Abstract
Abstract A mobile sensor network is a distributed collection of sensors, each of which has sensing, computation, communication, and locomotion capabilities. In particular, locomotion facilitates the ability to self-deployment. In such a network of self-deployable mobile sensors, it is difficult to evaluate the effectiveness of mobile sensor network deployment in a given target area because we cannot predict the coverage rate for the target area. The coverage rate will be changed due to the number of sensor required in the target area, connectivity degree to be maintained and unknown obstacles. In this article, we develop mobile sensor network simulator (MSNS) in order to visualize (1) coverage secured by mobile sensors and (2) avoidance of obstacle objects (building, road and wall, and so on) on the real map drawn by GML (Geography Markup Language). From a user, MSNS receives the number of mobile sensor nodes, connectivity degree, sensor node's sensing range, communication range, and supersonic wave range. And then it visualizes the location information of sensor nodes, connectivity degree, and sensing coverage, all of which change with simulation time. Thereby we can estimate how many nodes are required in a given target area, and also calculate coverage rate of the target area in advance to the real deployment of mobile sensors.
Highlights
Mobile sensor network is made up of group/groups of small low-power sensor nodes that can sense specific situations or collect information, and transmit that information to sink nodes using wireless ad hoc communication
mobile sensor network simulator (MSNS) visualizes the location information of sensor nodes, connectivity degree, and sensing coverage, all of which change with simulation-time
Conclusion and future research The MSNS developed in this article is the simulator that provides the information on sensing coverage of the target area where a number of mobile sensors are randomly deployed
Summary
Mobile sensor network is made up of group/groups of small low-power sensor nodes that can sense specific situations or collect information, and transmit that information to sink nodes using wireless ad hoc communication. Several nodes with various kinds of sensors for sound, heat, magnetic field, and infrared ray are randomly scattered in a target area These sensors move, voluntarily avoiding obstacles and other nodes, establish sensing coverage and configure their communication network [1]. MSNS visualizes the location information of sensor nodes, connectivity degree, and sensing coverage, all of which change with simulation-time. The previous studies [7] suggested self-deployment algorithm where Voronoi diagram was used First of all, they raised the question if sensors were enabled to observe detection area at the maximum while minimizing move time, moving distance of sensor and complexity of message for a random detection area.
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