Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies.

Over the coming years, the foresighted enormous increase in smart devices supporting Internet-of-Things (IoT) applications demand novelty in network design. A promising solution to the ever-increasing low-latency requirement of IoT applications is the development of fog network architecture. However, the presence of an enormous number of smart devices in fog networks affects the performance of the network. To harvest the benefits of fog networking necessitates finding optimal cloudlet selection strategies. This article formulates a mixed-integer non-linear programming (MINLP) problem that has the objective of latency minimization. An exhaustive search on our cache-enabled (CE) fog architecture cannot be applied because of the problem’s combinatorial and NP-hard nature. Similarly, the genetic algorithm (GA) cannot be used to find the solution because of the calculation of the number of generations. The increase in the number of IoT and fog nodes increases the solution search space, hence an Outer Approximation Algorithm (OAA) is proposed to arrive at the solution. Low complexity, convergence, and effectiveness of the proposed algorithm ensures the $\epsilon $ -optimal solution = 10 ⁻³ , obtained through standard problem solvers.

Green fog computing/networking is considered a promising way to get real-time communication for latency-sensitive Internet-of-Things (IoT) applications. Fog networks consist of fog/cloudlet nodes near the vicinity of users, served directly from the remote cloud server. The addition of cloudlet servers within IoT devices access scope gives rise to user interaction and power allocation problems. Within this paper, we have formulated a problem of joint resource and power allocation for fog networks enabled by the cache. The problem formulated is classified under the problem of NP-hard mixed-integer non-linear programming (MINLP). The goal of the proposed problem is to reduce the network's end-to-end energy in terms of overall network latency, under power, cache capacity, and Quality of Service (QoS) limitations. We are proposing the outer approximation algorithm (OAA) to achieve optimal performance of ε. The efficiency of the proposed algorithm is seen in terms of network key performance indicators (KPIs), such as throughput, number of associated devices and minimum criteria for data rates and latency.

Fog networks facilitate ultra-low latency through the use of data availability near the network edge in fog servers. Existing work in fog networks considers the objective of energy efficiency and low latency for internet-of-things (IoT) for resource allocation. These works provide solutions to energy efficiency and low latency resource allocation problem without consideration of secure communication. This article investigates the benefits of fog architecture from the perspective of three promising technologies namely device-to-device (D2D) communication, caching, and physical layer security. We propose security provisioning followed by mode selection for D2D-assisted fog networks. The secrecy rate maximization problem is formulated first, which belongs to mixed-integer nonlinear programming (MINLP) problem. It is NP-hard, that is why an exhaustive search for finding the solution is complex. Keeping in view the complexity, a nonlinear technique namely outer approximation algorithm (OAA) is applied. OAA is a traditional algorithm, whose results are compared with the proposed heuristic algorithm, namely the security heuristic algorithm (SHA). Performance of the network is observed for the different numbers of eavesdroppers, IoT nodes, and fog nodes.