Full Text:   <3365>

CLC number: TP393

On-line Access: 2008-03-08

Received: 2007-07-16

Revision Accepted: 2007-11-28

Crosschecked: 0000-00-00

Cited: 23

Clicked: 7201

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

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


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.

@article{title="Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics",
author="Yao-feng WEN, Yu-quan CHEN, Min PAN",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics
%A Yao-feng WEN
%A Yu-quan CHEN
%A Min PAN
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 4
%P 531-538
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071382

T1 - Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics
A1 - Yao-feng WEN
A1 - Yu-quan CHEN
A1 - Min PAN
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 4
SP - 531
EP - 538
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A071382

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


[1] Arici, T., Altunbasak, Y., 2004. Adaptive Sensing for Environment Monitoring Using Wireless Sensor Networks. Proc. Wireless Communications and Networking Conf., 3:2347-2352.

[2] Baran, B., Sosa, R., 2000. A New Approach for AntNet Routing. Proc. 9th Int. Conf. Computer Communications Networks, 10:303-308.

[3] Bonabeau, E., Dorigo, M., Theraulaz, G., 2000. Inspiration for optimization from social insect behavior. Nature, 406(6791):39-42.

[4] Chang, J.H., Tassiulas, L., 2000. Energy-Conserving Routing in Wireless Ad-hoc Networks. Proc. IEEE INFOCOM, 1:22-31.

[5] De Couto, D.S.J., Aguayo, D., Bicket, J., Morris, R., 2003. A High-Throughput Path Metric for Multi-Hop Wireless Routing. Proc. 9th Annual Int. Conf. on Mobile Computing and Networking, 9:134-146.

[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.

[7] Ding, N., Liu, P.X., Hu, C., 2005. Data Gathering Communication in Wireless Sensor Networks Using Ant Colony Optimization. Proc. Int. Conf. on Intelligent Robots and Systems, 8:729-734.

[8] Dorigo, M., Di Caro, G., 1999. Ant Colony Optimization: A New Meta-Heuristic. Proc. Congress on Evolutionary Computation, 2:1470-1477.

[9] Dorigo, M., Di Caro, G., Gambardella, L.M., 1999. Ant algorithms for discrete optimization. Artificial Life, 5(2):137-172.

[10] Dorigo, M., Bonabeau, E., Theraulaz, G., 2000. Ant algorithms and stigmergy. Future Generation Computer Systems, 16:851-871.

[11] Golmie, N., Cypher, D., Rebala, O., 2005. Performance analysis of low rate wireless technologies for medical applications. Computer Commun., 28(10):1266-1275.

[12] Nemeroff, J., Garcia, L., Hampel, D., Di Pierro, S., 2001. Application of Sensor Network Communications. Proc. Military Communications Conf., 1:336-341.

[13] Wu, C.M., Chen, Z., Jiang, M., 2006. The research on initialization of ants system and configuration of parameters for different TSP problems in ant algorithm. Acta Electronica Sinica, 34(8):1530-1533 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE