Abstract
This paper considers the scheduling problem of mobile sensors in a hybrid wireless sensor network (WSN) with obstacles. In a WSN, static sensors monitor the environment and report where events appear in the sensing field. Then, mobile sensors are dispatched to these event locations to perform in-depth analysis. The sensing field may contain obstacles of any shape and size. A big challenge is how to efficiently dispatch the mobile sensor to find an obstacle-avoiding shortest path. To remedy this issue, we propose an efficient scheduling mechanism based on connection graphs in this paper. Specifically, the region of network is divided into grid cells with the same size. Consequently, the search space of the shortest path is restricted to the connection graphs composed of some grid cells. Through simulation, we verify the effectiveness of our method. The paper contributes to developing an energy-efficient dispatch solution in the presence of obstacles.
Highlights
Recent advances in microelectronics technology, computer technology, and wireless communication technology have led to the development of low-cost and low-power sensor nodes that are small in size
We propose an efficient scheduling technique of the mobile sensor in the presence of obstacles, which aims to prolong the lifetime of the hybrid wireless sensor network (WSN) as long as possible
The search space contains 104 grid cells whose faces are labeled by a black circle and a white circle
Summary
Recent advances in microelectronics technology, computer technology, and wireless communication technology have led to the development of low-cost and low-power sensor nodes that are small in size. Sensor nodes have the ability to sense the nearby environment and perform simple computations and communication within a small region. With the advancement of embedded computing and wireless communication techniques, sensors can move around when installing sensing devices on mobile equipment. Mobile sensors are typically resource-rich devices with more energy, higher communication power, and more powerful sensing and computing capabilities. Mobile sensors collaborate for fulfilling high-level requirements. In this context, the efficient scheduling for mobile sensors while prolonging the lifetime of the network as long as possible is one of the major challenges. We focus on the energy-efficient scheduling for mobile sensors to find an obstacle-avoiding shortest path
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