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Research Paper | Electronics & Communication Engineering | India | Volume 12 Issue 3, March 2023 | Popularity: 5.4 / 10
Enhancing Node Activation in Sensor Networks Using MOCL - RFSA for Maximized Coverage, Connectivity, and Minimized Interference
R. Christal Jebi, S. Baulkani
Abstract: Wireless Sensor Networks (WSNs) can be used for surveillance, smart agriculture, and healthcare. However, WSNs face several challenges, such as coverage, connectivity, and interference issues that affect network performance. Therefore, a novel approach called the Multi objective Chaotic Learning based Red Fox Selective Activation algorithm (MOCL - RFSA) is proposed. The MOCL - RFSA method introduces six objective functions to maintain network performance. The objective function is formulated to guide the fox towards the areas of interest or target area, ensuring that nodes are activated optimally with maximum coverage, maximum connectivity, and minimum interference. The fox may leave the herd due to lack of food or be shot down by hunters. This behaviour is adapted in the proposed MOCL - RFSA algorithm to deactivate the unnecessary nodes (shot down) that cause coverage overlap in the network. This improved the network lifetime. Moreover, the proposed MOCL - RFSA algorithm introduces opposition learning and chaotic mechanism to overcome problems like local optima and premature convergence. The performance of MOCL - RFSA is estimated using simulation experiments, and the outcomes indicate that the proposed MOCL ? RFSA outperforms the existing methods with better network lifetime, residual energy, throughput, coverage, connectivity, and interference ratio.
Keywords: Coverage, Connectivity, Interference, Optimization, Selective Activation
Edition: Volume 12 Issue 3, March 2023
Pages: 1717 - 1724
DOI: https://www.doi.org/10.21275/SR23327231350
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