Haipeng Dai

Minimizing the number of mobile chargers for large-scale wireless rechargeable sensor networks☆

Abstract Traditional wireless sensor networks (WSNs) are constrained by limited battery energy that powers the sensor nodes, which impedes the large-scale deployment of WSNs. Wireless power transfer technology provides a promising way to solve this problem. With such novel technology, recent works propose to use a single mobile charger (MC) traveling through the network fields to replenish energy to every sensor node so that none of the nodes will run out of energy. These algorithms work well in small-scale networks. In large-scale networks, these algorithms, however, do not work efficiently, especially when the amount of energy the MC can provide is limited. To address this issue, multiple MCs can be used. In this paper, we investigate the minimum MCs problem (MinMCP) for two-dimensional (2D) wireless rechargeable sensor networks (WRSNs), i.e., how to find the minimum number of energy-constrained MCs and design their recharging routes in a 2D WRSN such that each sensor node in the network maintains continuous work, assuming that the energy consumption rate for all sensor nodes are identical. By reduction from the Distance Constrained Vehicle Routing Problem (DVRP), we prove that MinMCP is NP-hard. Then we propose approximation algorithms for this problem. Finally, we conduct extensive simulations to validate the effectiveness of our algorithms.

Quality of Energy Provisioning for Wireless Power Transfer

One fundamental question for wireless power transfer technology is the energy provisioning problem, i.e., how to provide sufficient energy to mobile rechargeable nodes for their continuous operation. Most existing works overlooked the impacts of node speed and battery capacity. However, we find that if the constraints of node speed and battery capacity are considered, the continuous operation of nodes may never be guaranteed, which invalidates the traditional energy provisioning concept. In this paper, we propose a novel metric-Quality of Energy Provisioning (QoEP)-to characterize the expected portion of time that a node sustains normal operation by taking into account node speed and battery capacity. To avoid confining the analysis to a specific mobility model, we study spatial distribution instead. As there exist more than one mobility models corresponding to the same spatial distribution, and different mobility models typically lead to different QoEPs, we investigate upper and lower bounds of QoEP in 1D and 2D cases. We derive tight upper and lower bounds of QoEP for 1D case with a single source, and tight lower bounds and loose upper bounds for general 1D and 2D cases with multiple sources. Finally, we perform extensive simulations to verify our theoretical findings.

Using Minimum Mobile Chargers to Keep Large-Scale Wireless Rechargeable Sensor Networks Running Forever

Wireless Rechargeable Sensor Networks (WRSNs) can be recharged after deployment for sustainable operations. Recent works propose to use a single mobile charger (MC) traveling through the network fields to recharge every sensor node. These algorithms work well in small scale networks. However, in large scale networks these algorithms do not work efficiently, especially when the amount of energy the MC can provide is limited. To address these challenges, multiple MCs can be used. In this paper, we investigate the minimum MCs problem (MinMCP) for rechargeable sensor networks: how to find the minimum number of energy-constrained MCs and design their recharging routes given a sensor network such that each sensor node in the WRSN maintains continuous work. Our results are three folds. We first prove that for any ϵ > 0, there is no (2-ϵ)-approximation algorithm for Distance Constrained Vehicle Routing Problem (DVRP) on a general metric space, which is the best as far as we know. By reducing from DVRP, we prove that MinMCP is NP-hard, and the inapproximability bound for MinMCP is the same as that of DVRP. Then we propose approximation algorithms for this problem. Finally, we conduct simulations to validate the effectiveness of our algorithms.

Impact of mobility on energy provisioning in wireless rechargeable sensor networks

One fundamental question in Wireless Rechargeable Sensor Networks (WRSNs) is the energy provisioning problem, i.e., how to deploy energy sources in a network to ensure that the nodes can harvest sufficient energy for continuous operation. Though the potential mobility of nodes has been exploited to reduce the number of sources necessary in energy provisioning problem in existing literature, the non-negligible impacts of the constraints of node speed and battery capacity on energy provisioning are completely overlooked, in order to simplify the analysis. In this paper, we propose a new metric - Quality of Energy Provisioning (QoEP) - to characterize the expected portion of time that a mobile node can sustain normal operation in WRSNs, which factors in the constraints of node speed and battery capacity. To avoid confining the analysis to a specific mobility model, we study spatial distribution instead. We investigate the upper and lower bounds of QoEP in one-dimensional case with one single source and multiple sources respectively. For single source case, we prove the tight lower bound and upper bound of QoEP. Extending the results to multiple sources, we obtain tight lower bound and relaxed upper bound in normal cases, together with tight upper bound for one special case. Moreover, we give the tight lower bounds in both 2D and 3D cases. Finally, we perform extensive simulations to verify our findings. Simulation results show that our bounds perfectly hold, and outperform the former works.

Practical scheduling for stochastic event capture in wireless rechargeable sensor networks

Existing scheduling schemes for stochastic event capture with rechargeable sensors either adopt simplified assumptions on event staying time or provide no performance guarantee. Considering the stochasticity of event staying time, we investigate the sensor scheduling problem aiming to maximize the overall Quality of Monitoring (QoM) in events capture application of wireless rechargeable sensor networks. We first provide a paradigm to calculate the QoM of a point of interests (PoI) and formulate the scheduling problem into an optimization problem. Although we find that that this problem is NP-complete, we prove that the objective function of the optimization problem is monotone submodular. Therefore we can express the problem as a maximization of a submodular function subject to a matroid constraint. Accordingly we can design an approximation algorithm which achieves a factor of 1/2 of the optimum. We evaluate the performance of our solution through simulations, and simulation results show that our scheme outperforms former works.

