Wei Wayne Li

Maximum Lifetime Scheduling for Target Coverage and Data Collection in Wireless Sensor Networks

Target coverage and data collection are two fundamental problems for wireless sensor networks (WSNs). Target coverage is needed to select sensors in a given area that can monitor a set of interesting points. Data collection is needed to transmit the sensed data from sensors to a sink. Since, in many applications, sensors are battery powered, it is expected that a WSN can work untended for a long period. This paper addresses the scheduling problems for both target coverage and data collection in WSNs with the objective of maximizing network lifetime. First, a polynomial-time approximation scheme is developed for the case where the density of target points is bounded, and then, a polynomial-time constant-factor approximation algorithm is developed for the general case. It is also proved that it is NP-hard to find a maximum lifetime scheduling of target cover and data collection for a WSN, even if all the sensors have the same sensing radius and the same transmission radius. Further, the practical efficiency of our algorithms is analyzed through simulation. These extensive simulation results show better performances of our algorithms compared with other research findings.

Determination of optimal call admission control policy in wireless networks

This paper investigates the optimal call admission control (CAC) policy for non-priority scheme (NPS) and reserved channel scheme (RCS) in wireless networks, respectively. Both new call and handoff call arrival processes are assumed to be Poisson processes, and the call holding times are exponentially distributed with different rate for new call and handoff calls. Admitting each call would bring a reward to the network provider but holding each call in the system would also incur some expenses (or cost) to the provider. We concentrate on the optimization problems of when to admit or reject a call in order to achieve the maximum total expected discounted reward. By establishing a discounted semi Markov decision process (SMDP) model, we verify that the optimal policies are state-related control limit policies for both NPS and RCS. Our numerical results explained in both tables and diagrams are consistent with our theoretic results.

Performance Evaluation of Wireless Cellular Networks with Mixed Channel Holding Times

In most analytical models for wireless cellular networks, the channel holding times for both new and handoff calls are usually assumed to be independent and identically distributed. However, simulation study and field data show that this assumption is invalid. In this paper, we present a new general analytical model in wireless cellular networks where channel holding times for new and handoff calls are distinctly distributed with different average values. For our proposed model, we first derive the explicit matrix product-form solution of the stationary probability for number of new and handoff calls in the system. We then show that the expression of the stationary probability for total number of calls in the system possesses a scalar product-form solution if and only if the expected channel holding times for both new and handoff calls are the same. Moreover, we derive analytical results for the blocking probabilities of new and handoff calls. Finally, we compare our new theoretical results with the corresponding simulation results and two already existing approximations. Through this comparison study, we show that our analytical results are indeed the same as the simulation results and that there are certainly significant estimation errors for the existing approximations.

Prolonging the Lifetime of Large Scale Wireless Sensor Networks via Base Station Placement

Network lifetime is a critical design goal of any battery operated large scale wireless sensor networks (WSNs). In this paper, we investigate how to prolong the lifetime of large scale WSNs via optimal placement of base stations (BSs). With multiple BSs placed, the sensor nodes can send their data to nearby sinks and may reduce energy consumption of relaying packets for other sensor nodes. Due to the high cost of BSs and geographical constraints in WSNs, we can only place a limited number of BSs in a few candidate locations. Considering wireless transmission features and flow routing, we formulate this base station placement (BSP) problem in WSNs into a mixed integer nonlinear programming (MINLP) problem. In view of the NP-hardness of the formulated problem, we develop the heuristic algorithm to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithm are close to the optimal one and the proposed scheme is effective in prolonging the lifetime of large scale WSNs.

Charging coverage for energy replenishment in wireless sensor networks

The energy of sensors is critical to the performance of any battery operated wireless sensor networks (WSNs). The new emerging wireless power transfer technology can potentially relieve the energy intension of WSNs. To effectively replenish the energy of sensors, in this paper, we investigate the minimum charging coverage problem, which aims to recharge a set of sensors in a given area with the minimum number of wireless chargers. We introduce a partition algorithm to address this charging coverage challenge, and through theoretical analysis, we prove that the proposed algorithm can develop a solution close to the optimal one with guaranteed approximation ratio.

