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The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to transmit wireless energy to the sensor node to improve the network’s throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by the joint scheduling of an MC and a DC. We proved we could transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selected a total horizon of 360° and then selected the horizon of each specific 90∘ area based on its antenna orientation. The DC’s orientation was scheduled for each time slot. Furthermore, multiple MCs were used to transmit energy for sensor nodes that could not be covered by the DC. We divided the rechargeable wireless sensor network into several zones via a Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtained the optimal charging locations of the MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research was to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network could increase, and the end delay could be maximized, with joint energy transmission. Extensive simulation results demonstrated that our RWSNs were designed to significantly improve network lifetime over the baseline method.

Recently, Unmanned Aerial Vehicles (UAVs) have emerged as an alternative solution to assist wireless networks, thanks to numerous advantages they offer in comparison to terrestrial fixed base stations. For instance, a UAV can be used to embed a flying base station providing an on-demand nomadic access to network services. A UAV can also be used to wirelessly recharge out-of-battery ground devices. In this paper, we aim to deal with both data collection and recharging depleted ground Internet-of-Things (IoT) devices through a UAV station used as a flying base station. To extend the network lifetime, we present a novel use of UAV with energy harvesting module and wireless recharging capabilities. However, the UAV is used as an energy source to empower depleted IoT devices. On one hand, the UAV charges depleted ground IoT devices under three policies: (1) low-battery first scheme; (2) high-battery first scheme; and (3) random scheme. On the other hand, the UAV station collects data from IoT devices that have sufficient energy to transmit their packets, and in the same phase, the UAV exploits the Radio Frequency (RF) signals transmitted by IoT devices to extract and harvest energy. Furthermore, and as the UAV station has a limited coverage time due to its energy constraints, we propose and investigate an efficient trade-off between ground users recharging time and data gathering time. Furthermore, we suggest to control and optimize the UAV trajectory in order to complete its travel within a minimum time, while minimizing the energy spent and/or enhancing the network lifetime. Extensive numerical results and simulations show how the system behaves under different scenarios and using various metrics in which we examine the added value of UAV with energy harvesting module.

When extending the life of Wireless Rechargeable Sensor Networks (WRSN), one challenge is charging networks as they grow larger. Overcoming this limitation will render a WRSN more practical and highly adaptable to growth in the real world. Most charging algorithms require a priori full knowledge of sensor nodes’ power levels in order to determine the nodes that require charging. In this work, we present a probabilistic algorithm that extends the life of scalable WRSN without a priori power knowledge and without full network exploration. We develop a probability bound on the power level of the sensor nodes and utilize this bound to make decisions while exploring a WRSN. We verify the algorithm by simulating a wireless power transfer unmanned aerial vehicle, and charging a WRSN to extend its life. Our results show that, without knowledge, our proposed algorithm extends the life of a WRSN on average 90% of what an optimal full knowledge algorithm can achieve. This means that the charging robot does not need to explore the whole network, which enables the scaling of WRSN. We analyze the impact of network parameters on our algorithm and show that it is insensitive to a large range of parameter values.

To reduce time delays during data collection and prolong the network lifetime in Wireless Rechargeable Sensor Networks (WRSNs), a type of high-efficiency data collection method based on Maximum Recharging Benefit (DCMRB) is proposed in this paper. According to the minimum number of the Mobile Data Collectors (MDCs), the network is firstly divided into several regions with the help of the Virtual Scan Line (VSL). Then, the MDCs and the Wireless Charging Vehicles (WCVs) are employed in each region for high efficient data collection and energy replenishment. In order to ensure the integrity of data collection and reduce the rate of packet loss, a speed adjustment scheme for MDC is also proposed. In addition, by calculating the adaptive threshold of the recharging request, those nodes with different energy consumption rates are recharged in a timely way that avoids their premature death. Finally, the limited battery capacity of WCVs and their energy consumption while moving are also taken into account, and an adaptive recharging scheme based on maximum benefit is proposed. Experimental results show that the energy consumption is effectively balanced in DCMRB. Furthermore, this can not only enhance the efficiency of data collection, but also prolong the network lifetime compared with the Energy Starvation Avoidance Online Charging scheme (ESAOC), Greedy Mobile Scheme based on Maximum Recharging Benefit (GMS-MRB) and First-Come First-Served (FCFS) methods.

Wireless capsule endoscopic devices are adopted for painless diagnosis of cancer and other diseases affecting the gastrointestinal tract as an alternative to traditional endoscopy. Although much work has been done to improve capsule performance in terms of active navigation, a major drawback is the limited available energy on board the capsule, usually provided by a battery. Another key shortcoming of active capsules is their limitation in terms of active functionalities and related costs. An inductive-based wireless recharging system for the development of an innovative capsule for colonoscopy is proposed in this paper; the aim is to provide fast off-line battery recovery for improving capsule lifecycle and thus reducing the cost of a single endoscopic procedure. The wireless recharging system has been properly designed to fit the dimensions of a capsule for colonoscopy but it can be applied to any biomedical devices to increase the number of times it can be used after proper sterilization. The current system is able to provide about 1 W power and is able to recharge the battery capsule in 20 min which is a reasonable time considering capsule operation time (10–15 min).

