However, most of the existing studies focused on improving the charging efficiency or minimizing the latency while very few studies improved the data accuracy of the network. This technology provides a new solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). With the help of wireless power transfer (WPT) technology, the mobile charger (MC) can transfer energy to the sensor nodes. Furthermore, from the results of the taxonomy mapping of these constraints, new gaps were identified related to developing existing research to produce better solutions. This paper also presents solutions from existing studies related to the constraints of implementing the WSN. This paper presents a taxonomy related to the constraints in implementing Wireless Sensor Networks. The main obstacles in implementing a wireless sensor network include three things: an effective and efficient data sending/receiving process, limited and easily depleted sensor energy/power, network security, and data security that is vulnerable to eavesdropping and destruction. Network management itself includes network configuration management, network performance management, network failure management, network security management, and network financing management. The implementation of the wireless sensor network includes five main parts, namely sender, receiver, wireless transmission media, data/information, network architecture/configuration, and network management. Sensors with low power, multifunction, supported by a combination of wireless network, microcontroller, memory, operating system, radio communication, and energy source in the form of an integrated battery enable a monitoring process of the monitoring area to run properly. One interesting research case is the use of WSN for the monitoring process by collecting data using sensors placed and distributed in locations based on a wireless system. Since 2015 research related to the use of WSN in various health, agriculture, security industry, and other fields has continued to grow. Wireless Sensor Network (WSN) is a collection of sensors communicating at close range by forming a wireless-based network (wireless). We also demonstrate latency performance improvements by applying a parallelization strategy. Our results show that the proposed solver improves the solution quality offered by Guided Local Search in most of the cases tested. We supplement Guided Local Search (via Google OR-Tools) with a Black Hole-inspired algorithm. We propose Rapid Online Metaheuristic-based Planner, ROMP, a multi-objective offline and online mission planner that can incorporate real-time state information from the power delivery vehicle and its local environment to deliver reliable, up-to-date and near-optimal mission planning. In this paper, we present a novel lightweight and reliable mission planner that solves the problem by combining offline search and online reevaluation. Many studies have demonstrated satisfactory performance of heuristic algorithms' ability to solve specific routing problems, however very few studies explore online updating (i.e., mission re-planning `on the fly') for such hybrid scenarios. This is decomposed as an NP-hard nonconvex optimization and nonlinear integer programming problem. ![]() Our work considers a hybrid Travelling Salesman Problem and Orienteering Problem scenario where the optimization objective is to jointly minimize discharged energy of the power delivery vehicle and maximize recharged energy of devices. ![]() Ensuring efficient and reliable performance of autonomous power delivery vehicles is very challenging in dynamic environments. Recharging Internet of Things devices using autonomous robots is an attractive maintenance solution. A modified version of the Black Hole algorithm is presented, which is shown to execute on average 35% faster than the state of the art genetic approach, while delivering comparable performance in simulation across 18 scenarios with varying area and density of sensor nodes deployed under different initialization scenarios. In this paper, a configurable optimal recharge scheduler is proposed that incorporates several evolutionary and clustering approaches. The optimal recharge scheduling problem considers minimizing discharged energy of drones while maximizing devices’ recharged energy. ![]() Improving the operating efficiency of power delivery vehicles is challenging due to complex dynamic environments and the need to solve difficult optimization problems to determine the best combination of routes, number of vehicles, and numerous safety thresholds prior to deployment. Wireless recharging by autonomous power delivery vehicles is an attractive maintenance solution for Internet of Things devices.
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