# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 4 Protocols/Algorithms
### Resource Allocation  
One way to meet the growing IIoT application requirements is to use fog computing. Interactions in real time takes place in fog application rather than batch processing. Services supported by fog include mobility, heterogeneity, working with cloud to extend cloud services, user optimization, etc. Resource allocation or resource management in any network is one of the biggest challenges. This defines new protocols for networking and communication. Many researchers have done work in this context and already proposed some protocols. Resource allocation was done with a specific defined objective, it can be maximizing energy efficiency or throughput; minimizing latency or power consumption or network cost. A joint min-max optimization problem of resource allocation and offloading decision making was proposed by the authors in [51]. They have proposed Computation-Offloading-Decision-Making and Resource-Allocation Algorithm (CORA) to minimize the maximal network cost (delay & energy consumption). Results were explained using fractional programming theory and lagrangian dual decomposition. Fog Radio Access Networks (F-RAN), an extension of the Services provided by Cloud Radio Access Network (C-RAN), has gathered attention globally and several advantages can be taken by providing functions of baseband signal processing near the edge or making the edge devices cache-enabled. F-RAN is the best example in which we are using edge devices as well as network devices (access points) as fog nodes to achieve the best results. Moreover, due to the cooperative communication, benefits of C-RAN such as enhanced spectral and energy efficiency are also conserved in F-RAN. Resource allocation is a challenge for the upcoming F-RAN. Authors in [52] have proposed a Stackelberg equilibrium (SE) for a hierarchical problem of network slicing customization between global radio resource manager (GRRM) and local radio resource managers (LRRMs). A game is formulated to alleviate the burdens in GRRM and LRRM; GRRM assigned resources to each slice and then LRRM in every slice provides resources to UEs. UEscould be divided into clusters on basis of respective objective functions and accordingly get resources. Authors have provide two algorithms for LRRM 1 slice and LRRM 2 slice with an objective function of maximizing high data rate and minimizing latency, respectively.  
To meet low latency, high throughput and connectivity requirements in future RANs, non-orthogonal multiple access (NOMA) is a promising technique. [PERSON] et al. in [53] have formulated a problem for maximizing the net utility under interference constraint for resource allocation problem for NOMA-based FRAN system model. Results were compared with the conventional orthogonal frequency division modulation (OFDM) technique. [PERSON] and [PERSON] [54] discuss platforms involving fog computing in which there is communication going on between the smart devices, fog, and cloud. A model for internet applications in future is presented by [PERSON] et al. [55] in which applications are delay-sensitive and distributed geographically. For improving the rendering performance of a webpage, [PERSON] et al. [56] exploit information available only at the network's edge. Companies that want to deliver content such as Netflix uses fog computing to reach their geographically distributed customers. As indicated by [57], ensuring significantly large streamed data to be delivered in the proximity of the end-user (customer), is done using fog computing.  
Minimizing the energy consumed in geographically distributed applications and resource allocation using fog computing is discussed in [58] for video services. Size of fog nodes deployment shows the application demand in that region. To maximize social welfare, some of the user data needs to be controlled. Optimization on large scale is also possible using proximal and distributed algorithms. The algorithm proposed gives a near-optimal solution.