Distributed adaptive node-specific signal estimation in heterogeneous and mixed-topology wireless sensor networks

Abstract

a b s t r a c tA wireless sensor network (WSN) is considered where each node estimates a number of node-specific desired signals by means of the distributed adaptive node-specific signal estimation (DANSE) algorithm. It is assumed that the topology of the WSN is constructed based on one of the two approaches, either a top-down approach where the WSN is composed of heterogeneous nodes, or a bottom-up approach where the nodes are not necessarily heterogeneous. In the top-down approach, nodes with the largest energy budgets are designated as cluster heads and the remaining nodes form clusters around these nodes. In the bottom-up approach, an ad hoc WSN is partitioned into a set of smaller substructures consisting of non-overlapping cliques that are arranged in a tree topology. These two approaches are shown to be conceptually equivalent, in that the same building blocks constitute both envisaged topologies, and the functionality of the DANSE algorithm is extended to such topologies. In using the DANSE algorithm in such topologies, the WSN converges to the same solution as if all nodes had access to all of the sensor signal observations, and provides faster convergence when compared to DANSE in a single tree topology with only a slight increase in per-node energy usage.

Energy-balance node-selection algorithm for heterogeneous wireless sensor networks

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Gravitational Clustering: A simple, robust and adaptive approach for distributed networks

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“…A recent and emerging research direction in distributed signal processing for wireless sensor networks (WSNs) is that of enabling cooperation among multiple heterogeneous devices dedicated to solve different signal processing tasks [1]- [5]. A crucial first step towards this so-called multiple devices multiple tasks (MDMT) paradigm is to answer the question: who observes what?…”

Route bundling in polygonal domains using Differential Evolution

“…Examples include the construction of a connected network to ensure complete coverage of an area of interest with the minimum number of nodes as possible [8], the degree-constrained minimum-weight connected dominated set for energy-efficient topology control in wireless sensor networks [9], the topological optimization for consensus-based clock synchronization protocols [10], the tree topology construction for heterogeneous wireless sensor networks [11], the self-stabilizing algorithms to construct rooted trees under the assumption of node disconnection [12] and a number of topology optimization for network coverage, connectivity, energy savings, delay minimization, optimal routing and broadcasting [13–17]. …”