# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 5 Challenges with Solutions
### Throughput/Rate/Capacity  
For network designing, a new paradigm known as Socially-Aware-Networking (SAN) has been of major interest [73]. To achieve efficient performance, SAN brings the human behavior and CPS together via intelligently designing of a network. This design should be adaptable as well for all environments. The resources available to mobile devices differ depending on the models and specifications. This resource availability results in a group of mobile devices that might be sufficient in terms of processing and storage parameters. Group of some might not be self-sufficient. The best solution to this problem is given by SAN and Fog-Radio Access Networking (F-RAN).  
D2D communication comprises the direct sharing of contents among mobile devices. Direct sharing is a key feature supporting D2D communication. For achieving efficient performance results, an imperative design of network embedded with all technologies is required. Research has shown that the system performance in terms of utility, throughput and energy efficiency is maximized using the download mode with the help of branch and bound algorithm [74]. [PERSON] et al. [75] and [PERSON] et al. [76] worked on improving network efficiency Mobile Edge Computing (MEC). MEC is a domain under fog and focuses on providing cloud computing capabilities near the edge of network. Efforts and improvements have been made in MEC in order for it to support 5G communications. Another target of MEC is to achieve access to information provided by the radio network for application development and distribution of the content. It is predicted that data traffic over mobile devices will increase manifold in the next few years. Such increasing needs must be satisfied using efficient mechanisms.  
A reliable service with an extremely low latency (URLLC) and high capacity is the foremost requirement of IIoT networks. Throughput maximization of the F-RAN system is compared with three different back-haul strategies for Small Cell Networks (SCN) in [77]. All these strategies namely, decode and forward, direct transmission and C-RAN were studied under delay threshold, rate constraint, and backhaul and fronthaul links. The authors proposed iterative algorithms for all strategies. For F-RAN, [PERSON] et al. in [78] formulated a non-convex optimization problem under fronthaul constraints. A hybrid semi-distributed resource allocation algorithm was proposed by the authors for the proposed weighted sum-rate maximization problem. Results show that there is a trade-off between maximum throughput and system latency. A multi-objective optimization problem was proposed by the authors in [79]. They have proposed three parallel algorithms to improve latency, throughput and resource management. A queuing model was studied under task buffering, offloading and resource allocation algorithms. Lyapunov drift was used by the authors to design the resource allocation policy. In results for better system performance, trade-off between latency and throughput is observed. [80] end-to-end performance is guaranteed after composition of problem as Multi-Constrained Optimal Path (MCOP). The authors propose a solution from the network architecture's perspective and cloud service relation. The proposed algorithm provides better results in terms of efficiency and effectiveness. QoS parameters (capacity, delay and cost) gets improved by the proposed network-cloud service provisioning system model.