Wenzheng Xu

Approximation Algorithms for Min-Max Cycle Cover Problems

As a fundamental optimization problem, the vehicle routing problem has wide application backgrounds and has been paid lots of attentions in past decades. In this paper we study its applications in data gathering and wireless energy charging for wireless sensor networks, by devising improved approximation algorithms for it and its variants. The key ingredients in the algorithm design include exploiting the combinatorial properties of the problems and making use of tree decomposition and minimum weighted maximum matching techniques. Specifically, given a metric complete graph G and an integer k > 0, we consider rootless, uncapacitated rooted, and capacitated rooted min-max cycle cover problems in G with an aim to find k rootless (or rooted) edge-disjoint cycles covering the vertices in V such that the maximum cycle weight among the k cycles is minimized. For each of the mentioned problems, we develop an improved approximate solution. That is, for the rootless min-max cycle cover problem, we develop a (531+ ε)-approximation algorithm; for the uncapacitated rooted min-max cycle cover problem, we devise a (631+ ε)-approximation algorithm; and for the capacitated rooted min-max cycle cover problem, we propose a (7 + ε)-approximation algorithm. These algorithms improve the best existing approximation ratios of the corresponding problems 6 + ε, 7 + ε, and 13 + ε, respectively, where ε is a constant with 0 <; ε <; 1. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the actual approximation ratios delivered by the proposed algorithms are always no more than 2, much better than their analytical counterparts.

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.

Efficient Scheduling of Multiple Mobile Chargers for Wireless Sensor Networks

In this paper, we study the deployment of multiple mobile charging vehicles to charge sensors in a large-scale wireless sensor network for a given monitoring period so that none of the sensors will run out of energy, where sensors can be charged by the charging vehicles with wireless energy transfer. To minimize the network operational cost, we first formulate a charging scheduling problem of dispatching multiple mobile charging vehicles to collaboratively charge sensors such that the sum of travelling distance (referred to as the service cost) of these vehicles for this monitoring period is minimized, subject to that none of the sensors will run out of energy. Due to NP-hardness of the problem, we then propose a novel approximation algorithm with a guaranteed approximation ratio, assuming that the energy consumption rate of each sensor does not change for the given monitoring period. Otherwise, we devise a heuristic algorithm through modifications to the approximation algorithm. We finally evaluate the performance of the proposed algorithms via experimental simulations. Simulation results show that the proposed algorithms are very promising, which can reduce the service cost by up to 20% in comparison with the service costs delivered by existing ones.

Throughput maximization for online request admissions in mobile cloudlets

In mobile cloud computing (MCC) paradigm, cloud service providers not only offer powerful cloud data centers but also provide small-scale cloudlets in some strategic locations for mobile users to access their rich resources. Due to the flexibility and locality of cloudlets, most requests of mobile users can be processed locally. However, the cloudlets usually have limited resources and processing abilities, which implies that they may not be capable to process every incoming request. Instead, some resource-intensive requests need to be sent to remote data centers for processing and such a processing is transparent to users. In this paper, we address the online request admission issue in a cloudlet with an objective to maximize the system throughput, for which we first propose a novel admission cost model to model critical resource consumptions. We then devise efficient control algorithms for online request admissions. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results indicate that the proposed algorithms are promising and outperform other heuristics.

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.

Capacitated cloudlet placements in Wireless Metropolitan Area Networks

In this paper we study the cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) that consists of many wireless Access Points (APs). Although most existing studies in mobile cloud computing mainly focus on energy savings of mobile devices by offloading computing-intensive jobs from them to remote clouds, the access delay between mobile users and the clouds usually is large and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and has been demonstrated to enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper we consider placing multiple cloudlets with different computing capacities at some strategic local locations in a WMAN to reduce the average cloudlet access delay of mobile users at different APs. We first formulate this problem as a novel capacitated cloudlet placement problem that places K cloudlets to some locations in the WMAN with the objective to minimize the average cloudlet access delay between the mobile users and the cloudlets serving their requests. We then propose a fast yet efficient heuristic. For a special case of the problem where all cloudlets have the identical computing capacity, we devise a novel approximation algorithm with a guaranteed approximation ratio. In addition, We also consider allocating user requests to cloudlets by devising an efficient online algorithm for such an assignment. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are promising and scalable.

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.