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Global trade depends on long-haul transportation, yet comfort for drivers on lengthy trips is sometimes neglected. Rough roads have a major negative influence on driver comfort and increase the risk of weariness, distracted driving, and accidents. Using Random Forest regression, a machine learning technique well-suited to examining big datasets and nonlinear relationships, this study examines the relationship between road roughness and driver comfort. Using the MIRANDA mobile application, data were gathered from 1,048,576 rows, including vehicle acceleration and values for the International Roughness Index (IRI). The Support Vector Regression (SVR) and XGBoost models were used for comparative analysis. Random Forest was preferred because of its ability to be deployed in real time and use less memory, even if XGBoost performed better in terms of training time and prediction accuracy. The findings showed a significant relationship between driver discomfort and road roughness, with rougher roads resulting in increased vertical acceleration and lower comfort levels (Road Roughness: SD—0.73; Driver’s Comfort: Mean—10.01, SD—0.64). This study highlights how crucial it is to provide smooth surfaces and road maintenance in order to increase road safety, lessen driver weariness, and promote long-haul driver welfare. These results offer information to transportation authorities and policymakers to help them make data-driven decisions that enhance the efficiency of transportation and road conditions.

The dynamic expressions of emotion convey both the emotional and functional states of an individual’s interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize human emotional states automatically can use various affective signals as inputs, such as visual, vocal and physiological signals. However, emotion recognition via a single modality can be affected by various sources of noise that are specific to that modality and the fact that different emotion states may be indistinguishable. This review examines the current state of multimodal emotion recognition methods that integrate visual, vocal or physiological modalities for practical emotion computing. Recent empirical evidence on deep learning methods used for fine-grained recognition is reviewed, with discussions on the robustness issues of such methods. This review elaborates on the profound learning challenges and solutions required for a high-quality emotion recognition system, emphasizing the benefits of dynamic expression analysis, which aids in detecting subtle micro-expressions, and the importance of multimodal fusion for improving emotion recognition accuracy. The literature was comprehensively searched via databases with records covering the topic of affective computing, followed by rigorous screening and selection of relevant studies. The results show that the effectiveness of current multimodal emotion recognition methods is affected by the limited availability of training data, insufficient context awareness, and challenges posed by real-world cases of noisy or missing modalities. The findings suggest that improving emotion recognition requires better representation of input data, refined feature extraction, and optimized aggregation of modalities within a multimodal framework, along with incorporating state-of-the-art methods for recognizing dynamic expressions.

The concept of integrating cooperative communication in non-orthogonal multiple access (NOMA) based networks (also known as cooperative NOMA) is inspired by the need to enhance network scalability with guaranteed fair resource allocation. However, sustaining the cooperative NOMA (C-NOMA) network lifetime becomes a challenging problem due to the energy bottleneck of the devices acting as relays for weak users. To alleviate this problem, the integration of radio frequency-based energy harvesting technique known as simultaneous wireless information and power transfer (SWIPT) in C-NOMA networks has been proposed. In this article, we present an extensive review of various contributions to the performance analysis of SWIPT-aided C-NOMA networks. To begin with, we identify critical guidelines needed for efficient network design. Moreover, we present an illustration of the system model in order to understand the fundamental relationship among different network components from a technical perspective. Further to this, we categorize and present an in-depth review of notable studies based on the type of relay employed, the number of relays deployed and the type of communication protocol adopted. Finally, we identify open areas worth exploring for future research works.

The emergence of various underwater wireless communication systems has been on the rise due to increasing human activities in the marine environment. In underwater wireless communication networks (UWCNs), several communication devices, such as sensors and autonomous underwater vehicles (AUV) are interconnected to expand communication coverage, monitoring, information gathering, and surveillance. These devices operate on batteries, making their replacement and recharging difficult. Consequently, sustaining the operational lifetime of UWCNs is deemed a major challenge. This leads to the development of various energy harvesting (EH) techniques to perpetuate the power supply to underwater devices. In this paper, we present a review of various energy sources and EH techniques applicable to UWCNs. To achieve this, we classify the energy sources into various categories in order to establish the peculiarities of each source and the type of harvester applicable to each category. Based on this classification, we present discussions on various contributions of articles related to applications of EH techniques in UWCNs. In addition to various insights gained from the presented papers, we establish that energy harvesters based on triboelectric effect, piezoelectric effect, sediment microbial fuel cell, acoustic, and optical power transfer are suitable for low-power (milliwatt-order) consuming devices such as sensors. Also, for devices with high power consumption requirement, such as AUV, solar and inductive power transfer-based harvesters should be employed. Furthermore, we identify several technical challenges that should be taken into consideration during the planning and system design phases. Finally, we highlight open research areas that could further improve the EH and communication processes in UWCNs.

The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.

