ALBERT

Description: We proposed two multiparty quantum secret sharing schemes based on n -particle Greenberger–Horne–Zeilinger-states (GHZ states), which are transformed from Einstein–Podolsky–Rosen pairs by entanglement swapping. In our schemes, the dealer imposes messages by performing local unitary operations ( I, x, i y, z ) on the n -particle GHZ state she holds, and the agents collaborate to deduce the dealer's messages by performing local unitary operations on their own qubit. The amount of dealer's secret message is positively related with the number of agents. The need of qubits is one-third less than the former schemes and, also, they can be reused for the next new round. The scheme (II) does not have to pre-share the code table, which increases the security without the risk of being stolen.

Description: Automatic fare collection (AFC) systems calculate the fare that the users must pay depending on the time of service (time-based) or the points of entrance and exit of the system (distance-based). The progressive introduction of Information and Communication Technologies allows the use of electronic tickets, which helps us to reduce costs and improve the control of the infrastructures. Nevertheless, these systems must be secure against possible fraud and they must also preserve users’ privacy. Therefore, we have studied the security requirements for the time-based and distance-based systems and we have proposed a protocol for each of the AFC systems. 1 The protocols offer strong privacy for honest users, i.e. the service provider is not able to disclose the identity of its users and, moreover, different journeys of the same user are not linkable between them. However, anonymity for users could be revoked if they misbehave. The protocols have been implemented in Android and its performance has been evaluated in two Android smartphones. The results remark that protocols are suitable to be used on an AFC system with a medium class mobile device although they offer a better experience with a high-class smartphone. The appearance in the market of more powerful mobile devices suggests a better usability of our proposal in a near future.

Description: Accurately locating unknown nodes is a critical issue in the study of wireless sensor networks (WSNs). Many localization approaches have been proposed based on anchor nodes, which are assumed to know their locations by manual placement or additional equipments such as global positioning system. However, none of these approaches can work properly under the adversarial scenario. In this paper, we propose a novel scheme called two-step secure localization (TSSL) stand against many typical malicious attacks, e.g. wormhole attack and location spoofing attack. TSSL detects malicious nodes step by step. First, anchor nodes collaborate with each other to identify suspicious nodes by checking their coordinates, identities and time of sending information. Then, by using a modified mesh generation scheme, malicious nodes are isolated and the WSN is divided into areas with different trust grades. Finally, a novel localization algorithm based on the arrival time difference of localization information is adopted to calculate locations of unknown nodes. Simulation results show that the TSSL detects malicious nodes effectively and the localization algorithm accomplishes localization with high localization accuracy.

Description: Distance-bounding protocols form a family of challenge–response authentication protocols that have been introduced to thwart relay attacks. They enable a verifier to authenticate and to establish an upper bound on the physical distance to an untrusted prover. We provide a detailed security analysis of a family of such protocols. More precisely, we show that the secret key shared between the verifier and the prover can be leaked after a number of nonce repetitions. The leakage probability, while exponentially decreasing with the nonce length, is only weakly dependent on the key length. Our main contribution is a high probability bound on the number of sessions required for the attacker to discover the secret, and an experimental analysis of the attack under noisy conditions. Both of these show that the attack's success probability mainly depends on the length of the used nonces rather than the length of the shared secret key. The theoretical bound could be used by practitioners to appropriately select their security parameters. While longer nonces can guard against this type of attack, we provide a possible countermeasure which successfully combats these attacks even when short nonces are used.

Description: In threshold public-key encryption (TPKE), the decryption key is divided into n shares, each one of which is given to a different decryption user in order to avoid single points of failure. A robust TPKE is that if threshold decryption of a valid ciphertext fails, the combiner can identify the decryption users that supplied invalid partial decryption shares. In this paper, we propose a practical and efficient TPKE scheme which is robust and non-interactive. Security against chosen-ciphertext attacks (CCAs) can be proved in the standard model under the hashed Diffie–Hellman assumption in bilinear groups. The security reduction is tight and simple. We use an instantiation hash function of the Kiltz's key encapsulation mechanism and Lai et al. 's chosen-ciphertext secure technique to construct a TPKE scheme. Moreover, our scheme is more simple and shown to be more efficient than currently existing CCA-secure TPKE schemes.

Description: The advances in the digital world (e.g. the Internet, Communications, etc.) are closing the gap between consumers and providers, in both Business to Consumer and Business to Business environments. Through the Internet, providers can offer their products directly to consumers, which increase their choices and allow them to contract a set of different services/products from different providers. But sometimes the consumer needs an all-or-nothing exchange with these providers: an atomic exchange. The consumer has negotiated/obtained a set of conditions that are only met if she acquires all the services/products. Thus, we need an Atomic Multi-Two-Party contract signing protocol. Even though digital signature of contracts is a topic that has been widely studied, it lacks a solution when applied to Atomic Multi-Two-Party scenarios. Here, we propose the first solution to solve this problem.

Description: Key-evolving cryptography is intended to mitigate the damage in case of a secret key compromise, one of the severest security threats to actual cryptographic schemes. In the public-key setting, the essential idea of key-evolving lies in updating the private key with time, while maintaining the same public key. Key evolution in encryption and signing has been well studied, especially in the identity-based (ID-based) setting where an entity's public key can be derived from that entity's identity information. From a more practical standpoint, however, one would like to use the primitive signcryption in the hierarchical ID-based setting. In this paper, we propose the first key-evolving hierarchical ID-based signcryption scheme that is scalable and joining-time-oblivious and allows secret keys to be updated autonomously. The security proofs of our construction depend on the bilinear Diffie–Hellman assumption and the computational Diffie–Hellman assumption in the random oracle model. To be specific, our proposal not only achieves the fundamental goals of confidentiality and authenticity, but also enjoys desirable properties of non-repudiation, ciphertext anonymity and strong forward security. Compared with the conventional sign-then-encrypt approach, our construction provides better efficiency in terms of the computation cost and the communication overhead.

Description: Cooperative Intelligent Transportation Systems (ITS) will turn up to us with safer and more efficient driving environments as well as convenient and infotainment features for future ITS stations. ETSI and ISO are completing the standardization of the building blocks of a reference communication architecture for cooperative ITS. Future ITS stations complying with this set of standards deployed in vehicles, at the roadside infrastructures and within the Internet are expected to communicate with each other through a combination of ITS dedicated communication protocols and legacy Internet protocols. However, in spite of the wide adoption of IPv6 for cooperative ITS communications, relatively little attention has been paid to the security issues related to IPv6 signaling and IPv6 transport communications. In this paper, we present our position on the emerging and urgent IPv6-related security issues that occur in communications between ITS stations complying with the ITS station reference architecture under standardization within ETSI TC ITS and ISO TC204.

Description: The objectives of this special issue are advanced security technologies and services for future computing environments, including, but not limited to, security primitives, protocols and security applications and services. Recent advances in security technologies and services for future computing environments have created a new class of the following: (i) Wireless sensor networks and radio-frequency identification security and privacy; (ii) security architectures for distributed network systems, P2P systems, cloud and grid systems; and (iii) security in e-commerce, mobile and wireless networks, and finally, security standards and assurance methods. All papers are expected to focus on novel approaches for advanced security technologies and services for future computing environments and to present high-quality results for tackling problems arising from the ever-growing advanced security technologies and services for future computing environments.

Description: In this paper, we present a statistical analysis of six traffic features based on entropy and distinct feature number at the packet level, and we find that, although these traffic features are unstable and show seasonal patterns like traffic volume in a long-time period, they are stable and consistent with Gaussian distribution in a short-time period. However, this equilibrium property will be violated by some anomalies. Based on this observation, we propose a Multi-dimensional Box plot method for Short-time scale Traffic (MBST) to classify abnormal and normal traffic. We compare our new method with the MCST method proposed in our prior work and the well-known wavelet-based and A Short-Timescale Uncorrelated-Traffic Equilibrium (ASTUTE) techniques. The detection result on synthetic anomaly traffic shows that MBST can better detect the low-rate attacks than wavelet-based and MCST methods, and detection result on real traffic demonstrates that MBST can detect more anomalies with lower false alarm rate than the two methods. Especially compared with ASTUTE, MBST performs much better for detecting anomalies involving a few large flows despite a little poor for detecting anomalies involving large number of small flows.

Description: This paper presents a benchmarking, performance analysis and optimization study of the OP2 ‘active’ library, which provides an abstraction framework for the parallel execution of unstructured mesh applications. OP2 aims to decouple the scientific specification of the application from its parallel implementation, and thereby achieve code longevity and near-optimal performance through re-targeting the application to execute on different multi-core/many-core hardware. Runtime performance results are presented for a representative unstructured mesh application on a variety of many-core processor systems, including traditional X86 architectures from Intel (Xeon based on the older Penryn and current Nehalem micro-architectures) and GPU offerings from NVIDIA (GTX260, Tesla C2050). Our analysis demonstrates the contrasting performance between the use of CPU (OpenMP) and GPU (CUDA) parallel implementations for the solution of an industrial-sized unstructured mesh consisting of about 1.5 million edges. Results show the significance of choosing the correct partition and thread-block configuration, the factors limiting the GPU performance and insights into optimizations for improved performance.

