A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs

Abstract

Because of the specific characteristics, like high vehicular mobility, unstable topology, and interruption of inter-vehicle wireless communication, it is hard to make a perfect decision on packet forwarding in highly dynamic topology VANETs. Especially in a sparse urban environment, the poor connectivity of nodes will mostly cause problems such as data packet loss and routing redundancy. Therefore, how to choose the best relay node becomes a key challenge in the design of fast and reliable routing protocols. This paper presents real-time effective information traffic routing (RTEIT), which provides an optimal route for forwarding the data packets toward their destination when choosing the relay node. RTEIT introduces a new network parameter named effective information traffic which can estimate the connectivity of nodes by the path that has been successfully created. Moreover, for avoiding unexpected communication interruption, we propose a new formula to evaluate the status of the links via considering the speed, direction, and location information. Finally, the node utility, as the criterion of routing decision, is calculated by effective information traffic and link status. We use the simulator of SUMO and NS-3 platform to evaluate RTEIT, and the results are compared with GPSR MOPR, and MM-GPSR. The evaluation results demonstrate that RTEIT outperforms in terms of packet loss rate, end-to-end delay, and network yield.

Mobile Adaptive Routing Algorithm for Road-Aware Infrastructure-Assisted Communication in Cognitive Internet of Vehicles

“…Jianhang Liu et al [16] presented a real-time effective information traffic routing (RTEIT) system that determines the optimal route by selecting the relay node and transmitting the data packets towards their destination. Real-time effective information traffic routing introduces a new network attribute called "effective information traffic," which can assess the connectivity of nodes based on efficiently generated paths.…”

A classification method for urban functional regions based on the transfer rate of empty cars

“…The relationship between the transfer rate of empty cars and the regional nature fully reflected. It can be seen from Figure 1 that the transfer rate of empty cars is low from A (the residential land) to B (the commercial land) at 8:00 AM because most individuals take taxies [4] to work from the residential land at this moment. Conversely, the transfer rate of empty cars is high from B (the commercial land) to A (the residential land) at 8:00 AM because almost nobody comes home from work at this moment.…”

Secure AODV Routing Strategies in Smart Cities for Vehicular Communication

“…The existing routing protocols used in VANETs fail to consider the essential aspect of securing the location privacy of vehicles. Consequently, this presents a substantial hazard to individual safety and information security [7]. AODV is a famous and widely utilized routing protocol in VANETs because of its reactive nature, which reduces control overhead and effectively adjusts to the changing vehicular network conditions [8].…”

“…Hybrid optimization and deep learning hybrid optimization-based deep learningThis technique classifies attacks in VANETs and selects Cluster Heads for routing based on proper feature selection[18] (7). Trust-based and fuzzy logic authentication and routing…”