Noncooperative and Cooperative Joining Strategies in Cognitive Radio Networks With Random Access

A cognitive radio (CR) system with retrial possibility and an admission cost for secondary users (SUs) to join the retrial group is investigated in this paper. If the SU finds the primary user (PU) band unavailable, it must decide with a probability estimate to either enter a retrial group or give up its service and leave the system. SUs in the retrial group independently retry after an exponentially distributed random time until they successfully access the spectrum. When the PU arrives, the SUs service on the band is interrupted. This interrupted SU is then assumed to occupy the PU band immediately when the PU completes its service. First, the noncooperative joining behavior of SUs that choose to maximize their benefit in a selfish distributed manner is investigated, and an inefficient Nash equilibrium is derived. Second, from the perspective of the social planner, the socially optimal joining strategy when SUs cooperate with each other is studied, and the corresponding Nash equilibrium is exactly derived. Finally, the result that an individually optimal strategy, in general, does not yield the socially optimal strategy is theoretically verified. Furthermore, to bridge the gap between the individually and socially optimal strategies, a novel strategy of imposing an admission fee on SUs to join the retrial group is proposed and investigated with the derivation of an optimal value for the admission fee. The numerical analysis indicates that the proposed admission fee as an equilibrium strategy and the socially optimal strategy of SUs improve efficiency in the utilization of the CR system.

Approximation algorithms for maximum target coverage in directional sensor networks

Target coverage is a fundamental problem in wireless sensor networks. To preserve energy, the minimum coverage problem, which aims at covering a given area or a set of interesting points with the minimum number of sensors, has been extensively studied for general sensor networks with omni-directional sensors. In this paper we address the coverage problem in directional sensor networks in which sensors have tunable directions and can only cover one direction at a time. Given a set of target points and a set of sensors, we investigate the problem of assigning directions to the sensors such that the number of target points in the sensing area is maximized. We prove the greedy algorithm is a polynomial time 1/2-factor approximation solution for this maximization problem and provide a tight example for this ratio. We also investigate a variant of this problem and provide an approximation solution for it with similar performance guarantee.

Efficient scheduling algorithms for on-demand wireless data broadcast

On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data items per request. However the multi-item request scheduling has not yet been thoroughly investigated for on-demand wireless data broadcasts. In this paper, we step-up on investigating this problem from viewpoint of theory and simulation. We develop a two-stage scheduling scheme to arrange the requested data items with the objective of minimizing the average access latency. The first stage is to select the data items to be broadcast in the next time period and the second stage is to schedule the broadcasting order for the data items selected in the first stage. We develop algorithms for the two stages respectively and analyze them both theoretically and practically. We also compare the proposed algorithms with other well known scheduling methods through simulation. The theoretical findings and simulation results reveal that significantly better access latency can be obtained by using our scheduling scheme rather than its competitors.

Collaborative Data and Energy Transmission for Energy-Rechargeable Mobile Devices

Mobile hotspots have made the dream of ubiquitous Internet access come true, while the widespread applications are still hindered by the limited power of smart phones. To address this issue, we propose a novel distributed cooperative data transmission scheme for energy-rechargeable mobile devices. In particular, we not only let a mobile phone help the nearby client devices connect to the Internet via its cellular accessing, but also let those clients replenish the mobile hotspot energy via wireless power transfer. We mathematically formulate the mutually beneficial relationship between mobile hotspots and clients into an optimization problem, with the objective of conducting the cooperative wireless data and energy transmission to maximize the system utility. Resorting to methods from combinatorics and matching theory, we develop a near optimal solution for many-to-one matching when there is a single mobile hotspot and a distributed matching strategy for the general case by considering the nature of data communication and the characteristic of wireless power transfer. By extensive simulation, we show that the proposed distributed solution achieves a performance close to the centralized method, and it outperforms the greedy matching strategy and the classic Gale-Shapley matching strategy in different scenarios.