Abstract
Wireless Sensor Networks are characterized by having specific requirements such as limited energy availability, low memory and reduced processing power. On the other hand, these networks have enormous potential applicability, e.g., habitat monitoring, medical care, military surveillance or traffic control. Many protocols have been developed for Wireless Sensor Networks that try to overcome the constraints that characterize this type of networks. Ant-based routing protocols can add a significant contribution to assist in the maximisation of the network lifetime, but this is only possible by means of an adaptable and balanced algorithm that takes into account the Wireless Sensor Networks main restrictions. This paper presents a new Wireless Sensor Network routing protocol, which is based on the Ant Colony Optimization metaheuristic. The protocol was studied by simulation for several Wireless Sensor Network scenarios and the results clearly show that it minimises communication load and maximises energy savings.
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References
Estrin, D., et al.: Embedded, Everywhere: A research Agenda for Network Systems of Embedded Computers, National Research Council Report (2001)
Handy, M., Haase, M., Timmermann, D.: Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection. In: 4th IEEE International Conference on Mobile and Wireless Communications Networks, Stockholm (2002)
Lindsey, S., Raghavendra, C.: PEGASIS: Power Efficient GAthering in Sensor Information Systems. In: ICC (2001)
Lindsey, S., Raghavendra, C., Sivalingam, K.: Data Gathering in Sensor Networks using the EnergyDelay Metric (2000)
Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed Diffusion: a scalable and robust communication paradigm for sensor networks. ACM Press, New York (2000)
Jeon, P., Rao, R., Kesidis, G.: Two-Priority Routing in Sensor MANETs Using Both Energy and Delay Metrics (in preparation, 2004)
Di Caro, G., Dorigo, M.: AntNet: Distributed Stigmergetic Control for Communications Networks. Journal of Artificial Intelligence Research (JAIR) 9, 317–365 (1998)
Zhang, Y., Kuhn, L., Fromherz, M.: Improvements on Ant Routing for Sensor Networks. In: Ants 2004, Int. Workshop on Ant Colony Optimization and Swarm Intelligence (September 2004)
Singh, G., Das, S., Gosavi, S., Pujar, S.: Ant Colony Algorithms for Steiner Trees: An Application to Routing in Sensor Networks. In: de Castro, L.N., von Zuben, F.J. (eds.) Recent Developments in Biologically Inspired Computing, pp. 181–206. Idea Group Publishing, USA (2004)
Zuniga, M.Z., Krishnamachari, B.: Integrating Future Large-Scale Wireless Sensor Networks with the Internet, Department of Electrical Engineering, UNiversity of Southern California (2002)
Alonso, J., Dunkels, A., Voigt, T.: Bounds on the energy consumption of routings in wireless sensor nodes. In: WiOpt 2004: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, Cambridge, UK (March 2004)
Ye, W., Heidemann, J.: Medium Access Control in Wireless Sensor Networks. In: Wireless Sensor Networks, Kluwer Academic Publishers, Dordrecht (2004)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Network Simulator-2: http://www.isi.edu/nsnam/ns/
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© 2006 Springer-Verlag Berlin Heidelberg
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Camilo, T., Carreto, C., Silva, J.S., Boavida, F. (2006). An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_5
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DOI: https://doi.org/10.1007/11839088_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
Online ISBN: 978-3-540-38483-0
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