Sensor replacement using mobile robots

  • Authors:
  • Yongguo Mei;Changjiu Xian;Saumitra Das;Y. Charlie Hu;Yung-Hsiang Lu

  • Affiliations:
  • School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA

  • Venue:
  • Computer Communications
  • Year:
  • 2007

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Abstract

Sensor replacement is important for sensor networks to provide continuous sensing services. Upon sensor node failures, holes (uncovered areas) may appear in the sensing coverage. Existing approaches relocate redundant nodes to fill the holes and require all or most sensor nodes to have mobility. However, mobility equipment is expensive while technology trends are scaling sensors to be smaller and cheaper. In this paper, we propose to use a small number of mobile robots to replace failed sensors for a large-scale static sensor network. We study algorithms for detecting and reporting sensor failures and coordinating the movement of robots that minimize the motion energy of mobile robots and the messaging overhead incurred to the sensor network. A manager receives failure reports and determines which robot to handle a failure. We study three algorithms: a centralized manager algorithm, a fixed distributed manager algorithm, and a dynamic distributed manager algorithm. Our analysis and simulations show that: (a) the centralized and the dynamic distributed algorithms have lower motion overhead than the fixed distributed algorithm; (b) the centralized algorithm is less scalable than the two distributed manager algorithms, and (c) the two distributed algorithms have higher messaging cost than the centralized algorithm. Hence, the optimal choice of the coordination algorithm depends on the specific scenarios and objectives being optimized.