Diffusion adaptive networks with imperfect communications: link failure and channel noise

Rastegarnia, Amir; Bazzi, Wael M.; Khalili, Azam; Chambers, J.A.

doi:10.1049/iet-spr.2012.0281

Cited by 19 publications

(21 citation statements)

References 27 publications

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“…Diffusion strategies are known to be robust to node and link failures as well as to high noise levels [7], [8], [9], [10] However, it is possible that a single adversarial agent that does not update its estimates according to the diffusion-based information processing rules, for instance by retaining a fixed value throughout, can fail other agents to converge to their true estimates. Resilience of diffusion-based distributed algorithms in the presence of such fixed-value Byzantine attacks has been studied in [2], [5].…”

Section: Introductionmentioning

confidence: 99%

Resilient Distributed Diffusion in Networks With Adversaries

Abbas

Koutsoukos

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation. In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and nonstationary multi-target localization.

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Section: Introductionmentioning

confidence: 99%

Resilient Distributed Diffusion in Networks With Adversaries

Abbas

Koutsoukos

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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“…The adaptive networks work with several agents distributed in an area gathering data in order to estimate a desired entity [1][2][3][4][5][6][7]. All of these agents have access to some measurements and with the help of their processing unit, they perform local estimations.…”

Section: Introductionmentioning

confidence: 99%

“…Diffusion strategy is a more practical mode of cooperation because finding a Hamiltonian cycle for the network topology of the incremental mode is sometimes not possible [5].…”

Section: Introductionmentioning

confidence: 99%

Channel Fading Effect Analysis on Diffusion Cooperation Strategies over Adaptive Networks

2019

KSII TIIS

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In this paper, we investigate the performance of the diffusion adaptation strategies for parameter estimation in wireless adaptive networks, where the nodes exchange information over noisy and fading wireless channels. This paper shows the differences between the effect of Rayleigh and Rician fading over wireless adaptive networks and proves that the Rician fading is a more practical model in such kinds of networks. Simulation results imply that the effect of Rayleigh fading is more degrading for the estimation process than Rician fading. Also, the simulation results show the performance of adapt then combine (ATC) diffusion algorithm is better than the combine then adapt (CTA) algorithm by merely considering noise in wireless channels. While the performance of CTA prevails ATC over the wireless adaptive network in the presence of noise plus channel fading.

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“…In addition, due to using the diffusion strategy and modeling the smart grid as an adaptive network, our framework is scale-free and an increase in the number of consumers does not increase the system calculations nor the communication burden significantly. Besides, as our proposed algorithm relies on the diffusion strategy, we expect that the framework will also work in the presence of imperfect communication and link failures [24] , [25] , [26] . Moreover, the theoretical models of the most important renewable energy resources (solar and wind) and the new objective function enable consumers to sell any excess energy back to the main grid at the right time and best price to make profit.…”

Section: Introductionmentioning

confidence: 99%

A distributed algorithm for demand-side management: Selling back to the grid

Latifi

Khalili

Rastegarnia

et al. 2017

Heliyon

Self Cite

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Demand side energy consumption scheduling is a well-known issue in the smart grid research area. However, there is lack of a comprehensive method to manage the demand side and consumer behavior in order to obtain an optimum solution. The method needs to address several aspects, including the scale-free requirement and distributed nature of the problem, consideration of renewable resources, allowing consumers to sell electricity back to the main grid, and adaptivity to a local change in the solution point. In addition, the model should allow compensation to consumers and ensurance of certain satisfaction levels. To tackle these issues, this paper proposes a novel autonomous demand side management technique which minimizes consumer utility costs and maximizes consumer comfort levels in a fully distributed manner. The technique uses a new logarithmic cost function and allows consumers to sell excess electricity (e.g. from renewable resources) back to the grid in order to reduce their electric utility bill. To develop the proposed scheme, we first formulate the problem as a constrained convex minimization problem. Then, it is converted to an unconstrained version using the segmentation-based penalty method. At each consumer location, we deploy an adaptive diffusion approach to obtain the solution in a distributed fashion. The use of adaptive diffusion makes it possible for consumers to find the optimum energy consumption schedule with a small number of information exchanges. Moreover, the proposed method is able to track drifts resulting from changes in the price parameters and consumer preferences. Simulations and numerical results show that our framework can reduce the total load demand peaks, lower the consumer utility bill, and improve the consumer comfort level.

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Diffusion adaptive networks with imperfect communications: link failure and channel noise

Cited by 19 publications

References 27 publications

Resilient Distributed Diffusion in Networks With Adversaries

Resilient Distributed Diffusion in Networks With Adversaries

Channel Fading Effect Analysis on Diffusion Cooperation Strategies over Adaptive Networks

A distributed algorithm for demand-side management: Selling back to the grid

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