The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization.

With previously unattainable performance metrics like terabit-per-second data rates, extremely low latency, and ubiquitous coverage, the next sixth-generation (6G) wireless communication technology promises to transform connectivity completely. This study highlights the major developments, difficulties, and potential uses of 6G while summarizing the state of research and development at the moment. The core technological foundations of 6G have been investigated, including terahertz (THz) frequency bands, in-band full duplex (IBFD) communication, and artificial intelligence/ML-driven network optimization. Additionally, it has been implied that 6G has the ability to facilitate transformational applications, such as body area networks, extended reality (XR), collaborating robotics (Cobots), smart grid 2.0, autonomous vehicles, intelligent healthcare systems, etc. Major challenges, including ultradense networking (UDN), spectrum allotment, security, and privacy, are evaluated severely. The purpose of this article is to guide future research and build a greater understanding of the revolutionary potential of 6G technology to restructure global communication networks by providing an overview of 6G research and development.

To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network.

The advent of sixth-generation (6G) networks promises revolutionary advancements in wireless communication, marked by unprecedented speeds, ultra-low latency, and ubiquitous connectivity. This research paper delves into the integration of Artificial Intelligence (AI) in 6G network applications, exploring the challenges and outlining future directions for this transformative synergy. The study investigates the key AI technologies for 6G: the potential of AI to optimize network performance, enhance user experience, and enable novel applications in diverse domains and AI-enabled applications. Analyzing the current landscape, the paper identifies key challenges such as scalability, security, and ethical considerations in deploying AI-enabled 6G networks. Moreover, it explores the dynamic interplay between AI and 6G technologies, shedding light on the intricate relationships that underpin their successful integration. The research contributes valuable insights to the ongoing discourse surrounding the convergence of AI and 6G networks, laying the groundwork for a robust and intelligent future communication infrastructure.

Indoor localization methods based on the Wi-Fireceived signal strength indicator (RSSI) ranging technology are sensitive to noise fluctuations and signal attenuations, which could lead to a significant localization error. Therefore, this study proposes an improved indoor localization algorithm using a deep randomized neural network (RandNN) with Wi-Fi-RSSI. We have conducted a real experiment testbed for Wi-Fi RSSI data collection from a complex indoor environment. An improved adaptive unscented Kalman filter (IAUKF) method is used to minimize noise fluctuations and signal attenuations in the raw Wi-Fi RSSI data collection. Moreover, we have investigated the deep RandNN in which the weights and biases of the input hyperparameter are initially randomized to obtain the best localization performance. For convenience, the presented localization model is known as RandNN-IAUKF. Furthermore, real experiments were conducted in a room surrounding working stations, walls, patriation separations, etc., to maximize the complexity of wireless signal propagation. The performance of the presented RandNN-IAUKF algorithm is assessed and compared with other well-known conventional localization approaches. Overall, the experimental results showed that the presented RandNN-IAUKF algorithm provides significant 95% and 67% location estimation errors only at 0.79 m and 1.31 m, respectively, outperforming conventional algorithms by approximately 30% under the same test environment