Recently, Wireless Rechargeable Sensor Networks (WRSNs) based platforms have become promising for broad applications. However, if an adversary disrupts the wireless charging process in WRSNs, sensors may die due to lack of timely energy supply, compromising the reliability and availability of systems relying on sensing tasks. In this paper, we develop a zero-cost power jamming attack in WRSNs, termed rEflection-based jaMmIng aTtack (EMIT), which introduces an off-the-shelf and inconspicuous reflector such as a Coca-Cola can that intentionally reflects the wave from the charger to destructively interfere with the charging wave at the target sensor. Our approach lifts the limitations of traditional charging attacks, including high cost, complex implementation and ease of detection. We conduct extensive field experiments to evaluate EMIT attack in different types of WRSNs. The results show that on average, the success rate of EMIT attack is 90% in WRSNs with fixed charging locations, and 75% in WRSNs with dynamic charging locations. Finally, we build a real-world WRSN on university campus to study the effectiveness of EMIT attack in complex scenarios. In total, EMIT attack causes 134 sensor deaths over 66 days.

Recently, deploying static directional chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Extensive simulations and field experiments are conducted to evaluate the performance of our solution. The results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 132.09% on average.

UAVs (Unmanned Aerial Vehicles) are promising tools for disaster monitoring, by obtaining valuable information of important PoIs (Points of Interest) with onboard cameras. Since people trapped at some PoIs are more likely in danger than people in other PoIs, different PoIs have different monitoring priorities, so that the PoIs with high monitoring priorities should be visited more often than those with low priorities. Unlike existing studies that assumed a UAV is required to fly to the location of a PoI to monitor the PoI, we observe that the a UAV can monitor a PoI as long as it hovers at any location around the PoI (e.g., 200 m away horizontally), thereby reducing the flying time of the UAV. In this paper, we first study a problem of finding a sequence of monitoring tours for an energy-constrained UAV to monitor PoIs in a disaster area for a monitoring period T (e.g., 72 hours) persistently, such that the maximum weighted monitoring interval of PoIs is minimized, where the weight associated with a PoI is its monitoring priority, and the monitoring interval of a PoI is the longest time between its two consecutive visits in period T. We then propose a novel approximation algorithm for the problem. We finally evaluate the algorithm performance based on both a real testbed and the simulation. The experimental results show that the maximum weighted monitoring interval by the proposed algorithm is up to 30% shorter than those by existing algorithms.

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%

With the maturation of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have been able to provide a continuous energy supply by scheduling a Mobile Charger (MC). However, traditional charging modes suffer from fixed charging areas that lack the ability to adapt to variable sensor distributions. This inflexibility yields a gap between energy supply and utilization, resulting in relatively low charging efficiency. To address this issue, we propose an adaptive charging mode that utilizes beam steering to dynamically adjust the charging area, thereby catering to different sensor distributions encountered during the charging process. First, we build a dual-symmetric steering charging model to describe the characteristics of dynamic beam steering, enabling precise manipulation of the charging area. Then, we develop a charging power discretization based on steering angle and charging distance to obtain a finite feasible charging strategy set for MC. We reformalize charging utility maximization under energy constraints as a submodular function maximization problem, and propose an approximate algorithm to solve it. Lastly, simulations and field experiments demonstrate that our scheme outperforms other algorithms by 43.9% on average.

To guarantee the reliability for WRSNs, placing sufficient static chargers effectively ensures charging coverage for the entire network. However, this approach leads to a considerable number of sensors located within charging overlaps. The destructive wave interference caused by concurrent charging in these overlaps may weaken sensors received power, thereby negatively impacting charging performance. This work addresses a CHArging utIlity maximizatioN (CHAIN) problem, which aims to maximize the overall charging utility while considering wave interference among multiple chargers. Specifically, given a set of stationary sensors, we investigate how to determine optimal positions for a fixed number of chargers. To tackle this problem, we first develop a charging model with wave interference, then propose a two-step charger placement scheme to identify the optimal charger positions. In the first step, we maximize the overall additive power of the waves involved in interference by selecting an appropriate initial position for each charger. Then, in the second step, we maximize the overall charging utility by finding the optimal final position for each charger around its initial position. Finally, to evaluate the performance of our scheme, we conduct extensive simulations and field experiments and the results suggest that CHAIN performs better than the existing algorithms.

