This paper presents the LightChain-RAN-RF, a lightweight blockchain-based architecture designed to enhance the security, privacy, and efficiency of Radio Frequency Identification (RFID) systems operating in Dense Reader Environments (DRE). By combining CSMA-based anti-collision protocols with mutual authentication, encrypted communication, and InterPlanetary File System (IPFS)-backed blockchain storage, the proposed method addresses key challenges such as reader collisions, energy consumption, and vulnerability to attacks like Man-In-The-Middle (MITM) and Sybil. RFID readers act as light blockchain nodes, ensuring secure, scalable interaction across distributed networks. The architecture is fully compatible with Open Radio Access Network (O-RAN) frameworks, allowing RFID readers to function as trusted edge devices in virtualized, Artificial intelligence (AI)-driven mobile infrastructures. Simulation results confirm significant improvements in throughput (almost 60%) and decreases in energy efficiency (almost$\mathbf{1. 4 ~ J}$), demonstrating the system's suitability for modern industrial IoT and mobile network applications.

Abstract Human activity recognition has been a popular research topic in recent years. The rapid development of deep learning techniques has greatly helped researchers to achieve success in this field. During the training process with deep learning techniques, features and time dependencies between them are well learned. However, researchers generally ignore the distribution of extracted features in the coordinate space despite their significant effect on classification and network convergence status. The present article utilizes a simple but effective centralized coordinate learning method that dispersedly spans extracted features across the coordinate space. This method causes the angle between the features of different classes to increase significantly. A hybrid loss function is also suggested to enhance the discriminative power of learned features. Some experiments were carried out on the OPPORTUNITY and the PAMAP2 datasets. The results showed that the proposed method outperformed the recently proposed deep learning methods, including the Deep ConvLSTM, CNN‐LSTM‐ELM, and Hybrid methods. This high efficiency was due to the identification of discriminative features.

Abstract The growth of the world's population, especially that of the elderly, along with the outbreak of infectious diseases such as COVID‐19 have caused hospitals and healthcare centres to become full, and even economical treatments cost a lot. On that account, the conjunction of wireless body area networks (WBAN) and Internet of Things (IoT) for healthcare and medical diagnosis has become really important, and is accordingly one of the most popular and attractive areas of the Internet of Things (IoT). In such an IoT, a wireless body area network (WBAN) consists of a miniature sample of the Internet of Medical Things (IoMT) that can be either implanted in the human body or wearable. Nowadays, IoT has made healthcare evaluation possible. Instead of the patient being constantly hospitalized for treatment, the condition of the person is sent to the health centre by the IoMT over the Internet. IoT enables wireless communication between smart devices on one side and almost anything on the other. Since this network deals with medical and critical conditions, data must be sent to a physician or practitioner in the prescribed period; this indicates that routing is one of the most critical issues. Thus, routing is considered a very important challenge in WBANs. The present study describes thermal (temperature)‐aware routing protocols in WBANs. Routing protocols in WBANs are divided into thermal (temperature)‐aware, QoS‐aware, security‐aware, cluster‐based, cross‐layered, postured‐based, cost‐effect, link‐aware, and opportunistic ones. In a WBAN, temperature rise in implant nodes can damage body tissues, which is dangerous for the patient. Accordingly, here, those algorithms were considered which are presented in thermal (temperature)‐aware protocols. This paper first introduces IoT‐based WBANs, their routing mechanism and challenges, after which it provides a detailed description of thermal (temperature)‐aware algorithms. Finally, the advantages and disadvantages of these algorithms are presented.

Abstract Today, a directional sensor network is a popular environment for solving the target coverage problem. Monitoring all targets in a DSN is a crucial challenge to scholars working in this field of study. Adjusting the angle and range of the sensors can be an efficient technique for improving the network performance. In this way, the network has the most extended lifespan and, at the same time, spends the least time to find the best cover set. In this method, each sensor dynamically adjusts its own sensing angle in order to find the targets by choosing the best range. The present study proposed a continuous learning automata‐based method to choose the optimum sensing angle for the sensors in a DSN. Then, to evaluate the proposed algorithm performance, its results were compared to those of a conventional automata‐based method whose algorithm worked based on continuous automata. The comparative analysis confirmed the superiority of the proposed method over the conventional automata‐based method regarding the extension of the network lifespan.

Summary Providing a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto‐scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale‐up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE‐K loop to auto‐scale the resources for multilayered cloud applications. K‐nearest neighbor (K‐NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.