A novel approach for particle swarm optimization‐based clustering with dual sink mobility in wireless sensor network

Summary There exist numerous high‐dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high‐dimensional problems. An appropriate approach to solve such problems is to apply multi‐agent systems (MASs) along with the metaheuristic algorithms. The present paper proposes a new approach based on the MASs and the concept of agent, which is named MAS as Metaheuristic (MAMH) method. In the proposed method, several basic and powerful metaheuristic algorithms are considered as separate agents, each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. Altogether, the proposed method was tested on 32 complex benchmark functions, the results of which indicated the effectiveness and powerfulness of the proposed method for solving high‐dimensional optimization problems. In addition, in this paper, the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection. According to the results, the proposed method exhibited a higher degree of precision in the detection of spam emails compared to other metaheuristic algorithms and methods.

A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

Mohammadzadeh

Gharehchopogh

2020

A novel framework for energy‐efficient compressive data gathering in heterogeneous wireless sensor network

Manchanda

Sharma

2020

Summary Wireless sensor network (WSN) suffers from the energy‐limited sensor nodes which consume energy heavily depending upon the magnitude of data which is transmitted or received by the nodes in the network. In this paper, our primary aim is to reduce the quantity of data transmitted to the data‐collecting sink, which helps in the energy preservation and eventually leads to network longevity. To address this concern, in this paper, we propose a novel framework for energy‐efficient compressive data gathering (NFECG) for heterogeneous WSN. NFECG works in four following phases; in the first phase, the cluster head (CH) selection is performed by considering remaining energy, “distance within the nodes and the sink,” and node density; in second phase, sleep scheduling is done among the cluster member nodes; further, in third phase, the compression of the aggregated data is performed at the CH level, and equivalent compressed sparse signals are generated which are transmitted to sink. In the last phase, at the sink, decompression is applied to retrieve the original signals. The simulation of NFECG is performed using MATLAB under two cases of different network area and number of nodes. We examine its performance for various performance metrics and also inspect for its scalable characteristics. The results show that for one of the two cases, it improves stability period and network lifetime by 52.59% and 46.09%, respectively, as compared to energy‐adjusted high‐level data total tree (EHDT) protocol, and also for the other case of network configuration, it acquires supreme performance.

A novel dynamic clustering approach for energy hole mitigation in Internet of Things‐based wireless sensor network

Dogra

Rani

Sharma

et al. 2021

Summary With the advancements in the technology, Internet of Things (IoT)‐based wireless sensor networks (WSNs) have shown a tremendous growth in rendering a huge number of applications across the globe. However, it is observed that IoT‐based sensor nodes suffer from energy limitations. To resolve this, cluster‐based topology is adapted by the various researchers for rendering green energy‐efficient solution for communication of IoT devices. In this paper, the dynamic and energy‐efficient clustering for energy hole mitigation (DECEM) is proposed. The proposed framework is composed of the following proposed attributes; network is divided in two halves of regions, in each half, a gateway node (GN) is selected that collects data from their corresponding half region. Further, in each cluster, two cluster heads (CHs) are selected among whom one is made active at a moment (remains active until 60% of its energy is consumed) and other stays in sleep mode. First, the GN is selected in each side of the network (divided into two halves), and later, clustering is done, and selection of two CHs in each cluster is performed. The parameters for the selection of GN and CHs include residual energy, separation between the node and the sink, the number of neighbour nodes and network's residual energy. The simulation experiments reveal that DECEM has enhanced stability period by 5% and 31% as compared to the MEEC and IDHR protocols, respectively. The network lifetime is gigantically improved by 56% as compared to MEEC protocol.