Mobility Management in 5G-enabled Vehicular Networks

Abstract

Over the past few years, the next generation of vehicular networks is envisioned to play an essential part in autonomous driving, traffic management, and infotainment applications. The next generation of intelligent vehicular networks enabled by 5G systems will integrate various heterogeneous wireless techniques to enable time-sensitive services with guaranteed quality of service and ultimate bandwidth usage. However, to allow the dense diversity of wireless technologies, seamless and reliable wireless communication protocols need to be thoroughly investigated in vehicular networks environment. Henceforth, efficient mobility management protocols that mitigate the challenges of vehicles’ mobility is essential to support massive data loads throughout various applications. In this article, we review different mobility management protocols and their ability to address issues related to 5G-enabled vehicular networks within the related works. First, we provide a broad view of existing models of vehicular networks and their applicability to the next generation of wireless networks. Next, we propose a classification of several vehicular network models that suit the 5G wireless network, followed by a thorough discussion of the mobility management challenges in each of these network models that need to be addressed and then discuss each of their benefits and drawbacks accordingly.

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

“…Adaptively chooses intersections through which data packets reach their destinations and improves the QoS on the basis of delay, connection probability, and PDR [105] Discusses the packet loss ratio minimization by optimally distributing video packets on multiple routes [106] Proposes a spider web based transmission mechanism to deal with emergency data in VANETs [107] Studies the proactive Fisheye source VANET protocol considering speed, altitude, QoS, etc., in a real traffic scenario [108] The reliability is ensured by selecting trustworthy and compatible routes with the minimum additional communications [109] The proposed routing approach leads to the reduction of the latency, enhancement of the delivery ratio, and minimization of the network overhead [110] Maintains a QoS guaranteed routing following a stable matching based routing approach that minimizes the end-to-end delay by using the best forwarder device [111] Discusses route discovery in V2X communications and highlights that DSDV must be preferred to AODV when V2X communications are performed various management protocols is presented in [113], along with issues pertaining to 5G networks. In that work, a brief description of existing VANET models, applications, classifications, benefits, and drawbacks are also presented.…”

Applications of Game Theory in Vehicular Networks: A Survey

“…VNs [3]- [8] have been extensively studied in different contexts, and recently published surveys cover many aspects of the VNs. Several surveys focus on the Quality-of-Service (QoS) of VNs involving the Medium Access Control (MAC) protocols [9]- [13], routing or message dissemination solutions [14]- [22], energy harvesting techniques [23], [24], and mobility management [25]. Furthermore, considering the vulnerability of VNs to various attacks, a great deal of studies investigate the security [26]- [28], privacy [29]- [32], trust [33], and comprehensive aspects (the former three metrics) [34], [35] of VNs.…”

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

“…Traffic signal control in vehicular networks enables traffic signal controllers at intersections to manage and control traffic, such as selecting traffic phases (e.g., the north-south and west-east bounds) following traffic rules (e.g., vehicles cannot make a right turn) for reducing congestion at intersections [57,58]. In [33], traffic signal controllers select their respective traffic phases in a distributed manner.…”