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.

Heterogeneous networks (HetNets) supported with relays and device to device communication can be considered as a promising solution to realize the ambitious targets of the future fifth generation networks in terms of energy efficiency and capacity. HetNets necessitate optimal power allocation, spectrum resource allocation, and cell selection to meet the quality of service requirements. In this paper, we formulate energy efficiency maximization problem in terms of resource allocation and cell selection for HetNets, where objective is to maximize the network throughput per unit network power consumption. Formulated optimization problem is non-linear fractional programming problem. We use Charnes-Cooper transformation to convert proposed fractional programming problem into concave optimization problem. We propose outer approximation algorithm (OAA) to solve the converted concave optimization problem. The proposed algorithm is evaluated by extensive simulation work. The performance of ε-optimal solution obtained by OAA method is shown for different network parameters, such as number of users, required date rate, and capacity of network.

Mobile edge/fog computing is a network architecture that brings the functionality of conventional centralized cloud to the edge nodes which are in close proximity of the end devices in internet of things (IoT) network. Fog networks have many advantages over traditional cloud networks, such as increased bandwidth utilization, enhanced security and privacy, better energy efficiency, improved performance and support for mobility. The most critical requirement of a fog architecture is to minimize the end to end latency in IoT networks, particularly in scenarios where large number of end devices (IoT nodes) and distributed computing fog nodes (cloudlets) are present. In this article, we formulated an optimization problem for joint cloudlet selection and latency minimization in fog network, subject to maximum work load and latency constraints. The problem can be epitomized as many-to-one matching game in which IoT nodes and cloudlets rank each other in order to minimize the latency. The proposed game belongs to a class of matching games with externalities. We propose an algorithm to solve this game which gives distributed and self-organizing solution. Extensive simulations have been carried out to validate the proposed algorithm.