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Congestion Aware Data Transmission in Mobile and Constrained IoT Network

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Abstract

The growth and adoption of Internet of Things (IoT) have been increasing at a rapid pace over the past few years. IoT is a network of huge number of connected devices which are mobile and constrained in nature. These IoT devices communicate and transmit data over the Internet. This huge amount of transmission causes congestion in the network, resulting in packet delivery delay and high packet loss. Handling the problem of congestion requires transmission of data effectively in a congestion-aware manner. This paper proposes an approach for congestion-aware data transmission in mobile and constrained IoT networks. In this study, we have performed hop-to-hop communication and the process of next hop selection considers the multiple parameters to make it congestion-aware and effective. The proposed method uses the Analytic Hierarchy Process (AHP) to perform multiparameter based decision making to pick the best suitable next hop. The proposed method is ideal for the environment where nodes have limited resources, high traffic and need timely delivery of data packets. The results are compared with state-of-the-art methods, and the performance of the proposed method was better in terms of throughput by 6%, packet delivery rate by 7.5%, and reduced average delay by 4% as well as buffer overflow condition by 2%.

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Funding

The authors declare that they don’t have competing interests and funding.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India

    Aastha Maheshwari & Rajesh Kumar Yadav

  2. Department of Computer Science and Engineering, H.N.B. Garhwal University, Srinagar, Uttarakhand, India

    Prem Nath

Authors
  1. Aastha Maheshwari
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  2. Rajesh Kumar Yadav
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  3. Prem Nath
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Correspondence to Aastha Maheshwari.

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Maheshwari, A., Yadav, R.K. & Nath, P. Congestion Aware Data Transmission in Mobile and Constrained IoT Network. Wireless Pers Commun 130, 2121–2136 (2023). https://doi.org/10.1007/s11277-023-10374-8

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