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Multi-access edge computing processes and stores data at the edge of the network, brings computing resources closer to the end users, significantly reduces latency for computation-intensive and time-sensitive applications. Unmanned aerial vehicles (UAVs) can accomplish rapid deployment of wireless networks as mobile base stations. Compared to terrestrial communications, UAVs have the outstanding advantage

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3GPP supports IoT applications through LTE-M (LTE for machine type of communication) and NB-IoT (Narrow Band-IoT) technologies. These mature technologies are suitable for a wide variety of applications. The future growth of sensor applications will lead to new use cases and scenarios. While many sensors can be supported by existing 3GPP technologies, additional support of satellite-IoT and low power/passive

rovides an overview of the technical articles and features of interest to readers that appear outside of this publication.

The employment of multi-access edge computing (MEC) promotes the emergence of novel computation-intensive and time-sensitive applications at the network edge. Resource-limited devices can offload their computing tasks to edge servers, thereby avoiding heavy computing loads and reducing energy consumption. However, the computing servers are usually embedded in the fixed access points (APs) or base stations

Cellular traffic prediction at mobile edges is extremely valuable to ultra high-reliability low-latency (URLLC) communication of 5G. Many network actions depend on this prediction technology, ranging from radio resource scheduling, edge node sharing, and traffic control, to network slicing and dynamic network function virtualization. However, accurate prediction for cellular traffic flow is a tough

In fog computing, processing, network, and storage resources are placed close to end users to ensure low latency in comparison to the latency experienced when accessing the cloud. One limitation of this solution, however, is that fog nodes are usually fixed, whereas demands are variable over time at all locations, which may result in either under- or overprovisioning. This limitation incurs high CAPEX

The unmanned aerial vehicle (UAV) swarm has drawn much attention for its wide range of potential applications. However, most UAV swarms rely on manual remote control or preprogramming, leaving autonomous cooperation as a challenging issue. Control and communication are two important pillars intra-autonomous-swarm, and the competition for limited resources between control and communication needs to

Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility, and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing. However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles

Wireless sensor networks have been widely adopted in various areas. However, large-scale sensor networks face four critical problems. Information transmissions through the network would cause the energy hole problem; deploying sensors in a distributed manner might cause the isolated problem; simultaneous transmissions lead to the communication collision problem; and the lack of intelligent computing

Benefiting from maneuverability, flexibility, and low-cost deployment, aerial base stations (ABSs) have emerged as a promising solution to meet the coverage demand when terrestrial BSs are overloaded and unavailable. Nevertheless, the high mobility of ABSs and the complicated interference incurred by the addition of ABSs inevitably cause the spatial-temporal discontinuity in air-to-ground (A2G) coverage

A new architecture with drone-assisted multi-access edge computing (MEC) is proposed for vehicular networks to support computation-intensive and delay-sensitive applications and services. Artificial intelligence (AI)-based resource management schemes are developed such that terrestrial and aerial spectrum, computing, and storage resources can be cooperatively allocated for guaranteeing the quality

Driven by the unprecedented high throughput and low latency requirements anticipated for next generation wireless networks, this article introduces an artificial intelligence (AI)-enabled framework in which unmanned aerial vehicles use non-orthogonal multiple access and mobile edge computing techniques to serve terrestrial mobile users (MUs). The proposed framework enables terrestrial MUs to offload

Unmanned aerial vehicle (UAV)-enabled edge computing is a promising paradigm to provide task offloading service for computation-intensive applications. Catering for highly dynamic edge networks, learning agents are placed on the UAVs to perceive environmental changes and adjust edge service strategies. These multiple agents build distributed intelligence in edge service scheduling. However, the agents

Computation offloading is a main issue in multi-access edge computing (MEC). In particular, in aerial MEC (AMEC) relying on UAVs/ drones, due to limited resources of single devices in providing services to end users, we always have to offload tasks to different devices within the network. However, in wireless communications, there are obstacles in transmission distance between devices sending out offloading

Aerial computing is a key enabling technique to provide seamless telecommunication and IT services for emerging computation-craving applications. As a drone employed in an aerial computing platform is limited in computation, caching, communication, and control capabilities, researchers have utilized multiple drones for efficient multi-modal multi-task processes, which also bring great challenges in

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The Internet of Things (IoT) has been extensively investigated as an enabling technology for situation awareness and data collection. Since power-limited or battery-free IoT devices may have limited transmission coverage, unmanned aerial vehicles (UAVs) can be employed as mobile edge computing (MEC) servers to collect and process data packets from multiple IoT devices due to their high mobility and

