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- Title
Faulty Nodes Detection for Reliable Data Transmission in Intelligent Wireless Sensor Networks.
- Authors
Rao, Maddu Srinivasa; Rao, D. Nagendra
- Abstract
One of the key elements of the internet of things (IoT) nowadays are wireless sensor networks (WSNs). Any IoT application uses a variety of sensor-based devices to collect data from real-world objects and send it to the base station (BS). In accordance with the sensor position, the BS evaluates this data. Any IoT-based application needs a high-performance intelligent WSN. IoT-enabled WSNs are high-performance intelligent WSNs, which are used throughout this article. Fault incidence likelihood in IoT-enabled WSNs is substantially higher than in conventional networks. The dependability of the IoT-enabled WSNs is affected by faulty nodes and broken connections. Incorporating multipath transmission, relay node location, and backup node selection, various faulttolerance techniques improve the network's dependability. These methods, however, have significant packet overhead, worse detection accuracy, and lengthy data transmission delays. For fault tolerance in IoT-enabled WSNs, a multi-objective red panda optimization method incorporating deep reinforcement learning (DRL) is suggested in this paper. This study's primary goal is to identify defective nodes using starting energy, transmission of packets rate, communication overhead, and packet delay measurements. In order to capture data in an energy-efficient manner and extend the network lifespan, a mobile sink (MS) is deployed. The suggested approach beat state-of-the-art techniques in terms of energy efficiency (EE), latency(L), packet delivery ratio (PDR) and network lifetime (NL) according to thorough simulations and theoretical research.
- Subjects
INTELLIGENT sensors; DEEP reinforcement learning; REINFORCEMENT learning; MULTICASTING (Computer networks); RED panda; DATA transmission systems; WIRELESS sensor networks; POSITION sensors
- Publication
International Journal of Intelligent Engineering & Systems, 2024, Vol 17, Issue 2, p26
- ISSN
2185-310X
- Publication type
Article
- DOI
10.22266/ijies2024.0430.03