Inter-contact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on inter-contact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the intercontact time, that is the time gap separating two contacts of the same pair of taxies, exhibits a light tail such as one of an exponential distribution, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the inter-contact time obeys a power law. By performing a least squares fit, we establish an exponential model that can accurately depict the tail behavior of the inter-contact time in VANETs. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Abstract-Large-scale wireless sensor networks are expected to play an increasingly important role in future civilian and military settings. Collaborative microsensors could be very effective in monitoring their operations. However, low power and in-network data processing make data-centric routing in wireless sensor networks a challenging problem. In this paper we propose heuristics to construct and maintain an aggregation tree in sensor networks. This aggregation tree can be used to facilitate data-centric routing. The main idea is to turn off the radio of all leaf nodes to save power, and thereby extending the network lifetime. Therefore, in order to save the number of broadcasting messages, only the non-leaf nodes in the tree are in charge of data aggregation and traffic relaying.In this paper, we propose an efficient energy-aware distributed heuristic to generate the aggregation tree, which we refer to as EADAT. Our EADAT algorithm makes no assumption on local network topology, and is based on residual power. It makes use of neighboring broadcast scheduling and distributed competition among neighbors. These novel concepts make EADAT very efficient and effective, as demonstrated by our simulation experiments with NS2.

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