The Internet of Things (IoT) plays a critical role in enabling the seamless integration of disparate devices. Future IoT will rapidly expand its coverage to offer future worldwide omnipresent applications and services by merging communications in diverse spatial domains to build the space–air–ground–ocean-integrated network (SAGOI-Net). SAGOI-Net will include a significant number of battery-powered

The proliferation of IoT devices in everyday life has made their security a critical requirement. Currently, those devices are not secure enough because of several reasons. First, manufacturers do not account much for security, releasing products that are vulnerable to attacks, thus leaving security issues that are unlikely to be resolved. Second, many IoT devices lack the processing power to run antivirus

Recent years have witnessed the exciting developments for the power grid. For instance, many traditional mechanical components are being replaced by modern electronics devices that can operate intelligently; new elements, such as renewable energy resources and various large-scale energy storage, are introduced into the grid to bring a new outlook on the system operation and control; smart appliances

Due to advancements in semiconductor technologies, Internet of Things (IoT) applications have penetrated into a wide spectrum of aspects of human lives. This widespread penetration is also thanks to significant contributions from many emerging technologies, e.g., artificial intelligence (AI) and blockchain [1] , [2] . The fast development of AI technologies like deep learning is a promising approach

The Internet of Things (IoT) is a major driving force for future sixth-generation (6G) wireless systems. With the emergence of various novel IoT applications, more data should be collected and transmitted. However, IoT devices are constrained by battery, transmit power, and processing capacity. Featured by line-of-sight communication links, favorable channels, and better coverage, aerial access networks

The future generation of the Internet of Things (IoT) systems is characterized by the fusion of technologies—from edge–fog–cloud computing to artificial intelligence (AI) and blockchain—closing the gap between the physical and digital worlds [A1]. Although these technologies have been developed separately over time, the synergy among them has taken a giant leap. We are witnessing a fast-paced convergence

Over the past decade, there has been huge rise in the number of Internet of Things (IoT) devices and networks often characterized by resource constraints on energy, memory, communication, and computation power and, thus, integration of security mechanisms in these networks are often neglected. As the attacks increase, it becomes essential to secure the networks with machine learning (ML)-based intrusion

The detail of the function PEO(.) in Section IV-B for this article was not available at the time of publication. It appears in Section IV-B as follows.

Enabling a satellite network with edge computing capabilities can complement the advantages further of a single terrestrial network and provide users with a full range of computing service. Satellite edge computing is a potentially indispensable technology for future satellite-terrestrial integrated networks. In this article, a three-tier edge computing architecture consisting of the terminal–satellite–cloud

With advances in spatial–temporal Internet of Things (IoT) technologies, human activity recognition (HAR) has played a major role in human safety and medical health. Recently, most works focus on activity deep feature extraction, offering promising alternatives to manually engineered features. However, how to extract the effective and distinguishable continuity activity features and meanwhile reduce

The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing “Things” with “Drones” while retaining incomparable features. Because of its vital applications, IoD technologies have attracted much attention in recent years. Nevertheless, gaining the necessary degree of public acceptability of IoD without demonstrating safety and security for human life is exceedingly difficult.

Internet of Things (IoT) aims to create a vast network with billions of things that can seamlessly create and exchange data, establishing intelligent interactions between people and objects around them. It is characterized with openness, heterogeneity, and dynamicity, which inevitably introduce severe security, privacy, and trust issues that hinder the widespread application of IoT. Trust management

For model inference of convolutional neural networks (CNNs), we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute- and memory-intensive CNNs on Internet of Things devices at the Edge is challenging as they typically have limited resources. One approach to address this challenge is to leverage all available resources across multiple edge devices

The trading of social media data has attracted wide research interests over years. In particular, the trading for Web browsing histories, when being applied to targeted advertising, produces tremendous economic value for data consumers. However, the disclosure of entire browsing histories, even in form of anonymous data sets, poses a huge threat to user privacy. Although some existing solutions have

With the development of multiaccess edge computing (also called mobile-edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired Quality of Service (QoS). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to limited resources, heterogeneous servers, and different geographical locations

Previous work on misbehavior detection and trust management for vehicle-to-everything (V2X) communication security is effective in identifying falsified and malicious V2X data. Each vehicle in a given region can be a witness to report on the misbehavior of other nearby vehicles, which will then be added to a “blacklist.” However, there may not exist enough witness vehicles that are willing to opt-in

In recent years, with an increasing number of Internet of Things (IoT) devices, general cloud computing mode is hard to process large amounts of data with high Quality of Service (QoS). Edge computing is put forward to relieve the pressure of cloud servers, but most of them only focused on allocating tasks depending on cloud servers or edge servers with the virtualization technology. Resource-constrained

