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Showing papers in "IEEE Communications Magazine in 2018"


Journal ArticleDOI
TL;DR: This article proposes a radically different approach, enabling deterministic, programmable control over the behavior of wireless environments, using the so-called HyperSurface tile, a novel class of planar meta-materials that can interact with impinging electromagnetic waves in a controlled manner.
Abstract: Electromagnetic waves undergo multiple uncontrollable alterations as they propagate within a wireless environment. Free space path loss, signal absorption, as well as reflections, refractions, and diffractions caused by physical objects within the environment highly affect the performance of wireless communications. Currently, such effects are intractable to account for and are treated as probabilistic factors. This article proposes a radically different approach, enabling deterministic, programmable control over the behavior of wireless environments. The key enabler is the so-called HyperSurface tile, a novel class of planar meta-materials that can interact with impinging electromagnetic waves in a controlled manner. The HyperSurface tiles can effectively re-engineer electromagnetic waves, including steering toward any desired direction, full absorption, polarization manipulation, and more. Multiple tiles are employed to coat objects such as walls, furniture, and overall, any objects in indoor and outdoor environments. An external software service calculates and deploys the optimal interaction types per tile to best fit the needs of communicating devices. Evaluation via simulations highlights the potential of the new concept.

860 citations


Journal ArticleDOI
TL;DR: The typical airborne connectivity requirements and characteristics are identified, the different propagation conditions for UAVs and mobiles on the ground with measurement and ray tracing results are highlighted, and simulation results are presented to shed light on the feasibility of providing LTE connectivity for Uavs.
Abstract: Many use cases of UAVs require beyond visual LOS communications. Mobile networks offer wide-area, high-speed, and secure wireless connectivity, which can enhance control and safety of UAV operations and enable beyond visual LOS use cases. In this article, we share some of our experience in LTE connectivity for low-altitude small UAVs. We first identify the typical airborne connectivity requirements and characteristics, highlight the different propagation conditions for UAVs and mobiles on the ground with measurement and ray tracing results, and present simulation results to shed light on the feasibility of providing LTE connectivity for UAVs. We also present several ideas on potential enhancements for improving LTE connectivity performance and identify fruitful avenues for future research.

575 citations


Journal ArticleDOI
TL;DR: A novel concept of edge computing for mobile blockchain and an economic approach for edge computing resource management are introduced and a prototype of mobile edge computing enabled blockchain systems are presented with experimental results to justify the proposed concept.
Abstract: Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications (e.g., finance, healthcare, and logistics), its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data (i.e., a block) to the blockchain. Solving the proof of work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.

417 citations


Journal ArticleDOI
TL;DR: Simulation results demonstrate that the FSO-based vertical backhaul/ fronthaul framework can offer data rates higher than the baseline alternatives, and thus can be considered a promising solution to the emerging back haul/fronthaul requirements of the 5G+ wireless networks, particularly in the presence of ultra-dense heterogeneous small cells.
Abstract: The presence of a super high rate, but also cost-efficient, easy-to-deploy, and scalable, backhaul/ fronthaul framework, is essential in the upcoming 5G wireless networks and beyond. Motivated by the mounting interest in unmanned flying platforms of various types, including UAVs, drones, balloons, and HAPs/MAPs/LAPs, which we refer to as networked flying platforms (NFPs), for providing communications services, and by the recent advances in free space optics (FSO), this article investigates the feasibility of a novel vertical backhaul/fronthaul framework where the NFPs transport the backhaul/fronthaul traffic between the access and core networks via pointto- point FSO links. The performance of the proposed innovative approach is investigated under different weather conditions and a broad range of system parameters. Simulation results demonstrate that the FSO-based vertical backhaul/ fronthaul framework can offer data rates higher than the baseline alternatives, and thus can be considered a promising solution to the emerging backhaul/fronthaul requirements of the 5G+ wireless networks, particularly in the presence of ultra-dense heterogeneous small cells. This article also presents the challenges that accompany such a novel framework and provides some key ideas toward overcoming these challenges.

