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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arici, T., Altunbasak, Y., 2004. Adaptive Sensing for Environment Monitoring Using Wireless Sensor Networks. Proc. Wireless Communications and Networking Conf., 3:2347–2352.
Baran, B., Sosa, R., 2000. A New Approach for AntNet Routing. Proc. 9th Int. Conf. Computer Communications Networks, 10:303–308. [doi:10.1109/ICCCN.2000.885506]
Bonabeau, E., Dorigo, M., Theraulaz, G., 2000. Inspiration for optimization from social insect behavior. Nature, 406(6791):39–42. [doi:10.1038/35017500]
Chang, J.H., Tassiulas, L., 2000. Energy-Conserving Routing in Wireless Ad-hoc Networks. Proc. IEEE INFOCOM, 1:22–31. [doi:10.1109/INFCOM.2000.832170]
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. [doi:10.1145/938985.939000]
Di Caro, G., Dorigo, M., 1997. AntNet: A Mobile Agents Approach to Adaptive Routing. Tech. Rep. IRIDIA/97-12, IRIDIA. Free Brussels University, Belgium.
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. [doi:10.1109/IROS.2005.1545067]
Dorigo, M., Di Caro, G., 1999. Ant Colony Optimization: A New Meta-Heuristic. Proc. Congress on Evolutionary Computation, 2:1470–1477. [doi:10.1109/CEC.1999.782657]
Dorigo, M., Di Caro, G., Gambardella, L.M., 1999. Ant algorithms for discrete optimization. Artificial Life, 5(2):137–172. [doi:10.1162/106454699568728]
Dorigo, M., Bonabeau, E., Theraulaz, G., 2000. Ant algorithms and stigmergy. Future Generation Computer Systems, 16:851–871. [doi:10.1016/S0167-739X(00)00042-X]
Golmie, N., Cypher, D., Rebala, O., 2005. Performance analysis of low rate wireless technologies for medical applications. Computer Commun., 28(10):1266–1275. [doi:10.1016/j.comcom.2004.07.021]
Nemeroff, J., Garcia, L., Hampel, D., Di Pierro, S., 2001. Application of Sensor Network Communications. Proc. Military Communications Conf., 1:336–341. [doi:10.1109/MILCOM.2001.985815]
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).
Author information
Authors and Affiliations
Corresponding author
Additional information
Project (No. 30470461) supported in part by the National Natural Science Foundation of China
Rights and permissions
About this article
Cite this article
Wen, Yf., Chen, Yq. & Pan, M. Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics. J. Zhejiang Univ. Sci. A 9, 531–538 (2008). https://doi.org/10.1631/jzus.A071382
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.A071382