Description: In this paper, we describe the integrated power, area and thermal modeling framework in the structural simulation toolkit (SST) for large-scale high performance computer simulation. It integrates various power and thermal modeling tools and computes run-time energy dissipation for core, network on chip, memory controller and shared cache. It also provides functionality to update the leakage power as temperature changes. We illustrate the utilization of the framework by applying it to explore interconnect options in manycore systems with consideration of temperature variation and leakage feedback. We compare power, energy-delay-area product (EDAP) and energy-delay product (EDP) of four manycore configurations-1 core, 2 cores, 4 cores and 8 cores per cluster. Results from simulation with or without consideration of temperature variation both show that the 4-core per cluster configuration has the best EDAP and EDP. Even so, considering that temperature variation increases total power dissipation, we demonstrate the importance of considering temperature variation in the design flow. With this power, area and thermal modeling capability, the SST can be used for hardware/software co-design of future exascale systems.

Description: With the advent of heterogeneous computing systems consisting of multi-core central processing units (CPUs) and many-core graphics processing units (GPUs), robust methods are needed to facilitate fair benchmark comparisons between different systems. In this paper, we present a benchmarking methodology for measuring a number of performance metrics for heterogeneous systems. Methods for comparing performance and energy efficiency are included. Consideration is given to further metrics, such as associated running costs and carbon emissions. We give a case study for these metrics applied to Bristol University Docking engine, a molecular mechanics-based docking application that has been ported to open computing language at the University of Bristol. Results are included for both AMD and NVIDIA GPUs, and for a highly optimized code on the latest x86 CPUs.

Description: Java embedded systems often include Java middleware classes installed on the client device. For higher performance, they can be compiled into machine code before runtime using an ahead-of-time compiler (AOTC). There are many approaches to AOTC, yet a bytecode-to-C (b-to-C) AOTC which translates the bytecode into the C code and then compiles it using an existing optimizing compiler such as gcc would be the most straightforward one. This paper explores a few important design and optimization issues of a b-to-C AOTC, including the compilation form for the translated C code, the call interfaces among translated and interpreted Java methods, and Java-specific optimizations by the AOTC that can complement the gcc optimizations. We evaluate these issues with our b-to-C AOTC implemented on the MIPS platform for the Sun's CDC VM to understand their performance impact.

Description: Frequent itemset mining over sliding window is an interesting problem and has a large number of applications. Sliding window is a widely used model for frequent itemset mining over data streams due to its capability to handle concept drift, its bounded memory and its low processing time. A sliding window-based algorithm requires an efficient data structure that can be updated as fast as possible when inserting and deleting transactions. Moreover, an innovative computing method is needed to provide the set of frequent patterns (FPs) with a little delay after the user issues a request for the mining results within a window. In this study, an efficient representation of the sliding window named blocked bit sequence is introduced which is aimed to store and maintain the content of the window. Moreover, by a novel technique this representation is exploited for efficiently extracting the set of FPs within the current window. Experimental evaluations on both real-life and synthetic data streams show that the proposed approach is faster than recently proposed algorithms in different phases of data stream mining.

Description: Dynamic voltage scaling (DVS) is one of the most effective techniques for reducing energy consumption on battery-operated embedded systems. According to the granularity of units to which voltage scaling is applied, the DVS problem can be divided into two subproblems: (i) inter-task DVS problem and (ii) intra-task DVS problem. A lot of effective DVS techniques have addressed either one of the two subproblems, but none of them have attempted to solve both simultaneously. This paper examines the problem of combined inter- and intra-task DVS, called the combined DVS (CDVS) problem. We solve the CDVS problem in two embedded system domains: one is systems with a sleep state and the other without sleep state. For systems without sleep state, we propose a close-to-optimal algorithm for the CDVS problem. We show that the algorithm is optimal when the power is a quadratically increasing function of the system's clock speed or the applied voltage level. For systems with a sleep state, we propose a refinement algorithm that fine-tunes the solution to the CDVS problem without sleep state to further reduce energy consumption by exploiting sleep state. Experimental results show that our proposed CDVS algorithm without sleep state is able to reduce the energy consumption by 12.5% on average over the results by the method that sequentially performs two existing inter- and intra-task DVS techniques, which are both optimal under no sleep state. Furthermore, our CDVS algorithm with a sleep state can reduce the energy consumption by 7.1% on average over the results by the conventional representative method that utilizes sleep state, but does not consider intra- and inter-task DVS simultaneously.

Description: Quantitative structure–activity relationships are regression models relating chemical structure to biological activity. Such models allow to make predictions for toxicologically relevant endpoints, which constitute the target outcomes of experiments. The task is often tackled by instance-based methods, which are all based on the notion of chemical (dis-)similarity. Our starting point is the observation by Raymond and Willett that the two families of chemical distance measures, fingerprint-based and maximum common subgraph-based measures, provide orthogonal information about chemical similarity. This paper presents a novel method for finding suitable combinations of them, called adapted transfer, which adapts a distance measure learned on another, related dataset to a given dataset. Adapted transfer thus combines distance learning and transfer learning in a novel manner. In our experiments, we visualize the performance of the methods by learning curves and present a quantitative comparison for 10 and 100% of the maximum training set size to show that transfer exploiting source datasets is effective with small training datasets. Additionally, we present an approach to select the source task in a data-driven manner. The relevant experiments include an example that shows that the selection of a meaningful source task is a critical factor for transfer learning.

Description: This paper addresses semantic data mining, a new data mining paradigm in which ontologies are exploited in the process of data mining and knowledge discovery. This paradigm is introduced together with new semantic subgroup discovery systems SDM-search for enriched gene sets (SEGS) and SDM-Aleph. These systems are made publicly available in the new SDM-Toolkit for semantic data mining. The toolkit is implemented in the Orange4WS data mining platform that supports knowledge discovery workflow construction from local and distributed data mining services. On the basis of the experimental evaluation of semantic subgroup discovery systems on two publicly available biomedical datasets, the paper results in a thorough quantitative and qualitative evaluation of SDM-SEGS and SDM-Aleph and their comparison with SEGS, a system for enriched gene set discovery from microarray data.

Description: Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes are not identical. Then we further refine this setting for the case where subsets of objects, for example, subsets of objects that represent different time points or different phenotypes, are contrasted. We show that this allows finding subgroups of objects that could not be found with classical subgroup discovery. To find such subgroups, we propose an approach that consists of two subgroup discovery steps and an intermediate, contrast set definition step. This approach is applicable in various application areas. An example is biology, where interesting subgroups of genes are searched by using gene expression data. We address the problem of finding enriched gene sets that are specific for virus-infected samples for a specific time point or a specific phenotype. We report on experimental results on a time series dataset for virus-infected Solanum tuberosum (potato) plants. The results on S. tuberosum 's response to virus-infection revealed new research hypotheses for plant biologists.

Description: Conditional functional dependencies (CFDs) have been proposed as a new type of semantic rules extended from traditional functional dependencies. They have shown great potential for detecting and repairing inconsistent data. Constant CFDs are 100% confidence association rules. The theoretical search space for the minimal set of CFDs is the set of minimal generators and their closures in data. This search space has been used in the currently most efficient constant CFD discovery algorithm. In this paper, we propose pruning criteria to further prune the theoretic search space, and design a fast algorithm for constant CFD discovery. We evaluate the proposed algorithm on a number of media to large real-world data sets. The proposed algorithm is faster than the currently most efficient constant CFD discovery algorithm, and has linear time performance in the size of a data set.

Description: Tide gauge observations along the coastline of Japan have recorded the land sinking due to the continuous subduction of the oceanic plates, indicating that stress and strain energies have been accumulating at the plate boundary, which would eventually cause large oceanic trench earthquakes like the 2011 Great East Japan Earthquake. The proposed method extracts such long-term activities of the Earth's crust together with rapid displacements related to earthquakes, even before the establishment of the global positioning system, from monthly mean data of the sea levels. A state space model decomposes the tide gauge time series into trend, seasonal, autoregressive and observation noise components, each of which are estimated using the particle filter algorithm. The spatial and temporal distributions of the extracted trend component clearly indicate high-risk regions, near which giant earthquakes have occurred or are predicted to occur. A multivariate analysis of the observatories located at the northeast coast of Japan successfully determines the past crustal displacement in the case of the 1978 Off-Miyagi Earthquake. The proposed method has the potential application for monitoring crustal activities related to the accumulation of earthquake energy.

Description: In an attribute-based system (ABS), users are identified by various attributes, instead of their identities. Since its seminal introduction, the attribute-based mechanism has attracted a lot of attention. However, current ABS schemes have a number of drawbacks: (i) the communication cost is linear in the number of the required attributes; (ii) the computation cost is linear in the number of the required attributes and (iii) there are no efficient verification algorithms for the secret keys. These drawbacks limit the use of ABS in practice. In this paper, we propose an attribute-based oblivious access control (ABOAC) scheme to address these problems, where only the receiver whose attributes satisfy the access policies can obtain services obliviously. As a result, the receiver does not release anything about the contents of the selected services and his attributes to the sender, and even the number and supersets of his attributes are protected. The sender only knows the number of the services selected by the authorized receiver. Notably, the costs of computation and communication are constant and independent of the number of required attributes. While, in the prior comparable schemes, both the costs of computation and communication are linear in the required attributes. Therefore, our ABOAC scheme provides a novel and elegant solution to protect user's privacy in the systems where both the bandwidth and the computing capability are limited, such as wireless sensor and actor networks, mobile ad hoc networks, etc..

Description: A building information model (BIM) is an electronic repository of structured, three-dimensional data that captures both the physical and dynamic functional characteristics of a facility. In addition to its more traditional function as a tool to aid design and construction, a BIM can be used throughout the life cycle of a facility, functioning as a living database that places resources contained within the building in their spatial and temporal context. Through its comprehension of spatial relationships, a BIM can meaningfully represent and integrate previously isolated control and management systems and processes, and thereby provide a more intuitive interface to users. By placing processes in a spatial context, decision-making can be improved, with positive flow-on effects for security and efficiency. In this article, we systematically analyse the authorization requirements involved in the use of BIMs. We introduce the concept of using a BIM as a graphical tool to support spatial access control configuration and management (including physical access control). We also consider authorization requirements for regulating access to the structured data that exists within a BIM as well as to external systems and data repositories that can be accessed via the BIM interface. With a view to addressing these requirements we present a survey of relevant spatiotemporal access control models, focusing on features applicable to BIMs and highlighting capability gaps. Finally, we present a conceptual authorization framework that utilizes BIMs.

Description: The importance of memory performance and capacity is a growing concern for high performance computing laboratories around the world. It has long been recognized that improvements in processor speed exceed the rate of improvement in dynamic random access memory speed and, as a result, memory access times can be the limiting factor in high performance scientific codes. The use of multi-core processors exacerbates this problem with the rapid growth in the number of cores not being matched by similar improvements in memory capacity, increasing the likelihood of memory contention. In this paper, we present WMTools , a lightweight memory tracing tool and analysis framework for parallel codes, which is able to identify peak memory usage and also analyse per-function memory use over time. An evaluation of WMTools , in terms of its effectiveness and also its overheads, is performed using nine established scientific applications/benchmark codes representing a variety of programming languages and scientific domains. We also show how WMTools can be used to automatically generate a parameterized memory model for one of these applications, a two-dimensional non-linear magnetohydrodynamics application, Lare2D . Through the memory model we are able to identify an unexpected growth term which becomes dominant at scale. With a refined model we are able to predict memory consumption with under 7% error.

Description: Autonomic systems exhibit self-managing behavior using various algorithms. Case-based reasoning is one the techniques that enable the autonomic manager to learn from past experience. Case-base is partitioned into some clusters in order to improve the retrieval efficiency. Deciding an appropriate number of clusters for a case-base is not a trivial problem. This paper proposes a randomized algorithm for determining the number of clusters to be formed of the case-base. Subsequently, a binary search-based case retrieval strategy has been applied to ensure enhanced retrieval time performance. The paper presents two versions of the randomized algorithm. The first version guarantees success but its computational cost is a function of random variable; the other guarantees a deterministic computational cost but success is not guaranteed. The performance of the proposed algorithms has been reported on a simulated case study of the Autonomic Forest Fire Application.

Description: Doubly hidden Markov models (DHMMs) have been widely used to analyze a type of time process whose driving factors are hierarchical and hierarchically correlated. A common issue of these models is that they implicitly assume that the dwell time of any system state is constant or exponentially distributed. This property comes from the standard hidden Markov models and causes the DHMM to limitations in some actual application environment, where an application has latent temporal structure and does not follow the exponential distribution but has the period-like or variable-period feature. Such problems are frequently encountered in practice, e.g. network traffic. In this paper, we remove this limitation by a new structural discrete approach named nested hidden semi-Markov model. The proposed model includes a nested latent semi-Markov chain and one observable discrete stochastic process. The bottom latent semi-Markov chain is the core layer and controls the second-layer semi-Markov chain that generates the observable process. The state duration of both the semi-Markov chains can be variable or explicit. The model makes no assumptions on the distribution of the state-duration and the observable processes. An efficient forward and backward recursion procedure is developed for estimating the generator of the proposed model and inferring the underlying state processes for a given observation sequence. To evaluate the performance of the proposed model, we apply the model to the arrival process of network traffic and compare its simulation traffic and the real traffic. The performance evaluation in the experiments includes time dynamic process, auto-correlation, cross-correlation, statistical distribution and self-similarity.

Description: Input/Output (I/O) operations can represent a significant proportion of the run-time of parallel scientific computing applications. Although there have been several advances in file format libraries, file system design and I/O hardware, a growing divergence exists between the performance of parallel file systems and the compute clusters that they support. In this paper, we document the design and application of the RIOT I/O toolkit (RIOT) being developed at the University of Warwick with our industrial partners at the Atomic Weapons Establishment and Sandia National Laboratories. We use the toolkit to assess the performance of three industry-standard I/O benchmarks on three contrasting supercomputers, ranging from a mid-sized commodity cluster to a large-scale proprietary IBM BlueGene/P system. RIOT provides a powerful framework in which to analyse I/O and parallel file system behaviour—we demonstrate, for example, the large file locking overhead of IBM's General Parallel File System, which can consume nearly 30% of the total write time in the FLASH-IO benchmark. Through I/O trace analysis, we also assess the performance of HDF-5 in its default configuration, identifying a bottleneck created by the use of suboptimal Message Passing Interface hints. Furthermore, we investigate the performance gains attributed to the Parallel Log-structured File System (PLFS) being developed by EMC Corporation and the Los Alamos National Laboratory. Our evaluation of PLFS involves two high-performance computing systems with contrasting I/O backplanes and illustrates the varied improvements to I/O that result from the deployment of PLFS (ranging from up to 25 x speed-up in I/O performance on a large I/O installation to 2 x speed-up on the much smaller installation at the University of Warwick).

Description: Counter-based power models have attracted the interest of researchers because they became a quick approach to know the insights of power consumption. Moreover, they allow one to overpass the limitations of measurement devices. In this paper, we compare different Top-down and Bottom-up Counter-based modeling methods. We present a qualitative and quantitative evaluation of their properties. In addition, we study how to extend them to support the currently ubiquitous dynamic voltage and frequency scaling (DVFS) mechanism. We propose a simple method to generate DVFS agnostic power models from the DVFS-specific models. The proposed method is applicable to models generated using any methodology and allows the reduction of the modeling time without affecting the fundamental properties of the models. The study is performed on an 18 DVFS states Intel ® Core™ 2 platform using the SPECcpu2006, NAS and LMBENCH benchmark suites. In our testbed, a 6 x reduction on the modeling time only increments 1% point on average the error in the predictions.

Description: This paper presents smdev , a shared memory communication middleware for multi-core systems. smdev provides a simple and powerful messaging application program interface that is able to exploit the underlying multi-core architecture replacing inter-process and network-based communications by threads and shared memory transfers. The performance evaluation of smdev on several multi-core systems has shown noticeable improvements compared with other Java shared memory solutions, reaching and even overcoming the performance of natively compiled libraries. Thus, smdev has obtained start-up latencies around 0.76 μ s and almost 90 Gbps bandwidth for point-to-point communications, as well as high performance and scalability both for collective operations and representative messaging kernels. This fact has motivated the integration of smdev in F-MPJ, our message-passing implementation in Java.

Description: With the gigantic growth of social-enabled web during the past years and the creation of new platforms that encompass the social communication, a new application paradigm is utilized. This application paradigm is enforced with the users’ tendency to associate social behavior with their daily demands. As this paradigm can be utilized in all communication environments (wired and wireless), there is a need to eliminate the opportunistic aggravation of the resource-sharing process in wireless communication and enable a mechanism that will consider the problem of content distribution by these devices. As wireless devices experience intermittent connectivity, resource exchange by these devices can rarely be successful due to the frequent variations in the communication and the dynamic changes in the topology. This work takes into account the proposed socio-technical model in order to strengthen the interactions among devices for enabling reliability and availability of the requested resources. In addition, the scheme considers the users’ modeled interaction metrics and their location profiling based on social parameters and allows on-demand resource sharing through the opportunistic socially oriented caching. Different concepts of social centralities are introduced and modeled in order to meet the improvements of resource sharing considering the temporal centrality characteristic of each node and the social interactions among users. The proposed model enables one, via the users’ social and location-aware interactions, and the introduced aging factor, to strengthen or weaken the diffusion process of highly ranked requested resources. The proposed collaborative opportunistic resource-sharing scheme is thoroughly evaluated through experimental simulation for the effectiveness of the resource-sharing efficiency as well as for the accuracy of end-to-end reliability and resource-sharing completeness.

Description: The peer churn rate in the peer-to-peer (P2P) video-on-demand streaming service is much higher than in the P2P live streaming service, which makes the data scheduling problem more challenging. First, the available upload bandwidth information used in the data scheduling scheme is often inaccurate due to the peer churn, which lets a peer make bad scheduling decisions and leads to load imbalance. The higher peer churn makes this problem worse. Secondly, the higher peer churn exacerbates the bandwidth contention problem which occurs between a newly joined peer and some already-existing peers. To tackle the above two challenges, we propose an event-driven high-priority first data scheduling scheme, called EHPF scheme. To tackle the first challenge, we design a piggyback mechanism based on the event-driven mechanism. To tackle the second challenge, we design a priority calculation strategy to differentiate the requests from the newly joined peers and those from the already-existing peers, and use the high-priority first policy to allocate the upload bandwidths of peers. Through simulations and a real environment experiment, we demonstrate that the EHPF scheme outperforms the periodical data scheduling scheme in terms of startup delay, streaming quality and load balancing.

