# On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks
###### Abstract
[20], in the context of incomplete mobile charger device capability, presented a real-time, on-demand charging scheduling scheme (RCSS) that examined the dynamic energy consumption of different nodes when creating the charging path and proposed the next node selection method. Simultaneously, a method for determining the feasibility of charging circuits was suggested to ensure charging efficiency and was based on an adaptive charging threshold through the charging procedure to decrease node death  
In Ref. [21], the authors started with a Linear Programming (LP) approach to solve the problem of scheduling a mobile charger, and they then moved on to an efficient scheme based on the gravitational search algorithm (GSA). Their solution consists of a novel agent perception as well as an effective fitness function. They ran extensive simulations on the proposed system to demonstrate how it outperforms two cutting-edge algorithms: first come, first served (FCFS), and nearest job next with preemption (NJNP). To extend the lifetime of sensors, researchers have used mobile chargers and external energy sources [22]. In their research, they looked at periodic charging time scheduling and charging path planning with multiple chargers. They described a slot-based periodic recharge time scheduling algorithm that includes a fine-grained node classification strategy to avoid wasted trips to energy-sufficient nodes and a balanced charging task assignment scheme to avoid charging famine. They also developed a charging path planning algorithm that allows for parallel energy replenishment using multiple chargers, which resulted in higher charging efficiency. The energy capacity issue can be solved using a wireless energy transfer (WET) technique (WRSNs). [PERSON] et al. [23] proposed a demand-based charging mechanism (DBCS) for wireless renewable sensor networks by improving charging programming in different ways: clustering approach, charging node selection, charging trail and charging timetable. Firstly, they presented a multipoint improved Kmeans (MIKmeans) clustering system that can group nodes based on their location, remaining energy and past contribution to balance energy usage. Secondly, the dynamic selection algorithm for charging nodes (DSACN) was developed to select nodes that need to be charged. Finally, they presented the DBCS to improve the efficiency of the mobile charging vehicle and developed simulated annealing based on performance and efficiency (SABPE) to minimize the charging time and path distance of the charging vehicle. [PERSON] and [PERSON] introduced Particle Swarm Optimization (PSO) in (1995), which is a randomly global swarm-based intelligence algorithm [24]. PSO is determined by the common behavior of animals such as a group of birds, a school of fish that protects against predators or a swarm of bees looking for food sources. Particle swarm optimization also has been used for scheduling charging models in wireless rechargeable sensor networks. To improve the wireless charging vehicle's journey time, the cost of the charger's travel path between nodes must be considered; [PERSON] et al. [22] presented a PSO-based heuristic for scheduling the journey path of a wireless charging vehicle that considers both travel cost and travel time. The Particle Swarm Charger Deployment (PSCD) and Improved PSCD (IPSCD) algorithms are introduced in [24] to almost optimize WRSN charger deployment by utilizing the particle swarm optimization (PSO) concept. Distances between chargers and nodes that were the same as the angles between them were used to calculate the charging efficiency in PSCD and IPSCD. To acquire statistics on charging efficiency, the authors conducted experiments using real-world wireless chargers. PSO has a fast convergence speed, which is best for wireless sensor network applications. However, this fast convergence speed can result in several drawbacks, including poor global search ability, low accuracy, fast convergence and stagnation.