Energy-Efficient Coverage of Wireless Sensor Networks Using Ant Colony Optimization With Three Types of Pheromones

• Published in: IEEE transactions on industrial informatics Vol. 7; no. 3; pp. 419 - 427
• Main Authors: Lee, Joon-Woo, Choi, Byoung-Suk, Lee, Ju-Jang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Ant colony optimization (ACO); Colonies; EEC; Energy use; energy-efficient coverage; Formicidae; Mathematical models; network lifetime; Networks; Optimization; Pheromones; Probabilistic logic; Sensor phenomena and characterization; Sensors; three types of pheromones; wireless sensor network (WSN); Wireless sensor networks
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2011.2158836

The Efficient-Energy Coverage (EEC) problem is an important issue when implementing Wireless Sensor Networks (WSNs) because of the need to limit energy use. In this paper, we propose a new approach to solving the EEC problem using a novel Ant Colony Optimization (ACO) algorithm. The proposed ACO algorithm has a unique characteristic that conventional ACO algorithms do not have. The proposed ACO algorithm (Three Pheromones ACO, TPACO) uses three types of pheromones to find the solution efficiently, whereas conventional ACO algorithms use only one type of pheromone. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI), and the other is used to form a sensor set that has as many sensors as an ant has selected the number of active sensors by using the former pheromone. The TPACO algorithm has another advantage in that the two user parameters of ACO algorithms are not used. We also introduce some techniques that lead to a more realistic approach to solving the EEC problem. The first technique is to utilize the probabilistic sensor detection model. The second method is to use different kinds of sensors, i.e., heterogeneous sensors in continuous space, not a grid-based discrete space. Simulation results show the effectiveness of our algorithm over other algorithms, in terms of the whole network lifetime.