Description: A reconfigurable interconnection network (RIN) is a custom-designed on-chip switching network yielding routing solutions for a pre-given set of applications. Like field programmable gate array (FPGA) routing networks, the RIN is used to make reconfigurable interconnections among functional blocks. Unlike FPGAs, the network topology of a RIN is irregular as it is designed for a given set of routing requirements and optimized for the area cost subject to given delay constraints. In this paper, we propose an automatic design scheme for RINs, including routing specification formulation, graph modelings, network topology designs, routing algorithms and multiplexer-based network circuit implementation. The choice of the design scheme is based on the existing routing network design practices and research, which give feasible solutions. Our scheme is to optimize the designs with the choice of design parameters. A computer-aided design (CAD) tool is developed based on the design scheme, which takes a set of routing requirements as input and produces the corresponding RIN network topology and network circuit in hardware description language format. We present the area costs of various RINs generated by the CAD tool subject to delay constraints, and illustrate the RIN design scheme with a reconfigurable multistream video system.

Description: Social tags have recently become popular for use as keywords for searching and annotating Web resources. The main limitation of using social tags is the focus on the keyword and human interpretability rather than on semantics and computer interpretability. Hence, although social tags are useful for information sharing and organizing, they lack the semantic metadata needed for the reasoning and inference functions that facilitate a personalized Web resource search. This study addresses this issue by defining a Semantic Tag-based Profile Framework (STPF) for integrating ontology technologies into user profiles and Web resource annotations. The proposed STPF is applied in a novel algorithm for automatically generating user profiles by combining the traditional term frequency scheme and the semantic technology. A Semantic Tag-based User Profile Platform (STUPP) based on the proposed STPF and algorithm is also used as a broker for personalized Web resource searches made for individual needs. The experimental results show that the proposed approach has higher precision and recall than the traditional term frequency scheme.

Description: Adaptive contrast enhancement (ACE) is a popular method for image contrast enhancement. In this method, enhancement is achieved by adding an amplified version of the high-frequency content of the image to its low-frequency content. The rationale behind that is supported by the fact that the human visual system is sensitive to discontinuities in images, which represent the high-frequency content of the image. Thus, emphasizing this content is expected to improve the perceived contrast. In this paper, a fuzzy ACE (FACE)-based enhancement method, FACE, is proposed. In this method, the contrast gain values are computed using a fuzzy inference system (FIS) whose parameters are entirely derived from the image local statistics. To the best of our knowledge, the computation of the ACE gain values using a FIS has never been addressed before. Experimental results have proved the capability of FACE in enhancing the image contrast with less noise amplification and overenhancement artifacts.

Description: K -nearest neighbor (KNN) rule is a simple and effective algorithm in pattern classification. In this article, we propose a local mean-based k -nearest centroid neighbor classifier that assigns to each query pattern a class label with nearest local centroid mean vector so as to improve the classification performance. The proposed scheme not only takes into account the proximity and spatial distribution of k neighbors, but also utilizes the local mean vector of k neighbors from each class in making classification decision. In the proposed classifier, a local mean vector of k nearest centroid neighbors from each class for a query pattern is well positioned to sufficiently capture the class distribution information. In order to investigate the classification behavior of the proposed classifier, we conduct extensive experiments on the real and synthetic data sets in terms of the classification error. Experimental results demonstrate that our proposed method performs significantly well, particularly in the small sample size cases, compared with the state-of-the-art KNN-based algorithms.

Description: High feature dimensionality of realistic datasets adversely affects the recognition accuracy of nearest neighbor (NN) classifiers. To address this issue, we introduce a nearest feature classifier that shifts the NN concept from the global-decision level to the level of individual features. Performance comparisons with 12 instance-based classifiers on 13 benchmark University of California Irvine classification datasets show average improvements of 6 and 3.5% in recognition accuracy and area under curve performance measures, respectively. The statistical significance of the observed performance improvements is verified by the Friedman test and by the post hoc Bonferroni–Dunn test. In addition, the application of the classifier is demonstrated on face recognition databases, a character recognition database and medical diagnosis problems for binary and multi-class diagnosis on databases including morphological and gene expression features.

Description: Causal discovery is highly desirable in science and technology. In this paper, we study a new research problem of discovery of causal relationships in the context of streaming features, where the features steam in one by one. With a Bayesian network to represent causal relationships, we propose a novel algorithm called causal discovery from streaming features (CDFSF) which consists of a two-phase scheme. In the first phase, CDFSF dynamically discovers causal relationships between each feature seen so far with an arriving feature, while in the second phase CDFSF removes the false positives of each arrived feature from its current set of direct causes and effects. To improve the efficiency of CDFSF, using the symmetry properties between parents (causes) and children (effects) in a faithful Bayesian network, we present a variant of CDFSF, S-CDFSF. Experimental results validate our algorithms in comparison with the existing algorithms of causal relationship discovery.

Description: Our aim is to realize semantic face image retrieval (FIR)\ by bridging the semantic-gap between low-level visual features of images and the high-level labels that describe facial features. This allows a user to retrieve face images based on a description of face features rather than an example image. We approach the semantic-gap problem by developing a fuzzy-based method of finding a mapping between the low-level features and the vocabulary that describes the semantics of the face features. The contribution of this paper is a new method of using fuzzy clustering and fuzzy inference methods to derive the degree of membership for each semantic label to a new image. Our experiments show that our approach has good results for annotating images, and provides a sound foundation for local face feature-based FIR systems. We have made available a demonstration system online. Further, our system is not domain specific and can be generalized and applied to other problems in the field of image retrieval.

Description: Expressiveness and convergence of evolutionary computation (EC) is studied using the evolutionary automata model. It turns out that all standard classes of evolutionary automata are equally expressive when they operate in the terminal mode, i.e. in the terminal mode, evolutionary finite automata (EFA) are as expressive as evolutionary pushdown automata, evolutionary linearly bounded automata, evolutionary Turing machines or evolutionary inductive Turing machines. For example, the simplest class of evolutionary automata, EFA, can accept all recursively enumerable languages (i.e. EFA have power of Turing machines) and even more—they can accept languages that are not recursively enumerable. Due to utilization of evolutionary automata, we obtain also very simple sufficient conditions for convergence of EC.

Description: Two-view methods have been well developed to identify human actions. However, in a case where the corresponding imaged points cannot induce distinguished measures, the performance of the methods deteriorates. For this reason, we propose a new view-invariant measure for human action recognition by enforcing tri-view constraints in this paper. This new measurement method can be tolerant to different rates of human actions and the anthropometric proportions. We apply our approach to video synchronization by imposing both the similarity ratio and the consistency in the trifocal tensor over entire video sequences. By testing on both synthetic and real data, our method has achieved higher tolerance to noise levels, as well as higher identification accuracy than the traditional two-view method. Experimental results demonstrate that our approach can identify human pose transitions, in spite of dynamic time-lines, different viewpoints and unknown camera parameters.

Description: In this paper, we propose an approach termed segment-based features (SBFs) to classify time series. The approach is inspired by the success of the component- or part-based methods of object recognition in computer vision, in which a visual object is described as a number of characteristic parts and the relations among the parts. Utilizing this idea in the problem of time series classification, a time series is represented as a set of segments and the corresponding temporal relations. First, a number of interest segments are extracted by interest point detection with automatic scale selection. Then, a number of feature prototypes are collected by random sampling from the segment set, where each feature prototype may include single segment or multiple ordered segments. Subsequently, each time series is transformed to a standard feature vector, i.e. SBF, where each entry in the SBF is calculated as the maximum response (maximum similarity) of the corresponding feature prototype to the segment set of the time series. Based on the original SBF, an incremental feature selection algorithm is conducted to form a compact and discriminative feature representation. Finally, a multi-class support vector machine is trained to classify the test time series. Extensive experiments on different time series datasets, including one synthetic control dataset, two sign language datasets and one gait dynamics dataset, have been performed to evaluate the proposed SBF method. Compared with other state-of-the-art methods, our approach achieves superior classification performance, which clearly validates the advantages of the proposed method.

Description: The Capsule Reviews are intended to provide a short succinct review of each paper in the issue in order to bring it to a wider readership. The Capsule Reviews were compiled by Fairouz Kamareddine. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK.

Description: The use of the Internet for business-critical and real-time services is growing day after day. Random node (link) failures and targeted attacks against the network affect all types of traffic, but mainly critical services. For these services, most of the time it is not possible to wait for the complete network recovery; the best approach is to act in a proactive way by improving redundancy and network robustness. In this paper, we study network resilience and propose a resilience factor to measure the network level of robustness and protection against targeted attacks. We also propose strategies to improve resilience by simple alterations in the network topology. Our proposal is compared with previous approaches, and experimental results on selected network topologies confirmed the effectiveness of the approach.

Description: The inclusion of encryption or evasive techniques in popular applications increased the importance of characterizing network traffic based on behavior. This study aims to characterize peer-to-peer (P2P) traffic from the perspective of host computers by focusing on the packet lengths. The article explores the dissimilarities between the lengths of Internet Protocol (IP) packets generated by P2P and non-P2P applications. The heterogeneity of those lengths was assessed using entropy and compared for different classes of applications, through the implementation of a sliding analysis window. Initial observations show that the lengths of the packets generated by P2P applications are more varied than those of non-P2P applications. These patterns were used to implement a method to identify hosts running P2P applications. Unlike previous studies on this area, we used the heterogeneity of the packet lengths instead of the length value per se , and a sliding window calculation procedure was adopted to allow real-time processing. The results of this study can be used for the characterization of traffic generated by P2P applications, as well as for traffic classification and management purposes.

