ALBERT

Description: Cloud computing technology has revolutionized the field of data management as it has enhanced the barriers of storage restrictions and high-cost establishment for its users. The benefits of the cloud have paved the way for its extensive implementation in large enterprises. However, the data in the cloud have succumbed to various security threats, and its privacy issues remain one of the biggest and topmost concerns for the data owners. Several techniques, such as Attribute-based Encryption (ABE), have been proposed by several researchers to preserve the privacy of the data. However, the issue of security still looms largely over the cloud. In the present work, we introduce the novel encryption model called “Advanced Encryption Standard–Cipher-text-Identity and Attribute-based Encryption” (AES–CP–IDABE) to preserve data privacy along with its access control. In the proposed scheme, the data have been double encrypted initially through the ABE, along with the attributes and the identity of the user. Secondly, the Advanced Encryption Standard (AES) is used to encrypt the encrypted data and provide it to the authorized users. The user access control is established using the digital signature with the help of user ID and security keys. Additionally, the set up includes Denial-of-Service (DoS) detection through IP address monitoring and control. The proposed scheme has also been evaluated for its performance in the communication between the user and the data owner, along with the user’s execution time. From the outcome, it is evident that the proposed scheme was more effective than the existing scheme of ABE over execution, encryption, and decryption time. Additionally, the performance over DoS detection and impact of attribute numbers for the proposed scheme was also studied to prove its effectiveness.

Description: Cloud has become one of the most widely used technologies to store data due to its availability, flexibility, and low cost. At the same time, the security, integrity, and privacy of data that needs to be stored on the cloud is the primary threat for cloud deployment. However, the increase in cloud utilization often results in the creation of a multi-user cloud environment, which requires its owners to manage and monitor the data more effectively. The security of information faces an additional threat, which is related to the increasing number of users and owners who deal with the data stored on the cloud. Many researchers have developed several frameworks and algorithms to address the security issues of the cloud environment. In the present work, a novel algorithm is proposed with the integration of Ciphertext Policy-Identity Attribute-based Encryption (CP-IDABE) and the Rivest–Shamir–Adelman (RSA) algorithm for securing the cloud. Both the owners and users are provided with the public and distinct secret keys that are generated by the Automated Certificate Authority (ACA). The attribute policy differentiates between the user and owner for accessing the cloud data. The proposed RSA-CP-IDABE algorithm also prevents the Man in the Middle (MITM) attack effectively. The performance of the proposed algorithm is evaluated for its time used for encryption, decryption, and execution for varying sizes of data. The obtained results are compared with the existing framework to show its effectiveness. The proposed algorithm can be enhanced with the revocation of privileges in the future.

Description: In recent decades, carbon (C) management is an important point on the agenda to identify the best viable mitigation strategies for its reduction. The study was conducted at Jaldapara National Park located in the Eastern Himalayan region of India. The study quantified litter production, decomposition, periodic nutrient release, soil fertility status, and soil organic carbon (SOC) of five major forest stands i.e., Tectona grandis (TGDS), Shorea robusta (SRDS), Michelia champaca (MCDS), Lagerstroemia parviflora (LPDS) and miscellaneous stand (MS). A stratified random nested quadrate method was adopted for sample collection. Results reveal that the greatest amount of litter production and decomposition was under MCDS followed by MS, LPDS, SRDS, and the smallest under TGDS. The material annual turnover through litter decomposition in all the stands varies between 96.46% and 99.34%. The content and amount of the available nutrients in litter varied significantly among the stands. Moreover, release of these nutrients was nearly equal to the amount available in the initial litter mass. In general, the magnitude of the total nutrient return was in the same order as the total litter fall and the nutrient availability was more closely related to litter nutrient content and soil organic carbon. The range of pH (4.86–5.16), EC (0.34–0.50), soil moisture (27.01–31.03) and available primary nutrients (N: (0.21–0.26 Mg/ha), P: (0.09–0.12 Mg/ha), K: (0.13–0.14 Mg/ha)) also varied significantly among the stands. Significant positive correlations were observed between SOC, N and K. Both the fertility indices exhibited no definite pattern in the stands but a significant correlation between the two indicates the healthy soil fertility status of the stands. SOC varies significantly under different forest stands, but the greatest content was found under MS. The estimated SOC ranges between 75.9 and 107.7 Mg ha−1 up to 60 cm and is reported to be below the Indian average of 182.94 Mg ha−1. The present study strongly recommends that Tectona grandis, Shorea robusta, Michelia champaca, and Lagerstroemia parviflora should be the important commercial timbers of the Eastern Himalayan region because they may help further to increase the C sink in agricultural and degraded landscapes.

Description: In the modern era, Gmelina arborea plantations are a hotspot of future research because of their high carbon sequestration potential. The present work was conducted during 2018 to 2020 on a young unmanaged Gmelina farm to understand the ecosystem’s carbon and its dynamics. The study area was categorized into three age classes: ≤5, 5–10, and 10–15 years. In a plantation, Gmelina trees (10%) were randomly selected while other trees (90%) were also taken into the consideration for ecosystem carbon. A stratified random nested quadrate sampling method was adopted for analyzing other vegetation forms under study. Overall, 51 individual species in the studied Gmelina farm were found which includes 23 tree species, 7 shrub species, 16 herbs, 2 climbers, and 3 species of ferns. The estimated quantitative vegetation parameters and diversity indices indicate that the plant assemblages were heterogeneous with native diverse species evenly distributed with fairly higher densities, frequencies, and abundance. Herbs were the most important species followed by shrubs and trees. Consequently, with the increasing age of plantation, the richness of plant species increased. Soil properties were significantly influenced by the age of the plantation but exhibited no discreet trend. Total biomass density and total carbon density increased with increasing plantation age while no drastic variation was found in available soil organic carbon (SOC) because of insignificant variability in litter production. Total carbon, available SOC (up to 60 cm depth) and ecosystem carbon in the three age class plantations fell in the ranges of 54.51–59.91, 48.18–55.73, and 104.81–110.77 Mg ha−1, respectively. The carbon sequestration potential of Gmelina arborea is higher compared to other reported species and highly supportive of converting unutilized agricultural landscapes to reduce the atmospheric carbon dioxide in future.

Description: Nowadays, many prevalent frameworks for medical care have been projected, studied, and implemented. The load and challenges of traditional hospitals are increasing daily, leading to inefficient service in the health system. Smart hospitals based on advanced techniques play a crucial part in advancing the health services of rural people. It spares the time and money involved in travel, and patient medical reports can be shared instantly with the experts regardless of geographical constraints. Currently, the role of technology in hospitals is limited due to various restrictions, such as the obtainability of a high spectrum, low latency, and high-speed network. In this paper, we focused on the implementation of an advanced waveform with high spectral performance. Filer Bank Multi-Carrier (FBMC) is considered a strong contender for the upcoming 5G-centered smart hospitals due to its high data rate, no leakage of the spectrum, and less sensitivity to frequency error. In addition, a comparison of the spectral utilization of orthogonal frequency division multiplexing (OFDM) and FBMC in terms of bit error rate (BER), peak power (PP), power spectral density (PSD), noise-PSD, capacity and magnitude, and phase response is illustrated. Numerical results show that the FBMC achieved a throughput gain of 1 dB and its spectral performance is better than the OFDM; hence, it is a better choice for the proposed application compared to the current standard OFDM.