Novel joint data collection and wireless charging algorithm for rechargeable wireless sensor networks

The sensing and data transmission in a wireless sensor network would lead to energy loss, resulting in failure to fulfil the duties. To address this issue, Wireless Rechargeable Sensor Networks (WRSN) have been created that can increase the overall network's lifetime. Existing researchers face a critical challenge due to limited battery capacity in sensor nodes, leading to constrained network lifetime. The existing approaches often plan to charge paths separately from data collection, which may not effectively optimize network life and data gathering efficiency. To overcome these issues, this work aims to address energy efficiency, data aggregation, and wireless charging challenges in WRSNs. This work proposes an innovative Joint Data Collection and Wireless Charging (JDCWC) algorithm for rechargeable Wireless Sensor Networks (WSNs). The proposed JDCWC algorithm aims to address the limitation of existing techniques by combining data gathering and energy replenishment into a single mobile element. By doing so, it simultaneously prolongs energy efficiency and enhances data collection in WRSNs. The algorithm optimally selects clustering points for charging or data gathering, ensuring efficient utilization of resources. Initially, the optimal clustering process is performed by the proposed Pelican Updated Blue Monkey Optimization (PUBMO) algorithm under the consideration of constraints including distance, energy and delay. Subsequently, proposes the Updated Sigmoid and Exponential kernel in Kernel Least Mean Square (USE-KLMS) algorithm for data aggregation, in which the duplicated data are rejected by updating the sigmoid and exponential kernel. The major issue of energy loss happens while transmitting and receiving data. Thus, the installation of wireless chargers takes place when the energy level of the cluster head gets lowered. The base station installs the wireless charger with the aid of a Mobile Vehicular Adhoc Network (VAN) under the consideration of constraints such as distance, trust, link quality and risk. After installing the wireless charger, the on-demand charging and data collection is carried out simultaneously. The proposed model is validated over the conventional models. Additionally, the JDCWC strategy has increased trust rates to their highest level. Additionally, the JDCWC offered the greatest trust rate (six) for round 600 when compared to traditional models.