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We consider the problem of distributed lossy linear function computation in a tree network. We examine two cases: 1) data aggregation (only one sink node computes) and 2) consensus (all nodes compute the same function). By quantifying the accumulation of information loss in distributed computing, we obtain fundamental limits on network computation rate as a function of incremental distortions (and...
Seismic tomographic imaging is a complex process for imaging the subsurface geological structures. It involves massive data acquisition, signal processing and computing. Traditionally, the voluminous data is logged in each station then manually gathered to a centralized location for post processing. It may take months to see the subsurface image. To see real-time subsurface dynamics, we developed...
Traffic from mobile wireless networks has been growing at a fast pace in recent years and is expected to surpass wired traffic very soon. Service providers face significant challenges at such scales including providing seamless mobility, efficient data delivery, security, and provisioning capacity at the wireless edge. In the Mobility First project, we have been exploring clean slate enhancements...
We consider the problem of estimating functions of distributed data using a distributed algorithm over a network. The extant literature on computing functions in distributed networks such as wired and wireless sensor networks and peer-to-peer networks deals with computing linear functions of the distributed data when the alphabet size of the data values is small, O(1). We describe a distributed randomized...
In many applications of Wireless Sensor Networks (WSN), a large number of sensor nodes are distributed over an area under investigation. The sensors collect ground data at definite time interval and forward it to the sink. In case of an event, it is often required to estimate the affected area and to identify the location of the event. Since, in WSN, communication is costlier than in-network computation...
In this paper we study distributed function computation in a noisy multi-hop wireless network, in which n nodes are uniformly and independently distributed in a unit square. We adopt the adversarial noise model, for which independent binary symmetric channels are assumed for any point-to-point transmissions, with (not necessarily identical) crossover probabilities bounded above by some constant ??...
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