Smartgrid is a paradigm that was introduced into the conventional electricity network to enhance the way generation, transmission, and distribution networks interrelate. It involves the use of Information and Communication Technology (ICT) and other solution in fault and intrusion detection, mere monitoring of energy generation, transmission, and distribution. However, on one hand, the actual and earlier smartgrid, do not integrate more advanced features such as automatic decision making, security, scalability, self-healing and awareness, real-time monitoring, cross-layer compatibility, etc. On the other hand, the emergence of the digitalization of the communication infrastructure to support the economic sector which among them are energy generation and distribution grid with Artificial Intelligence (AI) and large-scale Machine to Machine (M2M) communication. With the future Massive Internet of Things (MIoT) as one of the pillars of 5G/6G network factory, it is the enabler to support the next generation smart grid by providing the needed platform that integrates, in addition to the communication infrastructure, the AI and IoT support, providing a multitenant system. This paper aim at presenting a comprehensive review of next smart grid research trends and technological background, discuss a futuristic next-generation smart grid driven by artificial intelligence (AI) and leverage by IoT and 5G. In addition, it discusses the challenges of next-generation smart-grids as it relate to the integration of AI, IoT and 5G for better smart grid architecture. Also, proffers possible solutions to some of the challenges and standards to support this novel trend. A corresponding future work will dwell on the implementation of the discussed integration of AI, IoT and 5G for next-generation smart grid, using Matlab, NS2/NS3, Open-daylight and Mininet as soft tools and compare with related literature.

Metaheuristic optimization algorithms (MOAs) are popularly deployed for medical image enhancement (MIE) purposes. However, with an ever-increasing rate of newer MOAs being proposed in the literature, the question arises as to whether there exist any significant advantage(s) among these different MOAs, particularly as it pertains to MIE. In this paper, we explore this question by analyzing nine well-known MOAs for MIE, namely the artificial bee colony, cuckoo search, differential evolution, firefly, genetic algorithm, particle swarm optimization (PSO), covariance matrix adaptive evolutionary strategy (CMAES), whale optimization algorithm (WOA), and the grey wolf optimization (GWO) algorithms. First, instead of measuring an MOA’s performance based on the number of generations, we adopted the fitness computation rate (FCR), which enables MOAs to be compared in a fairer sense. Secondly, we used a combination of a well-known transformation function and a robust evaluation function as our objective function in the MOAs considered in our study. Then, medical images were obtained from the Medpix database with representative samples selected from across the different parts of the body for MIE evaluation purposes. Within the constraints of the datasets used, the results indicate that, while the GWO and WOA algorithms performed slightly better empirically than the other methods over an average of 1000 Monte Carlo trials, there was little/no statistical significant difference between the other methods. The timing performance also demonstrates that there was no significant difference in the real-time processing speeds of the various MOAs, particularly when evaluated under the same FCR. As a consequence, preliminary findings from our study suggest that employing a range of past and current MOAs or proposing newer MOAs for MIE may not necessarily guarantee substantial comparative enhancement benefits. This might suggest that under high FCR levels, any MOA can be utilized for MIE.

In recent years, the emergence of Industry 4.0 technologies has introduced manufacturing disruptions that necessitate the development of accompanying socio-technical solutions. There is growing interest for manufacturing enterprises to embrace the drivers of the Smart Industry paradigm. Among these drivers, human–robot physical co-manipulation of objects has gained significant interest in the literature on assembly operations. Motivated by the requirement for human dyads between the human and the robot counterpart, this study investigates recent literature on the implementation methods of human–robot collaborative assembly scenarios. Using a combination of strings, the researchers performed a systematic review search, sourcing 451 publications from various databases (Science Direct (253), IEEE Xplore (49), Emerald (32), PudMed (21) and SpringerLink (96)). A coding assignment in Eppi-Reviewer helped screen the literature based on ‘exclude’ and ‘include’ criteria. The final number of full-text publications considered in this literature review is 118 peer-reviewed research articles published up until September 2022. The findings anticipate that research publications in the fields of human–robot collaborative assembly will continue to grow. Understanding and modeling the human interaction and behavior in robot co-assembly is crucial to the development of future sustainable smart factories. Machine vision and digital twins modeling begin to emerge as promising interfaces for the evaluation of tasks distribution strategies for mitigating the actual human ergonomic and safety risks in collaborative assembly solutions design.

Visible light communications (VLC) is an emerging and promising concept that is capable of solving the major challenges of 5G and Internet of Things (IoT) communication systems. Moreover, due to the usage of light-emitting diodes (LEDs) in almost every aspect of our daily life VLC is providing massive connectivity for various types of massive IoT communications ranging from machine-to-machine, vehicle-to-infrastructure, infrastructure-to-vehicle, chip-to-chip as well as device-to-device. In this paper, we undertake a comprehensive review of the prospects of implementing VLC for IoT. Moreover, we investigate existing and proposed approaches implemented in the application of VLC for IoT. Additionally, we look at the challenges faced in applying VLC for IoT and offer solutions where applicable. Then, we identify future research directions in the implementation of VLC for IoT.