Locating Mobile Nodes with EASE : Learning Efficient Routes from Encounter Histories Alone

M. Grossglauser and M. Vetterli

IEEE/ACM Transactions on Networking, 14(3), p. 457- 469

This paper deals with last encounter routing (LER), a kind of protocol in which location information for nodes is essentially computed “on the fly” and there is no need to disseminate a location table and update it. They consider a grid toplogy (a torus, actually) on which nodes do a simple random walk. The walk time is much slower than the transmission time, so at any moment the topology is frozen with respect to a single packet transmission. Every node *i* maintains a table of pairs *(P _{ij}, A_{ij})* for each other node

*j*where

*P*is its last known position and

*A*is the age of that information. In the Exponential Age SEarch protocol (EASE) and GReedy EASE (GREASE), a packet keeps in its header an estimate of the position of its destination, and rewrites that information when it meets a node with a closer and more recent estimate. Because the location information and mobility processes are local, these schemes performs order-optimally (but with worse constants) to routing with location information, and with no overhead in the cost.

In particular, EASE computes a sequence of *anchors* for the packet, each one of has an exponentially closer estimate of the destination in both time and space. For example, each anchor could halve the distance to the destination as well as the age of that location estimate. This is similar in spirit to Kleinberg’s small world graphs paper, in which routing in a small world graph halves the distance, leading to a *log(n)* routing time, which comes from long hops counting the same as local hops. Here long hops cost more, so you still need *n ^{1/2}* hops. The paper comes with extensive simulation results to back up the analysis, which is nice.

What is nicer is the intuition given at the end:

Intuitively, mobility diffusion exploits three salient features of the node mobility processes: locality, mixing, and homogeneity…

Locality ensure[s] that aged information is still useful… Mixing of node trajectories ensures that position information diffuses around this destination node… Homogeneity ensure[s] that the location information spreads at least as fast as the destination moves.

The upshot is that location information is essentially free in an order sense, since the mobility process has enough memory to guarantee that suboptimal greedy decisions are not too suboptimal.