The potential of millimetre wave (mmWave) to provide dependability in 5G cellular networks has prompted the number of wireless communication network users to rise, indicating the progress of telecommunication standards. Despite the fact that the mmWave bands can support massive data rates, the effort required to deliver these data rates for end-to-end services while maintaining ultra-low-latency performance and reliability to support developing applications and use cases will necessitate a re-evaluation of all layers of the protocol stack. As a result, this paper examines millimetre wave characteristics and how they impact end-to-end transmission. In addition, this study examines the difficulties and potential solutions for offering dependable, end-to-end, and ultra-low-latency services in mmWave cellular networks. Aside from comparing mmWave and microwave, this article will also examine scheduling in order to mitigate the negative effects of intermittent connection in mmWave and fully use the capacity. This project's challenge and solution will be modelled using Deep Reinforcement Learning in order to do mathematical modelling and provide delay-propagation characteristics. Finally, the simulation results of physical modelling are provided to demonstrate the effectiveness of the suggested technique. Keywords: Delay, millimetre wave, Scheduling

ABSTRACT Cellular technology with long-term evolution (LTE)-based standards is a promising technology for smart grid communication networks. However, the integration of cellular technology and smart grid communications is a significant challenge due to the transmission of simultaneous and delay-sensitive smart grid data. The Device-to-device (D2D) communications are proposed as critical solutions to enhance the performance of the LTE. In this paper, we study energy efficiency and delay problems in D2D underlaying cellular communications for the future renewable electric energy delivery and management (FREEDM) system in smart grid with different message sizes and varying channel conditions. We adopt a D2D-assisted relaying framework to assist links that meet poor channel conditions in order to increase the data rate of intelligent energy management devices with large size report. We propose a joint power control and mode selection scheme to deal with the problem and develop a brute-force-based algorithm to find the solutions. We conduct simulations to show the effectiveness of the proposed scheme in balancing the tradeoff between energy efficiency and end-to-end delay and show significant energy efficiency improvements when exploiting the proposed scheme compared to direct and relaying schemes in a variety of different conditions. INDEX TERMS Energy efficiency, end-to-end delay, device-to-device communications, cellular networks, smart grids.

A person who experienced micro-sleep tends to have decreasing concentration in their driving and this is one of the reasons why the number of road accidents system was designed to detect the sypmtom of microsleep which is the drowsiness of the driver by putting emphasis on the driver’s eyes and mouth. The system utilized the machine learning algorithm, MATLAB simulator and a camera. The camera is pointed directly towards the driver’s face and when the camera captures features like drooping eyelids and yawning, an alarm will automatically set off to alert the driver and the condition of the driver.

One of the most crucial issues in mobile networks is ensuring reliable and stable connectivity during mobility. In recent years, numerous research has examined Fourth Generation (4G) and Fifth Generation (5G) mobile networks to address issues related to handover (HO) self-optimisation. Different approaches have been developed to identify the best Handover Control Parameters (HCPs) settings, including Time-To-Trigger (TTT) and Handover Margin (HOM), since managing HCP values is a key factor in determining the efficiency and accuracy of handover decisions. The purpose of this work is to address the challenges associated with HO management in Sixth Generation (6G) mobile networks, where a massive number of diverse devices, high mobility, and varying network conditions (e.g., Ultra Dense Network (UDN), Heterogeneous Network (HetNet) and Millimetre Waves (mmWaves)) are established. This, in turn, presents complex HO issues which require solutions that can adapt to different deployment settings. To provide a brief background of mobility management, the paper presents the basic HO concept, HO history, and HO procedure. Additionally, a comparison of HO in 4G to 6G is discussed to gain a better understanding of technology development and its effect on HO solutions. Furthermore, the basics of Mobility Robustness Optimisation (MRO) is explained, which involve HCP values. Previous MRO approaches have made significant advancements; however, they often lack adaptability and robustness to dynamic network conditions. By leveraging Artificial Intelligence (AI) algorithms with MRO techniques, this approach offers an improved solution to optimize handover decisions in a self-optimizing manner, thereby enhancing the overall network performance. To achieve this goal, it is necessary to analyze previous MRO and AI-integrated solutions and make comparisons between them. Additionally, the advantages and disadvantages of these solutions are considered. The contribution of this work lies in identifying the directions for HO self-optimization in 6G deployment. It demonstrates that deploying an AI-based solution would benefit future MRO deployments. This survey will aid in the analysis of mobility management challenges, particularly for the future mobile MRO self optimization implementation in future technologies.

Tipping or depositing large waste onto land using unauthorized and unlicensed methods are considered as illegal dumping. The increasing rate of illegal dumping becomes a crucial nation issue because this activity causes negative impacts to social, economy and environment. Thus, study on detecting the dumping activities is conducted to control the illegal dumping activities in Malaysia. Raspberry Pi with Python language is used as the microprocessor and a Raspberry Pi camera module with a microwave radar sensor are interfaced to it to capture the image of any vehicles entering the illegal dumping site. The image is captured to recognize the license plate of the vehicle. The method in this study is by using Open Automatic License Plate Recognition (ALPR), Open Computer Vision (CV) libraries and Optical Character Recognition (OCR) to detect the character of the plate registration number. The outcome of the study consists of recognition of Malaysia vehicles’ plate number and the automatic real time email notification on the illegal dumping case. The detection system can be used for case monitoring since the plate number recognition is done in real time. The system can be upgraded to ensure its sustainability in the harsh and isolated environment.

The desire for greater bandwidth is a problem for all network operators as the number of devices and bandwidth-hungry applications grows. These requirements cannot be provided by existing 5G cellular connectivity because spectrum resources are few and constrained. The increasing network density and number of devices puts a higher amount of traffic burden on the base station, which must be offloaded in order to decrease latency and satisfy mobile device users. A resource allocation scheduling method may pave the way for the effective use of restricted resources, which fulfils the aim of cellular communication and helps to reduce delay. Thus, proper resource allocation scheduling in Terahertz communication is therefore responsible for reducing latency in a communication system as well as increasing data rate, throughput, and system sum-rate.

The world developed rapidly to provide better standard of living to the human being. Industrial revolution 4.0 promising in great revenue, investment and technological advancement to the society and various sectors. This paper presents an overview of tomography system towards the industrial revolution 4.0. Tomography is essentially a technique for showing an image representation through solid objects, such as a pipeline or the human body, in the two-dimensional and three-dimensional cross sections. Several tomography sensors, including optical tomography systems, ultrasonic tomography systems and an electrical tomography system, are discussed in terms of their hardware and image reconstruction. To provide a clear view of tomography system, a few examples of tomography system application in medical and process industries are discussed.