An ACOR-Based Multi-Objective WSN Deployment Example for Lunar Surveying

Abstract

Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (3He), a potential enabling element for fusion reactors, on the Moon. 3He is abundant on the Moon’s surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACOR) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases.

Ant Colony Optimization ACO Based Autonomous Secure Routing Protocol for Mobile Surveillance Systems

“…The authors in [40], created to explicitly address the issue of node placement in a network that works in the similar way as the distribution of helium-3 (3He). 3He is on of the important components for fusion reactors, on the moon.…”

Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme

“…Ant Colonies Optimization (ACO) is one of the well-known representative SIAs, where complex collective behavior emerges from the behavior of ants. ACO is effective for solving Non-deterministic Polynomial (NP) hard discrete optimization problems, and has been successfully applied to a number of scientific and engineering problems, including grid-based deployment for wireless sensor networks [ 22 , 23 , 24 ]. ACO is also applied to the topology optimization [ 25 , 26 ] and routing algorithm [ 27 , 28 ] for wireless networks.…”

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

“…In [ 63 ], López-Matencio proposes and studies a new node placement algorithm as this is a critical aspects of WSN design. The node placement determines sensing capacities, network connectivity, network lifetime and other capabilities of the WSN.…”