Sandeep Verma

Optimization algorithms are crucial for energy-efficient routing in Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) because they help minimize energy consumption, reduce communication overhead, and improve overall network performance. By optimizing the routing paths and scheduling data transmission, these algorithms can prolong network lifetime by efficiently managing the limited energy resources of sensor nodes, ensuring reliable data delivery while conserving energy. In this work, we present Greylag Goose-based Optimized Clustering (GGOC), which aids in selecting the Cluster Head (CH) using the proposed critical fitness parameters. These parameters include residual energy, sensor sensing range, distance of a candidate node from the sink, number of neighboring nodes, and energy consumption rate. Simulation analysis shows that the proposed approach improves various performance metrics, namely network lifetime, stability period, throughput, the network’s remaining energy, and the number of clusters formed.

Wireless sensor networks (WSNs) can be conceptualized as large-scale, dynamic social systems where nodes interact to achieve collective objectives. These networks generate extensive data through interactions, offering opportunities for large-scale knowledge discovery to optimize operations and enhance resilience. However, challenges such as limited resources and susceptibility to distributed denial-of-service (DDoS) attacks necessitate efficient and secure mechanisms for managing these “social” interactions. This article proposes a lightweight trusted framework that applies computational modeling principles to clustering in WSNs. The framework employs bi-directional long short-term memory (Bi-LSTM) networks for malicious node detection, mirroring the role of anomaly detection in social systems, and uses the walrus optimization algorithm (WOA) for optimized cluster head (CH) selection. By considering parameters such as residual energy, proximity to base stations, node density, and trust value, WOA ensures effective “role assignment” within the network, similar to optimizing functional roles in human social systems. The Bi-LSTM model analyzes node behavior to exclude malicious actors, fostering trusted, and efficient clustering. Evaluated in simulated DDoS attack scenarios, the framework significantly reduces the impact of attacks by isolating malicious nodes while improving network performance and resilience. Metrics such as stability period, throughput, network lifetime, energy efficiency, and attack mitigation are analyzed, demonstrating the framework’s effectiveness. This research bridges the domains of social system modeling and WSN operations, providing an energy-efficient and secure solution for managing large-scale dynamic networks.

The Internet of Medical Things (IoMT) merges healthcare services with Internet of Things (IoT) technology to deliver remote patient monitoring and treatment in real-time. However, the body sensor nodes suffer from energy limitations and need to be operated under energy-efficient routing techniques. For large-area applications, the static network scenario of Wireless Body Area Network (WBAN) causes the hot-spot problem. To address the dynamic network scenario of WBAN, and to enhance the energy efficiency of body sensor nodes, we present Sink-mobility-based Energy Efficient Data Dissemination in IoMT (SEEDI). The four body sensor nodes that are continuously Energy Powered (EP), are deployed around each periphery of the rectangular-shaped network. The sink collects data from these body sensor nodes, which in turn collect data from the nearby patients in a continuous pattern. In addition, on each patient’s body, the Cluster Head (CH) selection is done using a recently proposed algorithm namely, Remora Optimization Algorithm (ROA) by optimizing the fitness parameters. It is observed that SEEDI outperforms the state-of-the-art routing algorithms of WBAN and also outperforms our previously presented algorithm i.e., Intelligent-Routing Algorithm for WBAN (I-RAW).

In this letter, we study a traffic-aware multi-beam optimal resource allocation strategy for fixed home users who are serviced through the utilization of simultaneous multiple beams generated by next-generation node B (gNB). The strategy takes into account the effects of rain and foliage attenuation. We propose a graphical methodology that is combined with a closed-form expression to compute the optimal coverage radius of gNB. Afterwards, the k-means clustering algorithm is utilized to determine an optimal location for the gNB. In this study, we also present an approach that employs non-linear programming (NLP) to allocate power and bandwidth among individual beams, with an objective of satisfying the traffic requirements of each user. From the results, it is evident that our proposed approach exhibits superior performance in scenarios characterized by rain and foliage attenuation, in comparison to alternatives relying on genetic algorithms and surrogate optimization. approaches.

Industrial cyber–physical systems (ICPSs) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, artificial intelligence (AI) has had a significant impact on wireless sensor networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based green routing for ICPS. While following the cluster-based routing, the election of cluster head (CH) is executed using our proposed AI-inspired extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.

The exponential growth in the smart cities and the massive deployment of wireless sensor network‐based Internet of Things (IoT) has resulted in generating the humongous data, which needs to be orchestrated. Further, it is observed that the resource‐constrained IoT devices act as stumbling block in the successful realization of cognitive smart cities. Hence, there is a high need to manage the data transmission from massive IoT devices and also to enhance the productivity of such devices. To address this issue, in this article, we present energy‐efficient routing paradigm for resource‐constrained IoT‐based cognitive smart city (EI‐CSC). We adapt Sooty tern optimization algorithm (STOA) due to its faster convergence and high ‘exploration and exploitation’ capabilities to perform energy efficient cluster‐based routing. We focus primarily on rendering the optimized solution to the cluster head selection problem through STOA. The outcomes of simulation analysis of EI‐CSC promises enhanced performance in the context of stability period and network lifetime by 83.4% and 107.7%, ‘respectively’ as compared to recently proposed ‘genetic algorithm and PSO based hybrid clustering algorithm’.

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and `security compromise' are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and `number of packets sent', which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT application and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.