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

Performance modelling of adaptive VANET with enhanced priority scheme

In this paper, we present an analytical and simulated study on the performance of adaptive vehicular ad hoc networks (VANET) priority based on Transmission Distance Reliability Range (TDRR) and data type. VANET topology changes rapidly due to its inherent nature of high mobility nodes and unpredictable environments. Therefore, nodes in VANET must be able to adapt to the ever changing environment and optimize parameters to enhance performance. However, there is a lack of adaptability in the current VANET scheme. Existing VANET IEEE802.11p’s Enhanced Distributed Channel Access; EDCA assigns priority solely based on data type. In this paper, we propose a new priority scheme which utilizes Markov model to perform TDRR prediction and assign priorities based on the proposed Markov TDRR Prediction with Enhanced Priority VANET Scheme (MarPVS). Subsequently, we performed an analytical study on MarPVS performance modeling. In particular, considering five different priority levels defined in MarPVS, we derived the probability of successful transmission, the number of low priority messages in back off process and concurrent low priority transmission. Finally, the results are used to derive the average transmission delay for data types defined in MarPVS. Numerical results are provided along with simulation results which confirm the accuracy of the proposed analysis. Simulation results demonstrate that the proposed MarPVS results in lower transmission latency and higher packet success rate in comparison with the default IEEE802.11p scheme and greedy scheduler scheme

Improving Performance of Opportunistic Routing Protocol using Fuzzy Logic for Vehicular Ad-hoc Networks in Highways

Vehicular ad hoc networks are an emerging technology with an extensive capability in various applications including vehicles safety, traffic management and intelligent transportation systems. Considering the high mobility of vehicles and their inhomogeneous distributions, designing an efficient routing protocol seems necessary. Given the fact that a road is crowded at some sections and is not crowded at the others, the routing protocol should be able to dynamically make decisions. On the other hand, VANET networks environment is vulnerable at the time of data transmission. Broadcast routing, similar to opportunistic routing, could offer better efficiency compared to other protocols. In this paper, a fuzzy logic opportunistic routing (FLOR) protocol is presented in which the packet rebroadcasting decision-making process is carried out through the fuzzy logic system along with three input parameters of packet advancement, local density, and the number of duplicated delivered packets. The rebroadcasting procedures use the value of these parameters as inputs to the fuzzy logic system to resolve the issue of multicasting, considering the crowded and sparse zones. NS-2 simulator is used for evaluating the performance of the proposed FLOR protocol in terms of packet delivery ratio, the end-to-end delay, and the network throughput compared with the existing protocols such as: FLOODING, P-PERSISTENCE and FUZZBR. The performance comparison also emphasizes on effective utilization of the resources. Simulations on highway environment show that the proposed protocol has a better QoS efficiency compared to the above published methods in the literature