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Secure Blockchain-Based Traffic Load Balancing Using Edge Computing and Reinforcement Learning

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Blockchain Cybersecurity, Trust and Privacy

Part of the book series: Advances in Information Security ((ADIS,volume 79))

Abstract

Congestion represents one of the major problems in constantly growing cities, and the expansion and modernization of the traffic system are a major priority to all government infrastructures. In effect, more efficient traffic represents a more efficient economy. Solving the issue of congestion by increasing the number of roads is not always the most cost-effective solution as it represents massive changes in city infrastructures that have been present for decades. Urban planning shapes the environment around us but fails to address specifically the future traffic clogging. Our research tackles the problem of traffic congestion by proposing a system for vehicle detection, identification, and count, allied with reinforcement learning for traffic congestion anticipation and prediction. The need for a real-time and efficient system led us to push the research and development onto an Edge Computing platform using IoT and secure transactions using Hyperledger Fabric blockchain. Blockchain intervenes as a security protocol in the proposed system. The native form of Internet of Things does not include security protocols which are important to a large scale implementation. Ensuring the security of transactions and permanent access control is the main reason why blockchain is rooted in our system architecture. Our project aims to reduce the traffic load on roads experiencing significant congestion, and improve overall city traffic system without costly investment into new communication infrastructures and city planning.

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Author information

Authors and Affiliations

  1. Kennesaw State University, Marietta, GA, USA

    Kevin Tiba

  2. College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USA

    Reza M. Parizi

  3. IBM Thomas J. Watson Research, New York, NY, USA

    Qi Zhang

  4. Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

  5. University of Guelph, Guelph, ON, Canada

    Hadis Karimipour

  6. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

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  1. Kevin Tiba
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  2. Reza M. Parizi
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  3. Qi Zhang
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  4. Ali Dehghantanha
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  5. Hadis Karimipour
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  6. Kim-Kwang Raymond Choo
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Corresponding author

Correspondence to Reza M. Parizi .

Editor information

Editors and Affiliations

  1. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  2. Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

  3. College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USA

    Reza M. Parizi

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Tiba, K., Parizi, R.M., Zhang, Q., Dehghantanha, A., Karimipour, H., Choo, KK.R. (2020). Secure Blockchain-Based Traffic Load Balancing Using Edge Computing and Reinforcement Learning. In: Choo, KK., Dehghantanha, A., Parizi, R. (eds) Blockchain Cybersecurity, Trust and Privacy. Advances in Information Security, vol 79. Springer, Cham. https://doi.org/10.1007/978-3-030-38181-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-38181-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38180-6

  • Online ISBN: 978-3-030-38181-3

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