Date Added: Jan 2012
In this paper, the authors study data gathering with compressive sensing from the perspective of in-network computation in random networks, in which n nodes are uniformly and independently deployed in a unit square area. They formulate the problem of data gathering to compute multiround random linear function. They study the performance of in-network computation with compressive sensing in terms of energy consumption and latency in centralized and distributed fashions. For the centralized approach, they propose a tree-based protocol for computing multiround random linear function.