{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T04:31:03Z","timestamp":1723264263766},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2012,3,8]],"date-time":"2012-03-08T00:00:00Z","timestamp":1331164800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Wireless Com Network"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>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.<\/jats:p>","DOI":"10.1186\/1687-1499-2012-95","type":"journal-article","created":{"date-parts":[[2012,3,8]],"date-time":"2012-03-08T19:14:06Z","timestamp":1331234046000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MSNS: mobile sensor network simulator for area coverage and obstacle avoidance based on GML"],"prefix":"10.1186","volume":"2012","author":[{"given":"Young-Sik","family":"Jeong","sequence":"first","affiliation":[]},{"given":"Youn-Hee","family":"Han","sequence":"additional","affiliation":[]},{"given":"James J","family":"Park","sequence":"additional","affiliation":[]},{"given":"SooYoung","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,3,8]]},"reference":[{"key":"252_CR1","first-page":"473","volume-title":"Third International Conference on Simulation of Adaptive Behavior, (SAB94) [From Animals to Animates]","author":"R Arkin","year":"1994","unstructured":"Arkin R, Ali K: Integration of reactive and telerobotic control in multi-agent robotic systems. 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