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%.

Neighbor discovery poses significant challenges in Underwater Optical Wireless Sensor Networks (UOWSNs) due to the unique characteristics of directional transceivers, line-of-sight communication, and mobility induced by water currents. Traditional methods typically rely on prerequisites and prior knowledge, such as centralized coordination, time synchronization, and information about the number of neighbors, which are often unavailable or impractical in underwater environments. In this paper, we make the first attempt to address the issue ofRobust andEfficientNeighborDiscovery (termed the REND problem) in UOWSNs with zero-knowledge. Here, zero-knowledge refers to the capability that enables sensors to identify neighbors in dynamic underwater optical channel conditions without prerequisites or prior knowledge. We design a zero-knowledge distributed directional neighbor discovery scheme inspired by gear meshing. We then propose a deterministic algorithm for the REND problem based on theoretical analysis. Additionally, to further reduce the discovery delay for the periodic REND problem, we develop a greedy-based approximation algorithm with a performance guarantee. Finally, extensive simulations demonstrate that the proposed scheme reduces the discovery delay by 34.9% on average and achieves an additional 54.4% reduction for periodic neighbor discovery. Furthermore, test-bed experiments are carried out to verify the applicability of our zero-knowledge scheme in real-world scenarios.

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.

Underwater Optical Wireless Communication (UOWC) is considered a promising approach, offering the potential for flexible and high-speed communication under the surface of the water. However, the interdependent relationship among three key performance elements, namely communication distance, bit error rate, and communication rate, has been largely overlooked. This oversight impedes the complete utilization of the system performance. In this work, we innovatively introduce a “UOWC Impossible Trinity” model and theorems to establish relationships among the three key performance elements, which clarify the inherent constraints within UOWC system optimization. Moreover, we formulate the Underwater Optical Communication Trade-offs (UOCT) Problem to maximize communication performance. Furthermore, we provide feasible non-dominated solution sets, considering the constraints of real environments and user demands of specific scenarios. Our model has been validated by extensive simulations, demonstrating that our approach not only clarifies fundamental limitations of UOWC systems, but also provides practical guidelines for designing and optimizing the systems. Our approach has been experimentally validated with an impressive accuracy of over 95%, surpassing conventional models, which not only enhances the understanding of UOWC system optimization but also validates the existence of inherent trade-offs. Furthermore, our approach demonstrates a significant increase in communication distance, outperforming traditional methods by more than 20%.

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.

Subset selection problem for task scheduling with general cost constraints exists widely in IoT applications. Its objective is to select several profitable tasks to execute under routing and cost constraints such that the total profit is maximized. Most prior arts only focus on either online tasks or offline tasks, which are usually inapplicable in practical applications where online tasks and offline tasks co-exist. In this paper, we study the subset selection problem for HybrId Task Scheduling with general cost constraints (HITS), in which both online and offline tasks are scheduled to maximize the overall profit. We first divide the HITS problem into online and offline subproblems and propose two algorithms to solve them with bounded approximation ratios. Furthermore, we propose an approximation algorithm for the hybrid scenario where both online and offline tasks are considered. Extensive simulations show that our proposed algorithm outperforms baseline algorithms by 21.5% averagely in profit and also performs well in pure online/offline scenarios. We further demonstrate the feasibility of our algorithm through test-bed experiments in a realistic scene.

As an epoch-making technology, wireless power transfer incredibly achieves energy transmission wirelessly, enabling reliable energy supplement for Wireless Rechargeable Sensor Networks (WRSNs). Existing methods mainly concentrate on performance improvement theoretically, neglecting the fact that most Commercial Off-The-Shelf (COTS) rechargeable sensors (e.g., WISP and Powercast) are not allowed to conduct sensing and energy harvesting tasks simultaneously, termedcharging exclusivity. Therefore, their schemes are not feasible for practical applications. In this paper, we focus on the charging exclusivity issue in stochastic events monitoring while improving network performance. In specific, we pay close attention to trading off charging and sensing tasks and formulate a combinatorial optimization problem with routing constraints. We introduce novel discretization techniques and investigate the routing problem to reformulate the original problem into maximization of a submodular function. With a slightly relaxed budget, the output of our proposed algorithm is better than$(1-1/e)/2$of the optimal solution to the original problem with a smaller charging radius$(1-\xi)D_{c}$. Through extensive simulations, numerical results show that in terms of charging utility, our algorithm outperforms baseline algorithms by 21.3% on average. Moreover, we conduct test-bed experiments to demonstrate the feasibility of our scheme in real scenarios.

