A Novel Topology-Scale-Adaptive and Energy-Efficient Clustering Scheme for Energy Sustainable Large-Scale SWIPT-Enabled WSNs

The emergence of the Simultaneous Wireless Information and Power Transfer (SWIPT) technology makes it possible to achieve energy sustainability in the Wireless Sensor Networks (WSNs). However, little attention was paid to the large-scale SWIPT-enabled WSNs. To this end, we synthesize the network Energy Efficiency (EE), energy sustainability conditions, and network throughput in the large-scale SWIPT-enabled WSNs, and propose a Topology-Scale-Adaptive and Energy Efficient Clustering Scheme (TSA-EECS). To be specific, the EE maximization problem is formulated as a fractional programming problem, which jointly optimizes the transmission power and the power splitting ratio of sensors. Subsequently, a Dinkelbach-based iterative algorithm is proposed to transform the problem and a Lagrangian function is presented to obtain a near-optimal solution through the gradient descent method. In addition, a BFOA-based Cluster Head (CH) selection algorithm is proposed to adapt to the large-scale network and reduce energy consumption for CH selection. Finally, extensive simulations are conducted to evaluate the effectiveness of TSA-EECS. Experimental results demonstrate that the proposed iterative algorithm converges after 15 iterations on average. In addition, compared with recent energy harvesting schemes, the network throughput of TSA-EECS is improved by 3-8%.