# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
## 1 Introduction  
Wireless sensor networks (WSNs) consist of spatially distributed autonomous devices using sensors to monitor physical or environmental conditions. A WSN system incorporates a gateway that provides wireless connectivity back to the wired/wireless world and distributed nodes equipped with radio transceivers, microcontrollers, external memory, and electronic circuits for interfacing with the sensors, and small-embedded batteries. WSNs also find their applications in military surveillance [1, 2], geo-fencing [3], healthcare monitoring [4, 5], environmental monitoring [6, 7, 8], and remote monitoring [9]. Additionally, sensors are the primary key sources of Big Data Analytics--the process of examining large data sets for customer-focused business decisions and market trends [10, 11]. The main limitation of WSNs is their batteries. Due to the finite capacity of batteries, the lifetime performance of WSNs is a fundamental bottleneck. The nodes consume energy for sensing, communicating, and data processing. The energy used in data communication is higher than any other process [12, 13]. It is well established that power maximization approaches such as conservation of energy [14; 15], ambient power or environmental energy harvesting [16; 17; 18; 19], incremental deployment and battery replacement [20; 21] exert a profound influence on the enhancement and enrichment of the lifetime of WSNs. In addition, these techniques evaluate the lifetime increment of the batteries, but on the contrary they are unable to reduce energy consumption. Harvesting natural energy, such as solar power [16; 18], vibrations [17], and the wind [19] is solely dependent on climatic conditions [22]. Thus, the source of energy is random and potentially sporadic, such as for solar-powered nodes which are exclusively dependent on the influx of the sun rays. The commercialization [23] of wireless charging (power transfer) technology is a promising option to address the energy limitations in WSNs [24]. [PERSON] et al. [25; 26] introduced the concept of wireless power transfer (WPT), which transfers power through magnetic fields between two coupled coils, a phenomenon called coupled resonators, as a function of the geometry, distance, and electrical properties of the devices used. The wireless power transfer proposed by this research group has high efficiency and the potential to charge devices from a relatively long distance. The wireless power transfer method developed by [PERSON] et al. has numerous advantages such as the WPT transmits energy via a non-radiative recombination process. The power transmission range can be further improved by the installation of repeaters between power transmitting and receiving devices. In contrast to battery substitution and wireless power transfer techniques, the innovation of the renewable wireless charging approach permits a charger to transfer power to the nodes remotely without strict management and environmental effects (air, dirt and chemicals) between them [27]. In the last decade, several researchers have conducted research pertaining to wireless charging frameworks, but herein, we have attempted to develop a technique which caters to the needs of WSNs i.e., a rechargeable wireless network which remains operational and minimizes energy consumption. Our research pathways have been delineated as below:  
1. In the environment of single-hop data routing, the distance between the nodes and base station may vary due to their randomly dispersed deployment which causes high-energy consumption. To overcome this, a weighted clustering algorithm is proposed which divides all nodes, based on their positions and remaining energy levels, into multiple groups called clusters. A cluster head is appointed to each cluster so that nodes can conserve energy via direct communication with their cluster head. Each cluster head has a higher amount of energy which is responsible for communicating with the base station and other nodes as well.