Wireless rechargeable sensor networks (WRSNs) have overcome the energy limitation bottleneck through wireless power transfer (WPT) technology. Traditional research has primarily focused on enhancing charging efficiency, while the critical issue of location privacy security arising from wireless charging has received scant attention. Additionally, sensors are vulnerable to detection and harm by malicious attackers, posing a significant threat to network integrity. In this paper, we propose two attack schemes, termed Least Squares Method (LSM) attack model and Centroid Method (CM) attack model for compromising sensor location privacy by exploiting charging power information and mobile charger behaviors. To counter such threats, we develop a scheme aimed at maximizing the node location privacy protection capabilities of the network. We propose a theoretical analysis to exploit the features of the proposed scheme. Finally, extensive test-bed experiments and simulations have been conducted to validate the effectiveness of our algorithms. The results demonstrate that our algorithms can protect at least 78% of the nodes without significantly compromising their survival rate.

Wireless Rechargeable Sensor Networks (WRSNs) promise to address the limited energy resource issue for sensor nodes through wireless power transfer technology. However, WRSNs are vulnerable to various security threats, such as compromised node attack and malicious mobile charger (MC) attack, which can disrupt the charging process and degrade charging efficiency. In this work, we investigate the eneRgy conversionEfficiency maximization problem unDer chargIng attackS(REDIS). We propose a blockchain-based framework that employs a lightweight multi-layer storage approach tailored for resource-constrained sensor nodes and features consensus algorithms that validate charging transactions. Furthermore, we introduce a validation node selection strategy that integrates consensus execution with charging scheduling, reducing energy consumption, and improving energy efficiency. Extensive simulations and experiments validate the effectiveness of our framework, improving energy efficiency by 30% and as much as 5 times in networks without attacks and those under full attacks, respectively.

Wireless rechargeable sensor networks (WRSNs) have become an important research topic as they show merit in long-term monitoring operations. Existing techniques focus on improving system performance, while the issue of thermal effects is overlooked, leading to discrepancies between theoretical results and real-world applications. In this work, we explore and exploit the impact of the thermal effect on charging performance. At first, the thermal effect is modeled based on the Newton-Richman cooling law, followed by a new theoretical charging model based on such effect. We jointly consider the influences of charger’s self-generated heat and ambient temperature on charging utility. To address the uncertainty of temperature variation problem, we developed an online learning scheme called tHermalEffectAdapTive charging algorithm (HEAT) based on the combined multi-armed bandit method. The proposed algorithm can dynamically schedule charging tasks adaptive to temperature fluctuations while guaranteeing a logarithmic regret bound. Extensive test-bed experiments and simulations are conducted. The results demonstrate that our scheme outperforms state-of-the-art methods by at least 24.9% in charging utility across various ambient temperature conditions.

Wireless Power Transfer (WPT) has revolutionized the field of Wireless Rechargeable Sensor Networks (WRSNs), enabling sustainable operation of sensor nodes. Traditional mobile charging methods often require sensors to be within line-of-sight or physically accessed by the mobile charger, which may potentially lead to user safety or privacy concerns. Addressing this concern, this work is the first to introduce and validate the feasibility ofThrough-Wallcharging. We formulate theWireless charging thrOughWalls (WOW) problem to simultaneously enhance user safety and maximize charging utility. Our approach leverages fundamental principles of electromagnetics to construct an accurate charging model for Magnetic Resonance Coupling-based WPT systems. Additionally, we thoroughly analyze the impact of wall obstruction and provide a generalized framework for through-wall charging. By employing discretization techniques and approximation algorithms, we derive a near-optimal solution to the WOW problem. Extensive simulations and test-bed experiments demonstrate that our proposed approach reduces the reliance on physical access to devices, simplifies deployment in complex environments, and thereby optimizes the travel paths of mobile chargers and enhances the overall performance and lifetime of WRSNs. Compared to conventional methods, our method benefits from more reasonable scheduling order and path construction, achieving an average energy efficiency improvement of 27.8%.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, existing 2D charging methods suffer from significant errors in 3D scenarios, which leads to a huge gap between theoretical results and practical applications, hindering the widespread adoption of WRSNs. In this paper, we address the chargIng utility maximizatioN problem In 3D environmenT (INIT) and provide a general solution suitable for any type of transceiver antenna. Specifically, we first establish an accurate 3D charging model to quantify the received power of sensors in 3D environments. Secondly, we design an angle-distance discretization scheme to determine appropriate charging spots for the Mobile Charger (MC). Then, we transform the mobile charging problem into a submodular function maximization problem and propose an approximation algorithm with guaranteed performance to solve it. Finally, our method has been extensively evaluated through experiments and simulations and has demonstrated considerable advantages over other comparison algorithms in real-world 3D environments. On average, it has achieved an impressive 34.8% improvement in charging utility and a remarkable 56.1% reduction in the number of dead sensors.

