Cognitive Radio Assisted OLSR Routing for Vehicular Sensor Networks

AbstractVehicular Sensor Network (VSN) emerged due to recent developments in Wireless Sensor Network (WSN) and functioning as a way for observing metropolitan environments and enabling vehicles to share relevant sensor data to assist safety, convenience and commercial applications. Data dissemination is an important aspect of these networks and requires timely delivery of important sensor information. In VSNs, rapid mobility of the vehicles causes recurrent topography modifications. The possibility of on-demand protocols that makes routing decisions reactively in Vehicular Networks are restricted owing to its structural instability and current routing protocols, operating in a table-driven fashion like OLSR are unable to cope up with the high demands imposed by vehicular applications. Furthermore, sensor data transmissions are accompanied by rapid fluctuations in the convention of licensed spectrum and acquire more number of channels to transmit huge bandwidth data and result in spectrum scarcity. Existing works on OLSR protocol failed to examine spectrum conditions and calculate utilization of channel. Cognitive Radio (CR) is a possible solution for guiding OLSR to discover unused frequency bands and utilize them opportunistically. This paper presents an optimal OLSR routing for efficient data communication using Cognitive Radio enabled Vehicular Sensor Networks (CR-VSNs). The proposed model was tested under simulated traffic of Chennai urban road map. Delay is observed to be minimal for data communications in CR-VSN

Hybrid-Vehfog: A Robust Approach for Reliable Dissemination of Critical Messages in Connected Vehicles

Vehicular Ad-hoc Networks (VANET) enable efficient communication between vehicles with the aim of improving road safety. However, the growing number of vehicles in dense regions and obstacle shadowing regions like Manhattan and other downtown areas leads to frequent disconnection problems resulting in disrupted radio wave propagation between vehicles. To address this issue and to transmit critical messages between vehicles and drones deployed from service vehicles to overcome road incidents and obstacles, we proposed a hybrid technique based on fog computing called Hybrid-Vehfog to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. Our proposed algorithm dynamically adapts to changes in an environment and benefits in efficiency with robust drone deployment capability as needed. Performance of Hybrid-Vehfog is carried out in Network Simulator (NS-2) and Simulation of Urban Mobility (SUMO) simulators. The results showed that Hybrid-Vehfog outperformed Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP), PEer-to-Peer protocol for Allocated REsource (PrEPARE), Fog-Named Data Networking (NDN) with mobility, and flooding schemes at all vehicle densities and simulation times

Detecting Traffic Conditions Model Based On Clustering Nodes Situations In VANET

In the last decade, cooperative vehicular network has been one of the most studied areas for developing the intelligent transportation systems (ITS). It is considered as an important approach to share the periodic traffic situations over vehicular ad hoc networks (VANETs) to improve efficiency and safety over the road. However, there are a number of issues in exchanging traffic data over high mobility of VANET, such as broadcast storms, hidden nodes and network instability. This paper proposes a new model to detect the traffic conditions using clustering traffic situations that are gathered from the nodes (vehicles) in VANET. The model designs new principles of multi-level clustering to detect the traffic condition for road users. Our model (a) divides the situations of vehicles into clusters, (b) designs a set of metrics to get the correlations among vehicles and (c) detects the traffic condition in certain areas. These metrics are simulated using the network simulator environment (NS-3) to study the effectiveness of the model