Green Fault Tolerant AIoT-Enabled Mobile Sink Data Collection Scheme in Sensor Networks

In the current era, the Internet of Things (IoT) is an emerging technology that finds its application in the smart agriculture, smart cities, healthcare, etc. The backbone of these IoT-based applications is Wireless Sensor Networks (WSNs). The sensors in the WSNs face various challenges such as hard...

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Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 73; no. 10; pp. 15385 - 15394
Main Authors Kaur, Gagandeep, Bhattacharya, Mahua
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial intelligence
Artificial Intelligence of Things (AIoT)
Data collection
Data models
energy hole
Fault tolerance
fault tolerant
Fault tolerant systems
green
intelligent
Internet of Things
network partitioning
Partitioning
Routing
Wireless sensor networks
wireless sensor networks (WSNs)
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Summary:In the current era, the Internet of Things (IoT) is an emerging technology that finds its application in the smart agriculture, smart cities, healthcare, etc. The backbone of these IoT-based applications is Wireless Sensor Networks (WSNs). The sensors in the WSNs face various challenges such as hardware failure and limited battery power. This causes network partitioning problems. Also, limited battery power causes energy-hole issues. In this paper, an Artificial Intelligence (AI)-based rendezvous points selection and rotation mechanism is proposed that provides green and fault-tolerant data collection in sensor networks. Also, an intelligent mobile sink-based data collection scheduling scheme is proposed that resolves energy holes and network partitioning issues. The simulations and testbed results demonstrate the efficiency of the proposed scheme.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3400880