Owing to the sensitive nature of healthcare data, the aforementioned approach to transferring patient data to central servers creates serious security and privacy issues. In addition, blockchain distributed ledger technology has introduced immutable storage and decentralized data management capability, which handles a large number of distributed nodes of E-Healthcare transactions via a serverless network, but in a limited manner because of blockchain-enabled resources. In this scenario, the medical industries are concerned about constituting an innovation in health information preservation and exchanging service delivery protocols without the connectivity of an untrusted third-party infrastructure. In this study, we proposed a blockchain hyperledger fabric-enabled consortium architecture called BIoMT, which provides security, integrity, transparency, and provenance to health-related transactions and exchanges sensitive clinical information in a serverless peer-to-peer (P2P) secure network environment. A consensus is designed and created to reduce the rate of blockchain resource constraints on the Internet of Medical Things (IoMT). The privacy of individual health transactions before sharing is protected using the NuCypher Re-Encryption mechanism, which increases security and provides medical ledger integrity and transparency. Smart contracts are created and deployed to automate device registration, exchange transactions, and ledger preservation in immutable storage (filecoin) after cross verification and validation. The experimental results show that the proposed BIoMT reduces the computational cost by 26.13%, and the robust medical node generation increases to 60.37%. Thus, only 31.79% and 74.21% of IoMT-related information and serverless P2P network usage are maintained and saved, respectively.

In recent years, unmanned aerial vehicles (UAVs) have become the trend for different types of research and applications. UAVs can accomplish some technical and risky tasks while still being safe, mobile, and inexpensive to operate. However, UAVs need flying ad-hoc networks (FANET) to operate in inaccessible or infrastructure-less areas. Subsequently, in many military and civil applications, the UAVs are connected ad hoc. FANET-based UAV systems have been developed for search and rescue, wildlife surveys, real-time monitoring, and delivery services. Maintaining the reliability and connectivity among UAV nodes in FANET becomes challenging because of the UAV movement, environmental conditions, energy efficiency, etc. Energy-aware routing protocols have become essential for developing advanced and effective FANETs. This paper presents a proposed Cross-Layer and Energy-Aware Ad-hoc On-demand Distance Vector (CLEA-AODV) routing protocol for improving FANET performance. The CLEA-AODV protocol is mainly divided into three sections: routing with AODV protocol, Glow Swarm Optimization (GSO)-based Cluster Head Selection, and Cooperative Medium Access Control (MAC). The cross-layer approach is implemented on the network layer and the data layer. The major parameters considered to evaluate the performance of the FANET are Packet Success Rate (PSR), Throughput (TP), End-to-End (E2E) delay, and packet drop ratio (PDR). The Network Simulator version 2 (NS2) is used to implement the CLEA-AODV protocol and evaluate the network performance. The results are compared with the standard AODV, Self-Organization Clustering-GSO (SOC-GSO), and Energy Efficient Neuro-Fuzzy Cluster-based Topology Construction with Meta-Heuristic Route Planning (EENFC-MRP) protocols. The results show that the CLEA-AODV surpasses these protocols in terms of PSR, TP, E2E delay, and PDR.

Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles from 2017-2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review.

Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed.

The need for sophisticated traceability systems has become essential in increasingly complex and globalized supply chains. The convergence of Blockchain (BC), Internet of Things (IoT), and Artificial Intelligence (AI) technologies offers promising solutions to enhance traceability systems across various sectors, particularly supply chain management (SCM). This paper presents a comprehensive bibliometric and systematic literature review to explore emerging trends, research patterns, and methodologies in integrating BC, IoT, and AI into traceability systems. In the study, 530 documents from the SCOPUS database for bibliometric analysis were examined, alongside a detailed review of 43 selected articles from multiple databases. The findings highlighted a significant increase in research output in recent years, with a dominant focus on agricultural supply chains and SCM. Notably, India and China lead the field in publications and citations. Furthermore, key authors and influential journals significantly contributed to advancing the research. The analysis also revealed a predominance of experimental and hybrid research methodologies, with Ethereum and Hyperledger Fabric emerging as the most widely adopted platforms for system implementations. The study identified critical research trends, including the growing role of AI-driven analytics, the importance of real-time IoT data collection, and the critical need for secure, tamper-proof data provided by BC. However, challenges, such as interoperability, scalability, and standardization, remain hindering the widespread adoption of these technologies. The paper proposes a four-layer conceptual framework for integrating BC, IoT, and AI into future traceability systems, emphasizing their potential to enhance transparency, security, and efficiency across various application areas. The paper concludes by offering directions for future research, highlighting the need for more empirical studies, industry-specific frameworks, and standardization to overcome existing limitations.

Efficient task scheduling on the cloud is critical for optimal utilization of resources in data centers. It became even more challenging with the emergence of 5G and IoT applications that generate massive number of tasks with stringent latency requirements. This gives birth to fog/edge computing - a complementary layer to cloud. Tasks latency in fog computing can be reduced as processing in the network is done closer to the end devices and users, but every task cannot be scheduled in the fog due to limited resources availability. Conventional scheduling algorithms often fail to exploit the heterogeneous resources; therefore, specially designed and well-tuned scheduling algorithms are desired for achieving better quality of service. In this study, the state-of-the-art task scheduling algorithms in the cloud and fog environments are investigated in a diverse set of dimensions. Among the relevant studies published between 2018-2022 and indexed in the Web-of-Science (WOS), SCOPUS, and Google Scholar databases, eighteen studies are selected for both the cloud and fog domains from WOS and Scopus, while seventeen studies are chosen for both the cloud and fog domains from Google Scholar. Thus, a total of 106 studies are included in this survey for the detail investigation. The scheduling algorithms are broadly classified into three categories such as heuristic, meta-heuristic, and hybrid meta-heuristic followed by detailed critical analysis. It has been observed that most of the scheduling algorithms are dynamic and non-preemptive in nature, while the higher fraction of the tasks is independent in comparison to bag of tasks andworkflows. Similarly, 97% of the studies focus on multiple objectives and 68% of the techniques are non-deterministic. Further, a total of twenty different scheduling objectives are identified with makespan, resource utilization, delay, load balancing, and energy consumption as the most significant metrics. The evaluation methods including simulations (51%), real experiments (4%), analytical equations (2%), and datasets (43%) etc. are surveyed. At the end, the open issues, challenges, and future directions are argued.