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      Autonomous multiple teams establishment for mobile sensor networks by SVMs within a potential field

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      Author
      Nazlibilek, S.
      Date
      2012
      Source Title
      Measurement: Journal of the International Measurement Confederation
      Print ISSN
      0263-2241
      Volume
      45
      Issue
      5
      Pages
      971 - 987
      Language
      English
      Type
      Article
      Item Usage Stats
      143
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      Abstract
      In this work, a new method and algorithm for autonomous teams establishment with mobile sensor network units by SVMs based on task allocations within a potential field is proposed. The sensor network deployed into the environment using the algorithm is composed of robot units with sensing capability of magnetic anomaly of the earth. A new algorithm is developed for task assignment. It is based on the optimization of weights between robots and tasks. The weights are composed of skill ratings of the robots and priorities of the tasks. Multiple teams of mobile units are established in a local area based on these mission vectors. A mission vector is the genetic and gained background information of the mobile units. The genetic background is the inherent structure of their knowledge base in a vector form but it can be dynamically updated with the information gained later on by experience. The mission is performed in a magnetic anomaly environment. The initial values of the mission vectors are loaded by the task assignment algorithm. The mission vectors are updated at the beginning of each sampling period of the motion. Then the teams of robots are created by the support vector machines. A linear optimal hyperplane is calculated by the use of SVM algorithm during training period. Then the robots are classified as teams by use of SVM mechanism embedded in the robots. The support vector machines are implemented in the robots by ordinary op-amps and basic logical gates. Team establishment is tested by simulations and a practical test-bed. Both simulations and the actual operation of the system prove that the system functions satisfactorily. © 2012 Elsevier Ltd. All rights reserved.
      Keywords
      Algorithms
      Magnetic field measurement
      Measurement
      Networks
      Robots
      Vectors
      Actual operation
      Area-based
      Background information
      Genetic backgrounds
      Inherent structures
      Initial values
      Knowledge base
      Logical gates
      Magnetic anomalies
      Mobile sensor networks
      Mobile units
      Multiple teams
      Potential field
      Sampling period
      SVM algorithm
      System functions
      Task allocation
      Task assignment
      Algorithms
      Knowledge based systems
      Magnetic field measurement
      Measurements
      Networks (circuits)
      Optimization
      Robots
      Sensor networks
      Vectors
      Support vector machines
      Permalink
      http://hdl.handle.net/11693/21453
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.measurement.2012.01.046
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      • Nanotechnology Research Center (NANOTAM) 1006
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