The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

Smart agriculture techniques have recently seen widespread interest by farmers. This is driven by several factors, which include the widespread availability of economically-priced, low-powered Internet of Things (IoT) based wireless sensors to remotely monitor and report conditions of the field, climate, and crops. This enables efficient management of resources like minimizing water requirements for irrigation and minimizing the use of toxic pesticides. Furthermore, the recent boom in Artificial Intelligence can enable farmers to deploy autonomous farming machinery and make better predictions of the future based on present and past conditions to minimize crop diseases and pest infestation. Together these two enabling technologies have revolutionized conventional agriculture practices. This survey paper provides: (a) A detailed tutorial on the available advancements in the field of smart agriculture systems through IoT technologies and AI techniques; (b) A critical review of these two available technologies and challenges in their widespread deployment; and (c) An in-depth discussion about the future trends including both technological and social, when smart agriculture systems will be widely adopted by the farmers globally.

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience. The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources, including AI-based chatbots. Session layer enhancements are also required, such as AI-based online proctoring and user authentication using Biometrics. These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms. It also needs the support of the Networking layer for IP-based geolocation features, the Virtual Private Network (VPN) feature, and the support of Software-Defined Networks (SDN) for optimum Quality of Service (QoS). Finally, in addition to these, non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Constant video surveillance of remote locations in rural areas, dense urbanized dwellings and strategic military sites is of paramount interest to law enforcing agencies. Aerial monitoring is now made possible by using dozens of camera mounted UAVs (Unmanned Aerial Vehicles) streaming video from different vantage points to a single command and control center. In this paper, we present an architecture for closed circuit monitoring of such sites, which may consist of multiple indoor and outdoor vantage points by using the legacy 4G LTE wireless network cellular infrastructure in and around the buildings consisting of several outdoor macro cells and indoor femto cells to build a closed-circuit monitoring framework for streaming real time video. Such a framework would lead to timely action in crime or disaster prevention. We investigate the technical performance of such a video streaming system using the metrics of throughputs, loss rates and delay in relation to the physical aspects of wireless propagation; multipath propagation loss, shadowing and fading models.

The recent race for autonomous or ‘driverless’ vehicles, has spawned a lot of research in the area of Internet of Autonomous Vehicles (IAVs). With the advent of the latest technology fueled by Artificial Intelligence and Machine Learning, Autonomous Vehicles (AVs) can now determine the best possible route to a destination based on the current traffic situation and take dynamic driving decisions accordingly, while preventing accidents. Field trials for single autonomous vehicles have been largely successful. However, as more autonomous vehicles will be added to the intelligent transport networks, current research is now centered around their synergistic coexistence in the offering of network-centric and user-centric services. This development is governed by borrowing several concepts from the legacy Internet to address the problems of IAVs. In this paper, we present an extensive overview of the research challenges in the IAVs. Moreover, our contributions in this paper are that (i) We show how the network-oriented cooperative client-server model will give way to a more unorthodox and ‘selfish’ decentralized and peer-to-peer (P2P) model, for example in the offering of navigation services on the IAV. (ii) We discuss how centralized architecture will give way to more distributed architectures for real-time information propagation over the IAV. (iii) We discuss how network-centric policies will begin to shift to user-centric under more beneficial revenue models by offering network-assisted quality of service (QoS) provisioning. (iv) We discuss in detail how vehicle traffic grooming in the IAV would present as much of a challenge as in the legacy Internet. (v) We discuss the disruptive role of value-added services on the IAV, and (iv) Finally, we discuss the problem of cyber threats in the IAV just as in the legacy Internet.