Description: When a vehicle requests a video streaming service from the Internet, it may not have enough bandwidth to receive good video quality. In order to resolve this problem, the vehicle is proposed to ask neighboring vehicles to help the video download process using a short-range wireless technique, i.e. Dedicated Short Range Communications (DSRCs). In this paper, this scenario is called cooperative streaming over vehicular networks. Since the topology of vehicular networks changes over time rapidly, three critical issues in the moving-vehicle-based cooperative streaming are: (i) how to form a cooperative group dynamically, (ii) how to schedule neighboring vehicles to help the video download process and (iii) how to estimate the possible change of network conditions. We propose corresponding control schemes to tackle the aforementioned issues in this paper. From the simulation results, we can find that different scheduling schemes are adaptive to different scenarios, and the proposed dynamic group formation scheme has better performance than that of periodic group formation scheme.

Description: The perfect hierarchical hypercube (HHC) interconnection network has been introduced in the literature recently. An HHC can connect many nodes while retaining a low degree and a small diameter. It is thus a suitable topology for interconnection networks of massively parallel systems. We describe in this paper an algorithm solving the set-to-set disjoint-path routing problem in perfect HHCs. In an HHC 2 m + m , given two sets of m +1 nodes S and D , the proposed algorithm can find m +1 node-disjoint paths between the nodes of S and the nodes of D of lengths at most ( m +1) (2 m + m + 4) + 3 in O ( m 2 2 2 m ) time complexity.

Description: In this paper, a new encryption scheme with three different modes of operations is proposed based on dual space-filling curves (SFSs) and a fractional wavelet transform (FrWT). This scheme is initially proposed for images and then extended to videos. The core idea behind the proposed schemes is to decompose an image/video first by the FrWT followed by the shuffling of each sub-band coefficients by means of a dual SFC. At last, an inverse FrWT is performed to get the encrypted image/video. A reliable decryption process is also proposed to construct the original image from the encrypted image. The experimental results, security and comparative analysis demonstrate the efficiency and robustness of the proposed scheme. Further, this paper also proposes an efficient implementation of an FrWT based on chaotic maps.

Description: We proposed an image signature method for content authentication, which applies a hierarchical approach to construct an image signature. In the first level, DWT and DCT are used to extract image features; then these features are encrypted by using sub-keys that are generated by a cryptographically hash function. In the second level, Karhunen–Loeve transformation is used to reduce the signature length. The main features of the proposed method are as follows: (i) It achieves a trade-off between robustness and tampering sensitivity. (ii) It provides a tool for image tampering detection and tampering localization. (iii) It can be used to detect the thumbnail of the large image to improve detection efficiency. (iv) It provides the compact signature, and the signature length is independent of the image size. Experimental results show that proposed method is robust for content-preserving manipulations such as JPEG compression, adding noise, filtering and Gamma correction, etc.

Description: For hiding messages into multiple least significant bit (MLSB) planes, a new weighted stego-image (WS)\ steganalysis method is proposed to estimate the ratio of messages hidden into each bit plane. First, a new WS with multiple weights is constructed, and it is proved that when the squared Euclidean distance between the WS and the cover image is minimal, the weight parameters are equal to the embedding ratios in MLSB planes. Afterward, based on this result and an estimation of cover image, a simple estimation equation is derived to estimate the embedding ratio in each bit plane. Experimental results show that the new steganalysis method performs more stably with the change of embedding ratios than typical structural steganalysis, and outperforms the typical structural steganalysis method on the estimation accuracy when the embedding ratio in any bit plane is larger than 0.4.

Description: We consider large queueing networks for which transient and stationary probability distributions are very difficult or impossible to obtain due to the state space explosion problem. In performance analysis, we need in general to study only a part of the network (a node or a path). Thus, we propose to define bounding systems that lead to compute bounds on performance measures of the considered subsystem. The original large state space is mapped into a smaller space to overcome the state space explosion problem, and bounds both on stationary and on transient performance measures are computed from these reduced-size models. This approach provides an interesting solution for complex networks since we have a trade-off between the quality of the bounds and the state space size, thus the computational complexity. As an application, we study a general multi-server queueing network, with finite capacity queues. We define bounding systems to compute blocking probabilities. The influence of parameters on the precision of the computed bounds are studied through some numerical examples in order to give more insights into the proposed approach.

Description: This special issue includes research papers related to a workshop held at the Royal Society on 21st of September 2010, honouring Erol Gelenbe on his 65th birthday. The workshop brought together some 40 participants including some of Erol's former and current PhD students, colleagues and friends with whom he has interacted over the years, both in Europe and the USA. The papers focus on probability models in computer science, at the core of the field of computer performance engineering, which Erol pioneered.

Description: This paper develops two themes. The first is to show that the task of ‘recognition’ can be realized by an artificial neural net, and that the structures thus generated can be in close analogy to those observed in nature. The second is to note that absolute signal levels are almost meaningless in biological contexts; for these, the current state of a system is revealed by dynamic rather than static behaviour. This was Walter Freeman's observation, which led him to the oscillatory models which he has developed so notably; see the reference list. Coupling of the two sets of ideas leads to some striking conclusions, developed in Whittle ( Neural Nets and Chaotic Carriers (1998) and Neural Nets and Chaotic Carriers , 2nd edn. (2010). Imperial College Press, London).

Description: We propose a probabilistic model based on stochastic geometry to analyze cognitive radio in a large wireless network with randomly located users sharing the medium with carrier sensing multiple access. Analytical results are derived on the impact of the interaction between primary and secondary users, on their medium access probability, coverage probability and throughput. These results can be seen as the continuation of the theory of priorities in queueing theory to spatial processes. They give insight into the guarantees that can be offered to primary users and more generally on the possibilities offered by cognitive radio to improve the effectiveness of spectrum utilization in such networks.

Description: An n -state generalized probabilistic automaton/array maps a list of d states stochastically into a next state, resulting in a degree- d polynomially nonlinear transformation of the n -component state-probability vector, in contrast with the linear transformation given by the conventional Markovian model. This nonlinearity introduces the possibility of chaotic behaviour as time (iteration) progresses. It is shown that for two-state systems, the d = 2 case is nonchaotic, while chaotic behaviour is found for degree d as low as 5. Examples, and open issues for other values of n and/or d , are also noted.

Description: Recently eXtensible Markup Language (XML) has achieved the leading role among languages for data representation and, thus, we can witness a massive boom of corresponding techniques for managing XML data. Most of the processing techniques, however, suffer from various bottlenecks worsening their time and/or space efficiency. We assume that the main reason is they consider XML collections too globally, involving all their possible features, although real-world data are often much simpler. Even though some techniques do restrict the input data, the restrictions are mostly unnatural. This paper aims to introduce Analyzer —a complex framework for performing statistical analyses of real-world documents. Exploitation of results of these analyses is a classical way how data processing can be optimized in many areas. Although this intent is legitimate, ad hoc and dedicated analyses soon become obsolete, they are usually built on insufficiently extensive collections and are difficult to repeat. Analyzer represents an easily extensible framework, which helps the user with gathering documents, managing analyses and browsing computed reports.

Description: Many fundamental problems in the area of distributed systems such as security, reliable routing, network survivability and broadening available bandwidth can be addressed through the use of disjoint paths between communication endpoints. Therefore, distributed solutions to the disjoint paths problem are of crucial importance to these fundamental problems. Disjoint paths naturally exist in traditional networks; however, these networks provide no guarantees regarding the presence and the number of available disjoint paths. On the other hand, network topologies structured based on Caley graphs such as hypercube, star networks and their variations possess many desirable properties related to disjoint paths. Therefore, it is anticipated that backbones of future networks will be structured following some Caley graph topologies. In this paper, we present a simple novel stabilizing and inherently stabilizing algorithm to route messages over all node-disjoint paths of optimal length from one non-faulty process to another in at most n +1 rounds in an n -dimensional hypercube network in the presence of crash failures. The proposed algorithm can tolerate up to 2 n –log ( n +1) process/link failures in the network and a maximum of ( L –2)/3 process/link failures on each disjoint path, where L is the number of processes on a disjoint path and the distance between any two failed processes/links is at least three. The proposed algorithm tolerates a large number of process and link failures, while delivering all n messages over optimal-length disjoint paths in the presence of maximum permissible process/link failures on each path. This is achieved by a simple uniform distributed algorithm using only local knowledge of failure locations.

Description: Acceleration of cryptographic applications on massive parallel computing platforms, such as Graphic Processing Units (GPUs), becomes a real challenge concerning practical implementations. In this paper, we propose a parallel algorithm for Elliptic Curve (EC) point multiplication in order to compute EC cryptography on these platforms. The proposed approach relies on the usage of the Residue Number System (RNS) to extract parallelism on high-precision integer arithmetic. Results suggest a maximum throughput of 9827 EC multiplications per second and minimum latency of 29.2 ms for a 224-bit underlying field, in a commercial Nvidia 285 GTX GPU. Performances up to an order of magnitude better in latency and 122% in throughput are achieved regarding other approaches reported in the related art. An experimental analysis of the scalability, based on OpenCL descriptions of the proposed algorithms, suggest that further advantage can be obtained from the proposed RNS approach for GPUs and EC curves supported by underlying finite fields of smaller size, regarding implementations on general purpose multi-cores.

