The application of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1:61(1+ϵ)2-approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors' transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework can reduce battery depletion by 20 percent and save vehicles' moving cost by 25 percent compared to previous works. By allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors.

In recent years, mobile data gathering in wireless sensor networks has attracted much interests in the research community. However, despite extensive efforts, many of previous work in this area lies only in theory and evaluates network performance with computer simulations, which leaves a large gap from reality. In this paper, we present the design and implementation of a general purpose, flexible platform for mobile data gathering in wireless sensor networks to evaluate network performance and algorithms in a practical setting. Instead of relying on hand-crafted theoretical models, our platform integrates both mobile data collector and sensor nodes to provide realistic performance evaluations. In addition, the platform adopts a modular design in mobile data collector and sensor nodes, and equips the mobile data collector with advanced computing capability, which makes it versatile for evaluating the performance of a wide-range of applications. Finally, as a case study, weimplement a wildlife monitoring system on our platform. Our experimental results demonstrate that real implementations can evaluate many practical performance factors which would have a great impact on the sensing results and are very difficult to fully capture by theoretical models and simulations. We expect that this platform can become a very powerful general tool for more accurate network simulations and facilitate performance optimization in wireless sensor networks.

Wireless charging has provided a convenient alternative to renew nodes' energy in wireless sensor networks. Due to physical limitations, previous works have only considered recharging a single node at a time, which has limited efficiency and scalability. Recent advances on multi-hop wireless charging is gaining momentum and provides fundamental support to address this problem. However, existing single-node charging designs do not consider and cannot take advantage of such opportunities. In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters. First, we present a realistic model that accounts for detailed physical factors to calculate charging efficiencies. Second, to achieve balance between energy efficiency and data latency, we propose a hybrid data gathering strategy that combines static and mobile data gathering to overcome their respective drawbacks and provide theoretical analysis. Then, we formulate multi-hop recharge schedule into a bi-objective NP-hard optimization problem. We propose a two-step approximation algorithm that first finds the minimum charging cost and then calculates the charging vehicles' moving costs with bounded approximation ratios. Finally, upon discovering more room to reduce the total system cost, we develop a post-optimization algorithm that iteratively adds more stopping locations for charging vehicles to further improve the results while ensuring none of the nodes will deplete battery energy. Our extensive simulations show that the proposed algorithms can handle dynamic energy demands effectively, and can cover at least three times of nodes and reduce service interruption time by an order of magnitude compared to the single-node charging scheme.

Recently, there have been a growing number of applications that power wireless sensor networks (WSNs) by wireless charging technology. Although previous studies indicate that wireless charging can deliver energy reliably, it still faces regulatory challenges to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies that wireless charging can provide. Fortunately, solar energy harvesting can provide high power density which is also risk-free. However, it is subject to weather dynamics. Therefore, in this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. First, we study a placement problem on how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1.61(1 + ϵ)2-approximation algorithm for the placement. Second, we establish an energy balance in the network and explore how to maintain such balance when sunlight is unavailable. Third, we consider combining wireless charging and mobile data gathering in a joint tour in such networks, and propose a polynomial-time scheduling algorithm. Our extensive simulation demonstrates that the hybrid framework can reduce battery depletion by 20% and save system cost by 25% compared to previous results.

Several recent works have studied mobile vehicle scheduling to recharge sensor nodes via wireless energy transfer technologies. Unfortunately, most of them overlooked important factors of the vehicles' moving energy consumption and limited recharging capacity, which may lead to problematic schedules or even stranded vehicles. In this paper, we consider the recharge scheduling problem under such important constraints. To balance energy consumption and latency, we employ one dedicated data gathering vehicle and multiple charging vehicles. We first organize sensors into clusters for easy data collection, and obtain theoretical bounds on latency. Then we establish a mathematical model for the relationship between energy consumption and replenishment, and obtain the minimum number of charging vehicles needed. We formulate the scheduling into a Profitable Traveling Salesmen Problem that maximizes profit - the amount of replenished energy less the cost of vehicle movements, and prove it is NP-hard. We devise and compare two algorithms: a greedy one that maximizes the profit at each step; an adaptive one that partitions the network and forms Capacitated Minimum Spanning Trees per partition. Through extensive evaluations, we find that the adaptive algorithm can keep the number of nonfunctional nodes at zero. It also reduces transient energy depletion by 30-50 percent and saves 10-20 percent energy. Comparisons with other common data gathering methods show that we can save 30 percent energy and reduce latency by two orders of magnitude.

