# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 5 5G and the Edge Cloud
### Smart Vehicles  
Vehicles are used in industry for logistical reasons, within warehouses and in-between sites. Although the current article does not mean to be comprehensive in this area, some elementary issues are discussed in connection with the edge cloud.  
In [59], electric vehicles cloud and edge (EVCE) computing is presented. EVCE is a network paradigm, which involves seamless connections in heterogeneous vehicular contexts to aggregate multiple geographically distributed electric vehicles (EVs) into a common resource pool. In EVCE computing, information and energy flow are dynamically exchanged during the vehicle-to-anything communications, including vehicle-to-grid (V2G), vehicle-to-infrastructure (V2I), and vehicle-to-vehicle (V2V). The purpose of this flow dynamical flow exchange is to achieve collaborative data sensing, information analyzing, and energy sharing. The article prioritize security issues for both information and energy interactions in EVCE computing. The authors define blockchain-inspired data coins and energy coins to achieve distributed consensus in context-aware vehicular applications. Finally, security schemes are proposed for cloud and edge computing to set up perspectives on vehicular applications.  
In [60], an edge computing-enabled computation offloading method, named ECO, is proposed. Although the computing tasks in the Internet of connected Vehicles (IoV) paradigm can be offloaded to the edge computing devices (ECDs), the wireless communication for computation offloading increases the risk of privacy leakage, which may lead to virtual vehicle hijacking or tracking, etc. ECO solves this privacy conflict in computation offloading challenge with novel privacy preservation technique for IoV. A genetic algorithm is adopted to realize multi-objective optimization to reduce the execution time and energy consumption of ECDs and prevent privacy conflicts of the computing tasks.  
Integrating fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. The authors of [61] propose a VFC model with three layers. Their model enables distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the authors formalize a VFC-enabled offloading scheme as an optimization problem by leveraging moving and parked vehicles as fog nodes. Finally, the proposed model is validated with a taxi-trajectory-based performance analysis, coming from a real-world scenario.  
In [62], the authors envision connected and autonomous vehicles (CAVs) as sophisticated computers on wheels. CAVs will have on-board sensors as data sources and there will be variety of services running on top to support autonomous driving or other functions. These envisioned novel services, especially the machine learning based applications, are expensive from the computational point of view. The authors propose an Open Vehicular Data Analytics Platform (OpenVDAP) for CAVs, which is a full-stack edge computing based platform including computing/communication unit in the CAVs. Furthermore, in OpenVDAP, an isolation-supported and security and privacy-preserved vehicle operation system is proposed. The authors created an edge-aware application library, which defines how to access and deploy edge-computing based vehicle applications. A novel scheduling strategy is presented, which enables CAVs to dynamically detect each service's status and the optimal offloading destination so that each service could be finished within an acceptable latency and limited bandwidth consumption. OpenVDAP is an open-source platform that offers free APIs and real-world vehicle data to the researchers and developers in the community.