With the advent of the Internet of Things (IoT) era, the ever increasing number of devices and emerging applications have triggered the need for ubiquitous connectivity and more efficient computing paradigms. These stringent demands have posed significant challenges to the current wireless networks and their computing architectures. In this article, we propose a high-altitude platform (HAP) network-enabled

Deep learning technologies have been widely exploited to predict mobile traffic. However, individually training deep learning models for various traffic prediction tasks is not only time consuming but also unrealistic, sometimes due to limited traffic records. In this article, we propose a novel deep meta-learning based mobile traffic prediction framework, namely, dmTP, which can adaptively learn to

The integration of fifth generation (5G) mobile network and satellite communication promises a promotion on the interconnection of everything, and has become one of the key development directions of beyond 5G and even 6G. As an inevitable development trend of mobile communication, the space-based Internet of Things (S-IoT) is expected to be commercially available in building an integrated information

Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful learning ability and potential applications. In contrast to other machine learning techniques that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a model

Reliable and flexible emergency networks are of paramount essence for disaster relief during or after the event of disasters due to the destruction or lack of terrestrial communication infrastructures. Thanks to fast deployment and flexible mobilities, unmanned aerial vehicles (UAVs) emerge as a promising paradigm to efficiently establish emergency networks and perform immediate disaster relief tasks

Reconfigurable intelligent surface (RIS) offers tremendous spectrum and energy efficiency in wireless networks by adjusting the amplitudes and/or phases of passive reflecting elements to optimize signal reflection. With the agility and mobility of unmanned aerial vehicles (UAVs), RIS can be mounted on UAVs to enable three-dimensional signal reflection. Compared to the conventional terrestrial RIS (TRIS)

Thanks to their flexibility and mobility, unmanned aerial vehicles (UAVs) have been widely applied in wireless networks. However, UAV communications may suffer from blockage and eavesdropping in practical scenarios due to the complex environment. Taking the recent advances in intelligent reflecting surface (IRS) to reconfigure the propagation environments, in this article, we employ IRS to enhance

Inter-slice radio resource allocation (IS-RRA) is a new layer of radio resource management introduced by network slicing in 5G and beyond mobile communication systems. Instead of focusing on the per-user service quality as in conventional packet schedulers, IS-RRA is required to ensure the service-level agreements on a per-slice basis, which is particularly challenging due to the diverse requirements

With the explosive development of the Internet of Things (IoT), massive equipments are involved in the indoor environment with the demand of precise localization to provide intelligent interconnection. The visible light positioning (VLP) system, with the light emitting diode (LED) and the photo-diode (PD) or image sensor as the transceiver, has attracted a lot of attention due to low cost, high accuracy

The ongoing deployments of the Internet of Things (IoT)-based smart applications are spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been significant recent interest in the concept of federated learning. Federated learning offers on-device machine learning without the need to transfer end-device

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Along with the unprecedented boom of 5G, the Internet of Things (IoT), and artificial intelligence, smart cities are making a great clamor. When billions of IoT devices are streaming in the smart city, realizing low-latency and high-bandwidth services has a significant impact on the benefits of smart cities. Thus, managing resources efficiently and intelligently to meet various service requirements

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Energy efficiency (EE) and spectrum efficiency (SE) are two key performance metrics in the sixth generation (6G) wireless communication networks due to the ultra-massive connectivity requirements in future Internet of Things scenarios such as smart cities and factories. Reconfigurable intelligent surface (RIS)-based symbiotic radio (SR) is a promising technology to simultaneously achieve high EE and

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Presents the front cover for this issue of the publication.

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Presents the table of contents for this issue of the publication.

Next generation wireless communication systems will connect billions of Internet of Thing (IoT) devices and user elements along with billions of people, enable machine-to-ma-chine communications across heterogeneous and dynamic environment, and provide low-latency computing and storage resources on demand at the deep edge as well as in the cloud. Tremendous research and development efforts have already

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. Also presents a call for papers for future issues of this publication.

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If 2020 taught the world something it is that adaptability is essential, and flexibility is key to social and economic stability. In times when health safety is paramount, with small IoT devices producing unimaginable amounts of data and automation driving productivity, the world is on the evolutionary path toward a more integrated knowledge-driven society. The next chapter of technology evolution

Provides an overview of the technical articles and features of interest to readers that appear outside of this publication.