Concurrent wireless charging offers significant performance improvements for Wireless Rechargeable Sensor Networks (WRSNs). However, wave interference, arising from interactions between electromagnetic waves from multiple chargers, disrupts this process. This results in uneven power distribution, potentially leading to significantly attenuated or even negligible energy reception at certain locations. This paper addresses this challenge by introducing the Concurrent cHarging with Efficient phaSe Scheduling (CHESS) problem. CHESS maximizes the energy received by critical sensors through a novel on-demand phase scheduling approach. To achieve this, we propose a practical charging model with charger phases and wave interference effects. Subsequently, a charger grouping algorithm reduces computational complexity, followed by a phase vector searching algorithm to identify optimal phases for maximizing sensor energy reception. Finally, a phase scheduling algorithm enables dynamic adaptation to the evolving energy demands. Simulations show significant efficiency improvements, outperforming baseline algorithms by an average of ${8 9. 8 \%}$

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

UAVs (Unmanned Aerial Vehicles) are promising tools for efficient data collections of sensors in IoT networks. Existing studies exploited both spatial and temporal data correlations to reduce the amount of collected redundant data, in which sensors are first partitioned into different clusters, a master sensor in each cluster then collects raw data from other sensors and compresses the received data. An energy-constrained UAV finally collects the maximum amount of compressed data from different master sensors. We however notice that the compressed data from only a portion of clusters are collected by the UAV in the existing studies, while the data from other clusters are not collected at all. In this paper, we study a problem of finding a data collection trajectory for an energy-constrained UAV, so that the accumulative utility of collected data is maximized, where the accumulative utility measures the quality of spatiotemporally correlated data collected from different clusters. We propose a novel 16+-approximation algorithm for the problem, where is a given constant with >0. Experimental results with real datasets show that the accumulative utility by the proposed algorithm is at least 23% larger than those by the existing studies, and the number of clusters collected by the proposed algorithm is from 45% to 105% larger than those by the existing studies.

To satisfy the heterogeneous service requirements in future internet of things (IoT), this paper investigates the novel framework for intelligent reflecting surface (IRS)-aided wireless powered over-the-air computation (AirComp) and communication networks, where the IoT devices first harvest energy from the downlink signal sent by the base station, and then conduct the information transmissions and AirComp in the uplink. In particular, the IRS is used to improve the efficiency of wireless energy transfer, and alleviate the harmful interference between the communication and AirComp signals. To balance the performance of such an integrated system, we present two joint beamforming and reflection optimization problems via minimizing the computation distortion and maximizing the sum rate, respectively. To solve the non-convex problems, we develop the alternating optimization framework with proved convergence, in which the penalty function-based method and variable substitution technique are exploited to acquire the optimal solutions of beamformers and reflection parameters. Finally, simulation results show that the proposed method realizes significantly higher computation accuracy and communication rate, in comparison with several existing benchmark methods.

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e.,back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by$49.5\%$and increases the energy usage effectiveness by$10.2\%$on average as compared with existing algorithms.

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average.

Benefitting from the maturation of Wireless Power Transfer technology, Wireless Rechargeable Sensor Networks have become a promising solution for prolonging network lifetime. In practical charging scenarios, obstacles are ubiquitous. However, most prior arts have failed to consider the combined impacts of the material, size, and location of obstacles on the charging performance, making these schemes unsuitable for real applications. In this article, we study a fundamental issue of W ireless ch A rger placement w I th obs T acles (WAIT), that is, how to place wireless chargers by comprehensively considering these parameters of obstacles, such that the overall charging utility is maximized. To tackle the WAIT problem, we first build a practical charging model with obstacles by introducing shadow fading, and conduct experiments to verify its correctness. Then, we design a piecewise constant function to approximate the nonlinear charging power. Afterwards, we develop a Dominating Coverage Set extraction algorithm to reduce the continuous solution space to a limited number. Finally, we prove the WAIT problem is a maximizing monotone submodular function problem, and propose a 1-1/e-ε approximation algorithm to address it. Extensive simulations and field experiments show that our scheme outperforms comparison algorithms by at least 20.6% in charging utility improvement.

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms.

Without the need for batteries, backscatter-based Wireless Powered Communication Network (WPCN) has been envisioned as a promising alternative to conventional wireless networks. Unfortunately, the unique phase cancellation problem in backscatter-based WPCN is essentially a phenomenon that severely affects connectivity and reliability of the network. Many arts have tried to tackle this issue either by using multiple antennas to employ the signal diversity, which increases the size and is not cost-efficient, or by making a repetition of the same information with different load impedances, which significantly decreases the throughput of network. In our paper, we propose an effective deployment scheme, aiming to fundamentally eliminate the phase cancellation problem. Specifically, we first build a practical communication model seeking the blind areas caused by phase cancellation. Then, a greedy algorithm and a minimum-weight graph based algorithm are proposed to elaborate topology of the network to ensure the connectivity. Finally, extensive experiments are carried out to evaluate the performance.

