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A two‐level clustering mechanism for energy enhancement in Internet‐of‐Things‐based wireless sensor networks
Author(s) -
Bany Salameh Haythem,
Obaidat Heba,
AlShamali Ahmad,
Jararweh Yaser
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4913
Subject(s) - computer science , computer network , wireless sensor network , cluster analysis , network packet , relay , energy consumption , node (physics) , key distribution in wireless sensor networks , efficient energy use , distributed computing , wireless network , wireless , telecommunications , ecology , power (physics) , physics , electrical engineering , structural engineering , quantum mechanics , machine learning , engineering , biology
Summary Wireless sensors are considered the key elements in enabling efficient Internet of Things (IoT) networking and services. One of the key objectives in designing a WSN is increasing network lifetime by reducing the overall network energy consumption. To achieve this, several techniques have been proposed to extend the network lifetime, such as clustering. Clustering divides the network into several clusters, each with its own member nodes and a cluster‐head (CH) node. In this paper, a novel clustering mechanism is developed with the objective of extending network lifetime through load balancing in wireless sensor networks (WSNs). This is achieved by electing a new node to a provides specific tasks in the WSN to decrease energy consumption and enhance network lifetime. This node is called a relay node that is responsible of delivering the received data packets from the CHs, and then forwards them to the central node (sink). Unlike previous clustering schemes, the proposed algorithm consists of three main processes: CH selection, relay node selection, and medium access control processes. These processes depend on energy level and user's distributions. The performance of the proposed algorithm is investigated through simulation experiments in terms of network lifetime under various network parameters. The results reveal that the proposed mechanism significantly enhances network lifetime compared to previously proposed algorithms.