Large-scale autonomous wireless sensor networks that provide complete situation awareness and ubiquitous computing environments will become an essential part of the future network infrastructure. Node collaboration is the key for the success of sensor networks due to the fact that each node itself is limited by sensing range, power, and processing ability. On the other hand, node competition is a consequence of networking multiple nodes with limited time, space, and frequency resources. For small-scale networks, node collaboration and competition can be optimally balanced by a centralized scheme that has the access to all the node information and further has the control over the whole network. However, in a large-scale sensor network, which may involve millions of nodes and cover a large geographic area, it is impossible to afford a centralized scheme that manages the network-wide functional collaboration and resource competition.
This research program focuses on large-scale sensor networks and investigates the fundamental mechanism that controls node collaboration and competition in a purely distributed manner, with joint considerations of the three key elements in sensor network design: topology control, information transmission, and information processing. The design methodology is motivated by some recent biological research results on how billions of cells in our body control their growth and interaction with each other in a both collaborative and competitive way. The deliverables are general theorems, performance bounds, and analytical system models, which capture the interactive dynamics of various aspects of large-scale networking. These models define not only the fundamental principles regarding collaboration and competition among neighboring nodes, but also the adaptation rules that control each node to learn the environment and adjust its behavior. They are crucial to the future design of distributed networking protocols that are embedded into each sensor node, which mimics the genetic code inherited in each biological cell.