In this paper, we study the employment of a mobile charger to charge lifetime-critical sensors under the multi-node partial-charging model, in which the charger can simultaneously charge the sensors within its charging range and each sensor may be partially charged each time. We notice that existing studies only scheduled the charger to minimize the number of dead sensors, but did not consider the charging scheduling for the sensors that have already run out of their energy, and the dead sensors will be last charged by the mobile charger. Then, their dead durations may be very long. In this paper, we consider not only how to minimize the number of dead sensors but also reduce the dead durations of sensors. To this end, we first formulate a sensor lifetime maximization problem, which is to find a charging tour for a mobile charger to charge sensors, such that the sum of sensor lifetimes is maximized. We then propose a novel$\frac{1}{3}$-approximation algorithm for the problem. We finally evaluate the performance of the proposed algorithm through experiments. Experimental results show that both the average and maximum sensor dead durations by the proposed algorithm are up to 70% shorter than those by existing algorithms.

Data collection with mobile elements can improve energy efficiency and balance load distribution in wireless sensor networks (WSNs). However, complex network environments bring about inconvenience of path design. This work addresses the network environment issue, by presenting an objective-variable tour planning (OVTP) strategy for mobile data gathering in partitioned WSNs. Unlike existing studies of connected networks, our work focuses on disjoint networks with connectivity requirement and serves delay-hash applications as well as energy-efficient scenarios respectively. We first design a converging-aware location selection mechanism, which macroscopically converges rendezvous points (RPs) to lay a foundation of a short tour. We then develop a delay-aware path formation mechanism, which constructs a short tour connecting all segments by a new convex hull algorithm and a new genetic operation. In addition, we devise an energy-aware path extension mechanism, which selects appropriate extra RPs according to specific metrics in order to reduce the energy depletion of data transmission. Extensive simulations demonstrate the effectiveness and advantages of the new strategy in terms of path length, energy depletion, and data collection ratio.

In this article we study a generalized team orienteering problem (GTOP), which is to find service paths for multiple homogeneous vehicles in a network such that the profit sum of serving the nodes in the paths is maximized, subject to the cost budget of each vehicle. This problem has many potential applications in IoTs and smart cities, such as dispatching energy-constrained mobile chargers to charge as many energy-critical sensors as possible to prolong the network lifetime. In this article, we first formulate the GTOP problem, where each node can be served by different vehicles, and the profit of serving the node is a submodular function of the number of vehicles serving it. We then propose a novel (1 - (1/e)1/2+e)-approximation algorithm for the problem, where ε is a given constant with 0 <; ε ≤ 1 and e is the base of the natural logarithm. In particular, the approximation ratio is about 0.33 when ε = 0.5. In addition, we devise an improved approximation algorithm for a special case of the problem where the profit is the same by serving a node once and multiple times. We finally evaluate the proposed algorithms with simulation experiments, and the results of which are very promising. Especially, the profit sums delivered by the proposed algorithms are up to 14% higher than those by existing algorithms, and about 93.6% of the optimal solutions.

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks ( WRNs ). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of the vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. We formalize the effective charging problem as a charging reward maximization problem ( CRMP ), where the amount of reward obtained by charging a device is inversely proportional to the residual lifetime of the device. Then, we prove that CRMP is NP-hard. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning ( RL ) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

Mobile charging can provide stable and reliable energy replenishment for wireless rechargeable sensor network (WRSN). However, relatively low charging utility exists in existing solutions. In this paper, we present a utility-based collaborative charging (UBCC) strategy to maximize the charging utility of mobile chargers (MCs) in large-scale WRSNs. Charging MCs and server MCs are employed to jointly achieve our goal by three aspects. First, a path merging scheme is designed to save the traveling paths of MCs. Unlike existing studies with entirely diverse movement trajectories of MCs, the same traveling path is assigned to both the departure charging MCs and the return MCs, which serve different charging areas. Second, an idle-difference alleviating scheme is devised to improve the utilization rate of MCs. Different from current solutions with a large difference of working hours of MCs, each charging MC is assigned the equal charging tasks, resulting in synchronous charging and simultaneous energy replenishment of MCs. Third, an energy-waste averting scheme is designed to maximize the energy utilization of MCs. The energy of each MC is just exhausted until the MC completes its charging tasks and traveling roles. Extensive simulation results demonstrate the advantages of UBCC in the charging cost and charging utility.

