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A Reliable Protocol for Data Aggregation and Optimized Routing in IoT WSNs based on Machine Learning

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Abstract

Data Aggregation for IoT-WSN, based on Machine Learning (ML), allows the Internet of Things (IoT) and Wireless Sensor Networks (WSN) to send accurate data to the trusted nodes. The existing work handles the dropouts well but is vulnerable to different attacks. In the proposed research work, the Data Aggregation (DA) based on Machine Learning (ML) fails the untrusted aggregator nodes. In the attack scenario, this paper proposes a Machine Learning Based Data Aggregation and Routing Protocol (MLBDARP) that verifies the network nodes and DA functions based on ML. This work is to authenticate the nodes to support the MLBDARP, a novel secret shared authentication protocol, and then aggregate using a secure protocol. MLBDARP types of the ML algorithm, such as Decision Trees (DT) and Neural Networks (NN). ML helps determine the probability of a successful Packet Delivery Ratio (PDR). This proposed ML model uses predictability value, Energy Consumption (EC), mobility, and node position. Simulation results proved that the proposed protocol of MLBDARP outperforms Differentiated Data Aggregation Routing Protocol (DDARP) and Weighted Data Aggregation Routing Protocol (WDARP) with Quality of Service (QoS) parameters of Network Throughput (NT), Routing Overhead (RO), End-to-End Delay (EED), Packet Delivery Ratio (PDR) and Energy Consumption (EC).

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  1. Institute of Advance Computing, SAGE University, Indore, India

    Neeraj Chandnani

  2. Military College of Telecommunication Engineering, Mhow, Research Centre, Affiliated to Devi Ahilya Vishwavidyalaya, Indore, Madhya Pradesh, India

    Neeraj Chandnani

  3. Faculty of Communication Engineering, Military College of Telecommunication Engineering, Mhow, Madhya Pradesh, India

    Chandrakant N. Khairnar

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Chandnani, N., Khairnar, C.N. A Reliable Protocol for Data Aggregation and Optimized Routing in IoT WSNs based on Machine Learning. Wireless Pers Commun 130, 2589–2622 (2023). https://doi.org/10.1007/s11277-023-10393-5

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