ACO-based Approach on Dynamic MSMD Routing in IoV Environment

Recently, the advance of the Internet of Things (IoT) and wireless communication technology, specifically Vehicles-to-Everything (V2X), makes a huge contribution to road transportation. The fully connected and autonomous system of road transportation can be basically made in practice by integrating V2X with a current autonomous vehicle. In this paper, we focus on dynamic traffic routing for IoT-based connected vehicles. First, we define the problem of identifying the best paths for all vehicles with different sources and different destinations, or multi-source multi-destination (MSMD) traffic flows. Then, Ant Colony Optimization (ACO)-based approach with coloring ants concept is proposed to solve the problem in a decentralized and self decision-making manner. The simulation is carried out on the NetLogo platform with a multi-intersection scenario. The simulation results show that the ACO-based routing approach outperforms the non-ACO-based approach in terms of average traveling time and the number of vehicles passing metrics.

[1]  Richard F. Hartl,et al.  A survey on dynamic and stochastic vehicle routing problems , 2016 .

[2]  Jason J. Jung,et al.  Computational negotiation-based edge analytics for smart objects , 2019, Inf. Sci..

[3]  Kris Braekers,et al.  The vehicle routing problem: State of the art classification and review , 2016, Comput. Ind. Eng..

[4]  Joel J. P. C. Rodrigues,et al.  Decentralized Consensus for Edge-Centric Internet of Things: A Review, Taxonomy, and Research Issues , 2018, IEEE Access.

[5]  Kponyo Jerry,et al.  NetLogo implementation of an ant colony optimisation solution to the traffic problem , 2015 .

[6]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[7]  Naveen K. Chilamkurti,et al.  Ant colony optimization algorithm with Internet of Vehicles for intelligent traffic control system , 2018, Comput. Networks.

[8]  Daiheng Ni,et al.  Calculation of traffic flow breakdown probability to optimize link throughput , 2010 .

[9]  Marco Dorigo,et al.  The hyper-cube framework for ant colony optimization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Jason J. Jung,et al.  Cooperative game-theoretic approach to traffic flow optimization for multiple intersections , 2017, Comput. Electr. Eng..

[11]  M. Mazhar Rathore,et al.  Vehicular traffic optimisation and even distribution using ant colony in smart city environment , 2018, IET Intelligent Transport Systems.

[12]  Athanasios V. Vasilakos,et al.  Future Internet of Things: open issues and challenges , 2014, Wireless Networks.

[13]  Bart De Schutter,et al.  Ant Colony Routing algorithm for freeway networks , 2013 .

[14]  Fatima de L. P. Duarte-Figueiredo,et al.  A 5G V2X Ecosystem Providing Internet of Vehicles † , 2019, Sensors.

[15]  Jason J. Jung,et al.  ACO-Based Dynamic Decision Making for Connected Vehicles in IoT System , 2019, IEEE Transactions on Industrial Informatics.

[16]  Nicolas Jouandeau,et al.  Swarm intelligence-based algorithms within IoT-based systems: A review , 2018, J. Parallel Distributed Comput..