The deployment of wearable or body-worn devices is increasing rapidly, and thus researchers’ interests mainly include technical and economical issues, such as networking, interoperability, security, power optimization, business growth and regulation. To address these issues properly, previous survey papers usually focused on describing the wireless body area network architecture and network protocols. This implies that deployment issues and awareness issues of wearable and BAN devices are not emphasized in previous work. To defeat this problem, in this study, we have focused on feasibility, limitations, and security concerns in wireless body area networks. In the aspect of the economy, we have focused on the compound annual growth rate of these devices in the global market, different regulations of wearable/wireless body area network devices in different regions and countries of the world and feasible research projects for wireless body area networks. In addition, this study focuses on the domain of devices that are equally important to physicians, sportsmen, trainers and coaches, computer scientists, engineers, and investors. The outcomes of this study relating to physicians, fitness trainers and coaches indicate that the use of these devices means they would be able to treat their clients in a more effective way. The study also converges the focus of businessmen on the Annual Growth Rate (CAGR) and provides manufacturers and vendors with information about different regulatory bodies that are monitoring and regulating WBAN devices. Therefore, by providing deployment issues in the aspects of technology and economy at the same time, we believe that this survey can serve as a preliminary material that will lead to more advancements and improvements in deployment in the area of wearable wireless body area networks. Finally, we present open issues and further research direction in the area of wireless body area networks.

Cloud computing technology is rapidly becoming ubiquitous and indispensable. However, its widespread adoption also exposes organizations and individuals to a broad spectrum of potential threats. Despite the multiple advantages the cloud offers, organizations remain cautious about migrating their data and applications to the cloud due to fears of data breaches and security compromises. In light of these concerns, this study has conducted an in-depth examination of a variety of articles to enhance the comprehension of the challenges related to safeguarding and fortifying data within the cloud environment. Furthermore, the research has scrutinized several well-documented data breaches, analyzing the financial consequences they inflicted. Additionally, it scrutinizes the distinctions between conventional digital forensics and the forensic procedures specific to cloud computing. As a result of this investigation, the study has concluded by proposing potential opportunities for further research in this critical domain. By doing so, it contributes to our collective understanding of the complex panorama of cloud data protection and security, while acknowledging the evolving nature of technology and the need for ongoing exploration and innovation in this field. This study also helps in understanding the compound annual growth rate (CAGR) of cloud digital forensics, which is found to be quite high at ≈16.53% from 2023 to 2031. Moreover, its market is expected to reach ≈USD 36.9 billion by the year 2031; presently, it is ≈USD 11.21 billion, which shows that there are great opportunities for investment in this area. This study also strategically addresses emerging challenges in cloud digital forensics, providing a comprehensive approach to navigating and overcoming the complexities associated with the evolving landscape of cloud computing.

Secure exchange of sensitive information between different entities is a serious challenge in today’s environment. Therefore, various cryptosystems have been created specifically for this purpose. Various operations are used in these cryptosystems. The most common operations used are substitution and permutation. The substitution-box (S-box), a look-up table that takes x bits as input and replaces them with y bits, is responsible for the substitution operation. The construction of an S-box with strong cryptographic attributes is an essential part of building a robust and secure cryptosystem.Numerous researchers have dedicated their efforts to devising intricate techniques for constructing S-boxes. In this study, a novel approach is proposed to design a robust and dynamic S-box based on a compound-chaotic map. The suggested method for constructing the S-box is characterized by its simplicity and resilience. Notably, the Tent-Sine chaotic system significantly expands the chaotic range, setting it apart from other chaotic systems and rendering it more suitable for cryptographic applications. This research introduces a promising avenue for enhancing the security and effectiveness of S-boxes, contributing to the advancement of cryptographic systems. A number of widely accepted metrics, such as Non-Linearity (NL), Strict Avalanche Criterion (SAC), Bit Independence Criterion (BIC), Linear Probability (LP), and Differential Probability (DP), are extensively exercised for the analysis and evaluation of the proposed S-Box’s cryptographic strength. The results of the proposed of S-box are compared with the S-boxes of other researchers, and it is observed that it is cryptographically sound and worthy of inclusion in modern cryptosystems.

