Review of Parameters in Routing Protocols in Vehicular Ad-hoc Networks

Vehicular Ad_hoc Network  (VANET) is a sophisticated elegance of devoted cellular network that permits automobiles to intelligently communicate for different   roadside infrastructure. VANETs bring with it some of demanding situations associated with Quality of Service (QoS) and performance. QoS relies upon on many parameters which includes packet transport ratio, bandwidth, postpone variance, records latency, etc. This paper, discuss numerous troubles associated with latency records, bandwidth usage, and transport of packet in VANETs. The demanding situations have been recognized in offering security, reliability and confidentiality of posted records. Finally, numerous packages of VANETs also are introduced in the modern computing scenario

A Hybrid Model to Extend Vehicular Intercommunication V2V through D2D Architecture

In the recent years, many solutions for Vehicle to Vehicle (V2V) communication were proposed to overcome failure problems (also known as dead ends). This paper proposes a novel framework for V2V failure recovery using Device-to-Device (D2D) communications. Based on the unified Intelligent Transportation Systems (ITS) architecture, LTE-based D2D mechanisms can improve V2V dead ends failure recovery delays. This new paradigm of hybrid V2V-D2D communications overcomes the limitations of traditional V2V routing techniques. According to NS2 simulation results, the proposed hybrid model decreases the end to end delay (E2E) of messages delivery. A complete comparison of different D2D use cases (best & worst scenarios) is presented to show the enhancements brought by our solution compared to traditional V2V techniques.Comment: 6 page

VANET Parameters and Applications: A Review

Vehicular Ad-hoc Network (VANET) represents a challenging class of mobile ad-hoc networks that enables vehicles to intelligently communicate with each other and with roadside infrastructure. VANET poses number of challenges in terms of Quality of Service (QoS) and its performance. Quality of Service depends on numerous parameters such as bandwidth, packet delivery ratio, data latency, delay variance etc. In this paper we have discussed various issues associated with data latency, efficient bandwidth utilization and packet delivery ratio in VANETs. Moreover, challenges in providing security, reliability and confidentiality of the disseminated data are elaborated. Finally, various applications of VANETs in current computing scenario are also presented

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Handover management in mobile WiMAX using adaptive cross-layer technique

The protocol type and the base station (BS) technology are the main communication media between the Vehicle to Infrastructure (V2I) communication in vehicular networks. During high speed vehicle movement, the best communication would be with a seamless handover (HO) delay in terms of lower packet loss and throughput. Many studies have focused on how to reduce the HO delay during lower speeds of the vehicle with data link (L2) and network (L3) layers protocol. However, this research studied the Transport Layer (L4) protocol mobile Stream Control Transmission Protocol (mSCTP) used as an optimal protocol in collaboration with the Location Manager (LM) and Domain Name Server (DNS). In addition, the BS technology that performs smooth HO employing an adaptive algorithm in L2 to perform the HO according to current vehicle speed was also included in the research. The methods derived from the combination of L4 and the BS technology methods produced an Adaptive Cross-Layer (ACL) design which is a mobility oriented handover management scheme that adapts the HO procedure among the protocol layers. The optimization has a better performance during HO as it is reduces scanning delay and diversity level as well as support transparent mobility among layers in terms of low packet loss and higher throughput. All of these metrics are capable of offering maximum flexibility and efficiency while allowing applications to refine the behaviour of the HO procedure. Besides that, evaluations were performed in various scenarios including different vehicle speeds and background traffic. The performance evaluation of the proposed ACL had approximately 30% improvement making it better than the other handover solutions

QoS-Aware 3D Coverage Deployment of UAVs for Internet of Vehicles in Intelligent Transportation

It is a challenging problem to characterize the air-to-ground (A2G) channel and identify the best deployment location for 3D UAVs with the QoS awareness. To address this problem, we propose a QoS-aware UAV 3D coverage deployment algorithm, which simulates the three-dimensional urban road scenario, considers the UAV communication resource capacity and vehicle communication QoS requirements comprehensively, and then obtains the optimal UAV deployment position by improving the genetic algorithm. Specifically, the K-means clustering algorithm is used to cluster the vehicles, and the center locations of these clusters serve as the initial UAV positions to generate the initial population. Subsequently, we employ the K-means initialized grey wolf optimization (KIGWO) algorithm to achieve the UAV location with an optimal fitness value by performing an optimal search within the grey wolf population. To enhance the algorithm's diversity and global search capability, we randomly substitute this optimal location with one of the individual locations from the initial population. The fitness value is determined by the total number of vehicles covered by UAVs in the system, while the allocation scheme's feasibility is evaluated based on the corresponding QoS requirements. Competitive selection operations are conducted to retain individuals with higher fitness values, while crossover and mutation operations are employed to maintain the diversity of solutions. Finally, the individual with the highest fitness, which represents the UAV deployment position that covers the maximum number of vehicles in the entire system, is selected as the optimal solution. Extensive experimental results demonstrate that the proposed algorithm can effectively enhance the reliability and vehicle communication QoS