Xiaojiang Ren

Maintaining sensor networks perpetually via wireless recharging mobile vehicles

The emerging wireless energy transfer technology based on magnetic resonant coupling is a promising technology for wireless sensor networks as it can provide a controllable and perpetual energy source to sensors. In this paper we study the use of minimum number of wireless charging mobile vehicles to charge sensors in a sensor network so that none of the sensors runs out of its energy, subject to the energy capacity imposed on mobile vehicles, for which we first advocate an flexible on-demand wireless charging paradigm that decouples sensor energy charging scheduling from data routing protocols design. We then formulate an optimization problem of scheduling mobile vehicles to charge lifetime-critical sensors with an objective to minimize the number of mobile vehicles deployed, subject to the energy capacity constraint on each mobile vehicle. As the problem is NP-hard, we devise an approximation algorithm with a provable performance guarantee for it. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results demonstrate that the proposed algorithm is promising, and the solution obtained is fractional of the optimal.

Use of a Mobile Sink for Maximizing Data Collection in Energy Harvesting Sensor Networks

In this paper we study data collection in an energy harvesting sensor network for traffic monitoring and surveillance purpose on busy highways, where sensors are densely deployed along a pre-defined path and a mobile sink travels along the path to collect data from one-hop sensors periodically. As the sensors are powered by renewable energy sources, the time-varying characteristics of energy harvesting poses great challenges on the design of efficient routing protocols for data collection in such energy harvesting sensor networks. In this paper we first formulate a novel data collection maximization problem that deals with multi-rate transmission mechanism and transmission time slot scheduling among the sensors. We then show the NPhardness of the problem and devise an offline algorithm with a provable approximation ratio for the problem by exploiting the combinatorial property of the problem, assuming that the global knowledge of the network topology and the profile of each sensor are given. We also develop a fast, scalable online distributed solution for the problem without the global knowledge assumption, which is more suitable for real distributive sensor networks. In addition, we consider a special case of the problem for which a optimal polynomial solution is given. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are very efficient, and the solutions are fractional of the optimum.

Delay-tolerant data gathering in energy harvesting sensor networks with a mobile sink

In this paper we consider data collection in an energy harvesting sensor network with a mobile sink, where a mobile sink travels along a trajectory for data collection subject to a specified tolerant delay constraint T. The problem is to find an optimal close trajectory for the mobile sink that consists of sojourn locations and the sojourn time at each location such that the network throughput is maximized, assuming that the mobile sink can only collect data from one-hop sensors, for which we first show that the problem is NP-hard. We then devise novel heuristic algorithms. We finally conduct extensive experiments to evaluate the performance of the proposed algorithms. We also investigate the impact of different parameters on the performance. The experimental results demonstrate that the proposed algorithms are efficient. To the best of our knowledge, this is the first kind of work of data collection for energy harvesting sensor networks with mobile sinks.

Data Collection Maximization in Renewable Sensor Networks via Time-Slot Scheduling

In this paper we study data collection in an energy renewable sensor network for scenarios such as traffic monitoring on busy highways, where sensors are deployed along a predefined path (the highway) and a mobile sink travels along the path to collect data from one-hop sensors periodically. As sensors are powered by renewable energy sources, time-varying characteristics of ambient energy sources poses great challenges in the design of efficient routing protocols for data collection in such networks. In this paper we first formulate a novel data collection maximization problem by adopting multi-rate data transmissions and performing transmission time slot scheduling, and show that the problem is NP-hard. We then devise an offline algorithm with a provable approximation ratio for the problem by exploiting the combinatorial property of the problem, assuming that the harvested energy at each node is given and link communications in the network are reliable. We also extend the proposed algorithm by minor modifications to a general case of the problem where the harvested energy at each sensor is not known in advance and link communications are not reliable. We thirdly develop a fast, scalable online distributed algorithm for the problem in realistic sensor networks in which neither the global knowledge of the network topology nor sensor profiles such as sensor locations and their harvested energy profiles is given. Furthermore, we also consider a special case of the problem where each node has only a fixed transmission power, for which we propose an exact solution to the problem. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are efficient and the solutions obtained are fractional of the optimum.

Monitoring Quality Maximization through Fair Rate Allocation in Harvesting Sensor Networks

In this paper, we consider an energy harvesting sensor network where sensors are powered by reusable energy such as solar energy, wind energy, and so on, from their surroundings. We first formulate a novel monitoring quality maximization problem that aims to maximize the quality, rather than the quantity, of collected data, by incorporating spatial data correlation among sensors. An optimization framework consisting of dynamic rate weight assignment, fair data rate allocation, and flow routing for the problem is proposed. To fairly allocate sensors with optimal data rates and efficiently route sensing data to the sink, we then introduce a weighted, fair data rate allocation and flow routing problem, subject to energy budgets of sensors. Unlike the most existing work that formulated the similar problem as a linear programming (LP) and solved the LP, we develop fast approximation algorithms with provable approximation ratios through exploiting the combinatorial property of the problem. A distributed implementation of the proposed algorithm is also developed. The key ingredients in the design of algorithms include a dynamic rate weight assignment and a reduction technique to reduce the problem to a special maximum weighted concurrent flow problem, where all source nodes share the common destination. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is very promising, and the solution to the weighted, fair data rate allocation and flow routing problem is fractional of the optimum.

The use of a mobile sink for quality data collection in energy harvesting sensor networks

In this paper we study data collection in an energy harvesting sensor network where sensors are deployed along a given path and a mobile sink travels along the path periodically for data collection. Such a typical application scenario is to employ a mobile vehicle for traffic surveillance of a given highway. As the sensors in this network are powered by renewable energy sources, the time-varying characteristics of energy harvesting poses great challenges on the design of efficient routing protocols for data collection in harvesting sensor networks. In this paper we first formulate a novel optimization problem as a network utility maximization problem, by incorporating multi-rate communication mechanism between sensors and the mobile sink and show the NP-hardness of the problem. We then devise a novel centralized algorithm for it, assuming that the global knowledge of the entire network is available. We also develop a distributed solution to the problem without the global knowledge assumption. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms are promising and very efficient.

Towards Perpetual Sensor Networks via Deploying Multiple Mobile Wireless Chargers

In this paper, we study the use of multiple mobile charging vehicles to charge sensors in a large-scale wireless sensor network for a given monitoring period, where sensors can be charged by the vehicles with wireless power transfer. Since each sensor may experience multiple charges to avoid its energy expiration for the period, we first consider a charging problem of scheduling the multiple mobile vehicles to collaboratively charge sensors so that none of the sensors will run out of its energy and the sum of traveling distance (referred to as the service cost) of these vehicles can be minimized. Due to NP-hardness of the problem, we then propose a novel approximation algorithm for it, assuming that sensor energy consumption rates do not change over time. Otherwise, we devise a heuristic algorithm through minor modifications to the approximation algorithm. We finally evaluate the performance of the proposed algorithms via simulations. Experimental results show that the proposed algorithms are very promising, which can reduce upto 45% of the service cost in comparison with the service cost delivered by a greedy algorithm.

Maintaining Large-Scale Rechargeable Sensor Networks Perpetually via Multiple Mobile Charging Vehicles

Wireless energy transfer technology based on magnetic resonant coupling has been emerging as a promising technology for wireless sensor networks (WSNs) by providing controllable yet perpetual energy to sensors. In this article, we study the deployment of the minimum number of mobile charging vehicles to charge sensors in a large-scale WSN so that none of the sensors will run out of energy, for which we first advocate a flexible on-demand charging paradigm that decouples sensor energy charging scheduling from the design of sensing data routing protocols. We then formulate a novel optimization problem of scheduling mobile charging vehicles to charge life-critical sensors in the network with an objective to minimize the number of mobile charging vehicles deployed, subject to the energy capacity constraint on each mobile charging vehicle. As the problem is NP-hard, we instead propose an approximation algorithm with a provable performance guarantee if the energy consumption of each sensor during each charging tour is negligible. Otherwise, we devise a heuristic algorithm by modifying the proposed approximation algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are very promising, and the solutions obtained are fractional of the optimal ones. To the best of our knowledge, this is the first approximation algorithm with a nontrivial approximation ratio for a novel scheduling problem of multiple mobile charging vehicles for charging sensors.

Quality-Aware Target Coverage in Energy Harvesting Sensor Networks

Sensing coverage is a fundamental problem in wireless sensor networks for event detection, environment monitoring, and surveillance purposes. In this paper, we study the sensing coverage problem in an energy harvesting sensor network deployed for monitoring a set of targets for a given monitoring period, where sensors are powered by renewable energy sources and operate in duty-cycle mode, for which we first introduce a new coverage quality metric to measure the coverage quality within two different time scales. We then formulate a novel coverage quality maximization problem that considers both sensing coverage quality and network connectivity that consists of active sensors and the base station. Due to the NP-hardness of the problem, we instead devise efficient centralized and distributed algorithms for the problem, assuming that the harvesting energy prediction at each sensor is accurate during the entire monitoring period. Otherwise, we propose an adaptive framework to deal with energy prediction fluctuations, under which we show that the proposed centralized and distributed algorithms are still applicable. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed solutions are promising.

On-demand energy replenishment for sensor networks via wireless energy transfer

In this paper, we study the use of a wireless charging vehicle (WCV) to replenish energy to sensors in a wireless sensor network so that none of the sensors will run out of its energy, where sensor batteries can be recharged. Specifically, we first propose a flexible on-demand sensor energy charging paradigm that decouples sensor energy replenishment and data collection into separate activities. We then formulate an optimization problem of wireless charging with an aim to maximize the ratio of the amount of energy consumed for charging sensors to the amount of energy consumed on traveling of the WCV as the WCV consumes its energy on both traveling and sensor charging. We also devise a novel algorithm for scheduling the tours of the WCV by jointly considering the residual lifetimes of sensors and the charging ratio of charging tours. We finally evaluate the performance of the proposed algorithm by conducting simulation. Experimental results show that the proposed algorithm is promising, and can improve the energy charging ratio of the WCV significantly.