Game of Sensors: An Energy-Efficient Method to Enhance Network Lifetime in Wireless Sensor Networks Using the Game of Life Cellular Automaton

Wireless sensor networks (WSNs) have increased in popularity since they play a relevant role in many applications, such as environmental monitoring, fire detection, and health care, to mention a few. These applications periodically collect data that is relayed to a single sink employing a many-to-one communication pattern. This pattern requires energy-efficient routing protocols since sensors closer to the sink node deplete their energy faster than those sensors located further away. As a result, several techniques have been proposed to solve this problem. For instance, some pieces of research split the network into concentric coronas to provide more energy resources in areas with heavier traffic. However, these techniques use either a predefined network deployment that is not well-suited for all sensor applications or do not always guarantee homogeneous energy consumption. This paper proposes a simple energy-aware routing method based on the Game of Life cellular automaton, which provides a homogeneous energy depletion while extending the network lifespan by considering factors such as residual energy, number of active neighbors, and a sleep schedule. To this end, a discrete dynamic model that takes into account different behaviors of WSN through a set of rules combined with a variation of the A-star algorithm is proposed. Simulation results show that the proposed model accurately balances the energy consumption rate while expanding the network lifetime compared to most recent research works. Furthermore, the proposed method can be combined with path-planning algorithms to improve energy consumption in sparse WSNs.

Automated summary

Wireless sensor networks (WSNs) are widely used in various applications and it is important to develop energy-efficient routing protocols. This paper proposes an energy-aware routing method based on the Game of Life cellular automaton, which achieves homogeneous energy consumption and extends the network lifetime by considering factors such as residual energy, number of active neighbors, and a sleep schedule. Simulation results show that the proposed method effectively balances energy consumption rate and improves network lifetime compared to previous research works.