360 citations


Journal ArticleDOI
TL;DR: In this article, four directions to tackle the crucial problem of distance limitation are investigated, namely, a distance-aware physical layer design, ultra-massive MIMO communication, reflectarrays, and intelligent surfaces.
Abstract: In the millimeter-wave (30-300 GHz) and terahertz (0.1-10 THz) frequency bands, the high spreading loss and molecular absorption often limit the signal transmission distance and coverage range. In this article, four directions to tackle the crucial problem of distance limitation are investigated, namely, a distance-aware physical layer design, ultra-massive MIMO communication, reflectarrays, and intelligent surfaces. Additionally, the potential joint design of these solutions is proposed to combine the benefits and further extend the communication distance. Qualitative and quantitative evaluations are provided to illustrate the benefits of the proposed solutions. The feasibility of mmWave and THz band communications up to 100 m in both line-of-sight and nonline- of-sight areas are demonstrated.

320 citations


Journal ArticleDOI
TL;DR: Key 5G building blocks (i.e., proximity services, mobile edge computing and network slicing) are explored in the context of vehicular communications, and associated design challenges are highlighted.
Abstract: 5G is ongoing, and it is an emerging platform that not only aims to augment existing but also introduce a plethora of novel applications that require ultra-reliable low-latency communication. It is a new radio access technology that provides building blocks to retrofit existing platforms (e.g., 2G, 3G, 4G, and WiFi) for greater coverage, accessibility, and higher network density with respect to cells and devices. It implies that 5G aims to satisfy a diverse set of communication requirements of the various stakeholders. Among the stakeholders, vehicles, in particular, will benefit from 5G at both the system and application levels. The authors present a tutorial perspective on vehicular communications using the building blocks provided by 5G. First, we identify and describe key requirements of emerging vehicular communications and assess existing standards to determine their limitations. Then we provide a glimpse of the adopted 5G architecture and identify some of its promising salient features for vehicular communications. Finally, key 5G building blocks (i.e., proximity services, mobile edge computing and network slicing) are explored in the context of vehicular communications, and associated design challenges are highlighted.

291 citations


Journal ArticleDOI
TL;DR: An SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support and a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented.
Abstract: The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment.

285 citations


Journal ArticleDOI
TL;DR: In this paper, the authors investigated the feasibility of multi-tier drone network architecture over traditional single-tier UAV networks and identified the scenarios in which drone networks can potentially complement the traditional RF-based terrestrial networks.
Abstract: Drones (or unmanned aerial vehicles) are expected to be an important component of 5G/ beyond 5G (B5G) cellular architectures that can potentially facilitate wireless broadcast or point-to-multipoint transmissions. The distinct features of various drones such as the maximum operational altitude, communication, coverage, computation, and endurance impel the use of a multi-tier architecture for future dronecell networks. In this context, this article focuses on investigating the feasibility of multi-tier drone network architecture over traditional single-tier drone networks and identifying the scenarios in which drone networks can potentially complement the traditional RF-based terrestrial networks. We first identify the challenges associated with multi-tier drone networks as well as drone-assisted cellular networks. We then review the existing state-of-the-art innovations in drone networks and drone-assisted cellular networks. We then investigate the performance of a multi-tier drone network in terms of spectral efficiency of downlink transmission while illustrating the optimal intensity and altitude of drones in different tiers numerically. Our results demonstrate the specific network load conditions (i.e., ratio of user intensity and base station intensity) where deployment of drones can be beneficial (in terms of spectral efficiency of downlink transmission) for conventional terrestrial cellular networks.

283 citations


Journal ArticleDOI
TL;DR: In this article, the authors propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance, and provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where an important event is being held and only authorized drones are allowed to fly over.
Abstract: Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance, and numerous public services. However, the deployment of amateur drones poses various safety, security, and privacy threats. To cope with these challenges, amateur drone surveillance has become a very important but largely unexplored topic. In this article, we first present a brief survey to show the stateof- the-art studies on amateur drone surveillance. Then we propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in detail, accompanied by the technical challenges and open issues. Furthermore, we provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where, for example, an important event is being held and only authorized drones are allowed to fly over.

