Author_Institution :
Center for Intell. Robot. & Autom. Res., Nat. Taiwan Univ., Taipei, Taiwan
Abstract :
Mobile sensor node deployment and power management are important issues in the wireless sensor network system. This study designs a mobile sensor node platform to achieve a highly accurate localization mechanism by using ultrasonic, dead reckoning, and radio frequency information which is processed through a particle filter algorithm. Mobile sensor node with accurate localization ability is of great interest to basic research works and applications, such as sensor deployment, coverage management, dynamic power management, etc. In this paper, we propose an efficient mobile sensor node deployment method, grid deployment, where the map is divided into multiple individual grids and the weight of each grid is determined by environmental factors such as predeployed nodes, boundaries, and obstacles. The grid with minimum values is the goal of the mobile node. We also design an asynchronous power management strategy in our sensor node to reduce power consumption of the sensor network. Several factors such as probability of event generation, battery status, coverage issues, and communication situations have also been taken into consideration. In network communication, we propose an asynchronous awakening scheme so that each node is free to switch on or off its components according to observed event statistics and make a tradeoff between communication and power consumption. The deepest sleep state period is determined by the residual power. By combining these methods, the power consumption of the sensor node can be reduced.
Keywords :
particle filtering (numerical methods); probability; telecommunication power supplies; wireless sensor networks; asynchronous awakening; asynchronous power management strategy; battery status; communication situation; coverage issue; coverage management; dynamic power management; event generation; grid deployment; localization ability; localization mechanism; mobile sensor node deployment; particle filter; power consumption; probability; radio frequency information; residual power; wireless sensor network system; Acoustics; Batteries; Mobile communication; Particle filters; Peer to peer computing; Robot sensing systems; Wireless sensor networks; Coverage; dynamic power management; mobile sensor node deployment; sensor network;