Abstract
We propose a second–order Cellular Automata (CA)–based approach to solve a problem of lifetime optimization in Wireless Sensor Networks (WSN). A WSN graph created for a given deployment of WSN in monitored area is considered as a multiagent system, where agents take part in a spatial Prisoner’s Dilemma game. We propose a local, agent–player oriented criterion which incorporates issues of area coverage and sensors energy spending. Agents act in such a way to maximize their profits what results in achieving by them a solution corresponding to Nash equilibrium. We show that the system is self–optimizing, i.e. is able to optimize a global criterion not known for players, related to a Nash equilibrium, which provides a balance between requested coverage and spending energy, and results in expanding WSN lifetime. The proposed approach is validated by a number of experimental results.
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Notes
- 1.
The WSN graph in Fig. 1(b) was obtained under the assumption that the number M of PoI is equal to \( 21 \times 21 = 441 \) as explained in the following section.
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Seredyński, F., Kulpa, T., Hoffmann, R., Désérable, D. (2022). Towards Self–optimizing Sensor Networks: Game–Theoretic Second–Order CA–Based Approach. In: Chopard, B., Bandini, S., Dennunzio, A., Arabi Haddad, M. (eds) Cellular Automata. ACRI 2022. Lecture Notes in Computer Science, vol 13402. Springer, Cham. https://doi.org/10.1007/978-3-031-14926-9_19
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