Wireless charging is a game-changing technology to provide reliable energy source for wireless sensor networks. Combining wireless charging with mobile data collection on a single mobile vehicle can mitigate the nonuniform energy distribution problem. However, data latency may be too long for some applications because vehicles have to spend significant time in charging before uploading data to the base station. In this paper, we propose a new framework that employs a dedicated vehicle for data collection and theoretically study the trade-offs between data latency and the number of recharging vehicles needed. We first study how to minimize data latency while ensuring all sensory data are collected and derive a latency bound. Then we establish a mathematical model to calculate the minimum number of recharging vehicles needed. Finally, we conduct simulations to validate the theoretical results and evaluate the efficiency of the framework. The results show that our scheme can reduce the number of nonfunctional nodes by 30-60%, and cut down data collection latency more than an order of magnitude compared to the previous work.

Wireless charging has provided a convenient alternative to renew sensors' energy in wireless sensor networks. Due to physical limitations, previous works have only considered recharging a single node at a time, which has limited efficiency and scalability. Recent advance on multi-hop wireless charging is gaining momentum to provide fundamental support to address this problem. However, existing single-node charging designs do not consider and cannot take advantage of such opportunities. In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters. First, we present a realistic model that accounts for detailed physical factors to calculate charging efficiencies. Second, to achieve balance between energy efficiency and data latency, we propose a hybrid data gathering strategy that combines static and mobile data gathering to overcome their respective drawbacks and provide theoretical analysis. Then we formulate multi-hop recharge schedule into a bi-objective NP-hard optimization problem. We propose a two-step approximation algorithm that first finds the minimum charging cost and then calculates the charging vehicles' moving costs with bounded approximation ratios. Finally, upon discovering more room to reduce the total system cost, we develop a post-optimization algorithm that iteratively adds more stopping locations for charging vehicles to further improve the results. Our extensive simulations show that the proposed algorithms can handle dynamic energy demands effectively, and can cover at least three times of nodes and reduce service interruption time by an order of magnitude compared to the single-node charging scheme.

Wireless sensor networks adopting static data gathering may suffer from unbalanced energy consumption due to non-uniform packet relay in such networks, especially in large scale networks. On the other hand, although mobile data gathering provides a reasonable approach to solving this problem, it inevitably introduces longer data collection latency due to the use of mobile data collectors. In the meanwhile, energy harvesting has been considered as a promising solution to relieve energy limitation in wireless sensor networks. In this paper, we consider a joint design of these two schemes and propose a novel two layer heterogeneous architecture for wireless sensor networks, which consists of two types of nodes: sensor nodes which are static and powered by solar panels, and cluster heads that have limited mobility and can be wirelessly recharged by power transporters. Based on this network architecture, we present a data gathering scheme, called Mobility Assisted Data Gathering (MADG), where sensor nodes are clustered around cluster heads that change their positions in each data gathering cycle, and the sensing data are forwarded to the data sink by these cluster heads working as data aggregation points. We evaluate the performance of the proposed scheme by extensive simulations and the results show that MADG provides significant improvement in terms of balancing energy consumption and the amount data gathered compared to previous work.

