On the Use of the Game of Life to Improve the Performance of Event-Driven Wireless Sensor Networks

Wireless Sensor Networks are composed of a set of sensors distributed within an area that monitor physical variables of the environment and send back information to a central node. Nodes cannot always remain active since they would swiftly drain the system’s energy. As such, some works have proposed the use of different on/off schemes to monitor the phenomena of interest efficiently but also to conserve energy as much as possible. To this end, the use of on/off protocols has been used before, but has no relation to the characteristics of the monitored events. However, in scenarios where the phenomena to monitor occur in a certain pattern or specific region, the use of more suited techniques to activate the nodes can yield better results. In this sense, we propose the use of cellular automata (CA), based on the Game of Life (GoL), in order to turn the nodes on and off, according to the patterns described by the automata. Cellular automata are discrete models consisting of a lattice or grid of cells in a finite number of states that remain or change into another state following pre-established rules commonly associated with the states of their neighbors. As such, we propose to activate/deactivate the nodes following the natural behavior of the GoL scheme. Since the initial state of the cellular automata directly modifies the pattern evolution of the GoL, we consider several possible patterns that can occur in practical systems in order to prove the effectiveness of our proposal. We evaluate the system performance in terms of successful event report probability and energy consumption, comparing our results to the conventional on/off schemes with a certain probability of nodes being in the on state. With this premise, we think CA is a good alternative to determine the on/off process in WSNs. We compared the system performance of the GoL patterns compared to the classical approach and found the cases where the GoL scheme performs better.