Description: The COordinate Rotation DIgital Computer (CORDIC) algorithm is a famous technique for realizing complex arithmetic functions using simple shift-add operations. This paper presents a novel completely scaling-free CORDIC algorithm in rotation mode for high performance hyperbolic computations. We target algorithm level improvements to achieve low area and power-delay product on FPGA. Instead of complex search algorithms, we use the most significant one bit detection technique for micro-rotation sequence identification, which helps in significantly reducing the number of pipelining stages. The proposed technique uses mathematical identities to extend the range of convergence. The eight-staged pipelined architecture implementation requires a ROM in the preprocessing unit for storing the initial coordinate values, while the ROM for storing the elementary angles is eliminated. The FPGA implementation of the proposed processor requires 46.35% less gates and has 31.81% less delay when compared with Xilinx Core IP-CORDIC v3.0. Moreover, on an average it consumes 75.96% less power when compared with Xilinx CORDIC v3.0. Hence, the proposed technique provides an area–power-delay efficient VLSI implementation for calculating hyperbolic functions and exponents. The detailed algorithm design, along with FPGA implementation and area and time complexities, is presented in this paper.

Description: We consider a single-server queue in discrete time, in which customers must be served before some limit sojourn time of geometrical distribution. A customer who is not served before this limit leaves the system: it is impatient. The service of customers, the loss due to impatience and the holding of customers in the queue induce costs. The purpose is to decide when to serve the customers so as to minimize them. We use a Markov decision process with infinite horizon and discounted cost. We establish the structural properties of the stochastic dynamic programming operator and we deduce that the optimal policy is of threshold type. In addition, we are able to compute explicitly the optimal value of this threshold in terms of the parameters of problem.

Description: Dynamically typed languages are becoming increasingly popular for different software development scenarios such as Web engineering, rapid prototyping or the construction of applications that require runtime adaptiveness. In contrast, statically typed languages have undeniable advantages such as early type error detection and more opportunities for compiler optimizations. Since both approaches offer different benefits, hybrid statically and dynamically typed programming languages have emerged, and some statically typed languages have also incorporated dynamic typing capabilities. In this paper, we present the minimal core of StaDyn , a hybrid typing language that performs static type inference of both statically and dynamically typed references. The type information gathered by the compiler is used to generate efficient .NET code, obtaining a significant runtime performance improvement compared with C# 4.0 and Visual Basic 10.

Description: Readahead is an important technique to deal with the huge gap between disk drives and applications. It has become a standard in modern operating systems and advanced storage systems. However, it is difficult to develop the common kernel readahead and to achieve full testing coverage of all cases. In this paper, we formulate the kernel read handling, caching and readahead behavior as an absorbing Markov decision process, and present performance evaluations to compare or verify various readaheads. We also introduce algorithms to find optimal prefetching policies for specific read pattern and present the convergence analysis. By exploiting sample-path-based methods, it becomes much easier to evaluate and optimize prefetching policies, which can provide valuable informations to help the design and improvement of practical readaheads. For illustration and verification, we present two examples and some experiments on the readaheads in Linux kernel. The results show that the model-based evaluations agree with the practice and the improved prefetching policy significantly outperforms the original one in Linux kernel for the specified workloads.

Description: Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment's realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent–crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators.

Description: Inspired by the swarm behaviours of social insects, research into the self-assembly of swarm robots has become an attractive issue in the robotic community. Unfortunately, there are very few platforms for self-assembly and locomotion in the field of swarm robotics. The Sambot is a novel self-assembling modular robot that shares characteristics with swarm robots and self-reconfigurable robots. Each Sambot can move autonomously and connect with the other. This paper discusses the concept of combining self-assembly and locomotion for swarm robots. Distributed control algorithms for self-assembly and locomotion are proposed. Using five physical Sambots, experiments were carried out on autonomous docking, self-assembly and locomotion. Our control algorithm for self-assembly can also be used to realize the autonomous construction and self-repair of robotic structures consisting of a large number of Sambots.

Description: Scanning of ports on a computer occurs frequently on the Internet. An attacker performs port scans of Internet protocol addresses to find vulnerable hosts to compromise. However, it is also useful for system administrators and other network defenders to detect port scans as possible preliminaries to more serious attacks. It is a very difficult task to recognize instances of malicious port scanning. In general, a port scan may be an instance of a scan by attackers or an instance of a scan by network defenders. In this survey, we present research and development trends in this area. Our presentation includes a discussion of common port scan attacks. We provide a comparison of port scan methods based on type, mode of detection, mechanism used for detection and other characteristics. This survey also reports on the available data sets and evaluation criteria for port scan detection approaches.

Description: Proxy blind signature is an important cryptographic primitive and plays an essential role in construction of the electronic cash (e-cash). Recently, Tan (2001, An offline electronic cash scheme based on proxy blind signature. Comput. J. , 54, 505–512) proposed a new proxy blind signature scheme and applied it to electronic cash. The scheme was claimed as being provably secure under the Discrete Log assumption, DBDH assumption and Chosen–Target CDH assumption in the random oracle model. In this paper, we show that Tan's proxy blind signature scheme is insecure by demonstrating several attacks in which a malicious original signer can forge both valid proxy signature keys of arbitrary proxy signers and a proxy blind signature on an arbitrary message with respect to any proxy signer directly. We also discuss some weaknesses in the e-cash scheme proposed by Tan.

Description: In recent times, there has been increasing interest in storing data securely in the cloud environment. To provide owners of data stored in the cloud with flexible control over access to their data by other users, we propose a role-based encryption (RBE) scheme for secure cloud storage. Our scheme allows the owner of data to store it in an encrypted form in the cloud and to grant access to that data for users with specific roles. The scheme specifies a set of roles to which the users are assigned, with each role having a set of permissions. The data owner can encrypt the data and store it in the cloud in such a way that only users with specific roles can decrypt the data. Anyone else, including the cloud providers themselves, will not be able to decrypt the data. We describe such an RBE scheme using a broadcast encryption algorithm. The paper describes the security analysis of the proposed scheme and gives proofs showing that the proposed scheme is secure against attacks. We also analyse the efficiency and performance of our scheme and show that it has superior characteristics compared with other previously published schemes.

Description: Although the objective of secure communication can be achieved by using cryptographic tools, the undeniability that results from cryptographic properties may create a potential threat to the sender of the message. Unfortunately, most existing deniable protocols only provide 1-out-of-2 deniability. When both parties (the sender and the receiver) are allowed to deny generating the message, a dispute might occur between these two parties. The 1-out-of-2 deniable protocol can result in an unfair resolution of the dispute. Therefore, we propose a new model of deniability, called 1-out-of- deniability, that can provide full deniability. The 1-out-of- deniability protocol allows the originator of the message to deny that he or she generated the message, since there are an infinite number of possible message generators; at the same time, all transmitted messages can be protected and authenticated between the sender and the intended receiver. Our design can be implemented by using any public-key cryptography technique. We also analyze the correctness of the proposed protocols based on logical rules, and two practical examples are given to illustrate our design.

Description: Each year, large amounts of money and labor are spent on patching the vulnerabilities in operating systems and various popular software to prevent exploitation by worms. Modeling the propagation process can help us to devise effective strategies against those worms’ spreading. This paper presents a microcosmic analysis of worm propagation procedures. Our proposed model is different from traditional methods and examines deep inside the propagation procedure among nodes in the network by concentrating on the propagation probability and time delay described by a complex matrix. Moreover, since the analysis gives a microcosmic insight into a worm's propagation, the proposed model can avoid errors that are usually concealed in the traditional macroscopic analytical models. The objectives of this paper are to address three practical aspects of preventing worm propagation: (i) where do we patch? (ii) how many nodes do we need to patch? (iii) when do we patch? We implement a series of experiments to evaluate the effects of each major component in our microcosmic model. Based on the results drawn from the experiments, for high-risk vulnerabilities, it is critical that networks reduce the number of vulnerable nodes to below 80%. We believe our microcosmic model can benefit the security industry by allowing them to save significant money in the deployment of their security patching schemes.

Description: Although analyzing complex systems could be a complicated process, current approaches to quantify system security or vulnerability usually consider the whole system as a single component. In this paper, we propose a new compositional method to evaluate the vulnerability measure of complex systems. By the word composition we mean that the vulnerability measure of a complex system can be computed using pre-calculated vulnerability measures of its components. We define compatible systems to demonstrate which components could combine. Moreover, choice, sequential, parallel and synchronized parallel composition methods are defined and the measurement of the vulnerability in each case is presented. Our method uses a state machine to model the system. The model considers unauthorized states and attacker capabilities. Furthermore, both the probability of attack and delay time to reach the target state are used to quantify vulnerability. The proposed approach would be useful to analyze complex systems which may have complicated models. This approach reduces the state space and complexity of computation. On the other hand, if a component is replaced by another one, the vulnerability measures of other components do not change. Thus, these quantities are reused in new computation. Therefore, the calculation of the vulnerability measure for a new system is simplified.

Description: The construction of gene regulatory networks from expression data is one of the most important issues in systems biology research. However, building such networks is a tedious task, especially when both the number of genes and the complexity of gene regulation increase. In this work, we adopt the S-system model to represent the gene network and establish a methodology to infer the model. Our work mainly includes an adaptive genetic algorithm-particle swarm optimization hybrid method to infer appropriate network parameters, and a gene clustering method to decompose a large network into several smaller networks for dimension reduction. To validate the proposed methods, different series of experiments have been conducted and the results show that the proposed methods can be used to infer S-system models of gene networks efficiently and successfully.

