Aggregation in Intersection Region for Distributed Heterogeneous Mobile Sensor Networks

来源 :Journal of Systems Science & Complexity | 被引量 : 0次 | 上传用户:wangzan1616
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This paper presents two aggregation strategies in convex intersection region for the distributed mobile sensor network(MSN) with heterogeneous dynamics. First, the authors analyze individual local perception model and dynamics model, set the intersection of all the local perceptions as the region of interest(ROI). The MSN consists of sensors with first-order dynamics and second-order dynamics. Then, the authors design a control strategy to ensure that individuals aggregate at a point in the ROI relying on their local perceptions and the locations of neighbors within their communication scope. The authors describe this situation of aggregation as rendezvous. In addition, the authors introduce artificial potential field to make sensors deploy dispersedly in a bounded range near the ROI,which the authors call dispersed deployment. Finally, the authors prove the stability of the proposed strategies and validate the theoretical results by simulations. This research is applied for the cooperative deployment and data collection of mobile platforms with different dynamics under the condition of inaccurate perception. This paper presents two aggregation strategies in convex intersection region for the distributed mobile sensor network (MSN) with heterogeneous dynamics. First, the authors analyze individual local perception model and dynamics model, set the intersection of all the local perceptions as the region of interest ( ROI). The MSN consists of sensors with first-order dynamics and second-order dynamics. Then, the authors design a control strategy to ensure that individuals aggregate at a point in the ROI relying on their local perceptions and the locations of neighbors within their communication scope. The authors describe this situation of aggregation as rendezvous. In addition, the authors introduce artificial potential field to make sensors deploy dispersedly in a bounded range near the ROI, which the authors call dispersed deployment. Finally, the authors prove the stability of the proposed strategies and validate the theoretical results by simulations. This research is applied for the cooper ative deployment and data collection of mobile platforms with different dynamics under the condition of inaccurate perception.
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