  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 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 pre-determined
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 2D 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.