Description: We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered.

Description: We investigate the assignment of assets to tasks where each asset can potentially execute any of the tasks, but assets execute tasks with a probabilistic outcome of success. There is a cost associated with each possible assignment of an asset to a task, and if a task is not executed there is also a cost associated with the non-execution of the task. As we proposed in [Gelenbe, E., Timotheou, S., and Nicholson, D. (2010). Fast distributed near optimum assignment of assets to tasks. Comput. J., doi:10.1093/comjnl/bxq010], we formulate the allocation of assets to tasks in order to minimize the overall expected cost, as a nonlinear combinatorial optimization problem. We propose the use of network flow algorithms which are based on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. We introduce three different schemes for the estimation of the arc costs and we investigate their performance compared with a random neural network algorithm and a greedy algorithm. We also develop an approach for obtaining tight lower bounds to the optimal solution based on a piecewise linear approximation of the considered problem.

Description: Building trust is a major concern in Peer-to-Peer networks as several kinds of applications rely on the presence of trusted services. Traditional techniques do not scale, produce very high overheads or rely on unrealistic assumptions. In this paper, we propose a new membership algorithm (Community Of Reputable PeerS, CORPS) for Distributed Hash Tables which builds a community of reputable nodes and thus enables the implementation of pseudo-trusted services. CORPS uses a reputation-based approach to decide whether a node can be a member of the group or not. We demonstrate the benefits of this approach and evaluate how much it improves the reliability of a trusted routing service.

Description: Many data sets exhibit skewed class distributions in which most cases are allocated to a class and far fewer cases to a smaller one. A classifier induced from an imbalanced data set has usually a low error rate for the majority class and an unacceptable error rate for the minority class. This paper provides a review on various methodologies that have tried to handle this problem. Afterwards, it presents an experimental study of these methodologies with a proposed cascade generalization ensemble that is applied in reweighted data and it concludes that such a framework can be a more effective solution to the problem. Our method improves the identification of a difficult small class, while keeping the classification ability of the other class in an acceptable level.

Description: Ants are generally believed to follow an intensive work routine. Numerous tales and fables refer to ants as conscientious workers. Nevertheless, biologists have discovered that ants also rest for extended periods of time. This does not only hold for individual ants. Interestingly, ant colonies exhibit synchronized activity phases that result from self-organization. In this work, self-synchronization in ant colonies is taken as the inspiring source for a new mechanism of self-synchronized duty-cycling in mobile sensor networks. Hereby, we assume that sensor nodes are equipped with energy harvesting capabilities such as, for example, solar cells. We show that the proposed self-synchronization mechanism can be made adaptive depending on variable energy resources. The main objective of this paper is to study and explore the swarm intelligence foundations of self-synchronized duty-cycling. With this purpose in mind, physical constraints such as packet collisions and packet loss are generally not considered.

Description: Vector field tomography is a field that has received considerable attention in recent decades. It deals with the problem of the determination of a vector field from non-invasive integral data. These data are modelled by the vectorial Radon transform. Previous attempts at solving this reconstruction problem showed that tomographic data alone are insufficient for determining a 2D band-limited vector field completely and uniquely. This paper describes a method that allows one to recover both components of a 2D vector field based only on integral data, by solving a system of linear equations. We carry out the analysis in the digital domain and we take advantage of the redundancy in the projection data, since these may be viewed as weighted sums of the local vector field's Cartesian components. The potential of the introduced method is demonstrated by presenting examples of vector field reconstruction.

Description: Historically, at the beginning of natural language processing, applications with industrial objectives were preferred, e.g. for a fully automated translation. Soon, during the 1960s, there were attempts to separate applications from basic theoretical research. Since this theoretical research encounters difficulties regarding its status as a separate discipline, and considering the constraints necessary to clarify the concept of ‘good application’, impossible to satisfy simultaneously (a real problem to solve, in response to a social demand, and a viable solution in terms of reliability, robustness, speed and cost), two main streams have emerged. The first one, as a computer technique, is intended to build applications based on a strict logic, using natural language to facilitate interaction with the computer, but not directly related to the human way of using language (this approach is designated as natural language processing ). Such pragmatic research accepts certain kinds of errors, but must lead to concrete results in limited time. The goal is to provide effective systems for real applications, able to respond effectively to requests addressed to them in fairly large areas; these systems are directly related to social and industrial productivity, which is the essential criterion of evaluation. Some technological developments, such as microcomputers, have made available to people specific applications of natural language processing and have enabled the emergence of small specialized firms. This produced, in the second half of the 1980s, the emergence of a ‘language industry’ and of the field of ‘linguistic engineering’. On the other hand, during the late 1960s, the gap between the social demand, the resources invested and the poor performance obtained led to the emergence of theoretical studies intended to formalize languages (as opposed to the more empirical machine translation). This leads to ‘pilot systems’, aimed at demonstrating the feasibility of complex theoretical approaches, but unable to operate outside a set of rather limited examples. The limits may be at different levels: more or less limited vocabulary or accepted sentences, knowledge about the field more or less complete, more or less developed reasoning and so on. These limits have a significant impact on communication itself. For natural language processing systems to be effective, they must make appropriate inferences from what is said and, conversely their behavior should allow the inferences that the users usually do when using their language. Thus, this position paper stresses that understanding the surface meaning of a natural language is not sufficient but that the goals, intentions and strategies of the participants in a dialogue must be understood.

Description: Ubiquitous smart environments equipped with low-cost and easily-deployable wireless sensor networks (WSNs) and ever-increasing widespread Mobile Ad hoc NETworks (MANETs) are opening brand new opportunities in environmental monitoring. This paper proposes an original solution for WSN/MANET integration based on the primary design guideline of opportunistically exploiting MANET overlays impromptu formed over the WSN to improve and boost the data collection task of a typical WSN. On the one hand, we adopt a cross-layer approach that exploits MANET connections to differentiate and fasten the delivery of sensed urgent data by pushing them over low-latency MANET paths. On the other hand, we take advantage of local cross-layer visibility of the WSN data collection procedures and protocols to carefully control and limit WSN–MANET coordination overhead. We claim that our proposed solution can obtain significant quality of service improvements via differentiation, by granting faster delivery times to urgent data with a very limited cost in most common execution scenarios.

Description: The Capsule Reviews are intended to provide a short succinct review of each paper in the issue in order to bring it to a wider readership. The Capsule Reviews were compiled by Fairouz Kamareddine. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK.

Description: Cache sharing has been a popular technique used to facilitate data access in mobile network environments. The key to this technique is to allow an efficient sharing of cache contents between neighboring nodes without introducing excessive amount of communication overhead. In this paper, we propose a cache-sharing protocol, called ‘Pull with Piggybacked Push (PPP)’, which exploits data request broadcasts by performing data pull and index push operations together. Taking advantage of both push- and pull-based approaches, PPP gains benefits from both ends: performance and communication overhead. PPP is unique in that it yields good performance over diverse operating environments, while accomplishing it with lower communication overhead than the previous methods. In addition, it adapts well to different data access patterns. As a result, better scalability is achieved by the proposed protocol.

Description: With recent advances in technologies such as radio-frequency identification and new standards such as the electronic product code, large-scale traceability is emerging as a key differentiator in a wide range of enterprise applications (e.g. counterfeit prevention, product recalls and pilferage reduction). Such traceability applications often need to access data collected by individual enterprises in a distributed environment. Traditional centralized approaches (e.g. data warehousing) are not feasible for these applications due to their unique characteristics such as large volume of data and sovereignty of the participants. In this paper, we describe an approach that enables applications to share traceability data across independent enterprises in a pure peer-to-peer (P2P) fashion. Data are stored in local repositories of participants and indexed in the network based on structured P2P overlays. In particular, we present a generic approach for efficiently indexing and locating individual objects in large, distributed traceable networks, most notably, in the emerging environment of the internet of things. The results from extensive experiments show that our approach scales well in both data volume and network size. A real-world returnable assets management system is also developed using the proposed techniques to demonstrate its feasibility.

Description: Detecting motion-based video change, such as different types of motion video or applications using different change detection algorithms in a Web system, is difficult. This paper designs and implements architecture for a real-time or near real-time on-demand motion video change detection system using the Sensor Observation Service (SOS) and the Web Processing Service (WPS) in the Sensor Web environment. Real-time or near real-time includes sensors that obtain motion video data, SOS provides motion video data to WPS and WPS processes this data. Three solution methods are introduced: the GetObservation operation of SOS by transaction, dynamical interaction between SOS and WPS, and WPS real-time or near real-time processing. On-demand means that a developer can choose different motion video change detection algorithms under different applications or different conditions. For this purpose, a flexible, standards-based and service-oriented WPS architecture is designed, which consists of three layers: the WPS interface layer, the field interface layer and the implementation layer. To test the proposed approach, a video change detection case of monitoring a road situation is shown, which was a demonstration for Open Geospatial Consortium Web Service phase 7. The results demonstrate that the proposed approach is feasible.

Description: Most traditional recommender systems lack accuracy in the case where data used in the recommendation process is sparse. This study addresses the sparsity problem and aims to get rid of it by means of a content-boosted collaborative filtering approach applied to a web-based movie recommendation system. The main motivation is to investigate whether further success can be obtained by combining ‘local and global user similarity’ and ‘effective missing data prediction’ approaches, which were previously introduced and proved to be successful separately. The present work improves these approaches by taking the content information of the movies into account during the item similarity calculations. The comparison of the proposed approach with the original methods was carried out using mean absolute error, and more accurate predictions were achieved.

