Mobilized Ad-Hoc Networks: A Reinforcement Learning Approach
With the cost of wireless networking and computational power rapidly dropping, mobile ad-hoc networks will soon become an important part of the society's computing structures. While there is a great deal of research from the networking community regarding the routing of information over such networks, most of these techniques lack automatic adaptivity. The size and complexity of these networks demand that the authors apply the principles of autonomic computing to this problem. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network.