In wireless sensor networks (WSNs), trap coverage has recently been proposed to trade off 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, practical issues, such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment, are still left untouched. In this article, 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. We design Localized Trap Coverage Protocol as the localized implementation of TCO. The performance of Minimum Weight Trap Coverage we find is proved to be at most O (ρ) times of the optimal solution, where ρ 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.

Intrusion detection is a significant application in wireless sensor networks (WSNs). S. Kumar et al have introduced the concept of barrier coverage, which deploys sensors in a narrow belt region to guarantee that any intrusion across the region is to be detected. However, the practical issues have not been investigated such as scheduling sensors energy-efficiently while guaranteeing the detection probability of any intrusion across the region based on probabilistic sensing model. Besides, the intruders may be humans, animals, fighter planes or other things, which obviously have diverse moving speeds. In this paper, we analyze the detection probability of arbitrary path across the barrier of sensors theoretically and take the maximum speed of possible intruders into consideration since the sensor networks are designed for different intruders in different scenarios. Based on the theoretical analysis of detection probability, we formulate Minimum Weight ε-Barrier Problem about how to schedule sensors energy-efficiently and prove it is NP-hard. We propose both global and local solutions to the problem. The global solution called Minimum Weight Barrier Algorithm is a bounded approximation algorithm, based on which a localized protocol for energy-efficient scheduling is designed. To evaluate our design, we analyze the performance of our approaches theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.

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.

Intrusion detection is a significant application in wireless sensor networks (WSNs). S. Kumar et al have introduced the concept of barrier coverage, which deploys sensors in a narrow belt region to guarantee that any intrusion across the region is to be detected. However, the practical issues have not been investigated such as scheduling sensors energy-efficiently while guaranteeing the detection probability of any intrusion across the region based on probabilistic sensing model, which is a more realistic sensing model. Besides, the intruders may be humans, animals, fighter planes or other things, which obviously have diverse moving speeds. In this paper, we analyze the detection probability of arbitrary path across the barrier of sensors theoretically and take the maximum speed of possible intruders into consideration since the sensor networks are designed for different intruders in different scenarios. Based on the theoretical analysis of detection probability, we formulate a Minimum Weight ∈-Barrier Problem about how to schedule sensors energy-efficiently. We show the problem NP-hard and propose a bounded approximation algorithm, called Minimum Weight Barrier Algorithm (MWBA) to schedule the activation of sensors. To evaluate our design, we analyze the performance of MWBA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.

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(ρ) times of the optimal solution, where ρ 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.