ALBERT

Description: Source localisation is an important component in the application of wireless sensor networks, and plays a key role in environmental monitoring, healthcare and battlefield surveillance and so on. In this article, the source localisation problem based on time-of-arrival measurements in asynchronous sensor networks is studied. Because of imperfect time synchronisation between the anchor nodes and the signal source node, the unknown parameter of start transmission time of signal source makes the localisation problem further sophisticated. The derived maximum-likelihood estimator cost function with multiple local minimum is non-linear and non-convex. A novel two-step method which can solve the global minimum is proposed. First, by leveraging dimensionality reduction, the maximum (minimum) distance maximum (minimum) time-of-arrival matching-based second-order Monte Carlo method is applied to find a rough initial position of the signal source with low computational complexity. Then, the rough initial position value is refined using trust region method to obtain the final positioning result. Comparative analysis with state-of-the-art semidefinite programming and min–max criterion-based algorithms are conducted. Simulations show that the proposed method is superior in terms of localisation accuracy and computational complexity, and can reach the optimality benchmark of Cramér–Rao Lower Bound even in high signal-to-noise ratio environments.

Description: “Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

Description: Equipped with micro wireless sensor nodes, a unmanned aerial vehicle) cluster can form an emergency communication network, which can have several applications such as environmental monitoring, disaster relief, military operations and so on. However, situations where there is excessive aggregation and small amount of dispersion of the unmanned aerial vehicle cluster may occur when the network is formed. To mitigate these, a solution based on a 3D virtual force driven by self-adaptive deployment (named as 3DVFSD) is proposed. As a result, the three virtual forces of central gravity, uniform force, and boundary constraint force are combined to act on each node of the communication network. By coordinating the distance between the nodes, especially the threshold of the distance between the boundary node and the boundary, the centralized nodes can be relatively dispersed. Meanwhile, the nodes can be prevented from being too scattered by constraining the distance from the boundary node to the end. The simulation results show that the 3DVFSD algorithm is superior to the traditional virtual force-driven deployment strategy in terms of convergence speed, coverage, and uniformity.

Description: Climate change brings many changes in a physical environment like plants and leaves. The flowers and plants get affected by natural climate and local weather extremes. However, the projected increase in the frost event causes sensitivity in plant reproduction and plant structure vegetation. The timing of growing and reproduction might be an essential tactic by which plant life can avoid frost. Flowers are more sensitive to hoarfrost than leaves but more sensitive to frost in most cases. In most cases, frost affects the size of the plant, its growth, and the production of seeds. In this article, we examined that how frost affects plants and flowers? How it affects the roots and prevents the growth of plants, vegetables, and fruits? Furthermore, we predicted how the frost will grow and how we should take early precautions to protect our crops? We presented the convolutional neural network model framework and used the conv1d algorithm to evaluate one-dimensional data for frost event prediction. Then, as part of our model contribution, we preprocessed the data set. The results were comparable to four weather stations in the United States. The results showed that our convolutional neural network model configuration is reliable.

Description: Intrusion detection systems play a vital role in traffic flow monitoring on Internet of Things networks by providing a secure network traffic environment and blocking unwanted traffic packets. Various intrusion detection systems approaches have been proposed previously based on data mining, fuzzy techniques, genetic, neurogenetic, particle swarm intelligence, rough sets, and conventional machine learning. However, these methods are not energy efficient and do not perform accurately due to the inappropriate feature selection or the use of full features of datasets. In general, datasets contain more than 10 features. Any machine learning–based lightweight intrusion detection systems trained with full features turn into an inefficient and heavyweight intrusion detection systems. This case challenges Internet of Things networks that suffer from power efficiency problems. Therefore, lightweight (energy-efficient), accurate, and high-performance intrusion detection systems are paramount instead of inefficient and heavyweight intrusion detection systems. To address these challenges, a new approach that can help to determine the most effective and optimal feature pairs of datasets which enable the development of lightweight intrusion detection systems was proposed. For this purpose, 10 machine learning algorithms and the recent BoT-IoT (2018) dataset were selected. Twelve best features recommended by the developers of this dataset were used in this study. Sixty-six unique feature pairs were generated from the 12 best features. Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. Similarly, 660 feature-pair-based lightweight intrusion detection systems were developed by training the 10 machine learning algorithms via each feature pair out of the 66 feature pairs. Moreover, the 10 intrusion detection systems trained with 12 best features and the 660 intrusion detection systems trained via 66 feature pairs were compared to each other based on the machine learning algorithmic groups. Then, the feature-pair-based lightweight intrusion detection systems that achieved the accuracy level of the 10 full-feature-based intrusion detection systems were selected. This way, the optimal and efficient feature pairs and the lightweight intrusion detection systems were determined. The most lightweight intrusion detection systems achieved more than 90% detection accuracy.

Description: Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.

Description: With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.