The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research.

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.

Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

Urban centers across the globe are under immense environmental distress due to an increase in air pollution, industrialization, and elevated living standards. The unmanageable and mushroom growth of industries and an exponential soar in population has made the ascent of air pollution intractable. To this end, the solutions that are based on the latest technologies, such as the Internet of things (IoT) and Artificial Intelligence (AI) are becoming increasingly popular and they have capabilities to monitor the extent and scale of air contaminants and would be subsequently useful for containing them. With centralized cloud-based IoT platforms, the ubiquitous and continuous monitoring of air quality and data processing can be facilitated for the identification of air pollution hot spots. However, owing to the inherent characteristics of cloud, such as large end-to-end delay and bandwidth constraint, handling the high velocity and large volume of data that are generated by distributed IoT sensors would not be feasible in the longer run. To address these issues, fog computing is a powerful paradigm, where the data are processed and filtered near the end of the IoT nodes and it is useful for improving the quality of service (QoS) of IoT network. To further improve the QoS, a conceptual model of distributed fog computing and a machine learning based data processing and analysis model is proposed for the optimal utilization of cloud resources. The proposed model provides a classification accuracy of 99% while using a Support Vector Machines (SVM) classifier. This model is also simulated in iFogSim toolkit. It affords many advantages, such as reduced load on the central server by locally processing the data and reporting the quality of air. Additionally, it would offer the scalability of the system by integrating more air quality monitoring nodes in the IoT network.

In this paper, we aim to present a cooperative relaying based two way wireless communication scheme which can provide both spectral and energy efficiency in future wireless networks. To this end, we propose a novel network coding based Dynamic Spectrum Leasing (DSL) technique in which the cognitive secondary users cooperatively relay the primary data for two-way primary communication. In exchange for the relaying services, the primary grants exclusive access to the secondary users for their own activity. We model the random geometry of the ad hoc secondary users using a Poisson point process. We devise a game theoretic framework for the division of leasing time between the primary cooperation and secondary activity phases. We demonstrate that under these considerations and employing network coding, DSL can improve the number of bits that are successfully transmitted by 54% as compared to un-coded direct two way primary communication. Also the energy costs of the proposed DSL scheme are more than 10 times lower. Employing DSL also enables the cognitive users to get reasonable time for their own transmission after increasing the primary spectral and energy efficiency.