Abstract
We address a task coverage problem to cover all given tasks with a given number of mobile sensors. In this context, we consider tasks as certain points or regions that should be probed by sensors. ...
Highlights
As our lives are increasingly threatened by disasters and environmental pollutants, such as earthquakes, tsunami, airborne particulate matter, and chemical contaminants, the use of sensors to monitor real-world environments has become a popular research area
The comparison with k-nearest neighbor (kNN)-random shows the effect of selecting proper initial tasks relative to maximum path cost (MPC)
These results indicate that the proposed method, which selects initial tasks in consideration of the shortest paths, reduces MPCs effectively, while kNN-random uniformly scatters initial tasks in the space to distribute tasks to each sensor
Summary
As our lives are increasingly threatened by disasters and environmental pollutants, such as earthquakes, tsunami, airborne particulate matter, and chemical contaminants, the use of sensors to monitor real-world environments has become a popular research area. Our goal is to find the initial locations and trajectories (paths) of a set of given sensors that allow such missions to be accomplished as quickly as possible It is to (1) find initial tasks, that is, find locations where sensors are to be deployed in advance and begin sensing as required, and (2) find an efficient set of search paths from the initial tasks that completely covers all tasks and minimizes the maximum cost among paths. Assuming a given number of mobile sensors and a set of predefined tasks, the goal is to find efficient paths that consist of the initial tasks and subsequent suitable tasks to solve the TCP with minimal maximum search cost. The proposed algorithm minimizes the maximum cost to accomplish a timecritical search and guarantees that mobile sensors completely cover all given tasks. For k available sensors along paths that cover all tasks
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