The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.

This paper focuses on downlink power allocation for a cognitive radio-based non-orthogonal multiple access (CR-NOMA) system in a femtocell environment involving device-to-device (D2D) communication. The proposed power allocation scheme employs the greedy asynchronous distributed interference avoidance (GADIA) algorithm. This research aims to optimize the power allocation in the downlink transmission, considering the unique characteristics of the CR-NOMA-based femtocell D2D system. The GADIA algorithm is utilized to mitigate interference and effectively optimize power allocation across the network. This research uses a fairness index to present a novel fairness-constrained power allocation algorithm for a downlink non-orthogonal multiple access (NOMA) system. Through extensive simulations, the maximum rate under fairness (MRF) algorithm is shown to optimize system performance while maintaining fairness among users effectively. The fairness index is demonstrated to be adaptable to various user counts, offering a specified range with excellent responsiveness. The implementation of the GADIA algorithm exhibits promising results for sub-optimal frequency band distribution within the network. Mathematical models evaluated in MATLAB further confirm the superiority of CR-NOMA over optimum power allocation NOMA (OPA) and fixed power allocation NOMA (FPA) techniques.

The channel assignment problem is a complex problem which requires that under certain constraints a minimum number of channels have to be assigned to mobile calls in the wireless mobile system. In this paper, we propose a new scheme, which is based on double band frequency and channel borrowing strategy. The proposed scheme takes into account factors such as limited bandwidth of wireless networks and the capacity of underlying servers involved in processing mobile calls. It aims to ensure end-to-end performance by considering the characteristics of mobile devices. This is achieved by determining the position of users (or mobile stations) in wireless mobile systems. The proposed scheme is simulated in order to investigate its efficiency within a specific area of a large city in Saudi Arabia. Experimental results demonstrate that the proposed scheme significantly improves the performance of mobile calls as well as reduces the blocking when the number of mobile call increases.

Future 6^th Generation (6G) networks will rely on Terahertz (THz) wireless communication as their main enabler for delivering both ultra-high data speed and minimal delay. THz wireless systems become crucial for upcoming communications by using Unmanned Aerial Vehicles (UAVs) together with Intelligent Reflecting Surfaces (IRS) while improving reliability and efficiency. In UAV-IRS-assisted networks, minimizing mission completion time and energy consumption is critical. However, achieving rapid mission execution often requires UAVs to operate at higher speeds, increasing energy usage and creating a trade-off that demands optimization. This paper addresses the challenge of optimizing UAV-IRS trajectories in THz networks to reduce mission time while adhering to energy constraints. Given the non-convex and NP-hard nature of the problem, traditional optimization methods are insufficient. To tackle this, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which provides an efficient, low-complexity solution for trajectory optimization. MADRL dynamically adapts UAV-IRS paths, balancing mission efficiency and energy savings. Simulation results demonstrate that the proposed MADRL-based approach outperforms existing benchmarks, achieving shorter mission times and near-optimal energy consumption across varying scenarios. By leveraging cooperative learning, the algorithm effectively handles complex environments with multiple users and IRS elements. This work highlights the potential of MADRL for UAV-IRS trajectory optimization, offering a scalable solution for energy-efficient and high-performance THz communication systems.

This chapter provides an overview of the diverse applications of ML in UAVs for object and people detection and tracking. It begins by examining the current landscape of ML-driven UAV technologies and their potential. The related work section discusses the advancements in object and people detection and tracking. The subsequent sections delve into the technical aspects, focusing on the next generation of UAV convolutional neural network (CNN) backbones, including the contextual multi-scale region-based CNN (CMSRCNN), single shot multibox detector (SSD), and you only look once (YOLO), highlighting their significance in enhancing detection capabilities. Furthermore, it explores practical applications of ML in UAVs, encompassing object and people detection and tracking, path planning, navigation, and image and video analysis. Challenges and complexities in vision-based UAV navigation are addressed. Additionally, it showcases the potential for UAV networks to locate objects in real time. Request access from your librarian to read this chapter's full text.

Terahertz (THz) communication networks, combined with Intelligent Reflecting Surfaces (IRS), hold significant potential for advancing the capabilities of Sixth-Generation (6G) wireless systems. The integration of these technologies offers opportunities for ultra-fast data transmission and substantial improvements in network capacity, meeting the growing demand for higher bandwidth and faster connectivity. This study investigates the use of flying IRS-assisted Unmanned Aerial Vehicles (UAVs) within a THz communication framework, aiming to overcome challenges in environments with complex signal propagation, such as urban settings. To facilitate this integration, we introduce the Fly-IRS algorithm, designed to optimize key aspects of network performance, including user device grouping, IRS phase shifts, and UAV positioning. These factors are critical for maximizing data transmission rates, boosting network capacity, and minimizing service disruptions that affect user experience. Our approach leverages a combination of Multi-Agent Deep Reinforcement Learning (MADRL) and Particle Swarm Optimization (PSO) to effectively handle user grouping, IRS phase shift optimization, and UAV trajectory planning. By merging the strengths of both techniques, the Fly-IRS algorithm enhances system adaptability and efficiency. Simulation results demonstrate significant improvements in data rates, with the Fly-IRS algorithm delivering up to 10% better performance compared to the MADRL method alone. These findings highlight the transformative potential of integrating THz communication networks with IRS and UAV technologies, emphasizing their ability to shape future wireless communication systems. This innovation could lead to more reliable, high-capacity networks, capable of addressing the advanced requirements of 6G, offering improved service quality even in difficult environments.