Under horizontal federated learning (HFL) in the Internet of Things (IoT) scenarios, different user data sets have significant similarities on the feature spaces, the final goal is to build a high-performance global model. However, not all features are great contributors when training the global HFL model, some features even impair the HFL. Besides, the curse of dimension will delay the training time

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EVs), making the data acquisition scenario gradually extend to the Internet

IoT devices offer insufficient protections against exploitation of critical programming errors (such as buffer overflows) it is therefore paramount to sufficiently test IoT software before deployment. A central source of these errors is the implementations of stateful network protocols used in the IoT (e.g., MQTT-SN). Unfortunately, comprehensive automated testing of such protocol implementations is

Location-based services have become prevalent and the risk of location privacy leakage increases. Most existing schemes use third-party-based or third-party-free system architectures; the former suffers from a single point of failure (SPOF) and the latter experiences a heavy load on user terminal equipment and higher communication costs. Ensuring location privacy while lowering system overhead becomes

With the rich sensing ability and extensive usage of various sensors, mobile crowdsensing (MCS) has become a new paradigm to collect sensing data for various sensing applications. In the modern urban environment, the multisource sensing information and the difference of mobile users make the sensing scenario more and more complex. To improve the applicability of different sensing scenarios, it is necessary

With the development of edge computing technology, the resource-limited Internet of Things (IoT) devices can offload computation-intensive artificial intelligence tasks, such as model training and inference to edge servers through resource trading. However, due to the increase in the number of intelligent applications and the rise of peer-to-peer (P2P) resource trading, the existing resource trading

Nowadays, as an essential technique for intelligent transportation, vehicular ad hoc networks (VANETs) has significantly improved people’s travel experience, providing richer, and smarter services for vehicles while ensuring driver safety. However, considering that VANETs are complex, some security challenges still remain, including but not restricted to privacy preserving of vehicles, authentication

The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The massive deployment of IoT devices, including sensors and actuators, is ushering in smart and connected communities of the future. The availability of real-time and high-quality sensor data is crucial for various IoT applications, particularly

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless

Weighted multidimensional scaling (WMDS), an algorithm extending multidimensional scaling (MDS), has been utilized in a broad spectrum of localization. However, there are still two unsolved theoretical questions: 1) The estimator provided by MDS depicts a relative placement of targets which require further Procrustes analysis to recover the actual placement, referred to as the absolute placement. This

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized

The evolution of the Internet of Things (IoT) has been driving the explosive growth of deep neural network (DNN)-based applications and processing demands. Hence, edge computing has emerged as a potential solution to meet these processing requirements. However, emerging IoT applications have increasingly demanded to run multiple DNNs to extract multifaceted knowledge, requiring more computational resources

Incorporating wireless power transfer (WPT) into mobile-edge computing (MEC) is an effective way to enhance the self-sustainability of the MEC network. However, it is susceptible to the effect of double-far-near. Meanwhile, inspired by the fact that nonorthogonal multiple access (NOMA) has shown its great potential in improving the spectral efficiency of the network over orthogonal multiple access

Nowadays, face recognition technology has been dramatically boosted by the advances in deep learning and big data fields. However, this also poses grand challenges in protecting personal identity information in intelligent applications of the Internet of Things (IoT). Existing methods based on the $K$ -Same algorithm have low effectiveness for protecting personal identity while preserving face attributes

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced indoor positioning system (IPS) has to handle diverse devices and the inherent heterogeneity in received signal strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting

Recently proposed split learning (SL) is a promising distributed machine learning paradigm that enables machine learning without accessing the raw data of the clients. SL can be viewed as one specific type of serial federation learning. However, deploying SL on resource-constrained Internet of Things (IoT) devices still has some limitations, including high communication costs and catastrophic forgetting

Radar-based solutions have attracted great attention in human activity recognition (HAR) for their advantages in accuracy, robustness, and privacy protection. The conventional approaches transform radar signals into feature maps and then directly process them as visual images. While effective, these image-based methods may not be the best solutions in terms of representation efficiency to encode the

A benchmark study of modern distributed databases (DDBs) (e.g., Cassandra, MongoDB, Redis, and MySQL) is an important source of information for selecting the right technology for managing data in edge–cloud deployments. While most of the existing studies have investigated the performance and scalability of DDBs in cloud computing, there is a lack of focus on resource utilization (e.g., energy, bandwidth

In this work, we present a cloud-based system for noncontact, real-time recognition, and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition, and gait analysis. To train deep learning models