Recently, legal practice has seen a significant rise in the adoption of Artificial Intelligence (AI) for various core tasks. However, these technologies remain in their early stages and face challenges such as understanding complex legal reasoning, managing biased data, ensuring transparency, and avoiding misleading responses, commonly referred to as hallucinations. To address these limitations, this paper introduces Legal Query RAG (LQ-RAG), a novel Retrieval-Augmented Generation framework with a recursive feedback mechanism specifically designed to overcome the critical shortcomings of standard RAG implementations in legal applications. The proposed framework incorporates four key components: a custom evaluation agent, a specialized response generation model, a prompt engineering agent, and a fine-tuned legal embedding LLM. Together, these components effectively minimize hallucinations, improve domain-specific accuracy, and deliver precise, high-quality responses for complex queries. Experimental results demonstrate that the fine-tuned embedding LLM achieves a 13% improvement in Hit Rate and a 15% improvement in Mean Reciprocal Rank (MRR). Comparisons with general LLMs reveal a 24% performance gain when using the Hybrid Fine-Tuned Generative LLM (HFM), the specialized response generation model integrated into the LQ-RAG framework. Furthermore, LQ-RAG achieves a 23% improvement in relevance score over naive configurations and a 14% improvement over RAG with Fine-Tuned LLMs (FTM). These findings underscore the potential of domain-specific fine-tuned LLMs, combined with advanced RAG modules and feedback mechanisms, to significantly enhance the reliability and performance of AI in legal practice. The reliance of this study on a proprietary model as the evaluation agent, combined with the lack of feedback from human experts, highlights the need for improvement. Future efforts should focus on developing a specialized legal evaluation agent and enhancing its performance by incorporating feedback from domain experts.

Since its inception more than a decade ago, Internet of Things (IoT) technology has been guiding people in the development of a world full of smart solutions in which all devices and physical objects, represented as "things," are interlinked with sensors using the Internet. In some areas, the delivery of medications to patients or receivers at their destinations remains highly informal and old. In smart medicine delivery, the medicine needs to maintain its original state while facing multiple environmental factors, like temperature fluctuations, humidity, etc. This paper presents an effective implementation of IoT (Internet of Things) for monitoring the transportation of medicines and vaccines, along with temperature control facilitated through mobile applications and sensor networks. The system employs mobile applications as the user interface, utilizes Arduino, MQTT (Message Queuing Telemetry Transport) for communication, incorporates a temperature sensor, and employs a mini portable cooling box. Designed for the generalized delivery of medicines/vaccines from sender to receiver, the system also suggests CRC-32 as an optimal algorithm for error detection instead of complex hash functions such as MD5 and SHA, ensuring better performance, smooth operation, and data integrity. In addition, elliptic curve based shared keys are used for protected data transmission. The accuracy of the proposed system is 89.88%, and the value of the F1-score is 0.686, which is greater than the threshold of 0.5, hence conclusively validating the authenticity and reliability of the proposed system.

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head selection where detection of such attacks is challenging. The dependency of WSN on batteries elevates the concern posed by these threats, making detection and isolation crucial, especially within the framework of energy-efficient clustering protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH). Various approaches have been proposed in prior research to deal with such attacks. However, the use of memory-efficient data structures has yet to be effectively addressed. In this article, considering the limitations of WSN, we utilize memory-efficient data structures named Bloom filters, count-min (CM) sketch, and cellular automata (CA) to address abnormal energy drainage. A CA-based trust model is used to choose the legitimate node as the cluster head. CM sketch is used to control the frequency of a node selected as a cluster head, achieving fairness in the cluster head selection process, and Bloom filters maintain the record of malicious nodes blocked from participating in the communication or cluster head selection process. CA and trust functions collectively keep a record of neighbors’ energy and their trust in the network. Grayhole, blackhole, and scheduling attacks are three well-known threats that lead to abnormal energy drainage in legitimate nodes. The proposed solution effectively detects and addresses abnormal energy drainage in WSN. Its impact is simulated and observed using ns2 IEEE 802.15.4 medium access control (MAC) and LEACH clustering protocols, specifically in the context of the mentioned attacks. The effectiveness of the proposed model was rigorously analysed, and it was observed that it reduces the energy consumption of WSN by approximately 16.66%, 48.33%, and 43.33% in the cases of grayhole, blackhole, and scheduling attacks, respectively. In terms of space/time complexity, its growth is linear O(n). The proposed solution also consumes 0.08-0.10 J more energy compared to the original LEACH as a cost of the solution, which is not more than 2% of the total initial energy. The tradeoff of implementing heightened security is worthwhile, as the proposed approach outperforms the original LEACH and related methods, effectively mitigating abnormal energy drainage in WSN and extending network lifetime, especially in challenging environments with persistent battery recharging challenges.