Description: How do animals use their habitat? Where do they go and what do they do? These basic questions are key not only to understanding a species’ ecology and evolution, but also for addressing many of the environmental challenges we currently face, including problems posed by invasive species, the spread of zoonotic diseases and declines in wildlife populations due to anthropogenic climate and land-use changes. Monitoring the movements and activities of wild animals can be difficult, especially when the species in question are small, cryptic or move over large areas. In this paper, we describe an Automated Radio-Telemetry System (ARTS) that we designed and built on Barro Colorado Island (BCI), Panama to overcome these challenges. We describe the hardware and software we used to implement the ARTS, and discuss the scientific successes we have had using the system, as well as the logistical challenges we faced in maintaining the system in real-world, rainforest conditions. The ARTS uses automated radio-telemetry receivers mounted on 40-m towers topped with arrays of directional antennas to track the activity and location of radio-collared study animals, 24 h a day, 7 days a week. These receiving units are connected by a wireless network to a server housed in the laboratory on BCI, making these data available in real time to researchers via a web-accessible database. As long as study animals are within the range of the towers, the ARTS system collects data more frequently than typical animal-borne global positioning system collars (~12 locations/h) with lower accuracy (approximately 50 m) but at much reduced cost per tag (~10X less expensive). The geographic range of ARTS, like all VHF telemetry, is affected by the size of the radio-tag as well as its position in the forest (e.g. tags in the canopy transmit farther than those on the forest floor). We present a model of signal propagation based on landscape conditions, which quantifies these effects and identifies sources of interference, including weather events and human activity. ARTS has been used to track 374 individual animals from 38 species, including 17 mammal species, 12 birds, 7 reptiles or amphibians, as well as two species of plant seeds. These data elucidate the spatio-temporal dynamics of animal activity and movement at the site and have produced numerous peer-reviewed publications, student theses, magazine articles, educational programs and film documentaries. These data are also relevant to long-term population monitoring and conservation plans. Both the successes and the failures of the ARTS system are applicable to broader sensor network applications and are valuable for advancing sensor network research.

Description: Energy efficiency is a key characteristic of modern wireless sensors. Assuming that the transceiver is the most power-consuming component of a typical sensor node, then a large advantage can be achieved at the data link layer where the medium access control (MAC) protocol controls the usage of the radio unit. Many MAC protocols have been developed for traditional wireless networks. Given that the sensor network is different from the traditional wireless network in many places, researchers are looking for a MAC protocol that is specifically designed and adapted to the sensor network. Moreover, most of the contributions in the wireless sensor network have assumed static nodes. However, some applications in the area of medical care and disaster response make use of the mobile sensor network. Thus, the present paper studies and simulates the sensor MAC (S-MAC), timeout MAC and mobility-aware S-MAC (MS-MAC) protocols in terms of their energy efficiency in different mobility situations. Furthermore, an enhancement of the MS-MAC protocol, named as enhanced MS-MAC, is introduced and simulated. The results show that this enhancement can significantly improve the energy efficiency when there is a reasonable packet delivery rate.

Description: According to Lee in 1996, computer scientists ‘are reaching the stage of development where each new generation of participants is unaware both of their overall technological ancestry and the history of the development of their speciality, and have no past to build upon’ [Lee, J.A.N. (1996) "Those who forget the lessons of history are doomed to repeat it" or, why I study the history of computing. IEEE Ann. Hist. Comput. 18, 54–62]. A technically and historically accurate account, as attempted here, can help us, computer scientists, grasp some of the fundamental ideas underlying our discipline. This paper describes some early contributions of Dijkstra by elaborating on his involvement in putting forward and implementing the recursive procedure as an ALGOL60 language construct. Particular attention is paid to Dijkstra's generalizing style of solving problems.

Description: This paper explores efficient ways to use flash memory to store online analytical processing (OLAP) data. The particular type of queries considered are range queries using the aggregate functions SUM, COUNT and AVG. The asymmetric cost of reads and writes for flash memory gives higher importance to how updates are handled in a flash memory environment. A popular data structure used for answering OLAP range-sum queries is the prefix sum cube. It allows the range-sum query to be answered in constant time. However, updating the prefix sum cube is very expensive. To overcome this, the -tree was proposed by Chun et al . (Dynamic update cube for range-sum queries. Proc. Int. Conf. Very Large Data Bases, San Francisco, CA, USA, 2001, pp. 521–530. Morgan Kaufmann Publisher). The -tree stores all updates to the prefix sum cube in a separate r-tree. This approach worked well for the hard disk where in-place updates are relatively cheap. However, for flash memory where in-place updates are very expensive, the -tree performs very poorly. We take a four-pronged approach to overcome the problem of expensive in-place updates. The first is efficient caching of updates in RAM. The second is writing out whole trees from RAM to flash memory instead of incrementally updating a disk resident tree. The third is we allow users to trade bounded amounts of accuracy for less updates via lossy compression. Finally, we use a quadtree index structure instead of the R-tree. We prove that the quadtree compression problem is NP-complete. A greedy heuristic is proposed to find near optimal solutions in polynomial time. Various experiments were conducted to compare the proposed algorithms against the existing -tree. The results show that our algorithms consistently outperformed -tree by factors of between 10 and 100. This demonstrates the importance of designing flash memory customized algorithms for OLAP range queries. In addition, among our algorithms, the error bound solutions with a small error bound setting significantly outperform the accurate solution in terms of performance for a variety of parameter settings. This indicates that the error bound algorithms offer users an effective trade-off between execution time and accuracy.

Description: This paper focuses on the performance evaluation of Compiler for Portable Checkpointing (CPPC), a tool for the checkpointing of parallel message-passing applications. Its performance and the factors that impact it are transparently and rigorously identified and assessed. The tests were performed on a public supercomputing infrastructure, using a large number of very different applications and showing excellent results in terms of performance and effort required for integration into user codes. Statistical analysis techniques have been used to better approximate the performance of the tool. Quantitative and qualitative comparisons with other rollback-recovery approaches to fault tolerance are also included. All these data and comparisons are then discussed in an effort to extract meaningful conclusions about the state-of-the-art and future research trends in the rollback-recovery field.

Description: There are various ways to measure the shape difference between two n -node rooted binary trees (binary trees for short). A rotation on a binary tree is a local restructuring that changes the tree into another one preserving the in-order sequence. The rotation distance between two binary trees is the minimum number of rotations needed to transform one into another. Till now, no polynomial–time algorithm exists for computing the rotation distance between any two binary trees. Recently, Lucas ( Comput. J. , 47, 259–269, 2004) presented an O ( n 2 )–time algorithm for finding the rotation distance between two binary trees, where the source tree is a degenerate tree and the destination tree is an angle tree. This paper improves the time-complexity to O ( n ) under this constraint.

Description: We discuss algorithm recognition (AR) and present a method for recognizing algorithms automatically from Java source code. The method consists of two phases. In the first phase, the recognizable algorithms are converted into the vectors of characteristics, which are computed based on static analysis of program code, including various statistics of language constructs and analysis of Roles of Variables in the target program. In the second phase, the algorithms are classified based on these vectors using the C4.5 decision tree classifier. We demonstrate the performance of the method by applying it to sorting algorithms. Using leave-one-out cross-validation technique, we have conducted an experimental evaluation of the classification performance showing that the average classification accuracy is 98.1% (the data set consisted of five different types of sorting algorithms). The results show the applicability and usefulness of roles of variables in AR, and illustrate that the C4.5 algorithm is a suitable decision tree classifier for our purpose. The limitations of the method are also discussed.

Description: Electron tomography (ET) is an important technique in biosciences that is providing new insights into the cellular ultrastructure. Iterative reconstruction methods have been shown to be robust against the noise and limited-tilt range conditions present in ET. Nevertheless, these methods are not extensively used due to their computational demands. Instead, the simpler method weighted backprojection (WBP) remains prevalent. Recently, we have demonstrated that a matrix approach to WBP allows a significant reduction in processing time both on central processing units and on graphics processing units (GPUs). In this work, we extend that matrix approach to one of the most common iterative methods in ET, simultaneous iterative reconstruction technique (SIRT). We show that it is possible to implement this method targeted at GPU directly, using sparse algebra. We also analyse this approach on different GPU platforms and confirm that these implementations exhibit high performance. This may thus help to the widespread use of SIRT.

Description: The source-location privacy problem in Wireless Sensor Networks has been traditionally tackled by the creation of random routes for every packet transmitted from the source nodes to the base station. These schemes provide a considerable protection level at a high cost in terms of message delivery time and energy consumption. This overhead is due to the fact that the data routing process is done in a blind way, without knowledge about the location of the attacker. In this work, we propose the Context-Aware Location Privacy (CALP) approach, which takes advantage of the ability of sensor nodes to perceive the presence of a mobile adversary in their vicinity in order to transmit data packets in a more energy-efficient and privacy-preserving manner. In particular, we apply the concepts of CALP to the development of a shortest-path CALP routing algorithm. A permissive and a strict version of the protocol are studied for different adversarial models and the proposed schemes are evaluated through simulation experiments in terms of privacy protection and energy consumption. Finally, we present the conclusions of the paper as well as possible extensions of this work.