Eventual Convergence of the Reputation-Based Algorithm in IoT Sensor Networks

Uncertainty in dense heterogeneous IoT sensor networks can be decreased by applying reputation-inspired algorithms, such as the EWMA (Exponentially Weighted Moving Average) algorithm, which is widely used in social networks. Despite its popularity, the eventual convergence of this algorithm for the...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-09, Vol.21 (18), p.6211
Main Authors: Lebiedź, Jacek, Cofta, Piotr, Orłowski, Cezary
Format: Article
Language:English
Subjects:
Algorithms
Communication
Data smoothing
eventual convergence
EWMA algorithms
Internet of Things
IoT sensor networks
Literature reviews
proof of convergence
Reputations
Sensors
Social networks
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Summary:Uncertainty in dense heterogeneous IoT sensor networks can be decreased by applying reputation-inspired algorithms, such as the EWMA (Exponentially Weighted Moving Average) algorithm, which is widely used in social networks. Despite its popularity, the eventual convergence of this algorithm for the purpose of IoT networks has not been widely studied, and results of simulations are often taken in lieu of the more rigorous proof. Therefore the question remains, whether under stable conditions, in realistic situations found in IoT networks, this algorithm indeed converges. This paper demonstrates proof of the eventual convergence of the EWMA algorithm. The proof consists of two steps: it models the sensor network as the UOG (Uniform Opinion Graph) that enables the analytical approach to the problem, and then offers the mathematical proof of eventual convergence, using formalizations identified in the previous step. The paper demonstrates that the EWMA algorithm converges under all realistic conditions.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21186211