Charging scheduling plays a significant role in wireless rechargeable sensor networks (WRSNs), which benefit from stable and reliable energy supplements via wireless charging. This paper proposes an importance-different charging scheduling (IDCS) strategy for improving charging utility as well as reducing the data loss. The unique feature of IDCS is that, it distinguishes nodes by means of different importance of data delivery. The Matroid theory is used to achieve our goals. First, two important factors are determined in the Matroid model, i.e., the deadline of the task and the penalty value of the task. Moreover, a greedy algorithm of task classification is designed to minimize the data loss. All tasks are divided into the early tasks and the delayed tasks, and the node with greater importance and shorter deadline has a higher priority of being included into the early tasks. In addition, a charging sequence adjustment approach is proposed to maximize the charging utility. This approach aims to exchange the sequence of different nodes in the trajectory of the mobile charger for exploring a shorter path. Several simulations verified the effectiveness and advantages of our charging scheduling strategy in terms of the node failure rate and total data loss.

With the maturation of wireless charging technology, Wireless Rechargeable Sensor Networks (WRSNs) has become a promising solution for prolong network lifetimes. Recently studies propose to employ a mobile charger (MC) to simultaneously charge multiple sensors within the same charging range, such that the charging performance can be improved. In this paper, we aim to jointly optimize the number of dead sensors and the energy usage effectiveness in such multi-node charging scenarios. We achieve this by introducing the partial charging mechanism, meaning that instead of following the conventional way that each sensor gets fully charged in one time step, our work allows MC to fully charge a sensor by multiple times. We show that the partial charging mechanism causes minimizing the number of dead sensors and maximizing the energy usage effectiveness to conflict with each other. We formulate this problem and develop a multi-node temporal spatial partial-charging algorithm (MTSPC) to solve it. The optimality of MTSPC is proved, and extensive simulations are carried out to demonstrate the effectiveness of MTSPC.

In this paper we study a team orienteering problem, which is to find service paths for multiple vehicles in a network such that the profit sum of serving the nodes in the paths is maximized, subject to the cost budget of each vehicle. This problem has many potential applications in IoT and smart cities, such as dispatching energy-constrained mobile chargers to charge as many energy-critical sensors as possible to prolong the network lifetime. In this paper, we first formulate the team orienteering problem, where different vehicles are different types, each node can be served by multiple vehicles, and the profit of serving the node is a submodular function of the number of vehicles serving it. We then propose a novel (1 - (1/e)1/2+ε)approximation algorithm for the problem, where c is a given constant with 0 ≤ ε ≤ 1 and ε is the base of the natural logarithm. In particular, the approximation ratio is no less than 0.32 when ε = 0.5. In addition, for a special team orienteering problem with the same type of vehicles and the profits of serving a node once and multiple times being the same, we devise an improved approximation algorithm. Finally, we evaluate the proposed algorithms with simulation experiments, and the results of which are very promising. Precisely, the profit sums delivered by the proposed algorithms are approximately 12.5% to 17.5% higher than those by existing algorithms.

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks (WRNs). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. Then, we formalize the effective charging problem as a charging reward maximization problem (CRMP), where the amount of reward obtained by charging a de-vice is inversely proportional to the residual lifetime of the device. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning (RL) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

In wireless rechargeable sensor networks (WRSNs), prior studies mainly focus on the optimization of power transfer efficiency. In this work, we consider the cost for building and operating WRSNs. In the network, sensor nodes can be charged by mobile chargers, that have limited energy which is used for charging and moving. We introduce a novel concept called “shuttling” and introduce an optimal charging algorithm, which is proven to achieve the minimum number of chargers in theory. We also point out the limitations of the optimal algorithm, which motivates the development of solutions named Push-Shuttle-Back (PSB). We formally prove that PSB achieves the minimum number of chargers and the optimal shuttling distance in a 1D scenario with negligible energy loss. When the loss in wireless charging is non-negligible, we propose to exploit detachable battery pack (DBP) and propose a DBP-PSB algorithm to avoid energy loss. We further extend the solution to 2D scenarios and introduce a new circle-based “shortcutting” scheme that improves charging efficiency and reduces the number of chargers needed to serve the sensor network. We carry out extensive simulations to demonstrate the performance of the proposed algorithms, and the results show the proposed algorithms achieve a low overall cost.

The authors of "Low-Cost Collaborative Mobile Charging for Large-Scale Wireless Sensor Networks" which appeared in August issue of this journal [ibid., vol. 16, no. 8, pp. 2213–2227, Aug. 2017] would like to correct a typo that occurred in Fig. 1. The numbers above the X axis were wrong. The corrected Fig. 1 is provided