Energy-balancing node scheduling inspired by gene regulatory networks for wireless sensor networks

Wireless sensor networks (WSNs) generally comprise a large number of tiny sensor nodes that perform network processing of the acquired data and then forwarding such data to the sink via multi-hop paths. The sensor nodes are resource constrained in terms of battery life, memory, and processing capability. Hence, a critical aspect in WSNs is power scarcity, which directly affects the network operation lifetime and the performance of applications. Furthermore, large-scale WSNs demand a high level of self-organisation so each node of the system autonomously makes decisions. In this respect, self-organising methods enhancing the network lifetime while achieving balanced energy are highly significant in WSNs. In this study, the authors apply gene regulatory network (GRN) principles to WSN system and design a new GRN-inspired model for autonomous node scheduling in WSNs. GRNs have received considerable attention from computational engineering for their robustness, scalability, and adaptability with simple local interactions and limited information. They apply cellular mechanisms of GRNs to WSNs and establish a metaphor between a multi-cellular system and a WSN system. Then, they propose a new model inspired by GRN so each sensor node autonomously schedules its state with local interaction based on sensor variable signalling while achieving the global object predefined by an application or user. Using control theory, they analyse system stability and derive steady states of the proposed system. They further derive the conditions of system parameters to ensure system convergence to a desired state. Simulation and numerical results are evaluated to provide insights into the effect of various system parameters on energy balancing and system stability.

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