In the past few years, dockless bike-sharing has become a popular means of public transportation and brought significant convenience to millions of citizens. As one of the key components of a shared bike, the smart locking/unlocking module has proposed a new challenge of how to provide robust power supplement for them. Current charging solutions for shared bikes are mainly based on mechanical power and solar power, and rarely take user experience and charging delay into consideration. In this article, we design a robust RF-based wireless charging system for dockless bike-sharing. Our system utilizes radio frequency (RF) power to provide stable charging service while preserving the quality of service. In our system, an RF wireless charging sensing node is integrated on the bike's basket, so that the mutual interference during charging process and space occupation can be reduced. In order to reduce charging delay, we first design an efficient charging direction scheduling algorithm for a single charger. Then, we extend the solution to multiple-charger scenarios via dynamic programming. Our system has been successfully implemented on a dockless bike-sharing system. The experimental results verify that our design can satisfy the charging demands of shared-bikes and achieve 85% of the optimal solution.

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. However, as a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes’ operation states: working or recharging, to achieve a desirable network utility. In our design, we first consider a single-hop special case of small-scale networks. We transform the operation state scheduling problem into a linear programming problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. The multi-hop operation state scheduling problem is proved to be NP-hard. We show that the spatiotemporal coupling caused by time-varying network topology makes the problem quite challenging. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85 percent of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. As a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes' operation states: working or recharging, to achieve a desirable network utility. We show that the operation state scheduling problem is quite challenging, since the time-varying network topology leads to spatiotemporal coupling of scheduling strategies. We first consider a single-hop special case of small-scale networks. We employ geometric programming to transfer it into a convex optimization problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85% of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.

Wireless charging technology has drawn great attention of both academia and industry in recent years, due to its potential of significantly improving the system performance of sensor networks. The emergence of an open-source experimental platform for wireless rechargeable sensor networks, Powercast, has made the theoretical research closer to reality. This pioneering platform is able to recharge sensor nodes much more efficiently and allows different communication protocols to be implemented upon users’ demands. Different from the RFID-based model widely used in the existing works, Powercast designs the charger and sink station separately. This leads to a new design challenge of cooperatively deploying minimum number of chargers and sink stations in wireless rechargeable sensor networks. Such a co-deployment issue is extremely challenging, since the deployments of chargers and sink stations are coupled, and each subproblem is known to be NP-hard. The key to the design is to understand the intrinsic relationship between data flow and energy flow, which is interdependent. In this article, we tackle this challenge by dividing it into two subproblems and optimizing charger and sink station deployment iteratively. Specifically, we first transform each subproblem to a max-flow problem. With this, we are able to select chargers or sink stations according to their contributions to the total flow rate. We design greedy-based algorithms with a guaranteed worst-case bound ln R/ξ for the subproblems of charger deployment and sink station deployment, respectively. Further, we address the original problem by designing an iterative algorithm that solves two subproblems alternatively to achieve a near optimal performance. We corroborate our analysis by extensive simulations under practical coefficient settings and demonstrate the advantage of the proposed algorithm.

Wireless rechargeable sensor networks have recently emerged as a promising platform that can effectively solve the power constraint problem suffered by traditional battery powered systems. The problem of determining the best charging routes for maximizing charging efficiency has been studied extensively. However, the task assignment problem, which plays a crucial role in efficiently utilizing the harvested energy and thus minimize the charging delay, has received rather limited attention. In this paper, we study the problem of assigning a given set of tasks in a wireless rechargeable sensor network while maximizing the charger's velocity to minimize the charging delay. We first propose an online task assignment algorithm, namely Lower Bound assignment (LB), that yields a quantifiable lower bound on the charging velocity while guaranteeing a feasible assignment. This algorithm further enables the transformation of our considered task assignment problem into a variation of the classical multiple knapsack problem. We then present a fully polynomial-time approximation scheme with a (2+ε)-approximation ratio, namely ACT, that is built upon an existing greedy algorithm designed for the original knapsack problem. Extensive experimental results presented herein demonstrate that ACT is able to achieve near-optimal performance in most cases, and can achieve more than 15% performance improvement compared to the baseline algorithms.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumption rates, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging requests sequence of nodes with their sequence on the TSP tours.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumptions, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging re- quests sequence of nodes with their sequence on the TSP tours. Experiment and simulation demonstrate ESync can reduce charger travel distance and nodes charging delay by about 30% and 40% respectively.

Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.

As a pioneering experimental platform of wireless rechargeable sensor networks, the Wireless Identification and Sensing Platform (WISP) is an open-source platform that integrates sensing and computation capabilities to the traditional RFID tags. Different from traditional tags, a RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold in order to power its sensing, computation and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - ε) by discretizing the charging power on a two-dimensional space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7% while the computational complexity is O((N/ε)2).

Energy constraint is a critical hurdle hindering the practical deployment of long-term wireless sensor network applications. Turning off (that is, duty cycling) sensors could reduce energy consumption, however, this would occur at the cost of low sensing fidelity due to sensing gaps introduced. Existing techniques focus mainly on scheduling a network with static anchors. Few methods provides a rigorous approach to confining sensing errors within desirable bounds while seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this work, we propose a sensing scheduling scheme, called MAS, to support mobile anchors in sensor networks. Within a node, we use a sensing probability bound to control tolerable sensing errors. While communicating with the mobile anchor, nodes trigger additional sensing activities to accommodate the QoS requirement in mobile communication. We validated the concept by constructing a lab-grade mobile anchor that fully supports 4G-LTE communications for monitoring applications. We further conducted simulations to investigate system performance. The simulation results demonstrated that the MAS achieved enhancement performance compared to several other sensing schemes.