Abstract
Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.
Highlights
With wide deployment of wireless sensors in the real world, such as air quality monitoring and intrusion detection, wireless sensor networks (WSNs) have attracted tremendous research attention.Sensor coverage is one of the fundamental issues in WSNs
The challenge of the minimum e-connected target coverage (CTC) problem we face is how to activate as few sensors as possible, to achieve high detection threshold e and provide connectivity for WSNs
Given the detection probability threshold e, we formally define the minimum e-connected target coverage problem based on our sensing model and network model as follows
Summary
With wide deployment of wireless sensors in the real world, such as air quality monitoring and intrusion detection, wireless sensor networks (WSNs) have attracted tremendous research attention. The CTC problem under both the Boolean sensing model and the probabilistic model has been proven to be NP-hard [1,18] This strategy minimizes the overall network cost as covering targets with the minimum number of sensor nodes. The challenge of the minimum e-CTC problem we face is how to activate as few sensors as possible, to achieve high detection threshold e and provide connectivity for WSNs. Much different from sensors based on the 0/1 coverage model, probabilistic sensors have to cooperate with each other to achieve threshold e. The key insight of MVMFA is that each augmenting path picked out by the pivotal Algorithm 2 FindPath has more flow and few inactive sensors This means that a sensor with a high detection probability, but passing few relaying sensors by, will be activated firstly.
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