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ACO-Based Dynamic Decision Making for Connected Vehicles in IoT System
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DOI：10.1109/TII.2019.2906886.png)
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摘要
En 中文
With the rapid development of the internet of things (IoT), connected vehicles are set to become a huge industry over the next few years. In this study, we take an investigation of the distributed intelligent traffic system by pushing intelligence into connected vehicles in terms of dynamic decision making for traversing a certain area (e.g., roundabout and intersection). In particular, we propose a model for the next generation of intelligent transportation system, which focuses on dynamic decision making of connected vehicles based on Ant Colony Optimization, a typical Swarm Intelligence (SI)-based algorithm. Specifically, we first present a communication framework among connected vehicles for sharing information of traffic flow. Then, by applying the concept of SI, connected vehicles are regarded as artificial ants which are able to self-calculate to make an adaptive decision following the dynamics of traffic flow. Furthermore, for evaluating the effectiveness of the proposed approach, we have constructed a framework to model and simulate the traffic system in IoT environment. Simulations with different scenarios of transportation systems indicate promising results comparing with previous works.
Keyword:
Connected vehicles
Internet of Things
Vehicle dynamics
Heuristic algorithms
Decision making
Routing
Ant colony optimization
connected vehicles
dynamic decision making
decentralized management
internet of things
intelligent transportation system
swarm intelligence
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