You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey 
Chapter Title:  4) Energy and Monetary Combination: 
1. Abstract of Cited Paper (BIB001):  Vehicular Edge Computing (VEC) has been studied as an important application of mobile edge computing in vehicular networks. Usually, the generalization of VEC involves large-scale deployment of dedicated servers, which will cause tremendous economic expense. We also observe that the Parked Vehicles (PVs) in addition to mobile vehicles have rich and underutilized resources for task execution in vehicular networks. Thus, we consider scheduling PVs as available edge computing nodes to execute tasks, and this leads to a new computing paradigm, called by parked vehicle edge computing (PVEC). In this paper, we investigate PVEC and explore opportunistic resources from PVs to run distributed mobile applications. PVs coordinate with VEC servers for collective task execution. First, a system architecture with primary network entities is proposed for enabling PVEC. We also elaborately design an interactive protocol to support mutual communications among them with security guarantee. Moreover, we measure the availability of opportunistic resources and formulate a resource scheduling optimization problem by using Stackelberg game approach. A subgradient-based iterative algorithm is presented to determine workload allocation among PVs and minimize the overall cost of users. Numerical results indicate that compared with existing schemes, PVEC serves more vehicles and reduces service fee for users. We also demonstrate that the Stackelberg game approach is effective and efficient. 
2. Abstract of Cited Paper (BIB002):  Vehicular edge computing is essential to support future emerging multimedia-rich and delay-sensitive applications in vehicular networks. However, the massive deployment of edge computing infrastructures induces new problems including energy consumption and carbon pollution. This motivates us to develop BEGIN (Big data enabled EnerGy-efficient vehIcular edge computiNg), a programmable, scalable, and flexible framework for integrating big data analytics with vehicular edge computing. In this article, we first present a comprehensive literature review. Then the overall design principle of BEGIN is described with an emphasis on computing domain and data domain convergence. In the next section, we classify big data in BEGIN into four categories and then describe their features and potential values. Four typical application scenarios in BEGIN including node deployment, resource adaptation and workload allocation, energy management, and proactive caching and pushing, are provided to illustrate how to achieve energy-efficient vehicular edge computing by using big data. A case study is presented to demonstrate the feasibility of BEGIN and the superiority of big data in energy efficiency improvement. Finally, we conclude this work and outline future research open issues. 
3. Abstract of Cited Paper (BIB003):  Low-Iatency communication is crucial to satisfy the strict requirements on latency and reliability in 5G communications. In this paper, we firstly consider a contract-based vehicular fog computing resource allocation framework to minimize the intolerable delay caused by the numerous tasks on the base station during peak time. In the vehicular fog computing framework, the users tend to select nearby vehicles to process their heavy tasks to minimize delay, which relies on the participation of vehicles. Thus, it is critical to design an effective incentive mechanism to encourage vehicles to participate in resource allocation. Next, the simulation results demonstrate that the contract-based resource allocation can achieve better performance. 
4. Abstract of Cited Paper (BIB004):  Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending the fog computing to conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a VFC system by combining both PVA and smart parking. A single- round multi-item parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatible, individual rational and budget balance. The simulation results confirmed the win-win performance enhancement to fog node controller (FNC), vehicles, and parking places from the proposed design. 
5. Abstract of Cited Paper (BIB005):  Mobile Edge Computing (MEC) is a promising solution to improve vehicular services through offloading computation to cloud servers in close proximity to mobile vehicles. However, the self-interested nature together with the high mobility characteristic of the vehicles make the design of the computation offloading scheme a significant challenge. In this paper, we propose a new Vehicular Edge Computing (VEC) framework to model the computation offloading process of the mobile vehicles running on a bidirectional road. Based on this framework, we adopt a contract theoretic approach to design optimal offloading strategies for the VEC service provider, which maximize the revenue of the provider while enhancing the utilities of the vehicles. To further improve the utilization of the computing resources of the VEC servers, we incorporate task priority distinction as well as additional resource providing into the design of the offloading scheme, and propose an efficient VEC server selection and computing resource allocation algorithm. Numerical results indicate that our proposed schemes greatly enhance the revenue of the VEC provider, and concurrently improve the utilization of cloud computing resources. 
6. Abstract of Cited Paper (BIB006):  Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme. 
7. Abstract of Cited Paper (BIB007):  Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending fog computing to the conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a smart VFC system, by combining both PVA and smart parking. A VFC-aware parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services by monetary rewards to compensate for their service cost. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatibility, individual rationality, and budget balance. We further provide an observation stage with dynamic offload pricing update to improve the offload efficiency and the profit of the fog system. The simulation results confirm the win–win performance enhancement to the fog node controller, the smart vehicles, and the parking places from the proposed design.

Literature Review:
Unlike previous MOO, BIB001 proposed the EV demands (i.e., charging and discharging) using a calendar policy whereby the calendars were scheduled by the appropriate fog data center based on the vehicle's location. The optimal calendar was then selected for each vehicle considering parameters, such as waiting time to plug in, the price at a specific time, distance to the EV public supply stations (EVPSS), and the demand/supply energy curve. However, the authors overlook the prices of the energy consumption in the optimisation. BIB002 proposed a Stackelberg game model between a service provider (leader) and PVs s(followers). The former was designed to minimise the overall cost of users, and the latter was to maximise the utility of PVs under the constraint of given rewards. To achieve the Stackelberg equilibrium, a subgradient-based iterative algorithm was proposed to determine the workload allocation among the PVs and concurrently minimised the overall cost of users that led to high complexity. The total amount of served vehicles reached twice compared to the existing schemes when the average workload was 2500 GHz, i.e., CPU cycles. BIB003 has slightly different Stackelberg game model, whereby the game leader, i.e., RSU, issued a computing service price to vehicles within its coverage in the first stage. The utility function of the service provider is based on the total provision revenue minus the total electricity cost, which the latter is not considered in BIB002 . The multiobjective optimisation is transformed into a single objective by introducing the linear weighting function. In the second stage, each vehicle as a game follower optimised its offloading proportion based on the computed service price. Then, the formulated Stackelberg game can be solved using backward induction iteratively, and the obtained strategy at each stage was shown converging to Nash equilibrium. Adopting a contract theoretic approach, BIB006 maximised the revenue of service provider by identifying the cost of computation resources and maximised the utility of vehicles based on computation resources and energy saving. Another solution is that the BS designed a contract associated with distinct performance of vehicle and rewarded the vehicles based on their vehicle types resulted in a maximum payoff BIB004 . However, the study seems initiated high number of contracts in the networks and the latency performance is not investigated. The work is then expanded by BIB007 ; a contract-matching algorithm consists of two stages, which are a contract-based incentive mechanism for vehicles to promptly share their resources and a pricing-based stable matching algorithm for the assignment of UE's tasks with the preferred vehicle. The key advantage of the work is that the scenario of vehicles' private information (i.e., preference on resource sharing and the total amount of available resources) is not known at the BS, which is called as information asymmetry, is compared with that of information symmetry. On the other hand, BIB005 proposed a single-round multi-item parking reservation auction for two different rules, allocation rule and payment rule to determine the parking allocation and the corresponding parking payment work, respectively. The work is then extended to BIB008 to a multi-round multi-item parking reservation auction for the optimal offload price. The simulation revealed that with the optimal offload price, the proposed multi-round auction can improve both the profit of the fog node controller and the utility of parked vehicles. The research makes no attempt for computational resources of other edge nodes except for the parked vehicles.