We examine the ability of networks formed by mobile sensor nodes to diffuse information in the case when communication is only possible during opportunistic encounters. Our setting assumes that mobile nodes are continuously sensing the world and acquiring new information. We form an abstract model of this situation and show by theoretical analysis, simulation, and real mobility data that the diffusion of information in this setting cannot be as efficient as when we allow arbitrary contact patterns between the nodes with the same overall contact statistics. This establishes a fundamental asymptotic limitation on the information diffusion capacity of such opportunistic mobile sensor networks - the encounter patterns arising out of physical motions in a geometric space are not ideal for information diffusion.

We consider the problem of distributing time-sensitive information from a collection of sources to mobile users traversing a wireless mesh network. Our strategy is to distributively select a set of well-placed nodes (warehouses) to act as intermediaries between the information sources and clusters of users. Warehouses are selected via the distributed construction of Hierarchical Well-Separated Trees (HSTs), which are sparse structures that induce a natural spatial clustering of the network. Unlike many traditional multicast protocols, our approach is not data driven. Rather, it is agnostic to the number and position of sources as well as to the mobility patterns of users. Whereas source-rooted tree multicast algorithms construct a separate routing infrastructure to support each source, our sparse and flexible infrastructure is precomputed and efficiently reused by sources and users, its cost amortized over time. Moreover, the route acquisition delay inherent in on-demand wireless ad hoc network protocols is avoided by exploiting the HST addressing scheme. Our algorithm ensures with high probability a guaranteed stretch bound for the information delivery path, and is robust to lossy links and node failure by providing alternative HST-induced routes. Nearby users are clustered and their requests aggregated, further reducing communication overhead.

We present a method to extract topology information from detection events of mobile entities moving through a network of binary sensors. We extract the topological structure of possible paths in the network by analyzing the time correlation of events at different sensors. The histograms of time delays between any two sensors contain the necessary information to reconstruct the network topology. This data is heavily corrupted by noise due to multiple agents in the network. We therefore use a mixture model of multiple Gaussian and a uniform distribution to explicitly isolate the noise. Our algorithm yields a graph representing the topology of our sensor network along with average travel time between nodes.