A Token-Based Approach for Distributed Computation in Sensor Networks

We consider distributed algorithms for data aggregation and function computation in sensor networks. The algorithms perform pairwise computations along edges of an underlying communication graph. A token is associated with each sensor node, which acts as a transmission permit. Nodes with active tokens have transmission permits; they generate messages at a constant rate and send each message to a randomly selected neighbor. By using different strategies to control the transmission permits we can obtain tradeoffs between message and time complexity. Gossip corresponds to the case when all nodes have permits all the time. We study algorithms where permits are revoked after transmission and restored upon reception. Examples of such algorithms include Simple-Random Walk (SRW), Coalescent-Random-Walk (CRW), and Controlled Flooding (CFLD) and their hybrid variants. SRW has a single node permit, which is passed on in the network. CRW, initially has a permit for each node but these permits are revoked gradually. The final result for SRW and CRW resides at a single (or few) random node(s) making a direct comparison with GOSSIP difficult. A hybrid two-phase algorithm switching from CRW to CFLD at a suitable predetermined time can be employed to achieve consensus. We show that such hybrid variants achieve significant gains in both message and time complexity. The per-node message complexity for n-node graphs, such as 2-D mesh, torii, and random geometric graphs, scales as O(polylog(n)) and the corresponding time complexity scales as O(n). The reduced per-node message complexity leads to reduced energy utilization in sensor networks.