# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 4 Protocols/Algorithms
### Load Balancing  
When one edge device has to do a lot of work e.g., computing tasks all by itself, it consumes resources. However, this can be reduced with the help of distributed architecture in which load is evenly distributed among the edge devices present in the network. This distribution of load is called load balancing. Load Balancing in combination with fog computing makes a formidable combination. A fog network can provide a platform for cooperation and coordination between edge devices in a load-balanced network. Authors have proposed a load balancing offloading algorithm for latency minimization in Vehicular fog computing (VFC). VFC is an integration of vehicular networks and fog computing technology, which is an efficient field to achieve real-time and location-aware for vehicles in a smart city. [PERSON] et al. in [59] has formulated a response time minimization problem using a three-layered decentralized network system model to balance the traffic among vehicles. These three layers are cloud layer with a high computing cloud data center, cloudlet layer processed the received data from vehicles before sending to the cloud center. Lower layer has vehicle clusters, clusters are made for traffic balancing. The fog-vehicle interface manages and alleviates the traffic load. Traffic overhead results in inefficient resource management, which is also a challenge to be solved for healthcare applications. WSNs based health monitoring systems have become a convenient choice as elderly people can frequently require health services. The basic requirements for healthcare applications are energy efficiency, high-response time, low-latency and real-time connection. Fog computing can be considered to be an important enabling technology for such time-sensitive applications. Authors in [60] provide a very significant critical review of existing solutions provided by researchers in the healthcare domain using fog computing. The authors in [61] proposed a fog-cloud hybrid solution to load-balancing problem. If a client's requirement is more critical, it will be handled by cloud otherwise servicing is done by foglets. Results shows that network utility can be enhanced in terms of latency and load balancing. They used iFogSim tool for experiments. [PERSON] et al. [62] proposed an Energy Balancing Algorithm (EBA) for Fog-IoT networks to reduce delay and energy consumption. The authors have proposed two optimization problems, first one is to find an optimal transmission rate and power for terminal nodes (TNs) and the second one is to find an efficient topology between TNs and fog nodes (FNs). The system model was designed under the constraint of channel conditions between TN and corresponding FN. A major challenge in edge computing is the inefficient deployment of resources as it reduces the overall efficiency of the network. This results in definite increase in power consumption. A low service block is preferred for maintaining lower latency, which will otherwise be detrimental to the high-performance requirements of edge computing. Unlike cloud computing, fog computing has a limitation of resources, which can be overcome if we allow cooperation between various data centers. A cooperative scheme by [PERSON] et al. [63] is proposed in which data centers near the edge exchange processing requests and shares the load of highly loaded data centers. The request arriving at a busy data center is forwarded to any other data center having the request buffer partially filled. The proposed scheme maintains a threshold of requests with the help of Markov Chains to make sure that load is equally distributed among all the data centers. This leads to much-improved performance as lightly loaded data centers can absorb the burden of heavily loaded ones during peak hours. Full use of edge resources cannot be done by cloud computing polymerization calculation [64]. The edge devices are not part of the cloud computing, which is undesirable for delay-sensitive requirements. [PERSON] et al.