279 citations


Journal ArticleDOI
TL;DR: A novel distributed deep learning scheme of cyber-attack detection in fog-to-things computing is proposed and experiments show that deep models are superior to shallow models in detection accuracy, false alarm rate, and scalability.
Abstract: The increase in the number and diversity of smart objects has raised substantial cybersecurity challenges due to the recent exponential rise in the occurrence and sophistication of attacks Although cloud computing has transformed the world of business in a dramatic way, its centralization hammers the application of distributed services such as security mechanisms for IoT applications The new and emerging IoT applications require novel cybersecurity controls, models, and decisions distributed at the edge of the network Despite the success of the existing cryptographic solutions in the traditional Internet, factors such as system development flaws, increased attack surfaces, and hacking skills have proven the inevitability of detection mechanisms The traditional approaches such as classical machine-learning-based attack detection mechanisms have been successful in the last decades, but it has already been proven that they have low accuracy and less scalability for cyber-attack detection in massively distributed nodes such as IoT The proliferation of deep learning and hardware technology advancement could pave a way to detecting the current level of sophistication of cyber-attacks in edge networks The application of deep networks has already been successful in big data areas, and this indicates that fog-tothings computing can be the ultimate beneficiary of the approach for attack detection because a massive amount of data produced by IoT devices enable deep models to learn better than shallow algorithms In this article, we propose a novel distributed deep learning scheme of cyber-attack detection in fog-to-things computing Our experiments show that deep models are superior to shallow models in detection accuracy, false alarm rate, and scalability

269 citations


Journal ArticleDOI
TL;DR: This article describes the LDPC code design philosophy and how the broad requirements of 5G NR channel coding led to the introduction of novel structural features in the code design, culminating in anLDPC code that satisfies all the demands of5G NR.
Abstract: Turbo codes, prevalent in most modern cellular devices, are set to be replaced by LDPC codes as the code for forward error correction. This transition was ushered in mainly because of the high throughput demands for 5G New Radio (NR). The new channel coding solution also needs to support incremental-redundancy hybrid ARQ, and a wide range of blocklengths and coding rates, with stringent performance guarantees and minimal description complexity. In this article, we first briefly review the requirements of the new channel code for 5G NR. We then describe the LDPC code design philosophy and how the broad requirements of 5G NR channel coding led to the introduction of novel structural features in the code design, culminating in an LDPC code that satisfies all the demands of 5G NR.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a privacy-preserving and efficient data aggregation scheme, which divides users into different groups, and each group has a private blockchain to record its members' data.
Abstract: Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home to smart building to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, an SM is installed at each home to collect the near-real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near-real-time data may disclose a user's private information. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this article, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups, and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonyms to hide users' identities, and each user may create multiple pseudonyms and associate his/ her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements and achieve better performance than other popular methods.

Journal ArticleDOI
TL;DR: In this paper, a novel air-ground integrated mobile edge network (AGMEN) is proposed, where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network.
Abstract: The ever increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation- heavy Internet of Things applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. Specifically, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted.

Journal ArticleDOI
TL;DR: The architecture and its security and privacy requirements are studied, and potential solutions to address challenging issues such as privacy leakage, data confidentiality protection, and flexible accessibility are outlined.
Abstract: A recent trend in both industry and research is the Internet of Drones, which has applications in both civilian and military settings. However, drones (also known as unmanned aerial vehicles) are generally not designed with security in mind, and there are fundamental security and privacy issues that need study. Hence, in this article, we study the architecture and its security and privacy requirements. We also outline potential solutions to address challenging issues such as privacy leakage, data confidentiality protection, and flexible accessibility, with the hope that this article will provide the basis for future research in this emerging area.

Journal ArticleDOI
TL;DR: A novel paradigm is proposed, 5G Intelligent Internet of Things (5G I-IoT), to process big data intelligently and optimize communication channels and the effective utilization of channels and QoS have been greatly improved.
Abstract: 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.

Journal ArticleDOI
TL;DR: A permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead.
Abstract: Today's vehicles are becoming cyber-physical systems that not only communicate with other vehicles but also gather various information from hundreds of sensors within them These developments help create smart and connected (eg, self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies, and maintenance service providers for various applications One such application that is becoming crucial with the introduction of self-driving cars is forensic analysis of traffic accidents The utilization of vehicle-related data can be instrumental in post-accident scenarios to discover the faulty party, particularly for self-driving vehicles With the opportunity of being able to access various information in cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data Specifically, we first integrate vehicular public key infrastructure (VPKI) to the proposed blockchain to provide membership establishment and privacy Next, we design a fragmented ledger that will store detailed data related to vehicles such as maintenance information/ history, car diagnosis reports, and so on The proposed forensic framework enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead

