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CLC number: TP393

On-line Access: 2008-03-08

Received: 2007-07-16

Revision Accepted: 2007-11-28

Crosschecked: 0000-00-00

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.4 P.531~538

http://doi.org/10.1631/jzus.A071382


Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics


Author(s):  Yao-feng WEN, Yu-quan CHEN, Min PAN

Affiliation(s):  State Specialized Laboratory of Biomedical Sensors, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yfwen@accelsemi.com, yqchen@mail.bme.zju.edu.cn

Key Words:  Ant colony optimization (ACO), Pheromones, Power consumption, Wireless sensor networks (WSNs)


Yao-feng WEN, Yu-quan CHEN, Min PAN. Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics[J]. Journal of Zhejiang University Science A, 2008, 9(4): 531~538.

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author="Yao-feng WEN, Yu-quan CHEN, Min PAN",
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T1 - Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics
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DOI - 10.1631/jzus.A071382


Abstract: 
To find the optimal routing is always an important topic in wireless sensor networks (WSNs). Considering a WSN where the nodes have limited energy, we propose a novel Energy*Delay model based on ant algorithms (“E&D ANTS” for short) to minimize the time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. Our goal is not only to maximize the lifetime of the network but also to provide real-time data transmission services. However, because of the tradeoff of energy and delay in wireless network systems, the reinforcement learning (RL) algorithm is introduced to train the model. In this survey, the paradigm of E&D ANTS is explicated and compared to other ant-based routing algorithms like AntNet and AntChain about the issues of routing information, routing overhead and adaptation. Simulation results show that our method performs about seven times better than AntNet and also outperforms AntChain by more than 150% in terms of energy cost and delay per round.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[6] Di Caro, G., Dorigo, M., 1997. AntNet: A Mobile Agents Approach to Adaptive Routing. Tech. Rep. IRIDIA/97-12, IRIDIA. Free Brussels University, Belgium.

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