Widespread deployment of the Internet of Things (IoT) has changed network services in which people, processes, data, and things connect each other. According to Cisco, 500 billion devices are expected to be connected to the Internet by 2030, where IoT is the network of these connected devices. Currently, most advanced IoT devices are equipped with visual sensors, making up the so-called visual IoT

Video streaming of the Visual Internet of Things (VIoT) has to guarantee the quality of service for latency-sensitive multimedia applications against jamming and interference. In this article, we propose a reinforcement learning (RL)-based low-latency VIoT video streaming scheme, which enables base stations to choose the streaming policy without relying on the known channel and jamming model. In this

For the Visual Internet of Things (VIoT), heterogeneous sensors such as visual cameras and acoustic microphones can be installed in devices to perform cognitive sensing and collect multiview data. Nonetheless, not every device has the same set of sensors due to deployment costs or sensor malfunctioning. This subsequently causes incomplete heterogeneous data, which means that data from various sensors

Nowadays, the Internet of Video Things (IoVT) is growing rapidly in terms of quantity and computation demands. In spite of the higher local computation capability on visual processing compared to conventional Internet of Things devices, IoVT devices need to offload partial visual processing tasks to the mobile edge computing (MEC) server wirelessly due to its larger computation demands. However, visual

Real-time crowd management systems play an important role in urban planning, disaster warning, and emergency evacuation. For example, through closed-circuit television (CCTV) cameras and artificial intelligence (AI) algorithms, crowd counting and abnormal event recognition can be realized. However, most of these applications are limited within the 2D coordinate system of each single camera, lacking

Connected and autonomous vehicles (CAVs) are promising due to their potential safety and efficiency benefits, and have attracted massive investment and interest from government agencies, industry, and academia. With more computing and communication resources available, both vehicles and edge servers are equipped with a set of cam-era-based vision sensors, also known as Visual IoT (VIoT) techniques

Reliable detection of objects is a crucial requirement to improve the safety of the visual Internet of Vehicles (V-IoVs). The resemblance of objects to each other or the background makes the detection of objects difficult. The traditional connectionist artificial intelligence (All-based object detection model is a potential approach to increase detection performance, but it has poor interpretability

Future wireless cellular networks are envisioned to not only enhance broadband access for human-centric applications, but also offer massive connectivity across tens of billions of devices for machine-centric applications empowered by Internet of Things (IoT) technologies (e.g., smart factory and smart city). To embrace the forthcoming era of IoT, the fifth-generation (5G) cellular communication standard

Wireless data aggregation (WDA), referring to aggregating data distributed at devices (e.g., sensors and smartphones), is a common operation in 5G-and-beyond machine-type communications to support the Internet of Things (IoT), which lays the foundation for diversified applications such as distributed sensing, learning, and control. Conventional WDA techniques, which are designed in a task-agnostic

Non-orthogonal multiple access (NOMA) is a promising evolution path to meet the requirements of massive machine type communications (mMTC) in 5G and beyond. However, the deployment of NOMA is hindered by the non-unified signal processing architectures of various NOMA schemes and the inflexibility resulting from the offline design paradigm. The block-wise optimized transceivers make its performance

Despite immense progress along different tracks, wireless connectivity for machine applications in remote areas is still very challenging. To address this vital need, in this article, we discuss and advocate the convergence between low-pow-er wide area network (LPWAN) grade massive machine-type communication (mMTC) wireless technologies and satellite (especially low Earth orbit, LEO) systems. In the

The Industrial Internet of Things (IIoT) revolutionizes future manufacturing facilities by integrating Internet of Things technologies into industrial settings. With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality of service (QoS) requirements. Although machine learning is regarded as a powerful data-driven

In multiple-input multiple-output (MIMO) systems, multiple radio frequency (RF) chains are usually required to simultaneously transmit multiple data streams. As a special MIMO technology, spatial modulation (SM) activates one transmit antenna by requiring one RF chain and exploits the index of the active antenna for information transfer at each time slot. Recently, reconfigurable metasurfaces have

Future integrated terrestrial-aerial-satellite networks will have to exhibit some unprecedented characteristics for the provision of both communications and computation services, and security for a tremendous number of devices with very broad and demanding requirements across multiple networks, operators, and ecosystems. Although 3GPP introduced the concept of self-organizing networks (SONs) in 4G

Sixth-generation (6G) wireless networks are envisioned to provide global coverage for the intelligent digital society of the near future, ranging from traditional terrestrial to non-terrestri-al networks, where reliable communications in high-mobility scenarios at high carrier frequencies would play a vital role. In such scenarios, the conventional orthogonal frequency division multiplexing (OFDM)