Topology-Independent Distributed Adaptive Node-Specific Signal Estimation in Wireless Sensor Networks

Abstract

A topology-independent distributed adaptive nodespecific signal estimation (TI-DANSE) algorithm is presented where each node of a wireless sensor network (WSN) is tasked with estimating a node-specific desired signal. To reduce the amount of data exchange, each node applies a linear compression to its sensors signal observations, and only transmits the compressed observations to its neighbors. The TI-DANSE algorithm is shown to converge to the same optimal node-specific signal estimates as if each node were to transmit its raw (uncompressed) sensor signal observations to every other node in the WSN. The TI-DANSE algorithm is first introduced in a fully connected WSN and then shown, in fact, to have the same convergence properties in any topology. When implemented in other topologies, the nodes rely on an in-network summation of the transmitted compressed observations that can be accomplished by various means. We propose a method for this in-network summation via a data-driven signal flow that takes place on a tree, where the topology of the tree may change in each iteration. This makes the algorithm less sensitive to link failures and applicable to WSNs with dynamic topologies. Index Terms-Distributed signal estimation, wireless sensor networks, Wiener filtering, ad-hoc topologies I. INTRODUCTIONW IRELESS sensor networks (WSNs) typically consist of a set of sensor nodes, that are deployed throughout a sensing environment to detect or estimate a signal or parameter

A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks With Arbitrary Topology

“…It is worth mentioning that it is not the use of an acyclic network in [14], [15] itself which is limiting, but rather the need for this network to be invariant over time. In [18], this point was exploited to form a fully distributed beamformer for use in general cyclic topologies. Like [14] and [15], [18] constructs a global beamformer as a composition of local beamformers at each node.…”

“…In [18], this point was exploited to form a fully distributed beamformer for use in general cyclic topologies. Like [14] and [15], [18] constructs a global beamformer as a composition of local beamformers at each node. Importantly, the method by which these local beamformers are combined does not depend on the underlying network topology.…”

“…This allows the network to vary between frames, overcoming the need for maintaining a fixed topology in all time instances. The method in [18] was shown to be iteratively optimal with its main drawback being a decrease in convergence rate compared to [14], requiring a larger number of frames to obtain near optimal performance.…”

“…The increasing usage of wireless portable devices equipped with microphones and limited power supplies, makes the notion of distributed beamforming in wireless acoustic sensor networks (WASNs) attractive compared to traditional centralized implementations [8]. The last decade, there are several proposed low-complexity distributed beamformers [9]- [18] that mainly focus on achieving a good trade-off between noise reduction and communication cost.…”

“…Furthermore, the authors demonstrated how the algorithm could perform near optimally with only a finite number of iterations, resulting in low transmission complexity. More recently, in [18] a topology independent distributed beamformer (i.e. able to operate in cyclic networks) was proposed.…”