JOURNAL ARTICLE

Energy-efficient and location-aware ant colony based routing algorithms for wireless sensor networks

Abstract

In recent years, advances in miniaturization, low-power circuit design, simple, low power, yet reasonably efficient wireless communication equipment, and improved small-scale energy supplies have combined with reduced manufacturing costs to make a new technological vision possible, Wireless Sensor Networks (WSN). As WSN are still a young research field, much activity is still on-going to solve many open issues. One is the data routing problem. As the size of the network increases, this problem becomes more complex due the amount of sensor nodes in the network. The meta-heuristic Ant Colony Optimization (ACO) has been proposed to solve this issue. ACO based routing algorithms can add a significant contribution to assist in the maximisation of the network lifetime and in the minimisation of the latency in data transmissions, but this is only possible by means of an adaptable and balanced algorithm that takes into account the WSN main restrictions, for example, memory and power supply. A comparison of two ACO based routing algorithms for WSN is presented, taking into account current amounts of energy consumption under a WSN scenario proposed in this work. Furthermore, a new routing algorithm is defined.

Keywords:
Computer science Wireless sensor network Ant colony optimization algorithms Energy consumption Geographic routing Routing (electronic design automation) Routing protocol Computer network Latency (audio) Multipath routing Static routing Distributed computing Algorithm Engineering Telecommunications Electrical engineering

Metrics

8
Cited By
1.47
FWCI (Field Weighted Citation Impact)
24
Refs
0.82
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Energy Efficient Wireless Sensor Networks
Physical Sciences →  Computer Science →  Computer Networks and Communications
Metaheuristic Optimization Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
Energy Harvesting in Wireless Networks
Physical Sciences →  Engineering →  Electrical and Electronic Engineering

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