# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 1 Introduction
Practically, we are beginning to understand the connection between and the enormous number of IIoT devices and a single cloud server. However, cloud-based systems are unable to meet the requirement such as heavy data computation, real-time device control, security and management results in insufficient support of IIoT application requirements [11]. Considering a wide variety of IoT scenarios, some of the challenges [10; 11; 12; 13; 14; 15; 16] in cloud computing are listed below:  
* Large distance between the cloud and edge devices causes propagation and transmission delays. * Large computational load on a single cloud server causes processing and queuing delays. * Increased number of smart devices has hindered meeting the bandwidth requirements. * Enormous number of smart devices will bring scalability, speed, and computational issues. * Wireless medium between cloud and smart devices brings resource management issues. * Heterogeneity property of smart devices in terms of accessing technology will bring difficulty in handling at the cloud. * Mobility of IoT devices bring service availability issues, cloud server may not be able to provide services due to network congestion and failure. * Security is a very critical thread, as the cloud is exposed to the whole world over the public internet
* Computing offloading every-time at cloud causes a loss in energy and battery lifetime. * Although data storage at cloud brings benefits to application developers, they should be careful of integrity and authentication demands of IIoT applications. * Cloud computing is a centralized and complex architecture for real-time applications of IIoT. All these limitations require a change, how and where we process data. These challenges motivate us to explore new decentralized approaches/solutions in IIoT domains. A new concept of fog computing is introduced by [PERSON] et al. [15] for handling data locally at the network edge in order to overcome the limitations of cloud architecture. Fog computing complements existing cloud architecture and has addressed the issue of latency and bandwidth efficiency [17]. Because of its distributed architecture, it calls for a strong check on QoS requirements to make it useful. Fog is mainly based on distributed networking with ubiquitous pervasive computing. It comprises small scale data centers or a group of computers known as cloudlets (fog clouds) that provide services to devices located in close proximity [17; 18]. The initial installation cost, latency, and energy consumption is far less as compared to that of the cloud, but the operational cost varies. Fog architecture can leverage computations either from dedicated edge servers or adhoc infrastructure. For promoting IIoT architecture with fog computing as a key enabler technology, a group of fog clouds can also be used. In fog computing, data processing in single server (fog cloud) helps in achieving real-time and reliable communication. It puts the safety and security of personal data back into our premises. Furthermore, a cost effective approach can be used in fog computing such that data transmission and storage fees can be reduced based on service premises. Therefore, fog computing has the potential to provide affordable solutions for large IIoT projects. Instead of being restricted to only one expensive cloud connection, fog computing gives the freedom to choose any hardware from Information Technology (IT) solutions. It supports all existing legacy devices and non-IIoT devices that never intended to be the part of IIoT application. This is not only economical but also more flexible. When it comes to speed, fog computing allows real-time processing and supports to process data as fast as our local system. Fog can be managed securely from remote places. It can be scaled and updated dynamically. It gives more security, better performance, and lower costs. Fog incorporates positive attributes of cloud and provides benefits that may support future IIoT applications [18; 19; 20; 21; 22; 23].