Several recent works have studied the schedule for mobile vehicles to recharge sensor nodes via wireless energy transfer technologies. Unfortunately, most of them overlooked the important factors of the vehicles' moving energy consumption and limited recharging capacity. These oversights may lead to problematic schedules or even stranded vehicles. In this paper, we study the recharging schedule that maximizes the recharging profit - the amount of replenished energy less the cost of vehicle movements - under these important constraints. We first derive the minimum number of vehicles needed for energy neutral condition and discover a set of desired network properties. Then we formulate the recharge schedule optimization into a Profitable Traveling Salesmen Problem with capacity and battery deadline constraints, which we prove to be NP-hard. We propose two algorithms to solve the problem. The first one is a greedy algorithm that maximizes the recharge profit at each step; the second one first adaptively partitions the network based on recharge requests, then forms Capacitated Minimum Spanning Tree in each partition followed by route improvements. Finally, we evaluate and compare the performance of proposed algorithms and validate the correctness of theoretical results through extensive simulations. Given a sufficient number of vehicles, the adaptive algorithm can keep the number of nonfunctional nodes at zero. Compared to the greedy algorithm, it reduces the percentage of transient energy depletion by 30-50% with 10-20% energy saving on vehicles.

Using vehicles equipped with wireless energy transmission technology to recharge sensor nodes over the air is a game-changer for traditional wireless sensor networks. The recharging policy regarding when to recharge which sensor nodes critically impacts the network performance. So far only a few works have studied such recharging policy for the case of using a single vehicle. In this paper, we propose NETWRAP, an NDN based Real Time Wireless Recharging Protocol for dynamic wireless recharging in sensor networks. The real-time recharging framework supports single or multiple mobile vehicles. Employing multiple mobile vehicles provides more scalability and robustness. To efficiently deliver sensor energy status information to vehicles in real-time, we leverage concepts and mechanisms from named data networking (NDN) and design energy monitoring and reporting protocols. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. Then we study how to minimize the total traveling cost of vehicles while guaranteeing all the sensor nodes can be recharged before their batteries deplete. We formulate the recharge optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions with low overhead, we present an algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also ensures the perpetual operation of networks. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed design. The results also validate the correctness of the theoretical analysis and show significant improvements that cut the number of nonfunctional nodes by half compared to the static scheme while maintaining the network overhead at the same level.

Recent years have witnessed the rapid development and proliferation of techniques on improving energy efficiency for wireless s`ensor networks. Although these techniques can relieve the energy constraint on wireless sensors to some extent, the lifetime of wireless sensor networks is still limited by sensor batteries. Recent studies have shown that energy rechargeable sensors have the potential to provide perpetual network operations by capturing renewable energy from external environments. However, the low output of energy capturing devices can only provide intermittent recharging opportunities to support low-rate data services due to spatial-temporal, geographical or environmental factors. To provide steady and high recharging rates and achieve energy efficient data gathering from sensors, in this paper, we propose to utilize mobility for joint energy replenishment and data gathering. In particular, a multi-functional mobile entity, called SenCar in this paper, is employed, which serves not only as a mobile data collector that roams over the field to gather data via short-range communication but also as an energy transporter that charges static sensors on its migration tour via wireless energy transmissions. Taking advantages of SenCar’s controlled mobility, we focus on the joint optimization of effective energy charging and high-performance data collections. We first study this problem in general networks with random topologies. We give a two-step approach for the joint design. In the first step, the locations of a subset of sensors are periodically selected as anchor points, where the SenCar will sequentially visit to charge the sensors at these locations and gather data from nearby sensors in a multi-hop fashion. To achieve a desirable balance between energy replenishment amount and data gathering latency, we provide a selection algorithm to search for a maximum number of anchor points where sensors hold the least battery energy, and meanwhile by visiting them, the tour length of the SenCar is no more than a threshold. In the second step, we consider data gathering performance when the SenCar migrates among these anchor points. We formulate the problem into a network utility maximization problem and propose a distributed algorithm to adjust data rates at which sensors send buffered data to the SenCar, link scheduling and flow routing so as to adapt to the up-to-date energy replenishing status of sensors. Besides general networks, we also study a special scenario where sensors are regularly deployed. For this case we can provide a simplified solution of lower complexity by exploiting the symmetry of the topology. Finally, we validate the effectiveness of our approaches by extensive numerical results, which show that our solutions can achieve perpetual network operations and provide high network utility.

