An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
Source abstract
Abstract
Wireless sensor networks (WSNs) include several sensor nodes that have limited capabilities. The most critical restriction in WSNs is energy resources. Moreover, since each sensor node’s energy resources cannot be recharged or replaced, it is inevitable to propose various methods for managing the energy resources. Furthermore, this procedure increases the network lifetime. In wireless sensor networks, the cluster head has a significant impact on system global scalability, energy efficiency, and lifetime. Furthermore, the cluster head is most important in combining, aggregating, and transferring data that are received from other cluster nodes. One of the substantial challenges in a cluster-based network is to choose a suitable cluster head. In this paper, to select an appropriate cluster head, we first model this problem by using multi-factor decision-making according to the four factors, including energy, mobility, distance to centre, and the length of data queues. Then, we use the Cluster Splitting Process (CSP) algorithm and the Analytical Hierarchy Process (AHP) method in order to provide a new method to solve this problem. These four factors are examined in our proposed approach, and our method is compared with the Base station Controlled Dynamic Clustering Protocol (BCDCP) algorithm. The simulation results show the proposed method in improving the network lifetime has better performance than the base station controlled dynamic clustering protocol algorithm. In our proposed method, the energy reduction is almost 5% more than the BCDCP method, and the packet loss rate in our proposed method is almost 25% lower than in the BCDCP method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Efficient Wireless Sensor Networks · IoT-based Smart Home Systems · Energy Harvesting in Wireless Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Combinatorial and Geometric Algorithms Lab., Department of Mathematical Sciences, Yazd University, Yazd 352, Iran
Department of Mathematics, Chabahar Maritime University, Chabahar 545, Iran
Institute of IR 4.0, The National University of Malaysia, Bangi 43600 UKM, Malaysia
School of Mathematical Sciences, Kean University (Wenzhou Campus), Wenzhou 325060, China
Department of Mathematics & Statistics, McMaster University, Hamilton, ON L8S 4K1, Canada
Center for Dynamics, Faculty of Mathematics, Technische Universität Dresden, 01062 Dresden, Germany
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