{"id":164,"date":"2015-02-04T22:58:55","date_gmt":"2015-02-05T06:58:55","guid":{"rendered":"http:\/\/ara.cse.unr.edu\/?page_id=164"},"modified":"2015-06-18T12:04:06","modified_gmt":"2015-06-18T19:04:06","slug":"project-1-distributed-sensing-learning-and-control-in-mobile-sensor-networks","status":"publish","type":"page","link":"https:\/\/ara.cse.unr.edu\/?page_id=164","title":{"rendered":"Project 1: Distributed Sensing, Learning and Control in Mobile Sensor Networks"},"content":{"rendered":"

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(Supported by the US Department of Defense)<\/p>\n

Authors: Hung M. La, Ronny S. Lim\u00a0and Weihua Sheng<\/p>\n

Introduction:<\/p>\n

Sensor network, especially mobile sensor network (MSN) has been extensively studied in recent years. An MSN has some advantages over a stationary sensor network (SSN) such as: the adaptation to environmental changes and the reconfigurability for better sensing performance. Therefore, MSN can be applied in many applications such as target tracking and protection of endangered of species. Figure 1 shows the example of target surveillance<\/p>\n

Goal:<\/p>\n

We are developing a framework for cooperative sensing, learning and control in distributed fashion for mobile sensor networks. Our goal is to design a control law to run on each mobile sensor platform to obtain the desired collective behavior. Example tasks include target(s) tracking in noisy or noisy-free environments, formation control, multi-agent learning to avoid predators, distributed sensor fusion for scalar field mapping.\u00a0 Our constraints are that each sensor platform may have limited computational power, sensing and communication capabilities.<\/p>\n

This research can benefit battlefield surveillance, security monitoring.<\/p>\n

Publications of this project<\/p>\n