Junkun Li

Energy-Efficient Probabilistic Area Coverage in Wireless Sensor Networks

As the binary sensing model is a coarse approximation of reality, the probabilistic sensing model has been proposed as a more realistic model for characterizing the sensing region. A point is covered by sensor networks under the probabilistic sensing model if the joint sensing probability from multiple sensors is larger than a predefined threshold ε. Existing work has focused on probabilistic point coverage since it is extremely difficult to verify the coverage of a full continuous area (i.e., probabilistic area coverage). In this paper, we tackle such a challenging problem. We first study the sensing probabilities of two points with a distance of d and obtain the fundamental mathematical relationship between them. If the sensing probability of one point is larger than a certain value, the other is covered. Based on such a finding, we transform probabilistic area coverage into probabilistic point coverage, which greatly reduces the problem dimension. Then, we design the ε-full area coverage optimization (FCO) algorithm to select a subset of sensors to provide probabilistic area coverage dynamically so that the network lifetime can be prolonged as much as possible. We also theoretically derive the approximation ratio obtained by FCO to that by the optimal one. Finally, through extensive simulations, we demonstrate that FCO outperforms the state-of-the-art solutions significantly.

Energy-Efficient Coverage Based on Probabilistic Sensing Model in Wireless Sensor Networks

Network coverage is one of the most critical issues to implement Wireless Sensor Networks (WSNs). It is important to find out a sensor set with maximal residual energy to cover all points of interest (PoIs). This issue was named as minimum weight sensor coverage problem (MWSCP) based on a boolean disc model and a probabilistic sensing model in the literature. In this paper, we introduce intelligent algorithms to solve this problem, yielding a better solution to MWSCP to extend network lifetime. Simulation results are conducted to demonstrate the effectiveness of our proposed algorithm in terms of network lifetime over existing algorithms.

Trapping Mobile Targets in Wireless Sensor Networks: An Energy-Efficient Perspective

Mobile target detection is a significant application in wireless sensor networks (WSNs). In fact, it is rather expensive to require every part of the region of interest (RoI) to be covered in a large-scale WSN for target detection. Trap coverage has been proposed to trade off between sensing performance and the cost of sensor deployments. It restricts the farthest distance that a target can move without being detected rather than providing full coverage to the region. However, the results cannot be directly applied in a real WSN since the detection pattern of a sensor in practical scenarios follows a probabilistic sensing model. Moreover, the trap coverage model does not consider the various moving speeds of targets, which is important for trapping targets. To extend the concept of mobile target trapping into a real large-scale WSN, we analyze the detection probability of a mobile target in the sensor network theoretically and define probabilistic trap coverage in this paper, which restricts the farthest displacement of a mobile target with a detection probability less than the threshold. We develop the theory of circle graph, which can be generally applied in the area of intrusion detection such as trap coverage and barrier coverage. We further study the practical issue of how to schedule sensors to maximize the lifetime of a network while guaranteeing probabilistic trap coverage. A localized protocol is proposed to solve the problem, and the performance of the protocol is theoretically analyzed. The lower bound of lifetime acquired by the protocol is nearly half the optimum lifetime. To evaluate our design, we perform extensive simulations to compare our algorithm with the state-of-the-art solution and demonstrate the superiority of our algorithm.

On energy-efficient trap coverage in wireless sensor networks

In wireless sensor networks (WSNs), trap coverage has recently been proposed to tradeoff between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, the practical issues such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment is still left untouched. In this paper, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. The performance of Minimum Weight Trap Coverage we find is proved to be at most O(\rho) times of the optimal solution, where

Energy-efficient probabilistic full coverage in wireless sensor networks

\rho

On Energy-Efficient Trap Coverage in Wireless Sensor Networks

is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution.

Introduction to Area Coverage in Sensor Networks

It is a common class of applications with wireless sensor network to provide full coverage to the region of interest (ROI), such as environment monitoring, military detection and agricultural observation. Existing literatures on full coverage are mostly based on the binary sensing model to simplify the problem. However, the results are far from the reality since binary sensing model as a coarse approximation is too conservative. The probabilistic sensing model has been proposed as a more realistic model to characterize the sensing region. In this paper, we introduce the concept of ε-full coverage based on probabilistic model, i.e., every point in ROI has at least a probability ε of being covered by sensors. We explore the mathematic relationship between the probabilities of two adjacent points being covered and transform ε-full coverage problem into point coverage problem. Then, we design ε-full coverage optimization (FCO) to select a subset of sensors to provide ε-full coverage dynamically so that the lifetime of network is prolonged. This algorithm outperforms the state-of-the-art solution significantly, which we have validated by simulations.

Energy-Efficient Capture of Stochastic Events in Sensor Networks

In wireless sensor networks (WSNs), trap coverage has recently been proposed to tradeoff between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, the practical issues such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment is still left untouched. In this paper, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. The performance of Minimum Weight Trap Coverage we find is proved to be at most O(\rho) times of the optimal solution, where

Energy-Efficient Trap Coverage in Sensor Networks

\rho

Trapping Mobile Intruders in Sensor Networks

is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution.