A Target Coverage Scheduling Scheme Based on Genetic Algorithms in Directional Sensor Networks

Abstract

As a promising tool for monitoring the physical world, directional sensor networks (DSNs) consisting of a large number of directional sensors are attracting increasing attention. As directional sensors in DSNs have limited battery power and restricted angles of sensing range, maximizing the network lifetime while monitoring all the targets in a given area remains a challenge. A major technique to conserve the energy of directional sensors is to use a node wake-up scheduling protocol by which some sensors remain active to provide sensing services, while the others are inactive to conserve their energy. In this paper, we first address a Maximum Set Covers for DSNs (MSCD) problem, which is known to be NP-complete, and present a greedy algorithm-based target coverage scheduling scheme that can solve this problem by heuristics. This scheme is used as a baseline for comparison. We then propose a target coverage scheduling scheme based on a genetic algorithm that can find the optimal cover sets to extend the network lifetime while monitoring all targets by the evolutionary global search technique. To verify and evaluate these schemes, we conducted simulations and showed that the schemes can contribute to extending the network lifetime. Simulation results indicated that the genetic algorithm-based scheduling scheme had better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

A novel multi-objective coverage optimization memetic algorithm for directional sensor networks

“…17 We propose an algorithm to solve the MSCD problem in the literature. 18 The network in Figure 1 comprises three targets and three directional sensors. …”

“…The network lifetime that is obtained by the three algorithms increases along with the number of sensors that are deployed in area A. MOCOMA outperforms GA 18 and GA 17 in extending the network lifetime. Figure 6 shows the average number of sensors per cover set that is used by the algorithms under different numbers of sensors.…”

“…We take the average value of the experiments as the result of each algorithm. The proposed algorithm is compared with GA 18 and GA 17 from the aspects of network lifetime. The experimental results show that the proposed algorithm outperforms the GA 18 and GA 17 in terms of extending the network lifetime.…”

“…This article extended the network lifetime by producing more cover sets. Gil and Han 18 proposed two heuristic algorithms that are based on the greedy and GAs to address the target coverage problem of the DSN. Although the greedy algorithm can rapidly solve the problem, the results are significantly influenced by the search method and, in several cases, may fall into the local optimal solution.…”

“…Gil and Han 18 proposed a maximum cover set for addressing the Maximum Set Covers for DSNs (MSCD) problem in DSNs, which involves the identification of cover sets that can monitor all targets in an energyefficient way and the maximization of network lifetime by assigning different scheduling times to each cover set. The cover set in the DSN can be classified into disjoint and non-disjoint.…”