Journal ArticleDOI
TL;DR: A method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques, demonstrating up to 90-99 percent experimental accuracy at transmitter- receiver distances varying between 2-50 ft over a noisy, multi-path wireless channel.
Abstract: Advances in software defined radio (SDR) technology allow unprecedented control on the entire processing chain, allowing modification of each functional block as well as sampling the changes in the input waveform This article describes a method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques The key benefit of this approach is that ML operates on raw I/Q samples and distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device No higher-level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing and coexistence of multiple protocols in a shared spectrum The contributions of the article are as follows: (i) The operational blocks in a typical wireless communications processing chain are modified in a simulation study to demonstrate RF impairments, which we exploit (ii) Using an overthe- air dataset compiled from an experimental testbed of SDRs, an optimized deep convolutional neural network architecture is proposed, and results are quantitatively compared with alternate techniques such as support vector machines and logistic regression (iii) Research challenges for increasing the robustness of the approach, as well as the parallel processing needs for efficient training, are described Our work demonstrates up to 90-99 percent experimental accuracy at transmitter- receiver distances varying between 2-50 ft over a noisy, multi-path wireless channel

Journal ArticleDOI
TL;DR: The key transceiver design challenges, including channel estimation, signal detector, channel information feedback and transmit precoding, are discussed and a mixed-ADC architecture is introduced as an alternative technique of improving overall system performance.
Abstract: Nowadays, mmWave MIMO systems are favorable candidates for 5G cellular systems. However, a key challenge is the high power consumption imposed by its numerous RF chains, which may be mitigated by opting for low-resolution ADCs, while tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges, including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system with ADC quantizers are discussed.

Journal ArticleDOI
TL;DR: In this article, the authors investigate the various sources of end-to-end delay of current wireless networks by taking 4G LTE as an example and propose and evaluate several techniques to reduce the end to end latency from the perspectives of error control coding, signal processing, and radio resource management.
Abstract: Fifth-generation cellular mobile networks are expected to support mission critical URLLC services in addition to enhanced mobile broadband applications. This article first introduces three emerging mission critical applications of URLLC and identifies their requirements on end-to-end latency and reliability. We then investigate the various sources of end-to-end delay of current wireless networks by taking 4G LTE as an example. Then we propose and evaluate several techniques to reduce the end-to-end latency from the perspectives of error control coding, signal processing, and radio resource management. We also briefly discuss other network design approaches with the potential for further latency reduction.

Journal ArticleDOI
Xiufang Shi1, Chaoqun Yang1, Xie Weige1, Chao Liang1, Zhiguo Shi1, Jiming Chen1 
TL;DR: An anti-drone system at Zhejiang University is developed, named ADS-ZJU, which combines multiple passive surveillance technologies to realize drone detection, localization, and radio frequency jamming.
Abstract: In recent years, drones have undergone tremendous development. Due to the low price and ease of use, drones have been widely utilized in many application scenarios, which potentially pose great threats to public security and personal privacy. To mitigate these threats, it is necessary to deploy anti-drone systems in sensitive areas to detect, localize, and defend against the intruding drones. In this article, we provide a comprehensive overview of the technologies utilized for drone surveillance and the existing anti-drone systems. Then we develop an anti-drone system at Zhejiang University, named ADS-ZJU, which combines multiple passive surveillance technologies to realize drone detection, localization, and radio frequency jamming. Furthermore, we discuss the challenges and open research issues in such a system.

Journal ArticleDOI
TL;DR: This article presents a mobility-aware hierarchical MEC framework for green and low-latency IoT, and deploys a game theoretic approach for computation offloading in order to optimize the utility of the service providers while also reducing the energy cost and the task execution time of the smart devices.
Abstract: IoT, a heterogeneous interconnection of smart devices, is a great platform to develop novel mobile applications. Resource constrained smart devices, however, often become the bottlenecks to fully realize such developments, especially when it comes to intensive-computation-oriented and low-latency-demanding applications. MEC is a promising approach to address such challenges. In this article, we focus on MEC applications for IoT, and address energy efficiency as well as offloading performance of such applications in terms of end-user experience. In this regard, we present a mobility-aware hierarchical MEC framework for green and low-latency IoT. We deploy a game theoretic approach for computation offloading in order to optimize the utility of the service providers while also reducing the energy cost and the task execution time of the smart devices. Numerical results indicate that the proposed scheme does brings significant enhancement in both energy efficiency and latency performance of MEC applications for IoT.

