A Novel Software-Defined Drone Network (SDDN)-Based Collision Avoidance Strategies for on-Road Traffic Monitoring and Management
In present road traffic system, drone-network based traffic monitoring using the Internet of Vehicles (IoVs) is a promising solution. However, camera-based traffic monitoring does not collect complete data, cover all areas, provide quick medical services, or take vehicle follow-ups in case of an incident. Drone-based system helps to derive important information (such as commuter's behavior, traffic patterns, vehicle follow-ups) and sends this information to centralized or distributed authorities for making traffic diversions or necessary decisions as per laws. The present approaches fail to meet the requirements such as (i) collision free, (ii) drone navigation, and (iii) less computational and communicational overheads. This work has considered the collision-free drone-based movement strategies for road traffic monitoring using Software Defined Networking (SDN). The SDN controllable drone network results in lesser overhead over drones and provide efficient drone-device management. In simulation, two case studies are simulated using JaamSim simulator. Results show that the zones-based strategy covers a large area in few hours and consume 5 kWs to 25 kWs energy for 150 drones (Case study 1). Zone-less based strategies (case study-2) show that the energy consumption lies between 5 kWs to 18 kWs for 150 drones. Further, the use of SDN-based drones controller reduces the overhead over drone-network and increases the area coverage with a minimum of 1.2% and maximum of 2.6%. Simulation (using AnyLogic simulator) shows the 3D view of successful implementation of collision free strategies.
Kumar, Adarsh; Krishnamurthi, Rajalakshmi; Nayyar, Anand; Luhach, Ashish Kr; Khan, Mohammad S.; and Singh, Anuraj. 2020. A Novel Software-Defined Drone Network (SDDN)-Based Collision Avoidance Strategies for on-Road Traffic Monitoring and Management. Vehicular Communications. https://doi.org/10.1016/j.vehcom.2020.100313 ISSN: 2214-2096