A Swarm-based Scheduling Algorithm for Lifetime Improvement of Visual Sensor Networks

Subject Areas : International Journal of Smart Electrical Engineering

Mir Gholamreza  Mortazavi ¹ (Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Mirsaeid  Hosseini Shirvani ² (Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran)
Arash  Dana ³ (Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Mahmood  Fathy ⁴ (School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran|School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran)

Received: 2023-12-12 Accepted : 2024-01-28 Published : 2024-03-28

Keywords: Visual Sensor Network (VSN), Directional Sensor Network (DSN), Discrete Cuckoo Search Optimization Algorithm (DCSA), Network Lifetime Expansion. Scheduling,

Abstract :

Visual sensor networks (VSNs) apply directional sensors that can be configured only in one direction and also can be set in one of the possible observing ranges. In this battery-resource-limited environment, battery management and network lifetime expansion are still important challenges. The target coverage problem in such networks, in which all of the specified targets must be continuously observed and monitored by administrators is formulated as an integer linear programming problem (ILP) that is an NP-Hard problem. Although several approaches have been presented in the literature to solve the aforementioned problem, the majority of them suffer from getting stuck in the local trap and low exploration in search space. To address the issue, a discrete cuckoo-search optimization algorithm (DCSA) is extended to solve this combinatorial problem. The discrete operator of the proposed algorithm is designed in such a way that explore search space efficiently and lead to balancing in the local and global search process. The proposed algorithm was examined in different conducted scenarios. The returned results of simulations of numerous scenarios show the dominance of the proposed algorithm in comparison with other existing approaches in terms of network lifetime maximization. In other words, the proposed DCSA has 19.75% and 13.75% improvement in terms of network average lifetime expansion against HMNLAR and GA-based approaches respectively in all scenarios.