Diffusion LMS for clustered multitask networks

Chen, Jie; Richard, Cédric; Sayed, Ali H.

doi:10.1109/icassp.2014.6854652

Cited by 27 publications

(24 citation statements)

References 22 publications

(48 reference statements)

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“…1 we now show that even though the WSN consists of a fully connected topology and several tree topologies (here assumed to be stars), the DANSE algorithm is still able to converge to the same solution, at each node, as if each node had access to all of the sensor signal observations in the entire WSN. Even though convergence and optimality of the DANSE algorithm in a fully connected 4 It is noted that the nodes share many sensor signal observations in between two iterations of the DANSE algorithm, such that sufficiently accurate covariance matrix estimates can be computed to solve (16). Furthermore, G i nq only changes whenever node n effectively performs an update of its node-specific parameters.…”

Section: Convergence and Optimalitymentioning

confidence: 98%

“…From [3], it is known that the signal estimated n , for every node n in the cluster, will then converge to the same LMMSE solution as if it had access to all sensor signal observations within the cluster. This convergence of the cluster can be thought of as a single DANSE update in the fully connected network that is composed of the CHs, i.e., it is as if the CH of cluster k has solved (16), where the MNs of cluster k are viewed as virtual sensors of the CH. After this first cluster has converged, the same process happens in the next cluster where again the T-DANSE algorithm is run until convergence and this is repeated for all K clusters in the network.…”

Section: Convergence and Optimalitymentioning

confidence: 99%

“…These methods have also been extended to distributed hierarchical WSNs, as the one envisaged in this paper, where the parameter estimation takes place using diffusion or consensus based algorithms [13][14][15][16]. In [17,18] the WSN is tasked with estimating a desired signal where each of the received signals at the individual clusters are correlated with one another.…”

Section: Introductionmentioning

confidence: 98%

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

2015

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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.

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Section: Convergence and Optimalitymentioning

confidence: 98%

Section: Convergence and Optimalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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

2015

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“…A similar case of disturbance was analyzed by [39]. More recent advances were investigated in [40] [41] [42].…”

Section: Introductionmentioning

confidence: 99%

Biologically Based Control of a Fleet of Unmanned Aerial Vehicles Facing Multiple Threats

El-Férik

2020

IEEE Access

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This paper addresses a set of multiagent, unmanned, aerial vehicles (UAVs) in a mission within a threat-prone environment. Each UAV is considered as a nonholonomic, nonlinear model moving in a two dimensional space. The system is composed of a fleet of UAVs, competing UAVs and a target. The approach proposed in this paper is based on a biological model representing collective behavior in predator-prey systems. The fleet of UAV's (prey) is moving cohesively towards a target and they can come under attack from competing UAV's (predators) which are also expected to avoid a certain area around the target, treating it as an obstacle that they have to avoid. Prey and predators are expected to obey certain social behaviors within their respective species that include coherent motion, separation to avoid collision, and alignment. The prey are also connected through an adaptable network. During attacks, this networked population of prey could be divided into several small groups that are still connected and naturally observe the same social behavior. To identify these groups and their members, the density-based algorithm DBSCAN is used. The aim of this work is to use this biologically inspired model along with a robust feedback linearization controller to achieve both target pursuance and effective evasion from two predators. Simulation results demonstrate the different aspects and features provided in the proposed approach. INDEX TERMS Nonlinear nonholonomic system; Biology-based control; Density-based clustering, Adapt-then-combine (ATC) algorithm;

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“…To improve the robustness of diffusion networks in the presence of disturbances, adaptive combiners are added to the networks as in [31]. A similar case of disturbance was analyzed by [32] Cattivelli et al More recent advances are investigated in [33][34][35]. Lately, [36] and [37] investigated the time-varying formation control of aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

Biologically Inspired Control Of A Fleet Of Uavs With Threat Evasion Strategy

ElźFerik

Thompson

2016

Asian Journal of Control

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In this paper, navigation algorithms for a fleet of multiple nonholonomic UAVs capable of evading a chasing predator and also pursuing a desired target are proposed. The proposed biologically-inspired navigation algorithms are used to define path planning trajectories which are tracked by a designed backstepping tracking controller. We implement the group of nonholonomic UAVs in an adaptive network, specifically inspired by the relationship between a school of fish and a predator. This approach approximately simulates an air combat field. To put this in context, the aim is to use a biologically inspired algorithm along with a designed controller to achieve both target pursuance and effective evasion from a predator. This is equivalent to having multiple UAVs on the same mission of attacking a target, while also aware of a predator on pursuit. The UAVs aim to maneuver and evade the predator while also coordinating their movement and behaviors in a cooperative and coherent manner.

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Diffusion LMS for clustered multitask networks

Cited by 27 publications

References 22 publications

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

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

Biologically Based Control of a Fleet of Unmanned Aerial Vehicles Facing Multiple Threats

Biologically Inspired Control Of A Fleet Of Uavs With Threat Evasion Strategy

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