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Roulette Wheel Selection-Based Computational Intelligence Technique to Design an Efficient Transmission Policy for Energy Harvesting Sensors

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Optimization in Machine Learning and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

An expeditious improvement in Internet of Things (IoT) leads to an enormous growth in the number of connected devices, most of them indulge in portable applications, which in turn promotes the usage of energy harvesting (EH) sensors as a viable option. This paper focuses on designing an efficient transmission policy for energy harvesting sensors by estimating the state of an EH sensor through channel gain estimation using different computational intelligence techniques. Automatic repeat request (ARQ)-based packet transmission has been considered where the transmitting node receives either a positive acknowledgement (ACK) or negative acknowledgement (NACK) for every transmitted packet. These feedback signals help in getting channel state information (CSI). Random and sporadic nature of the energy harvesting phenomenon through natural resources has been taken care at the EH node. Performance of policies designed based on different computational intelligence techniques is evaluated and compared by considering outage or packet drop probability as the performance metric. In this paper, we are proposing a new intuitive but effective computational technique that uses roulette wheel (RW) selection to estimate the channel gain, and its performance is compared with artificial neural network (ANN) and extreme learning machine (ELM) under the same simulation environment. We also propose a new collaborative transmission policy among the nodes of a wireless sensor network (WSN) to achieve even better performance.

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Author information

Authors and Affiliations

  1. Communication Systems, NIT, Trichy, India

    Shaik Mahammad & Vineetha Yogesh

  2. Department of Electronics and Communication Engineering, NIT, Tiruchirappalli, India

    E. S. Gopi

Authors
  1. Shaik Mahammad
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  2. E. S. Gopi
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  3. Vineetha Yogesh
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Corresponding author

Correspondence to E. S. Gopi .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

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Mahammad, S., Gopi, E.S., Yogesh, V. (2020). Roulette Wheel Selection-Based Computational Intelligence Technique to Design an Efficient Transmission Policy for Energy Harvesting Sensors. In: Kulkarni, A., Satapathy, S. (eds) Optimization in Machine Learning and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0994-0_12

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