Mobility in sensor networks or ad-hoc networks, in order to increase the coverage, connectivity and probably throughput.


In a random deployment, the node density should increase as O(logL + k log log L) to provide k-coverage in a network with a size of L. [1] proposes a hybrid network structure such that k-coverage is achieved with a constant density O(k), comprising static sensors and a small fraction O(1/sqrt(k)) of mobile sensors. Furthermore, the maximum distance that any sensor has to move is bounded as O((logL)^0.75).


Gupta and Kumar [2] studied the critical common range for connectivity of n randomly distributed nodes. Xue and Kumar [3] studied the relationship between connectivity and node degree. Li and Hou [4] consider the problem of deploying as few as possible additional wireless nodes to connect all network components. But how to dispatch mobile nodes in a decentralized manner? Is there any difference if some nodes in the network already are mobile? How many mobile nodes is needed v.s. how long is the moving distance.


What if the objective is system throughput? [5] uses machine learning techniques to learn the moving policies. This also reveal that AI people are also working on this topic.


  • 1. Trade-offs Between Mobility and Density for Coverage in Wireless Sensor Networks, Mobicom 2007.
  • 2. P. Gupta and P. R. Kumar, Critical power for asymptotic connectivity in wireless networks, 1998.
  • 3. F. Xue and P. R. Kumar, The number of neighbors needed for connectivity of wireless networks, 2004.
  • 4. Ning Li and Jennifer C. Hou, Improving connectivity of wireless ad-hoc networks
  • 5. Y. Chang, T. H., and L. P. Kaelbling. Mobilized ad-hoc networks: A reinforcement learning approach, MIT AI Laboratory, 2003

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