The deployment of mobile edges in vehicular environments

The provision of online traffic information and applications in vehicular environment can be offered via fixed ground Roadside Units (RSUs). Nevertheless, employing flying RSUs that are carried by Unmanned Aerial Vehicles (UAVs) would bring the capabilities of Mobile Edge Computing (MEC) to the vehicular environment, where they can be deployed dynamically in accordance to traffic safety events and congestion conditions. In this paper, we propose a novel method for the deployment of flying RSUs based on swarm intelligence. Our proposed method relies on two fundamental properties of the dynamic deployment of edges in vehicular environments, which are self-organization and the division of workload among RSUs. These two properties are necessary and sufficient to obtain a swarm intelligent behavior. The term “swarm” can refer to any restrained collection of interacting agents or individuals, which are — in this case — the travelling vehicles and RSUs.

[1]  Ismail Güvenç,et al.  UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges , 2017, IEEE Communications Magazine.

[2]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[3]  Aniruddha S. Gokhale,et al.  Maximizing Vehicular Network Connectivity through an Effective Placement of Road Side Units Using Voronoi Diagrams , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[4]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[5]  Fethi Filali,et al.  Roadside units placement within city-scaled area in vehicular ad-hoc networks , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).

[6]  Ejaz Ahmed,et al.  A survey on mobile edge computing , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[7]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[8]  Dusˇan Teodorovic,et al.  MODELING BY MULTI-AGENT SYSTEMS : A SWARM INTELLIGENCE APPROACH , 2003 .

[9]  Baber Aslam,et al.  Optimal roadside units placement in urban areas for vehicular networks , 2012, 2012 IEEE Symposium on Computers and Communications (ISCC).

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