Optimizing Energy Efficiency for Minimum Latency Broadcast in Low-Duty-Cycle Sensor Networks

Multihop broadcasting in low-duty-cycle Wireless Sensor Networks (WSNs) is a very challenging problem, since every node has its own working schedule. Existing solutions usually use unicast instead of broadcast to forward packets from a node to its neighbors according to their working schedules, which is, however, not energy efficient. In this article, we propose to exploit the broadcast nature of wireless media to further save energy for low-duty-cycle networks, by adopting a novel broadcasting communication model. The key idea is to let some early wake-up nodes postpone their wake-up slots to overhear broadcasting messages from its neighbors. This model utilizes the spatiotemporal locality of broadcast to reduce the total energy consumption, which can be essentially characterized by the total number of broadcasting message transmissions. Based on such model, we aim at minimizing the total number of broadcasting message transmissions of a broadcast for low-duty-cycle WSNs, subject to the constraint that the broadcasting latency is optimal. We prove that it is NP-hard to find the optimal solution, and design an approximation algorithm that can achieve a polylogarithmic approximation ratio. Extensive simulation results show that our algorithm outperforms the traditional solutions in terms of energy efficiency.

Energy-Efficient Broadcast Scheduling with Minimum Latency for Low-Duty-Cycle Wireless Sensor Networks

For low-duty-cycle wireless sensor networks, multihop broadcasting is a challenging problem, since every node has its own working schedules. In this paper, we design a novel broadcasting algorithm, of which key idea is to let some early wake-up nodes postpone their wake-up slots to overhear broadcasting message from its neighbors. This design utilizes the spatiotemporal locality of broadcasting to reduce the number of transmissions. We show that to find the broadcasting schedule with minimal latency and optimized total energy consumption is NP-hard, and then design an approximation algorithm that can guarantee the optimality of broadcasting latency and achieve a polylogarithmic approximation ratio for total energy consumption. Compared with the traditional solution, extensive experimental results show that our algorithm achieves the minimal broadcasting latency while reducing energy consumption significantly.

Radiation constrained wireless charger placement

Wireless Power Transfer has become a commercially viable technology to charge devices because of the convenience of no power wiring and the reliability of continuous power supply. This paper concerns the fundamental issue of wireless charger placement with electromagnetic radiation (EMR) safety. Although there are a few wireless charging schemes consider EMR safety, none of them addresses the charger placement issue. In this paper, we propose PESA, a wireless charger Placement scheme that guarantees EMR SAfety for every location on the plane. First, we discretize the whole charging area and formulate the problem into the Multidimensional 0/1 Knapsack (MDK) problem. Second, we propose a fast approximation algorithm to the MDK problem. Third, we optimize our scheme to improve speed by double partitioning the area. We prove that the output of our algorithm is better than (1 - ϵ) of the optimal solution to PESA with a smaller EMR threshold (1 - ϵ/2)Rt and a larger EMR coverage radius (1 + ϵ/2)D. We conducted both simulations and field experiments to evaluate the performance of our scheme. Our experimental results show that in terms of charging utility, our algorithm outperforms the prior art by up to 45.7%.

Practical scheduling for stochastic event capture in energy harvesting sensor networks

Existing scheduling schemes for stochastic event capture with rechargeable sensors either adopt simplified assumptions on events properties or provide no performance guarantee. Considering the stochasticity of event staying time and event capture utility, we investigate the sensor scheduling problem aiming to maximise the overall quality of monitoring QoM in event capture application of energy harvesting sensor networks. We first provide a paradigm to calculate the QoM of a point of interests PoI and formulate the scheduling problem as an optimisation problem. Although we find that this problem is NP-complete, we prove that the problem can be cast as maximisation of a submodular function subject to matroid constraints. Accordingly, we can design centralised and distributed algorithms, each of which achieves a factor of 1=2 of the optimum. We evaluate the performance of our solution through simulations, and simulation results show that our scheme outperforms former works.

Towards energy-fairness for broadcast scheduling with minimum delay in low-duty-cycle sensor networks

We are the first to investigate the load-balanced minimum end-to-end delay broadcast scheduling problem (LB-MEBS) for low-duty-cycle WSNs.We prove that LB-MEBS problem is NP-hard.We propose an approximation algorithm to address LB-MEBS problem.We also propose an efficient distributed solution to address LB-MEBS problem.Extensive simulation are conducted to validate the effectiveness of our proposed solutions. Broadcast scheduling for low-duty-cycle wireless sensor networks (WSNs) has been extensively studied recently. However, existing solutions mainly focused on optimizing delay and (or) total energy consumption without considering load distribution among nodes. Due to limited energy supply for sensor nodes, heavily loaded sensors often run out of energy quickly, reducing the lifetime of the whole network. In this paper, we target at minimizing the maximum transmission load of a broadcast schedule for low-duty-cycle WSNs, subject to the constraint that each node should have the minimum end-to-end delay under the broadcast schedule. We prove that it is NP-hard to find the optimal schedule. Then, we devise a Load-Balanced Parents Assignment Algorithm (LBPA-A) that achieves λ-approximation ratio, where λ denotes the maximum number of neighbors that are scheduled to wake up at the same time and is typically a small number in low-duty-cycle WSNs. Further, we introduce how to solve this problem in a distributed manner. Our simulation results reveal that compared with the traditional solutions, our proposed LBPA-A and distributed solution both exhibit much better average performance in terms of energy-fairness, total energy consumption and delivery ratio.