Journal
IEEE TRANSACTIONS ON RELIABILITY
Volume 71, Issue 3, Pages 1219-1229Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2020.3046556
Keywords
Task analysis; Blockchain; Peer-to-peer computing; Monitoring; Smart cities; Roads; Security; Budgeted auction; Internet of Vehicles (IoV); lightweight blockchain; reliability; truthfulness
Categories
Funding
- National Science Foundation [1747818, 1907472]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1907472] Funding Source: National Science Foundation
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Real-time traffic monitoring is essential in smart cities for understanding traffic conditions and preventing accidents. This article proposes a reliable and efficient traffic monitoring system that integrates blockchain and IoT technologies, crowdsourcing tasks to vehicles. The system includes a lightweight blockchain-based information trading framework and auction mechanism to incentivize vehicle participation and ensure budget constraints. Numerical simulations confirm the reliability and efficiency of the framework and algorithms.
Real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous accidents. In this article, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of Vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget but also maintains the truthfulness of the auction process that prevents some vehicles from offering unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly.
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