An Ant Colony Optimization Approach for Maximizing the Lifetime of Heterogeneous Wireless Sensor Networks

Abstract

-Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist for addressing the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs is progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) in solving combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor of the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs.

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

“…The benchmark algorithms include (1) the proposed GATPC, (2) the proposed GA with the Boolean disk model which is commonly used in literature (GABD) [14,15], (3) the proposed GA without repair mechanism (GAW), (4) a discrete version of particle swarm optimization (PSO) algorithm [39], and (5) an ant colony optimization (ACO) algorithm [40] which were used in two similar wireless coverage problems, as well as (6) the former PSO with the repair mechanism proposed in this paper (Algorithm 2), which is named PSOR.…”

“…As no repair mechanism is used in GAW, PSO, and ACO, a solution is assigned the maximal fitness if it does not satisfy the required coverage rate, aiming to eliminate unqualified solutions. To get tailored for the TPC problem, the ACO in [40] has the following adaptations. The construction graph in pheromone is deposited between two APs and is calculated by Eq.…”

“…The construction graph in pheromone is deposited between two APs and is calculated by Eq. (10) in [40]. The heuristic information is based on the increment in the actual coverage rate.…”

“…The probability to select a transmit power level is calculated by Eq. (12) in [40], except that the index k refers to a transmit power level of an AP. An ant selects a transmit power level using Eq.…”

A survey on natural computing techniques

“…al. [24] proposes an Ant Colony Optimization-based Approach for Maximizing the Number of Connected Covers (ACO-MNCC) to support the lifetime of heterogeneous WSNs. The procedure relies upon finding the most extraordinary number of disjoint related spreads that satisfy both distinguishing extension and framework organize.…”