This paper proposes a synchronous device discovery solution for ad-hoc networks based on the observations that time synchronization, along with an FDM based channel resource allocation, can lead to gains in terms of energy consumption, discovery range, and the number of devices discovered. These attributes are important for the success of proximity-aware networking, where devices autonomously find peer-groups over human mobility scales. In this paper, we develop the PHY and MAC protocols to enable autonomous device discovery. Using both simulations and stochastic-geometry based analysis, we validate our design, and argue that there can be significant gains over a conventional Wi-Fi based solution.

Interference is a central feature of the wireless channel. However, in many cases, the interferer's activity can be bursty and it is overly pessimistic to assume that interference is always present. In this paper, we use a degraded message set formulation of the two-user interference channel to study the statistical gains that can be harnessed from bursty interference. We consider a linear deterministic model of the Gaussian interference channel and characterize the degraded message set capacity region.

Abstract-We consider a scenario in which a wireless sensor network is formed by randomly deploying n sensors to measure some spatial function over a field, with the objective of computing a function of the measurements and communicating it to an operator station. We restrict ourselves to the class of type-threshold functions (as defined in [2]), of which max, min, and indicator functions are important examples; our discussions are couched in terms of the max function. We view the problem as one of message passing distributed computation over a geometric random graph. The network is assumed to be synchronous; the sensors synchronously measure values, and then collaborate to compute and deliver the function computed with these values to the operator station. Computation algorithms differ in (i) the communication topology assumed, and (ii) the messages that the nodes need to exchange in order to carry out the computation. The focus of our paper is to establish (in probability) scaling laws for the time and energy complexity of the distributed function computation over random wireless networks, under the assumption of centralised contention-free scheduling of packet transmissions. Firstly, without any constraint on the computation algorithm, we establish scaling laws for the computation time and energy expenditure for one time maximum computation. We show that, for an optimal algorithm, the computation time and energy expenditure scale, respectively, as Θ n log n and Θ(n) asymptotically as the number of sensors n → ∞. Secondly, we analyze the performance of three specific computation algorithms that may be used in specific practical situations, namely, the Tree algorithm, Multi-Hop transmission, and the Ripple algorithm (a type of gossip algorithm), and obtain scaling laws for the computation time and energy expenditure as n → ∞. In particular we show that the computation time for these algorithms scales as Θ √ n log n , Θ(n) and Θ √ n log n , respectively; whereas the energy expended scales as Θ(n), Θ n n log n and Θ n √ n log n , respectively. Finally, simulation results are provided to show that our analysis indeed captures the correct scaling; the simulations also yield estimates of the constant multipliers in the scaling laws. Our analyses throughout assume a centralized optimal scheduler and hence our results can be viewed as providing bounds for the performance with practical distributed schedulers.

We consider a scenario in which a wireless sensor network is formed by randomly deploying n sensors to measure some spatial function over a field, with the objective of computing a function of the measurements and communicating it to an operator station. We restrict ourselves to the class of type-threshold functions (as defined in [2]), of which max , min , and indicator functions are important examples: our discussions are couched in terms of the max function. We view the problem as one of message-passing distributed computation over a geometric random graph. The network is assumed to be synchronous, and the sensors synchronously measure values and then collaborate to compute and deliver the function computed with these values to the operator station. Computation algorithms differ in 1) the communication topology assumed and 2) the messages that the nodes need to exchange in order to carry out the computation. The focus of our paper is to establish (in probability) scaling laws for the time and energy complexity of the distributed function computation over random wireless networks, under the assumption of centralized contention-free scheduling of packet transmissions. First, without any constraint on the computation algorithm, we establish scaling laws for the computation time and energy expenditure for one-time maximum computation. We show that for an optimal algorithm, the computation time and energy expenditure scale, respectively, as  ffiffiffiffiffiffiffi n log n

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