Journal ArticleDOI
TL;DR: It is argued that semi-supervision is a must for smart cities to address the phenomenon of wasting unlabeled data, and a three-level learning framework is proposed that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstraction.
Abstract: The development of smart cities and their fast-paced deployment is resulting in the generation of large quantities of data at unprecedented rates Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards that benefit from the availability of such data Moreover, the highly dynamic nature of smart cities calls for a new generation of machine learning approaches that are flexible and adaptable to cope with the dynamicity of data to perform analytics and learn from real-time data In this article, we shed light on the challenge of underutilizing the big data generated by smart cities from a machine learning perspective In particular, we present the phenomenon of wasting unlabeled data We argue that semi-supervision is a must for smart cities to address this challenge We also propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstraction The proposed framework is scalable to meet the needs of smart city services Fundamentally, the framework benefits from semi-supervised deep reinforcement learning where a small amount of data that has users' feedback serves as labeled data, while a larger amount without such users' feedback serves as unlabeled data The framework utilizes a mix of labeled and unlabeled data to converge toward better control policies instead of wasting the unlabeled data This article also explores how deep reinforcement learning and its shift toward semi-supervision can handle the cognitive side of smart city services and improve their performance by providing several use cases spanning the different domains of smart cities We also highlight several challenges as well as promising future research directions for incorporating machine learning and high-level intelligence into smart city services

Journal ArticleDOI
TL;DR: A taxonomy of edge computing is established by classifying and categorizing existing literature, and by doing so, it is revealed the salient and supportive features of different edge computing paradigms for IoT.
Abstract: Remarkable advancements in embedded systems- on-a-chip have significantly increased the number of commercial devices that possess sufficient resources to run full-fledged operating systems. This change has extended the potential of the IoT. Many early IoT devices could only collect and send data for analysis. However, the increasing computing capacity of today's devices allow them to perform complex computations on-site, resulting in edge computing. Edge computing extends cloud computing capabilities by bringing services close to the edge of a network and thus supports a new variety of services and applications. In this work, we investigate, highlight, and report on recent advances in edge computing technologies with respect to measuring their impact on IoT. We establish a taxonomy of edge computing by classifying and categorizing existing literature, and by doing so, we reveal the salient and supportive features of different edge computing paradigms for IoT. Moreover, we present the key requirements for the successful deployment of edge computing in IoT and discuss a few indispensable scenarios of edge computing in IoT. Several open research challenges are also outlined.

Journal ArticleDOI
TL;DR: In this article, the authors investigated the integration of NOMA with CR into a holistic system, namely a cognitive nOMA network, for more intelligent spectrum sharing and proposed cooperative relaying strategies to address inter-network and intra-network interference.
Abstract: Two emerging technologies toward 5G wireless networks, namely non-orthogonal multiple access (NOMA) and cognitive radio (CR), will provide more efficient utilization of wireless spectrum in the future. In this article, we investigate the integration of NOMA with CR into a holistic system, namely a cognitive NOMA network, for more intelligent spectrum sharing. Design principles of cognitive NOMA networks are perfectly aligned to functionality requirements of 5G wireless networks, such as high spectrum efficiency, massive connectivity, low latency, and better fairness. Three different cognitive NOMA architectures are presented, including underlay NOMA networks, overlay NOMA networks, and CR-inspired NOMA networks. To address inter-network and intra-network interference, which largely degrade the performance of cognitive NOMA networks, cooperative relaying strategies are proposed. For each cognitive NOMA architecture, our proposed cooperative relaying strategy shows its potential to significantly lower outage probabilities. We discuss open challenges and future research directions on implementation of cognitive NOMA networks.

Journal ArticleDOI
TL;DR: The 5G-Smart Diabetes system is proposed, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes.
Abstract: Recent advances in wireless networking and big data technologies, such as 5G networks, medical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation, this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients.