Recent years have witnessed several technological breakthroughs in wireless sensor networks (WSN), yet energy continues to be an indispensable resource despite these advancements. The amount of energy that is available in a WSN has a direct bearing on how well it functions, how well it performs, and how long it will continue to operate. Because of the limitations imposed on them by cost and size, sensor nodes almost always come outfitted with a constrained amount of energy. As a direct consequence of this, their batteries will need to be replaced at regular intervals. However, the replacement is only sometimes a viable alternative; in fact, there are some situations in which it is unlikely to be achievable and entirely improbable. Because of this, there is an urgent need for more feasible solutions, which include energy harvesting or wireless energy transfer, as well as the creation of power at the sensor nodes themselves or their delivery of power to them. These options are among the options that are now available. This study intends to accomplish the following three primary goals: In the first step of this process, we will investigate prospective renewable energy resources and information on their qualities and uses in wireless sensor networks (WSN). Second, this study examines various methods for charging batteries and the various ways each of these methods might be applied to wireless sensor networks (WSN).

Energy recharging has received much attention in recent years. Several recharging mechanisms were proposed for achieving perpetual lifetime of a given Wireless Sensor Network (WSN). However, most of them require a mobile recharger to visit each sensor and then perform the recharging task, which increases the length of the recharging path. Another common weakness of these works is the requirement for the mobile recharger to stop at the location of each sensor. As a result, it is impossible for recharger to move with a constant speed, leading to inefficient movement. To improve the recharging efficiency, this paper takes “recharging while moving” into consideration when constructing the recharging path. We propose a Recharging Path Construction (RPC) mechanism, which enables the mobile recharger to recharge all sensors using a constant speed, aiming to minimize the length of recharging path and improve the recharging efficiency while achieving the requirement of perpetual network lifetime of a given WSN. Performance studies reveal that the proposed RPC outperforms existing proposals in terms of path length and energy utilization index, as well as visiting cycle.

Wireless sensor networks are a group of spatially distributed nodes deployed to sense, gather, and transmit data to the sink for further analytics. Due to continuous operations, the battery-equipped sensor nodes (SNs) drain energy rapidly, and replacing them is a hectic task. Wireless energy transfer (WET) is evolved as a promising innovation to recharge the SNs battery wirelessly to address the challenges. A WET is embedded in a vehicle called a mobile charger (MC) and traveled in the network to recharge the SNs. However, scheduling the mobile charger over the network before a sensor node dies is challenging. In this work, we introduced a partial charging strategy to avoid the long waiting time for MC because full recharging of a single node takes a long time. The partial charging strategy preempts the current charging node and moves to the newly requested node to minimize the network’s dead nodes. However, it will increase the traveling distance. Hence, adequate charging time and MC traveling path are required. In this context, this paper proposes a deep reinforcement learning-based mobile charger scheduling strategy called dynamic partial mobile charger scheduling using deep-Q-networks (DPMCS). The proposed DPMCS learns from the environment and decides each sensor’s charging duration in an identified tour. Experimental results reveal that the proposed DPMCS outperforms well compared to the existing studies, enhance the lifetime and diminish the dead nodes count.

These days everything runs on electricity, without electricity it becomes hard to spend time. Our daily use items like phones, laptops, and other items work on electricity. So electricity is our basic requirement and its connection is establishing rapidly. This is a great opportunity for us to shift to an electric vehicle as nature warns us of pollution. Also, there are different sources available for generating electricity and this electricity can be easily used to move the car. To move the car through electricity, an electricity bank i.e., batteries are installed inside the car which needs to be recharged after a fixed mile run. This recharging can be done in different manners but the need of better fast and efficient charging system has been increases. People don’t want to carry different chargers for different devices or wait for long hours at changing stations so to make charging in an easy way this paper purposed a charging system based on wire-free technology. This paper elaborates on the concept of wireless charging and improves the concept of stationary wireless electric car charging.

Wireless power transfer techniques to transfer energy have been widely adopted by wireless rechargeable sensor networks (WRSNs). These techniques are aimed at increasing network lifetime by transferring power to end devices. Under these wireless techniques, the incurred charging latency to replenish the sensor nodes is considered as one of the major issues in wireless sensor networks (WSNs). Existing recharging schemes rely on rigid recharging schedules to recharge a WSN deployment using a single global charger. Although these schemes charge devices, they are not on-demand and incur higher charging latency affecting the lifetime of a WSN. This paper proposes a collaborative recharging technique to offload recharging workload to local chargers. Experiment results reveal that the proposed scheme maximizes average network lifetime and has better average charging throughput and charging latency compared to a global charger-based recharging.