Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Mobile Cloud Computing is a new field of research that aims to study mobile agents (people, vehicles, robots) as they interact and collaborate to sense the environment, process the data, propagate the results and more generally share resources. Mobile agents collectively operate as Mobile Clouds that enable environment modeling, content discovery, data collection and dissemination and other mobile applications in a way not possible, or not efficient, with conventional Internet Cloud models and mobile computing approaches. In this paper, we discuss design principles and research issues in mobile cloud computing. We then focus on the Mobile Vehicular Cloud and review cloud applications ranging from urban sensing to intelligent transportation. <s> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized $Q$ -learning with compact state representation algorithm, which is named $QC$ -learning. Moreover, a decentralized version of the $QC$ -learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized $QC$ -learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction. <s> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction. <s> BIB003 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency. <s> BIB004 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Connected vehicles provide advanced transformations and attractive business opportunities in the automotive industry. Presently, IEEE 802.11p and evolving 5G are the mainstream radio access technologies in the vehicular industry, but neither of them can meet all requirements of vehicle communication. In order to provide low-latency and high-reliability communication, an SDN-enabled network architecture assisted by MEC, which integrates different types of access technologies, is proposed. MEC technology with its on-premises feature can decrease data transmission time and enhance quality of user experience in latency-sensitive applications. Therefore, MEC plays as important a role in the proposed architecture as SDN technology. The proposed architecture was validated by a practical use case, and the obtained results have shown that it meets application- specific requirements and maintains good scalability and responsiveness. <s> BIB005 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> With the significant population growth in megacities everywhere, traffic congestion is becoming a severe impediment, leading to long travel delays and large economic loss on a global scale. Platooning is a promising intelligent transportation framework that can improve road capacity, on-road safety, and fuel efficiency. Furthermore, enabling inter-vehicle communications within a platoon and among platoons (in a multiplatoon) can potentially enhance platoon control by keeping constant inter-vehicle and inter-platoon distances. However, an efficient resource allocation (RA) approach is required for the timely and successful delivery of inter-vehicle information within multiplatoons. In this paper, subchannel allocation scheme and power control mechanism are proposed for LTE-based inter-vehicle communications in a multiplatooning scenario. We jointly consider the evolved multimedia broadcast multicast services and device-to-device (D2D) multicast communications to enable intra- and inter-platoon communications such that a desired tradeoff between the required cellular resources and the imposed communication delay can be achieved. Simulation results are given to demonstrate that the proposed approaches can reduce the communication delay comparing to D2D-unicast based RA scheme, especially in a multiplatoon scenario with a large number of vehicles. <s> BIB006 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC. <s> BIB007 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Over the last few years, we have witnessed an exponential increase in the computing and storage capabilities of smart devices that has led to the popularity of an emerging technology called edge computing. Compared to the traditional cloud-computing- based infrastructure, computing and storage facilities are available near end users in edge computing. Moreover, with the widespread popularity of unmanned aerial vehicles (UAVs), huge amounts of information will be shared between edge devices and UAVs in the coming years. In this scenario, traffic surveillance using UAVs and edge computing devices is expected to become an integral part of the next generation intelligent transportation systems. However, surveillance in ITS requires uninterrupted data sharing, cooperative decision making, and stabilized network formation. Edge computing supports data processing and analysis closer to the deployed machines (i.e., the sources of the data). Instead of simply storing data and missing the opportunity to capitalize on it, edge devices can analyze data to gain insights before acting on them. Transferring data from the vehicle to the edge for real-time analysis can be facilitated by the use of UAVs, which can act as intermediate aerial nodes between the vehicles and edge nodes. However, as the communication between UAVs and edge devices is generally done using an open channel, there is a high risk of information leakage in this environment. Keeping our focus on all these issues, in this article, we propose a data-driven transportation optimization model where cyber-threat detection in smart vehicles is done using a probabilistic data structure (PDS)- based approach. A triple Bloom filter PDS- based scheduling technique for load balancing is initially used to host the real-time data coming from different vehicles, and then to distribute/collect the data to/from edges in a manner that minimizes the computational effort. The results obtained show that the proposed system requires comparatively less computational time and storage for load sharing, authentication, encryption, and decryption of data in the considered edge-computing-based smart transportation framework. <s> BIB008 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> The autonomous vehicles (AVs), like that in knight rider, were completely a scientific fiction just a few years ago, but are now already practical with real-world commercial deployments. A salient challenge of AVs, however, is the intensive computing tasks to carry out on board for the real-time traffic detection and driving decision making; this imposes heavy load to AVs due to the limited computing power. To explore more computing