Journal ArticleDOI
TL;DR: An in-depth view of channel modeling in the THz band, based on the deterministic, statistical, and hybrid methods is provided, which lays the foundation for reliable and efficient ultra-broadband wireless communications in theTHz band.
Abstract: Terahertz band (0.1-10 THz) communication is envisioned as a key technology to support ultra-broadband wireless systems for beyond 5G. For realization of efficient wireless communication networks in the THz band, it is imperative to develop channel models that can accurately and efficiently characterize the THz spectrum peculiarities. This article provides an in-depth view of channel modeling in the THz band, based on the deterministic, statistical, and hybrid methods. The state-of-the-art THz channel models in single-antenna and ultra-massive MIMO systems are extensively reviewed, respectively. Furthermore, the open challenges and potential research directions are highlighted regarding THz propagation modeling. Associated with the channel models, key physical parameters of the THz channel and their implications for wireless communication design are analyzed. The provided analysis lays the foundation for reliable and efficient ultra-broadband wireless communications in the THz band.

Journal ArticleDOI
TL;DR: This article proposes an architecture of edge computing for IoT-based manufacturing and analyzes the role of edge Computing from four aspects including edge equipment, network communication, information fusion, and cooperative mechanism with cloud computing.
Abstract: Edge computing extends the capabilities of computation, network connection, and storage from the cloud to the edge of the network. It enables the application of business logic between the downstream data of the cloud service and the upstream data of the Internet of Things (IoT). In the field of Industrial IoT, edge computing provides added benefits of agility, real-time processing, and autonomy to create value for intelligent manufacturing. With the focus on the concept of edge computing, this article proposes an architecture of edge computing for IoT-based manufacturing. It also analyzes the role of edge computing from four aspects including edge equipment, network communication, information fusion, and cooperative mechanism with cloud computing. Finally, we give a case study to implement the active maintenance based on a prototype platform. This article aims to provide a technical reference for the deployment of edge computing in the smart factory.

Journal ArticleDOI
TL;DR: The goal of this survey article is to study various potential cyber and physical threats that may arise from the use of UAVs, and subsequently review various ways to detect, track, and interdict malicious drones.
Abstract: Unmanned aerial vehicles, also known as drones, are expected to play major roles in future smart cities, for example, by delivering goods and merchandise, serving as mobile hotspots for broadband wireless access, and maintaining surveillance and security. The goal of this survey article is to study various potential cyber and physical threats that may arise from the use of UAVs, and subsequently review various ways to detect, track, and interdict malicious drones. In particular, we review techniques that rely on ambient radio frequency signals (emitted from UAVs), radars, acoustic sensors, and computer vision techniques for detection of malicious UAVs. We present some early experimental and simulation results on range estimation of UAVs and receding horizon tracking of UAVs. Finally, we summarize common techniques that are considered for interdiction of UAVs.

Journal ArticleDOI
TL;DR: The basic system architecture for THz wireless links with bandwidths of more than 50 GHz into optical networks is discussed and the role of PBF is highlighted, which is required in order to overcome the propagation losses, as well as the physical layer and medium access control challenges.
Abstract: This article discusses the basic system architecture for THz wireless links with bandwidths of more than 50 GHz into optical networks. New design principles and breakthrough technologies are required in order to demonstrate terabit- per-second data rates at near zero latency using the proposed system concept. Specifically, we present the concept of designing the baseband signal processing for both the optical and wireless links and using an E2E error correction approach for the combined link. We provide two possible electro-optical baseband interface architectures, namely transparent optical-link and digital- link architectures, which are currently under investigation. THz wireless link requirements are given as well as the main principles and research directions for the development of a new generation of transceiver front-ends that will be capable of operating at ultra-high spectral efficiency by employing higher-order modulation schemes. Moreover, we discuss the need for developing a novel THz network information theory framework, which will take into account the channel characteristics and the nature of interference in the THz band. Finally, we highlight the role of PBF, which is required in order to overcome the propagation losses, as well as the physical layer and medium access control challenges.

Journal ArticleDOI
TL;DR: Key differences in the propagation characteristics between the microwave and mmWave bands are explained, and examples of how these differences impact 5G system design are given.
Abstract: Fifth generation cellular systems will be deployed in the microwave and millimeterwave (mmWave) frequency bands (i.e., between 0.5100 GHz). Propagation characteristics at these bands have a fundamental impact on each aspect of the cellular architecture, ranging from equipment design to real-time performance in the field. While we have a reasonable understanding of the propagation characteristics at microwave (< 6 GHz) frequencies, the same cannot be said for mmWave. This article explains key differences in the propagation characteristics between the microwave and mmWave bands, and further gives examples of how these differences impact 5G system design.