Network correlated data gathering with explicit communication: NP-completeness and algorithms

Abstract

Abstract-We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.

Amplify and Forward for Correlated Data Gathering over Hierarchical Sensor Networks

“…This is because under symmetric conditions TDMA achieves capacity of the GMAC [5]. SB-TDMA is a version of the scheme used in [7], [6] and [24] in our setup. We study the performance of these schemes for N sensor nodes transmitting through the GMAC.…”

“…This scheme requires complete network knowledge. The lossy version of the problem using high resolution quantization is given in [6].…”

Energy scaling laws for distributed inference in random fusion networks

“…For ν ∈ [0, 2), in (21), substitute f 1 (x) with τ (x) 1 p , f 2 (x) with 1, p = 2 2−ν > 1 to obtain (20). ✷ The above result implies that, in the context of i.i.d.…”

“…One line of approach is the use of distributed compression with the aim of reconstructing all the measurements at the fusion center. Examples of such approaches can be found in [20]- [22].…”

Does Compressed Sensing Improve the Throughput of Wireless Sensor Networks?

“…Secondly, distributed source coding technique, such as Slepian-Wolf coding [3], may be applied to allow non-collaborative data compression at the sources, but the lack of prior knowledge of the data correlation structure could render it impossible to perform the coding operations. Finally, whereas collaborative in-network compression makes it possible to discover the data correlation structure through information exchange [4], [5], the resulting high computation and communication load may potentially offset the benefit of this aggregation technique.…”