power and enable scalable autonomous driving, in this paper, we propose a collaborative task computing scheme for AVs, in which the AVs in proximity dynamically share idle computing power among each other. This, however, raises another fundamental problem on how to incentivize AVs to contribute their computing power and how to fully utilize the pool of group computing power in an optimal way. This paper studies the problem by modeling the issue as a market-based optimal computing resource allocation problem. In specific, we develop a software-defined network (SDN) architecture and consider a star topology where a centered AV outsources its computing tasks to the surrounding AVs for its autonomous driving. A market mechanism is developed in which the surrounding AVs sell their computing power at a cost based on their local idle computing resources. Then, we classify the tasks requested by the centered AV into two types which are task with time to live (TTL) and task without TTL, respectively. With different task types, we define corresponding cost models of the centered AV and formulate them as two minimization problems. The optimal solutions of the problems are achieved to guide the centered AV to wisely allocate computing tasks to surrounding AVs towards minimal cost. Finally, the performance of the proposed scheme is evaluated using simulations, which show that the proposed scheme can result in the guaranteed computing performance yet the lowest costs compared with other conventional schemes. <s> BIB009 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Vehicular network aims at providing intelligent transportation and ubiquitous network access. Edge computing is able to reduce the consumption of core network bandwidth and serving latency by processing the generated data at the network edge, and social network is able to provide precise services by analyzing user’s personal behaviors. In this paper, we propose a new network system referred to as vehicular social edge computing (VSEC) that inherits the advantages of both edge computing and social network. VSEC is capable of improving the drivers’ quality of experience while enhancing the service providers’ quality of service. In order to further improve the performance of VSEC, the network utility is modeled and maximized by optimally managing the available network resources via two steps. First, the total processing time is minimized to achieve the optimal payment of the user to each edge device for each kind of the required resource. Second, a utility model is proposed, and the available resources are optimally allocated based on the results from the first step. The two optimization problems are solved by the Lagrangian theory, and the closed-form expressions are obtained. Numerical simulations show different capacities in different scenarios, which may provide some useful insights for VSEC design. <s> BIB010 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Recently, due to the increasing popularity of enjoying various multimedia services on mobile devices (e.g., smartphones, ipads, and electronic tablets), the generated mobile data traffic has been explosively growing and has become a serve burden on mobile network operators. To address such a serious challenge in mobile networks, an effective approach is to manage data traffic by using complementary technologies (e.g., small cell network, WiFi network, and so on) to achieve mobile data offloading. In this paper, we discuss the recent advances in the techniques of mobile data offloading. Particularly, based on the initiator diversity of data offloading, we classify the existing mobile data offloading technologies into four categories, i.e., data offloading through small cell networks, data offloading through WiFi networks, data offloading through opportunistic mobile networks, and data offloading through heterogeneous networks. Besides, we show a detailed taxonomy of the related mobile data offloading technologies by discussing the pros and cons for various offloading technologies for different problems in mobile networks. Finally, we outline some opening research issues and challenges, which can provide guidelines for future research work. <s> BIB011 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> This paper surveys the literature of opportunistic offloading. Opportunistic offloading refers to offloading traffic originally transmitted through the cellular network to opportunistic network, or offloading computing tasks originally executed locally to nearby devices with idle computing resources through opportunistic network. This research direction is recently emerged, and the relevant research covers the period from 2009 to date, with an explosive trend over the last four years. We provide a comprehensive review of the research field from a multi-dimensional view based on application goal, realizing approach, offloading direction, etc. In addition, we pinpoint the major classifications of opportunistic offloading, so as to form a hierarchical or graded classification of the existing works. Specifically, we divide opportunistic offloading into two main categories based on application goal: traffic offloading or computation offloading. Each category is further divided into two smaller categories: with and without offloading node selection, which bridges between subscriber node and the cellular network, or plays the role of computing task executor for other nodes. We elaborate, compare, and analyze the literatures in each classification from the perspectives of required information, objective, etc. We present a complete introductory guide to the researches relevant to opportunistic offloading. After summarizing the development of the research direction and offloading strategies of the current state-of-the-art, we further point out the important future research problems and directions. <s> BIB012 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes, where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multiuser multiserver VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer nonlinear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading algorithm compared to the benchmark solutions. <s> BIB013 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game’s usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game’s model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios. <s> BIB014 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the other researchers. <s> BIB015 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> I. INTRODUCTION <s> The Internet of Vehicles (IoV) is an emerging paradigm that is driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications that will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend on communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and cloud computing will not be sufficient due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. EIS will provide not only low-latency content delivery and computation services but also localized data acquisition, aggregation, and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies, and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open-research problems are identified. <s> BIB016
Recent developments in the edge computing field have led to a renewed interest in vehicular edge networks. With the increasing number of AVs on the road, the computation at the cloud platform becomes a great of concern. Each AV is envisaged to generate 1 GB data for every second BIB003 and on average 30 TB data in a day. Any AV relying on the cloud BIB001 for data computation might crash because of millions of vehicles transmitting and receiving immense of data from a data center for processing. It is even crucial with the emerging of smart vehicles equipped with a massive number of sensors and human computer interaction devices for supporting intelligent traffic and navigation applications, such as active driving safety assistance, smart parking and live traffic management BIB004 , BIB005 . Due to the proximity issue of CC to vehicles, CC suffers from serious shortcomings such as latency, high overhead and efficiency, particularly for time-sensitive vehicle applications BIB008 , BIB013 . Therefore, fast computation and decision making are necessary to deliver short response times, dynamic processing and low dependency on the cloud networks. This is viable when the intelligence and processing capabilities are pushed down closer to where the data originates that leveraged on nearby vehicles, Road Side Units (RSUs), Base Stations (BSs) and Mobile Edge Computing (MEC) servers known as edge nodes. This type of computing is called Vehicular Edge Computing. The VEC is feasible since AVs have computing power of 106 Dhrystone Million Instructions Executed per Second (DMIPS) in the near future  , which is tens times of the current laptops that make cars called as computers-on-wheels. Although AVs are generally equipped with onboard units (OBUs), the computing and storage capability is only on small-scale that make them dependent on other computational resources. Some on-board multimedia applications require stringent deadline and high computation, particularly the realtime applications, thus the limited computing and storage resources of vehicles are hard to support computation-intensive applications. The OBUs can perform simple computations, collect local data from sensing devices, and upload data to the edge nodes either stationary or mobile. By extending CC to the edge of the networks, the stationary edge node, such as RSUs, BSs and MEC servers can provide high reliability, high bandwidth, and low-latency computing services for the requesting vehicles. This greatly reduces the communication delay and avoids congestion. In addition, the MEC server can obtain the user's surrounding environment information in realtime and hence it can optimise the services dynamically and rapidly. Another feasible solution is for AVs to offload its data to other nearby AVs with cheap payments, but with a limited storage. Enabling cooperative driving among AVs, such as platoon-based driving BIB006 , [9] , convoy-based driving BIB009 , Internet of Vehicles (IoV) BIB016 and Vehicular Social Networks (VSN) BIB010 can aid VEC becomes possible. The key issue of VEC is the computation offloading mechanism whereby the vehicles must select optimum edge nodes in-real time by satisfying the latency requirement, low cost and high energy efficiency. Also, with such schemes the service provider can still gain some profits. Another critical issue is where the contents are cached at what edge nodes and then delivered directly to the corresponding vehicles. Much uncertainty still exists about the data computation offloading techniques and also content caching and delivery, i.e., downloading that are closely related to the optimisation problem of resource management in VEC. From the literature, ComOf and CachDel mostly involve with the optimisation of scheduling arXiv:1912.07803v1 [cs.NI] 17 Dec 2019 and task allocation mechanisms, which are the primary scope of our survey paper. Existing surveys generally discussed the mobile data offloading technologies BIB011 particularly in cellular networks BIB002 , mobile edge computing BIB007 , opportunistic offloading BIB012 and game theory in multi-access edge computing BIB014 . However, very little attention has been paid to the VEC BIB015 . Since the ComOf and CachDel mechanisms are of paramount importance to both VEC and VFC, this motivates us to fill in the gap by conducting a comprehensive survey on both offloading and downloading scenarios. The contributions of our survey work in this field are threefold. First, from a comprehensive literature surveys, the general VEC architecture is defined in terms of its layer, the types of communication technology, and vehicle applications. Second, our work summarises existing VEC architectures in terms of layers, fog nodes, communication technology, vehicle applications as well as the mobility model used. Third, we reviews the optimization techniques for ComOf and CachDel problems for VEC. Figure 1 illustrates the taxanomy of this paper. The rest of the paper is organised as follows. Section II explains a general VEC architecture as well as existing VEC architectures that consist of VEC layers, edge nodes, communication technologies, vehicle applications and mobility model used. The computation offloading and content delivery issues are also discussed in the section. Section III discusses the data computation offloading solutions in three different groups, which are single objective optimisation, hybrid optimisation, and multiobjective optimisation. Section IV presents content caching and delivery approaches for VEC in two groups, which are homogeneous and heterogeneous edge nodes. Section V highlights the key challenges, open issues and future works. The paper is concluded in Section VI. The abbreviations used in the paper are listed in Table I. 