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.

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Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios

In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”.

https://www.mdpi.com/1424-8220/20/9/2533/pdf

Edge Machine Learning for AI-Enabled IoT Devices: A Review

Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review

The sixth-generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air–space–ground integrated network Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as an aerial base station (BS) and supporting IoT deployment in remote and disaster areas A UAV-supported clustered nonorthogonal multiple access (C-NOMA) system is put forward in this article Specifically, the UAV provides services to IoT terminals as an aerial BS based on the wireless-powered communication (WPC) technique According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints As the formulated problem suffers nonconvexity and complication, an efficient iterative algorithm is proposed to address it First, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on the Lagrange multiplier and bisection method is proposed Then, for a fixed clustering state and subslot duration, we optimize the UAV trajectory Finally, we solve these two subproblems alternatively until the objective function converges The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results

UAV-Supported Clustered NOMA for 6G-Enabled Internet of Things: Trajectory Planning and Resource Allocation

Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have opened a revolutionized way for mobile crowdsensing (MCS). Conventional AI models, built on aggregation of UAVs’ sensing data (typically contain private and sensitive user information), may arise severe privacy and data misuse concerns. Federated learning, as a promising distributed AI paradigm, has opened up possibilities for UAVs to collaboratively train a shared global model without revealing their local sensing data. However, there still exist potential security and privacy threats for UAV-assisted crowdsensing with federated learning due to vulnerability of central curator, unreliable contribution recording, and low-quality shared local models. In this paper, we propose SFAC, a s ecure f ederated learning framework for U A V-assisted M C S. Specifically, we first introduce a blockchain-based collaborative learning architecture for UAVs to securely exchange local model updates and verify contributions without the central curator. Then, by applying local differential privacy, we design a privacy-preserving algorithm to protect UAVs’ privacy of updated local models with desirable learning accuracy. Furthermore, a two-tier reinforcement learning-based incentive mechanism is exploited to promote UAVs’ high-quality model sharing when explicit knowledge of network parameters are not available in practice. Extensive simulations are conducted, and the results demonstrate that the proposed SFAC can effectively improve utilities for UAVs, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes.

Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

The Internet of Things is a novel paradigm with access to wireless communication systems and artificial intelligence technologies, which is considered to be applicable to a variety of promising fields and applications. Meanwhile, the development of the fifth-generation cellular network technologies creates the possibility to deploy enormous sensors in the framework of the IoT and to process massive data, challenging the technologies of communications and data mining. In this article, we propose a novel paradigm, 5G Intelligent Internet of Things (5G I-IoT), to process big data intelligently and optimize communication channels. First, we articulate the concept of the 5G I-IoT and introduce three major components of the 5G I-IoT. Then we expound the interaction among these components and introduce the key methods and techniques based on our proposed paradigm, including big data mining, deep learning, and reinforcement learning. In addition, an experimental result evaluates the performance of 5G I-IoT, and the effective utilization of channels and QoS have been greatly improved. Finally, several application fields and open issues are discussed.

From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies

Faced with constant increasingly complicated communication network architecture issues and blossoming traffic demand over wireless systems, it is still insufficient for dynamic spectrum resource allocation in the fifth generation (5G) networks if cognitive radio (CR) technology lacks intelligence. For an efficient real-time process, a distributed cognitive cellular network is proposed in this article, which integrates artificial intelligence and CR technology into a sophisticated multi-agent system (MAS). It is a novel paradigm for 5G cellular communication networks. Balancing the resource allocation among primary users, secondary users, and base stations is of fundamental importance for dynamic time-frequency-space resource allocation to improve the utilization of the spectrum resources in the cognitive cellular network. We introduce a four-layer distributed networking framework and establish a hierarchical MAS model. Furthermore, we expound the key methods and technologies and evaluate the effectiveness through numerical simulations. Finally, challenges and open issues are discussed.

Intelligent Cognitive Radio in 5G: AI-Based Hierarchical Cognitive Cellular Networks

In this paper, an unmanned aerial vehicle (UAV)-enabledspace-air-ground integrated relay system, in which UAV is equipped with phased-array antennas to receive satellite’s signal while utilizing non-orthogonal multiple access (NOMA) schemes to transmit data to land-based users, is proposed. For UAV signal collection, a detection-vector optimization problem can be addressed by introducing a serial of transformations into a second-order cone programming problem. Furthermore, the UAV-ground NOMA communication is summarized into a max-min problem in terms of UAV’s energy efficiency (EE), which is a fractional mixed-integer non-convex problem and has been widely recognized as hard work. Thus, a two-step solution is proposed. Firstly, we transfer the max-min fractional problem into a subtractive one. After that, the transformed problem is further divided into three sub-problems, which are used for jointly performing UAV trajectory optimization, NOMA scheduling optimization, and NOMA power allocation. In addition, the non-convexity of the above-mentioned problem is well solved by using the proposed iterative algorithm, achieving a rapid convergence. Numerical results illustrate the superiority of the proposed EE-NOMA scheme, which achieves three times higher energy-efficient than the conventional spectrum efficiency scheme, and NOMA is shown to globally outperform the orthogonal multiple access in UAV EE circumstance.

NOMA-Based Energy-Efficiency Optimization for UAV Enabled Space-Air-Ground Integrated Relay Networks

Design space exploration is usually required during the conceptual design of hypersonic vehicle. Artificial neural network (ANN) is widely used for issues of design space exploration. However, gaps such as the low prediction accuracy of ANN with small training data and inefficiency of data generation are still unaddressed. In this paper, for investigation of the variation of the maximum takeoff weight (MTOW) of hypersonic vehicles as the configuration changes, a method combing physics-informed neural network (PINN) and knowledge based engineering (KBE) is developed to study the issue. In this method, PINN is used as the predictor of MTOW, the prior knowledge of engineers is able to be embedded into the loss function and guide the training of PINN. Compared with that of traditional neural network, the prediction accuracy of PINN is higher especially with small training data. Moreover, the KBE is used to guide the design process of generating training data and the efficiency of data generation can be improved. In order to account for the validity of the proposed method, the MTOW prediction of hypersonic vehicles with different wing configurations has been investigated. The results showed that the prediction accuracy of neural network can be improved with small training data. 

Estimation of hypersonic vehicle weight using Physics-Informed neural network supported by knowledge based engineering

This paper proposes a complete network capacity analysis framework for low-earth-orbit (LEO) mega-constellations, where a network capacity estimation problem considering the link packet loss rate is formulated with the support of a time-variant network topology model and a task distribution model. This problem is solved in two steps. First, without considering the link packet loss rate, an improved fully polynomial-time approximation (IFPTA) algorithm is proposed to provide a sub-optimal solution to the multi-commodity flow (MCF) problem, in which a simpler definition of a commodity is given and proved to be equivalent to the original problem. Second, a Jackson-network-based capacity fallback approach is proposed to control the link packet loss rate below a given threshold. Numerical results illustrate the superiority of the proposed IFPTA algorithm in terms of accuracy and time complexity compared to existing solutions. In addition, the capacity characteristics of mega-constellations are analyzed by utilizing the proposed capacity analysis framework, including the relationship between constellation size and capacity, network capacity bottlenecks, and the influence of task distributions.

Dong Chen

Papers

From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies

Intelligent Cognitive Radio in 5G: AI-Based Hierarchical Cognitive Cellular Networks

NOMA-Based Energy-Efficiency Optimization for UAV Enabled Space-Air-Ground Integrated Relay Networks

Estimation of hypersonic vehicle weight using Physics-Informed neural network supported by knowledge based engineering

Capacity Analysis of LEO Mega-Constellation Networks