Adaptive Asynchronous Gossip Algorithms for Consensus in Heterogeneous Sensor Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F25%3A10257947" target="_blank" >RIV/61989100:27510/25:10257947 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10960517" target="_blank" >https://ieeexplore.ieee.org/document/10960517</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JIOT.2025.3559242" target="_blank" >10.1109/JIOT.2025.3559242</a>
Alternative languages
Result language
angličtina
Original language name
Adaptive Asynchronous Gossip Algorithms for Consensus in Heterogeneous Sensor Networks
Original language description
The Internet of Things (IoT) connects a wide range of sensors and devices in environments that are often dynamic and resource-constrained, where efficient distributed solutions are essential for ensuring robust and scalable operation. This article presents a novel adaptive consensus algorithm, tailored for distributed signal processing in heterogeneous sensor networks, with a focus on distributed estimation and target tracking. The algorithm addresses the challenge posed by networks where intelligent sensors have limited sensing, computation, and communication capabilities, resulting in diverse quality of locally available information, and leading to neighbor-based information exchanges. It employs asynchronous gossip protocols to randomly exchange information between nodes, ensuring robustness to time synchronization and network topology uncertainties, while limiting computational and communication costs. The adaptation mechanism operates in two complementary ways. First, we account for variations in the quality of local processing results, ensuring that asymptotic behavior of the consensus scheme accurately reflects this diversity. Second, we introduce a novel adaptation of the rates at which nodes initiate communication, using the available local information. This enables fast information dissemination and provides a solution that is both effective and efficient. We show that, under appropriate network connectivity assumptions, the results obtained by the algorithm converge to the desired asymptotic values in the mean square sense. Numerical simulations demonstrate the algorithm’s properties and effectiveness, particularly in modeling visual surveillance networks where fixed cameras are augmented by moving drones to extend the coverage area.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
—
Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Internet of Things Journal
ISSN
2327-4662
e-ISSN
2327-4662
Volume of the periodical
12
Issue of the periodical within the volume
13
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
25516-25532
UT code for WoS article
001513326000003
EID of the result in the Scopus database
2-s2.0-105002683578