An Incremental Self-Deployment Algorithm for Mobile Sensor Networks
Autonomous Robots
Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks
IEEE Transactions on Computers
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Movement-Assisted Sensor Deployment
IEEE Transactions on Mobile Computing
Adaptive Triangular Deployment Algorithm for Unattended Mobile Sensor Networks
IEEE Transactions on Computers
On efficient deployment of sensors on planar grid
Computer Communications
Event-Based Motion Control for Mobile-Sensor Networks
IEEE Pervasive Computing
Adaptive flocking control for dynamic target tracking in mobile sensor networks
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Simple movement control algorithm for bi-connectivity in robotic sensor networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Tempo: An energy harvesting mote resilient to power outages
LCN '10 Proceedings of the 2010 IEEE 35th Conference on Local Computer Networks
A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network
Computer Communications
Strictly Localized Sensor Self-Deployment for Optimal Focused Coverage
IEEE Transactions on Mobile Computing
Hi-index | 0.24 |
Based on the flocking behaviors, this paper proposes three distributed, scalable, and stable deployment algorithms in homogeneous mobile sensor networks. We first address the deployment problem of driving sensors to surround a target of interest (TOI) which is either stationary or mobile. To maximize coverage area without coverage hole inside the network, sensors are relocated to approximate an equilateral triangle tessellation centered at the TOI. Since each sensor calculates the control input based on local and 1-hop neighborhood information, the algorithm is scalable. It is proved that a steady network state can be reached when the velocities of all sensors coincide with that of the TOI. We further extend the algorithm to handle sensor deployment under two other situations: (1) sensors are deployed uniformly along a predicted path of the TOI; (2) sensors are deployed according to a distribution density function where algorithms using both global map and local information are provided. Simulation results demonstrate the proposed algorithm can achieve reliable deployment with satisfactory moving distance.