ACO based qos routing algorithm for wireless sensor networks

  • Authors:

  • Wenyu Cai;Xinyu Jin;Yu Zhang;Kangsheng Chen;Rui Wang

  • Affiliations:

  • Department of Information Science & Electronic Engineering, College of Information Science & Engineering, Zhejiang University, Hangzhou, China;Department of Information Science & Electronic Engineering, College of Information Science & Engineering, Zhejiang University, Hangzhou, China;Department of Information Science & Electronic Engineering, College of Information Science & Engineering, Zhejiang University, Hangzhou, China;Department of Information Science & Electronic Engineering, College of Information Science & Engineering, Zhejiang University, Hangzhou, China;Department of Information Science & Electronic Engineering, College of Information Science & Engineering, Zhejiang University, Hangzhou, China

  • Venue:
  • UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
  • Year:
  • 2006

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Abstract

In this paper, we proposed an approach for Quality of Service (QoS) routing algorithm of Wireless Sensor Networks (WSNs) based on Ant Colony Optimization (ACO). The special characteristics of WSNs need to reduce the computational complexity and energy consumption of the QoS routing algorithm especially. We note that ACO algorithm using collective intelligence of artificial ants as intelligent agents is very appropriate to solve the combinatorial optimization problems in a fully distributed way, so in this paper we use modified ACO approach to solve Delay Constraint Maximum Energy Residual Ratio (DCMERR) QoS routing problem of WSNs. The QoS routing solution proposed in this manuscript, which is named as ACO based QoS routing algorithm (ACO-QoSR), searches for the best paths, which are satisfied with the QoS requirements with intelligent artificial ants. To overcome the problem of limited energy in WSNs, there are some modifications to enhance ACO’s convergence rate. ACO-QoSR algorithm is the tradeoff between a certain guaranteed QoS requirements and acceptable computational complexity. The simulation results verify that ACO-QoSR algorithm can reduce the selected paths’ delay and improve the selected paths’ normalized energy residual ratio at the similar levels of routing overhead.