Benefitting from the discovery of wireless power transfer (WPT) technology, the wireless rechargeable sensor network (WRSN) has become a promising way for lifetime extension for wireless sensor networks. In practical WRSN scenarios, obstacles can be found almost everywhere. Most state-of-the-art researches believe that obstacles will always degrade signal strength, and omit the influence of obstacles for simplifying the computation process. However, overlooking the positive impacts of obstacles on signal propagation is inconsistent with the intrinsic features of electromagnetic waves. To address this issue, in this paper, we explore the wireless signal propagation process and provide a theoretical charging model to enhance the charging efficiency by leveraging obstacles. Through utilizing the concept of the Fresnel Zone model, we re-formalize the wireless charging model and discretize the charging area and charging time to determine the best charging locations as well as charging duration. We model the chargingEfficiencyMaximization withObstacles (EMO) problem as a submodular function maximization problem and propose a cost-efficient algorithm to solve it. Finally, test-bed experiments and extensive simulations are both conducted to verify that our schemes outperform baseline algorithms by$33.46\%$on average in charging efficiency improvement.

Benefitting from the recent breakthrough of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have become an important research topic. Most prior arts focus on system performance enhancement in the ideal environment that ignores the impact of obstacles. This contradicts the practical applications in which obstacles can be found almost anywhere and have dramatic impacts on energy transmission. In this paper, we concentrate on the problem of charging a practical WRSN in the presence of obstacles to maximize the charging utility under specific energy constraints. First, we propose a new theoretical charging model with obstacles based on the Fresnel diffraction model and conduct experiments to verify its effectiveness. Then, we propose a spatial discretization scheme to obtain a finite feasible charging position set for mobile charger (MC), which largely reduces computation overhead. Afterwards, we re-formalize charging utility maximization with energy constraints as a submodular function maximization problem and propose a cost-efficient algorithm with an approximation guarantee to solve it. In addition, we present a theoretical analysis and a relevant mathematical proof of our algorithm. Finally, we demonstrate that our scheme outperforms other competing algorithms by 20.5% on average in terms of charging utility through test-bed experiments and extensive simulations.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, inaccurate discretization methods and imprecise charging models yield a huge gap between theoretical results and practical applications, making it difficult for wide adoptions. In this paper, we focus on designing a precise charging method for maximizing charging utility when line-of-sight (LOS) and none-line-of-sight (NLOS) charging cases exist in complicated environments. First, we design discretization methods for charging area and charging orientation for precisely constructing the charging model. Then, we develop a novel electromagnetic wave reflection model to describe the signal propagation model in the presence of obstacles. We formalize the mobile charging problem into a submodular function maximization problem which can be solved by a proposed algorithm with an approximation guarantee. Finally, extensive experiments and simulations demonstrate that our schemes outperform comparison algorithms by 32.5% on average in charging utility in complicated environments.

As a novel solution for IoT applications, wireless rechargeable sensor networks (WRSNs) have achieved widespread deployment in recent years. Existing WRSN scheduling methods have focused extensively on maximizing the network charging utility in the fixed node case. However, when sensor nodes are deployed in dynamic environments (e.g., maritime environments) where sensors move randomly over time, existing approaches are likely to incur significant performance loss or even fail to execute normally. In this work, we focus on serving dynamic nodes whose locations vary randomly and formalize the dynamic WRSN charging utility maximization problem (termed MATA problem). Through discretizing candidate charging locations and modeling the dynamic charging process, we propose a near-optimal algorithm for maximizing charging utility. Moreover, we point out the long-short-term conflict of dynamic sensors that their location distributions in the short-term usually deviate from the long-term expectations. To tackle this issue, we further design an online learning algorithm based on the combinatorial multi-armed bandit (CMAB) model. It iteratively adjusts the charging strategy and adapts well to nodes’ short-term location deviations. Extensive experiments and simulations demonstrate that the proposed scheme can effectively charge dynamic sensors and achieve a higher charging utility compared to baseline algorithms in both long-term and short-term.

Wireless rechargeable sensor networks (WRSNs) have become a hot research issue owing to the breakthrough of wireless power transfer (WPT) technology. Previous theoretical schemes are mostly designed for 2-D networks, and few of them are tailored for 3-D scenarios, making them not suitable for wide adoptions in practical applications. In this paper, we address the issue of how to serve a 3-D WRSN with an unmanned aerial vehicle (UAV). Our main concern is to maximize the charged energy for sensors supplied by the UAV, which has energy constraints. We respectively develop a spatial discretization scheme to construct a finite feasible set of charging spots for the UAV in a 3-D environment and a temporal discretization scheme to determine the appropriate charging duration for each charging spot. Then, we reduce the problem into a submodular maximization problem with routing constraints and present a cost-efficient algorithm (CEA) with a provable approximation ratio to solve it. Finally, test-bed experiments are conducted to show the feasibility of our schemes in practical scenarios. Extensive simulations are taken to verify the superior performance of our algorithm in charged energy and robustness. The charged energy of our scheme outperforms other competing methods by at least$18.2\%$.

Wireless Rechargeable Sensor Networks (WRSNs) with perpetual network lifetime have been used in many Internet of Things (IoT) applications, like oceanic monitoring and precision agriculture. Rechargeable sensors, together with an Unmanned Aerial Vehicle (UAV), are collaboratively employed for fulfilling periodic coverage missions. However, traditional coverage solutions are normally based on static deployment of sensors and not suitable for such novel coverage requirements. In this paper, we propose the concept of Period-Area Coverage (PAC) problem, which requires the data of the overall area must be collected/monitored periodically. To solve the PAC problem, we employ a UAV that simultaneously acts as a mobile charger and sensor. It is responsible for charging nearly exhausted sensors and sensing vacant regions to realize complete event monitoring. To maximize the energy efficiency of the UAV, we propose a heuristic hexagon-based scheduling algorithm (HSA) which can also balance energy consumption. Furthermore, we develop an emergent node charging scheduling method to prevent node exhaustion, and introduce a grid-based boustrophedon scheduling algorithm (GBSA) to reduce the complexity. Finally, we present a charging re-allocation mechanism to further enhance energy efficiency. Extensive simulations demonstrate that the proposed schemes can solve the PAC problem and enhance energy efficiency by at least 18.2% compared to prior arts. Test-bed experiments conducted both in agriculture and oceanic monitoring applications validate the applicability of the proposed scheme in practical scenarios.

Wireless rechargeable sensor networks have become a hot research issue as it can overcome the limited energy bottleneck of wireless sensor networks owing to the recent breakthrough of wireless power transfer technology. Though network lifetime is prolonged and sensor nodes can sustain immortally, the issue of network robustness is overlooked, yielding most theoretical work unsuitable for practical applications when confronting with unpredictable packet loss. In this paper, we address the network robustness issue by maximizing the charging utility in a risk-averse view. First, we build a risk-averse model based on the concept of CVaR (Conditional Value at Risk), which trades-off charging utility and risk aversion for quantifying robustness. Then, we propose a spatial discretization scheme to construct a charging route for mobile charger, which can reduce computational overhead. Afterwards, a path optimization scheme is designed to further improve the charging utility. We convert the original problem into the submodular function maximization problem and propose a method with a performance guarantee while maximizing the system robustness. Finally, testbed experiments and simulations are conducted, and the results demonstrate that our schemes outperform comparison algorithms by at least 22.4% in effective energy in the presence of risks to guarantee system robustness.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as it can overcome the energy bottleneck problem of wireless sensor networks. However, inaccurate discretization methods and imprecise charging models yield a huge gap between theoretical results and practical applications, making it difficult for wide adoptions. In this paper, we focus on designing a precise charging method for maximizing charging utility when line-of-sight (LOS) and none-line-of-sight (NLOS) charging cases exist in complicated environments. First, we design discretization methods for charging area and charging orientation for precisely constructing the charging model. Then, we develop a novel electromagnetic wave reflection model to describe the signal propagation model in the presence of obstacles. We formalize the mobile charging problem into a submodular function maximization problem which can be solved by a proposed algorithm with an approximation guarantee. Finally, extensive experiments and simulations demonstrate that our schemes outperform comparison algorithms by 31.45% on average in charging utility in complicated environments.