The rapid popularization of wireless power transfer (WPT) technology promotes the wide adoption of wireless rechargeable sensor networks (WRSNs). Traditional methods only focus on how to optimize network performance, and most of them overlook the energy waste issue induced by WPT. In this paper, we explore the potentials of recycling wasted energy when using WPT by means of freeloading. Specifically, with a slight modification on hardware, we expand the functionality of the mobile chargers (MCs), enabling them to harvest and recycle the WPT-induced wasted energy in the air to serve more sensors, which promotes energy efficiency. We model the problem, termed MEFree, as maximizing network energy efficiency by utilizing a limited number of freeloading MCs and scheduling their freeloading behaviors. Through jointly scheduling freeloading and charging tasks, the proposed scheme is able to solve the problem with a (1 − 1/e)/2 approximation ratio with a slightly relaxed budget. Extensive simulations are conducted and corresponding numerical results show that our proposed scheme significantly improves network energy efficiency by at least 18.8% and outperforms baseline algorithms by 19.1% on average in various aspects. Our test-bed experiments further demonstrate the practicability of our scheme in actual scenes.

As an epoch-making technology, wireless power transfer incredibly achieves energy transmission wirelessly, enabling reliable energy supplement for wireless rechargeable sensor networks (WRSNs). Existing methods mainly concentrate on performance improvement theoretically, neglecting the fact that most Commercial Off-The-Shelf (COTS) rechargeable sensors (e.g., WISP and Powercast) are not allowed to conduct sensing and energy harvesting tasks simultaneously, termed charging exclusivity. Therefore, their schemes are not feasible for practical applications. In this paper, we focus on the charging exclusivity issue in stochastic events monitoring while improving network performance. In specific, we pay close attention to trading off charging and sensing tasks and formulate a combinatorial optimization problem with routing constraints. We introduce novel discretization techniques and investigate the routing problem to reformulate the original problem into the maximization of a submodular function. With a slightly relaxed budget, the output of our proposed algorithm is better than (1 1/e)/2 of the optimal solution to the original problem with a -smaller charging radius (1 - ξ)Dc. Through extensive simulations, numerical results show that in terms of charging utility, our algorithm outperforms baseline algorithms by 21.3% on average. Moreover, we conduct test-bed experiments to demonstrate the feasibility of our scheme in real scenarios.

Recent breakthrough in wireless power transfer technology has enabled wireless sensor networks to operate virtually forever with the help of mobile chargers (MCs), thus generating the concept of wireless rechargeable sensor networks (WRSNs). However, existing studies mainly focus on developing charging tours with fixed network topology, most of which are not suitable for networks with dynamic topology, usually leading to massive packet/data loss. In this work, we explore the problem of charging scheduling for WRSNs with multiple MCs when confronting with dynamic topology. To minimize the energy cost to prolong the network lifetime, we convert the charging scheduling problem into a vehicle routing problem, which is proved to be NP-hard. Then we model the problem as a cooperative game taken among sensors and propose a cooperative game theoretical charging scheduling (CGTCS) algorithm to construct the optimal coalition structure. Then, we design an adaptive optimal coalition structure updating algorithm (AOCSU) to update the optimal coalition structure, which works well with network dynamics. We discuss the reasonability and feasibility to guarantee the cooperation among sensors through carefully designing the characteristic function and allocating cost based on Shapley value. Finally, test-bed experiments and simulations are conducted, revealing that CGTCS outperforms other related works in terms of expenditure ratio, total traveling cost, and charging time.

The ubiquitous and fine-grained features of WiFi signals make it promising for achieving device-free authentication. However, traditional methods suffer from drawbacks such as sensitivity to environmental dynamics, low accuracy, long delay, etc. In this paper, we introduce how to validate human identity using the ubiquitous WiFi signals. We develop WiAU, a device-free authentication system which only utilizes a Commodity Off-The-Shelf (COTS) router and a laptop. We describe the constitutions of WiAU and how it works in detail. Through collecting channel state information (CSI) profiles, WiAU automatically segments coherent activities and walking gait using an automatic segment algorithm (ASA). Then, a ResNet algorithm with two dedicated loss functions is designed to validate legal users and recognize illegal ones. Finally, experiments are conducted from different scenes to highlight the superiorities of WiAU in terms of high accuracy, short delay and robustness, revealing that WiAU has an accuracy of over 98% in recognizing human identity and human activities respectively.