# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 5 Challenges with Solutions
### Power Consumption/Energy Efficiency  
Objects are extensively being connected together using the IoT technologies. Heterogeneous smart objects, in the context of hardware and software, can perform efficiently in the availability of memory and high computational power. Network's complexity increases day by day, because of scalability issues of smart objects supporting IIoT applications.  
Virtualization has played a very crucial role in improving the overall efficiency of the data centers and now research has also been done on how virtualization can help in fog networks. Virtualization makes it easiest to deploy fog functionalities on an existing node (by isolating and securing fog services in a virtual machine or container). It also helps in conserving energy by efficiently consolidating the tasks on a single fog node. A comparison in terms of power consumption was studied in [69] between C-RAN and F-RAN using network function virtualization (NFV) technology. The authors have formulated a mixed integer linear programming (MILP) problem, results for F-RAN are 30% more improved in terms of power-saving as compared to C-RAN.  
[PERSON] et al. [70] have developed a platform using Fog-Function Virtualization, which works and builds on the concept of Network Function Virtualization (NFV) for multiple IoT applications. This, further with the help of node constellation creation, helps in easy deployment and reduces the cost considerably as less energy is required for running the system due to efficiently performing virtualization. Task scheduling is necessary as it helps with the load balancing aspect of networking and can provide services to multiple users. Cooperative games between the containers and brokers are studied for energy-efficient task selection algorithm. [PERSON] et al. [71] have achieved efficiency with the help of container-as-a-service (CaaS). Lightweight containers have been used which considerably reduce the energy consumption by a container migration techniques. This kind of virtualization is more cost-efficient for distributed architecture, where a large number of devices with different running applications/processes can be allocated to resources efficiently. Results achieved by the authors prove that the system is more energy-efficient.  
Graph-Based Heuristic algorithms were proposed by the authors in [72]. Type of problem is the integer linear programming (ILP) problem, the objective is to increase the energy efficiency under association and capacity threshold constraints for hybrid cloud-fog RAN (CF-RAN) architecture. Minimization of latency and power consumption is also addressed in the proposed system model. Fog computing along with NFV gives excellent outcomes in terms of reducing latency and power consumption. Energy can be saved by incorporating techniques, such as Message Queue Telemetry Transport (MQTT) in a fog-based environment [22]. In this scheme, the number of transmissions is reduced to save energy of the end devices. Using energy-efficient routing protocols is imperative to achieve energy efficiency. MQTT supports sensor data in real time due to its many-to-many communication nature. The concept of MQTT focuses on introducing another layer between the fog and cloud with lower complexity. MQTT broker place is at the fog layer. The intermediate layer is responsible for predicting the future measurements, and acts as a gateway for the upper layer. It helps to offload the computationally expensive tasks from the cloud to save in the storage memory of the fog server. This results in a reduced number of transmissions as the update only occurs in case of a mismatch.