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One of the main issues in rechargeable wireless sensor networks (RWSNs) is the stainability of network operation. Recently, wireless power transmission technology has been applied in RWSNs to transmit wireless power from chargers to sensor nodes. In this paper, we focus on how a charger changes its direction to cover different nodes for energy provisioning and, how to sense maximum data from the area of network. We intend to tackle the challenge that how to cover different nodes from each direction so that the charger can provide maximum power to maintain network performance. In this regard, a direction scheduling algorithm is proposed to fill the charging demand. Furthermore, a proposed Linear Programming based solution is proposed to determine maximum data sensing rate. The simulation results show that the proposed method performs better compared with existing methods.

In this chapter, we study mobile intruder detection in wireless sensor networks (WSNs) under the probabilistic sensing model. Existing trap coverage for intruder detection does not consider the various moving speeds of intruders. To extend the concept of mobile intruders trapping into a real large-scale WSN, we analyze the detection probability of a mobile intruder in the sensor network theoretically and define probabilistic trap coverage which restricts the farthest displacement of a mobile intruder 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 about how to schedule sensors to maximize the lifetime of network while guaranteeing probabilistic trap coverage. A localized protocol is proposed to solve the problem and the performance of the protocol is analyzed theoretically. We demonstrate the advantages of the proposed algorithm by comparing with the state-of-the-art solutions.

We consider a high density of sensors randomly placed in a geographical area for event detection. Prior work in this area does not consider the event dynamics. In this chapter, we show that knowledge about the event dynamics can be exploited for significant energy savings. We discuss energy-aware optimization of the periodic schedule for the cases of synchronous, asynchronous and regionally synchronous networks. Under the periodic scheduling, coordinated sleep by the sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We show that the asynchronous network exceeds any regionally synchronous network in the coverage intensity, thereby increasing the effectiveness of the event detection. When the sensor density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy efficient.

In this chapter, we introduce the background of area coverage problem in wireless sensor networks. We first describe the deployment schemes of sensor nodes and present the classification of coverage in sensor networks. Then we elaborate on different types of sensing models, which are frequently employed in existing works. Finally, we present a range of existing literatures and applications on area coverage.

In this chapter, we study trap coverage in sensor networks, which is recognized to be efficient for intruder detection. 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. We show that the performance of Minimum Weight Trap Coverage is at most O(ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. We perform extensive simulations to demonstrate the effectiveness of the proposed algorithm.