2 research outputs found
Computing in the sky: A survey on intelligent ubiquitous computing for UAV-assisted 6G networks and industry 4.0/5.0
Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation
paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication
networks. These networks have an avenue for generating a considerable amount of
heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments.
However, storing and processing massive data with limited computational capability
and energy availability at local nodes in the IoT network has been a significant difficulty, mainly
when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the
massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved
as a promising computing paradigm leveraged with efficient technology to improve the quality of
services of edge devices and network performance better than cloud computing networks, addressing
challenging problems of latency and computation-intensive offloading in a UAV-assisted framework.
This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G
networks over smart environments. We highlight the utility of UAV computing and the critical role
of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and
latency of IoT data in smart environments. We present the reader with an insight into UAV computing,
advantages, applications, and challenges that can provide helpful guidance for future research
Machine learning for smart environments in B5G networks: Connectivity and QoS
The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works