Latency and Network Lifetime Trade-Off in Geographic Multicast Routing for Multi-Sink Wireless Sensor Networks
The deployment of multiple sinks in Wireless Sensor Networks may provide better reliability, timely communication and longevity, depending on the routing strategy. Moreover, geographic routing is a powerful strategy to avoid the costs related to maintaining high network knowledge. In this paper, we present a Geographic Multicast Routing solution (GeoM), focused on the latency and network lifetime trade-off. Our solution considers a linear combination of network metrics during the decision process of the next hop. Packets are forwarded to all sinks, and duplications are defined on the fly during the forwarding. The network lifetime is addressed with an energy balance strategy and a trade-off between progress and energy cost. We make use of the maximum energy consumption as an indication of the network lifetime. Simulation results show that GeoM has an overall better performance than the existing solutions, with improvements of approximately 11% for the average latency, and 54% for the maximum energy consumption.
KeywordsMulti-Sink Wireless Sensor Networks Routing Multicast Latency Network lifetime Geographic routing
This work is partially supported by the Brazilian National Council for Scientific and Technological Development (CNPq). Computations have been performed on the supercomputer facilities of the Mésocentre de calcul de Franche-Comté.
- 1.Buettner, M., Yee, G.V., Anderson, E., Han, R.: X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks. In: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, pp. 307–320. ACM (2006)Google Scholar
- 6.Contiki OS: The Open Source OS for the Internet of Things. http://www.contiki-os.org/. Accessed 20 Mar 2018
- 7.Facility FIT: FIT IoT-LAB. https://www.iot-lab.info/. Accessed 20 Mar 2018
- 9.He, X., Kamei, S., Fujita, S.: Autonomous multi-source multi-sink routing in wireless sensor networks. Inf. Media Technol. 7(1), 488–495 (2012)Google Scholar
- 10.Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: 33rd Annual Hawaii International Conference on System Sciences (HICSS), pp. 1–10. IEEE (2000)Google Scholar
- 13.Mitton, N., Simplot-Ryl, D., Stojmenovic, I.: Guaranteed delivery for geographical anycasting in wireless multi-sink sensor and sensor-actor networks. In: 28th Annual IEEE Conference on Computer Communications (INFOCOM), pp. 2691–2695 (2009)Google Scholar
- 14.Mitton, N., Simplot-Ryl, D., Voge, M.-E., Zhang, L.: Energy efficient k-anycast routing in multi-sink wireless networks with guaranteed delivery. In: Li, X.-Y., Papavassiliou, S., Ruehrup, S. (eds.) ADHOC-NOW 2012. LNCS, vol. 7363, pp. 385–398. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31638-8_29CrossRefGoogle Scholar
- 15.Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., Voigt, T.: Cross-level sensor network simulation with COOJA. In: Proceedings of 31st IEEE Conference on Local Computer Networks, pp. 641–648. IEEE (2006)Google Scholar