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.

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.

Supporting ultra-reliable and low-latency communications (URLLC) is key for vehicular traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service (QoS)-aware resource allocation framework for vehicle-to-vehicle (V2V) communication is proposed. The proposed scheme incorporates physical proximity and traffic demands of vehicles to minimize the total transmission power over the allocated resource blocks (RBs) under reliability and queuing latency constraints. A Lyapunov framework is used to decompose the power minimization problem into two interrelated sub-problems: RB allocation and power optimization. To minimize the overhead introduced by frequent information exchange between vehicles and the roadside unit (RSU), the resource allocation problem is solved in a semi-distributed fashion. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles into disjoint zones based on mutual interference. Second, a per-zone matching game is proposed to allocate RBs to each vehicle user equipment (VUE) based on vehicles’ traffic demands and their latency and reliability requirements. In the formulated one-to-many matching game, VUEs pairs and RBs rank one another using preference relations that capture both the queue dynamics and interference. To solve this game, a semi-decentralized algorithm is proposed using which the VUEs and RBs can reach a stable matching. Finally, a latency- and reliability-aware power allocation solution is proposed for each VUE pair over the assigned subset of RBs. Simulation results for a Manhattan model show that the proposed scheme outperforms a state-of-art baseline and reaches up to 45% reduction in the queuing latency and 94% improvement in reliability.

This paper presents an experimental realization of multiband 5G new radio (NR) optical front haul (OFH) based radio over fiber (RoF) system using digital predistortion (DPD). A novel magnitude-selective affine (MSA) based DPD method is proposed for the complexity reduction and performance enhancement of RoF link followed by its comparison with the canonical piece wise linearization (CPWL), decomposed vector rotation method (DVR) and generalized memory polynomial (GMP) methods. Similarly, a detailed study is shown followed by the implementation proposal of novel neural network (NN) for DPD followed by its comparison with MSA, CPWL, DVR and GMP methods. In the experimental testbed, 5G NR standard at 20 GHz with 50 MHz bandwidth and flexible-waveform signal at 3 GHz with 20 MHz bandwidth is used to cover enhanced mobile broad band and small cells scenarios. A dual drive Mach Zehnder Modulator having two distinct radio frequency signals modulates a 1310 nm optical carrier using distributed feedback laser for 22 km of standard single mode fiber. The experimental results are presented in terms of adjacent channel power ratio (ACPR), error vector magnitude (EVM), number of estimated coefficients and multiplications. The study aims to identify those novel methods such as MSA DPD are a good candidate to deploy in real time scenarios for DPD in comparison to NN based DPD which have a slightly better performance as compared to the proposed MSA method but has a higher complexity levels. Both, proposed methods, MSA and NN are meeting the 3GPP Release 17 requirements.

Ever necessity of tremendous data traffic and mas-sive deployment of industrial Internet-of-Things (IIoT) devices operating in higher bands, i.e., millimeter-wave (mmWave) and terahertz (THz), have encouraged academia and industry to transition towards the future sixth-generation (6G) wireless communication networks. Nevertheless, the recent emergence of 6G enablers, i.e., reflecting intelligent surface (RIS) and massive multiple-input multiple output (mMIMO), has the ability to po-tentially change the previous paradigm of indoor mm Wave wire-less communication by modifying the propagation environment. It can control and establish the favorable and tunable wireless channel responses by exploiting the multipath and diversity of the propagation environment. Therefore, this next cutting-edge technology is capable of massively improving the performance of mmWave-mMIMO-enabled IIoT data transmissions, making it a feasible solution for 6G networks. In this paper, we propose a RIS-assisted mmWave-mMIMO for a multi-cells indoor factory propagation environment, which provides, 1) aid in mitigating the impact of radio frequency (RF) interference from interferes in closed vicinity (i.e., neighbor cells) by employing metasurface laminated walls, and 2) increased the mmWave-mMIMO sys-tem performance by controlling and tuning the indoor factory channel conditions. Our results indicate that the proposed RIS-assisted mmWave-mMIMO system outperforms the benchmark link capacity performance in the presence of interference

Artificial intelligence has transformed multiple input multiple output (MIMO) technology into a promising candidate for six-generation networks. However, several interference signals impact the data transmission between various antennas; therefore, sophisticated signal detection techniques are required at the MIMO receiver to estimate the transmitted data. This paper presents an optimized AI-based signal detection technique called AIDETECT for MIMO systems. The proposed AIDETECT network model is developed based on an optimized deep neural network (DNN) architecture, whose efficiency lies in its lightweight network architecture. To train and test the AIDETECT network model, we generate and process the data in a suitable form based on the transmitted signal, channel information, and noise. Based on this data, we calculate the received signal at the receiver end, where the received signal and channel information were integrated into the AIDETECT network model to perform reliable signal detection. Simulation results show that at a 20-dB signal-to-noise ratio (SNR), the proposed AIDETECT technique achieves between 97.33% to 99.99% better performance compared to conventional MIMO detectors and is also able to accomplish between 25.34% to 99.98% better performance than other AI-based MIMO detectors for the considered performance metrics. In addition, due to lightweight network architecture, the proposed AIDETECT technique has also achieved much lower computational complexity than conventional and AI-based MIMO detectors.