Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

Performance Analysis of a Two-Hop MIMO Mobile-to-Mobile via Stratospheric-Relay Link Employing Hierarchical Modulation

Next generation wireless communication networks intend to take advantage of the integration of terrestrial and aerospace infrastructures. Besides, multiple-input multiple-output (MIMO) architecture is the key technology, which has brought the wireless gigabit vision closer to reality. In this direction, high-altitude platforms (HAPs) could act as relay stations in the stratosphere transferring information from an uplink to a downlink MIMO channel. This paper investigates the performance of a novel transmission scheme for the delivery of mobile-to-mobile (M-to-M) services via a stratospheric relay. It is assumed that the source, relay, and destination nodes are equipped with multiple antennas and that amplify-and-forward (AF) relaying is adopted. The performance is analyzed through rigorous simulations in terms of the bit-error rate (BER) by using a recently proposed 3D geometry-based reference model in spatially correlated flat-fading MIMO channels, employing a hierarchical broadcast technique and minimum mean square error (MMSE) receivers

A Review on Software-Based and Hardware-Based Authentication Mechanisms for the Internet of Drones

During the last few years, a wide variety of Internet of Drones (IoD) applications have emerged with numerous heterogeneous aerial and ground network elements interconnected and equipped with advanced sensors, computation resources, and communication units. The evolution of IoD networks presupposes the mitigation of several security and privacy threats. Thus, robust authentication protocols should be implemented in order to attain secure operation within the IoD. However, owing to the inherent features of the IoD and the limitations of Unmanned Aerial Vehicles (UAVs) in terms of energy, computational, and memory resources, designing efficient and lightweight authentication solutions is a non-trivial and complicated process. Recently, the development of authentication mechanisms for the IoD has received unprecedented attention. In this paper, up-to-date research studies on authentication mechanisms for IoD networks are presented. To this end, the adoption of conventional technologies and methods, such as the widely used hash functions, Public Key Infrastructure (PKI), and Elliptic-Curve Cryptography (ECC), is discussed along with emerging technologies, including Mobile Edge Computing (MEC), Machine Learning (ML), and Blockchain. Additionally, this paper provides a review of effective hardware-based solutions for the identification and authentication of network nodes within the IoD that are based on Trusted Platform Modules (TPMs), Hardware Security Modules (HSMs), and Physically Unclonable Functions (PUFs). Finally, future directions in these relevant research topics are given, stimulating further work

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods

Handover Management in 5G Vehicular Networks

Fifth-Generation (5G) vehicular networks support novel services with increased Quality of Service (QoS) requirements. Vehicular users need to be continuously connected to networks that fulfil the constraints of their services. Thus, the implementation of optimal Handover (HO) mechanisms for 5G vehicular architectures is deemed necessary. This work describes a scheme for performing HOs in 5G vehicular networks using the functionalities of the Media-Independent Handover (MIH) and Fast Proxy Mobile IPv6 (FPMIP) standards. The scheme supports both predictive and reactive HO scenarios. A velocity and alternative network monitoring process prepares each vehicle for both HO cases. In the case of predictive HO, each time the satisfaction grade of the vehicular user drops below a predefined threshold, the HO is initiated. On the other hand, in the case of reactive HO, the vehicle loses the connectivity with its serving network and connects to the available network that has obtained the higher ranking from the network selection process. Furthermore, the HO implementation is based on an improved version of the FPMIPv6 protocol. For the evaluation of the described methodology, a 5G vehicular network architecture was simulated. In this architecture, multiple network access technologies coexist, while the experimental results showed that the proposed scheme outperformed existing HO methods

ANN-Based Control of a Multiboat Group for the Deployment of an Underwater Sensor Network

Underwater sensor networks (USNs) can be used for several types of commercial and noncommercial applications. However, some constraints resulting from the nature of aquatic environments severely limit their use. Due to constraints such as large propagation latency, low-bandwidth capacity, and short-distance communications, a large number of USN nodes are deployed to provide reliability in most applications. In this study, an unattended deployment approach based on the use of an autonomous boat group is proposed. A map of the deployment zone and optimal locations of USN nodes are fed into the onboard computers of the boat group. After processing these data and determining paths to be followed, the boat group deploys sensor nodes at predetermined locations. During the deployment, the boat group is controlled by an artificial neural network- (ANN-) based control system for reducing path errors. A set of performance evaluations is given to prove efficiency of the proposed control system. Performance results show that the boat group can successfully follow a predefined path set and deploy USN nodes. The tradeoffs between energy consumptions, end-to-end delay, and number of hops between underwater relay nodes of energy-efficient USN are also examined. The results indicate that increasing the number of hops reduces the total energy consumption and the end-to-end delay

A Group Handover Scheme for Supporting Drone Services in IoT-Based 5G Network Architectures

Next generation mobile networks are expected to integrate multiple drones organized in Flying Ad Hoc Networks (FANETs) to support demanding and diverse services. The highly mobile drones should always be connected to the network in order to satisfy the strict requirements of upcoming applications. As the number of drones increases, they burden the network with the management of signaling and continuous monitoring of the drones during data transmission. Therefore, designing transmission mechanisms for fifth-generation (5G) drone-aided networks and using clustering algorithms for their grouping is of paramount importance. In this paper, a clustering and selection algorithm of the cluster head is proposed together with an efficient Group Handover (GHO) scheme that details how the respective Point of Access (PoA) groups will be clustered. Subsequently, for each cluster, the PoA elects a Cluster Head (CH), which is responsible for manipulating the mobility of the cluster by orchestrating the handover initiation (HO initiation), the network selection, and the handover execution (HO execution) processes. Moreover, the members of the cluster are informed about the impending HO from the CH. As a result, they establish new uplink and downlink communication channels to exchange data packets. In order to evaluate the proposed HO scheme, extensive simulations are carried out for a next-generation drone network architecture that supports Internet of Things (IoT) and multimedia services. This architecture relies on IEEE 802.11p Wireless Access for Vehicular Environment (WAVE) Road Side Units (RSUs) as well as Long-Term Evolution Advanced (LTE-A) and IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX). Furthermore, the proposed scheme is also evaluated in a real-world scenario using a testbed deployed in a controlled laboratory environment. Both simulation and real-world experimental results verify that the proposed scheme outperforms existing HO algorithms

A Survey on Machine-Learning Techniques for UAV-Based Communications

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security