Leveraging AI and Blockchain in MANETs to enhance Smart City Infrastructure and Autonomous Vehicular Networks

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S. Gopalakrishnan
E.D. Kanmani Ruby
D. Hemanand
R. Anitha
D. Suresh
Shruti Bhargava choubey

Abstract

The incorporation or combination of Artificial Intelligence (AI) and blockchain technology into Mobile Ad Hoc Networks (MANETs) shows important factor for modern and advance smart city infrastructure and autonomous vehicular networks. This paper describes the complementary potential of the technologies to help the built-in difficulties of MANETs includes flexibility, protection, and data integrity. AI techniques such as machine learning and reinforcement learning, are emphasized to improve routing protocols to optimize data transmission rates, and decrease latency. Blockchain technology using Practical Byzantine Fault Tolerance (PBFT) and other consensus mechanisms, gives a tight and decentralized architecture for data handling assuring trust and integrity amidst network nodes. The appeal of these incorpoarted technologies is especially related for smart cities which depand on collection of data and evaluation for effective handling of urban operations such as flow of traffic, environmental observing, and consumption of energy. Autonomous vehicular networks needing rigd and strong communication and data transfer between vehicles and infrastructure, also help from the enhanced network functions and security provided by AI and blockchain incorpoaration. Experimental evaluation denotes improvements in crucial performance metrics. Sensor 2 persists the highest data transmission rate of 12 Mbps. Sensor 4 had the decreased at 9 Mbps. Latency measurements observed that Sensor 2 recorded the lowest latency at 45 ms, with Sensor 3 having the highest at 55 ms.

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Leveraging AI and Blockchain in MANETs to enhance Smart City Infrastructure and Autonomous Vehicular Networks (S. Gopalakrishnan, E. K. Ruby, D. Hemanand, R. Anitha, D. Suresh, & S. B. choubey , Trans.). (2024). Babylonian Journal of Machine Learning, 2024, 112-120. https://doi.org/10.58496/BJML/2024/011