Mobile vehicles equipped with wireless energy transmission technology can recharge sensor nodes over the air. When to recharge which nodes, and in what order, critically impact the network performance. So far only a few works have studied the recharging policy for a single mobile vehicle. In this paper, we study how to coordinate the recharging activities of multiple mobile vehicles, which provide more scalability and robustness than a single vehicle. We leverage concepts and mechanisms from NDN (Named Data Networking) to design energy monitoring protocols that deliver energy status information to mobile vehicles in an efficient manner. Then we study how to minimize the total traveling cost of multiple vehicles while ensuring no node failure. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. We formulate the optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions and reduce computational overhead, we present a heuristic algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also guarantees the perpetual operation of networks. Finally, we conduct extensive simulations to demonstrate the effectiveness and efficiency of our proposed design, and validate the correctness of theoretical analysis.

A mobile vehicle equipped with wireless energy transmission technology can move around a wireless sensor network and recharge nodes over the air, leading to potentially perpetual operation if nodes can always be recharged before energy depletion. When to recharge which nodes, and in what order, critically impact the outcome. So far only a few works have studied this problem and relatively static recharging policies were proposed. However, dynamic changes such as unpredictable energy consumption variations in nodes, and practical issues like scalable and efficient gathering of energy information, are not yet addressed. In this paper, we propose NETWRAP, an NDN based Real Time Wireless Recharging Protocol for dynamic recharging in wireless sensor networks. We leverage concepts and mechanisms from NDN (Named Data Networking) to design a set of protocols that continuously gather and deliver energy information to the mobile vehicle, including unpredictable emergencies, in a scalable and efficient manner. We derive analytic results on energy neutral conditions that give rise to perpetual operation. We also discover that optimal recharging of multiple emergencies is an Orienteering problem with Knapsack approximation. Our extensive simulations demonstrate the effectiveness and efficiency of the proposed framework and validate the theoretical analysis.

Recent studies have shown that environmental energy harvesting technologies have the potential to provide perpetual operation to wireless sensor networks. However, due to the large variations of the ambient energy source, such networks could only support low-rate data services and the performance is affected by many unpredictable environmental factors. To deliver energy to sensor nodes reliably, in this paper, we apply the novel wireless power transmission technology to rechargeable sensor networks by introducing a mobile actuator to replenish sensor energy wirelessly. We first establish an analytical model based on stochastic wireless energy replenishment to obtain a variety of performance metrics. Then based on the theoretical results, we further propose battery-aware mobile energy replenishment scheme and present two heuristic algorithms: (1) linear adaptation sensor activation with prioritized recharge; and (2) battery-aware activation with selective recharge. We validate the theoretical results and evaluate the performance of the proposed algorithms through extensive simulations. The results demonstrate that a good design of sensor activation with effective control of mobile energy replenishment can provide substantial performance improvement.

Current study on prolonging lifetime for wireless sensor networks (WSNs) mainly focuses on two techniques. The first technique is to reduce energy consumption of sensor nodes, while the second technique is to recharge sensor nodes by harvesting energy from the ambient environment or RF based energy transmission. However, neither of these two techniques are able to guarantee network lifetime and network performance. In order to achieve perpetual operation for WSNs while providing high network utility, in this paper we propose an optimal wireless energy replenishment and mobile data gathering mechanism (OWER-MDG) which charges sensor nodes effectively and collects data from the network using a mobile vehicle (SenCar). We study the application of OWER-MDG in WSNs and provide an efficient algorithm which maximizes network utility. Our numerical results demonstrate the performance advantage of OWER-MDG and provide a guidance on parameter selection for system design.