The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above,

With the explosive growth of content request services in the vehicle network, there is an urgent need to speed up the response process of content requests and reduce the backhaul burden on base stations (BSs). However, most traditional content caching strategies only consider the content popularity or cluster-based caching strategies individually, and the access paths are fixed. This article proposes

This work proposes a novel millimeter-wave (mm-wave) wideband circularly polarized (CP) antenna array for Internet of Things (IoT) applications. The proposed design addresses the issues of having IoT sensors deployed in remote locations or over large geographical regions. Elliptically shaped dielectric resonator antennas (DRAs) are used as array elements to improve radiation characteristics and achieve

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS consider time-division duplexing (TDD) systems, in which the same phase shifts can be applied to both uplink and downlink due to the channel reciprocity. However, for frequency-division

Federated learning (FL) is a distributed machine learning (ML) technique that allows numerous Internet of Things (IoT) devices to jointly train an ML model using a centralized server for help. Local data never leaves each IoT device in FL, so the local data of IoT devices are protected. In FL, distributed IoT devices usually collect their local data independently, so the data set of each IoT device

Performing an effective media access control (MAC) protocol suffers from strong dependencies between Underwater Acoustic Networks’ upper and lower layers: 1) the network layer frequently uses geo-routing protocols, which do not provide the specific next-hop for MAC protocols, resulting in serious data collisions and 2) in such scenarios with the unknown next-hop, fixed orthogonal frequency-division

Low-cost inertial measurement unit (IMU) is gradually applied for providing reliable positioning and navigation information in the area of Internet of Things (IoT) applications recently. However, the accuracy of IMU is highly influenced by inertial sensor errors in global navigation satellite system-denied and indoor navigation environment. In order to improve the accuracy and robustness of IMU, rotation

Histopathology diagnosis is an important standard for breast tumors identifying. However, histopathology image analysis is complex, tedious, and error-prone, due to the super-resolution image. In recent years, deep learning technology has been successfully applied to histopathology image analysis and made great progress. The well-known deep neural networks usually have tens of million parameters, which

The pioneering field of tactile Internet (TI) will enable the transfer of human skills over long distances through haptic feedback. Realizing this demands a roundtrip latency of sub-5 ms. In this work, we investigate the capability of Wi-Fi 6 and existing TI scheduling/multiplexing schemes in meeting this stringent latency constraint. Taking the concrete example of the state-of-the-art video-haptic

Duty cycle management is critical for the energy-neutral operation of batteryless devices. Many efforts have been made to develop an effective duty cycling method, including machine-learning-based approaches, but existing methods can barely handle the dynamic harvesting environments of batteryless devices. Specifically, most machine-learning-based methods require the harvesting patterns to be collected

As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQCs) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This article verifies the potential of QRL, which will be further realized by implementing quantum multiagent

Mobile-edge computing (MEC) raises the problem of selfish user devices that utilize less computing resources than expected to execute offloading tasks or maliciously discard computation tasks. However, most of the existing work either focused on the task offloading or concentrated on the trust mechanism in MEC systems. By jointly considering the two challenges, in this article, we propose a selfish-aware

Precisely synchronized communication is a major precondition for many industrial applications. At the same time, hardware cost and power consumption need to be kept as low as possible in the Internet of Things (IoT) paradigm. While many wired solutions on the market achieve these requirements, wireless alternatives are an interesting field for research and development. This article presents a novel

Positioning, navigation, and timing (PNT) is essential for Internet of Things (IoT) communications and location-based services. Although global navigation satellite system (GNSS) can provide accurate PNT in open areas, obtaining reliable PNT is still a considerable technical challenge in complex urban environments. This is because the GNSS signals are more likely to be affected by multipath interference

Vehicular sensing has become attracting an increasing research interest for cost-effective monitoring in urban areas. Even though multiple types of sensing data are required to form a multidimensional sensing map in urban sensing applications, most of the previous works have only considered the sensing quality of single sensor type. In this article, we formulate an optimization problem of task allocation

Emotion recognition or detection is broadly utilized in patient–doctor interactions for diseases, such as schizophrenia and autism and the most typical techniques are speech detection and facial recognition. However, features extracted from these behavior-based emotion recognitions are not reliable since humans can disguise their emotions. Recording voices or tracking facial expressions for a long

As indoor space is the primary place for pedestrian activities, obtaining intelligent monitoring of indoor pedestrians is crucial for intelligent video surveillance. Previous studies have verified the effectiveness of spatiotemporal constraints in multitarget multicamera tracking (MTMCT). Pedestrians are generally subjected to fine spatiotemporal constraints within buildings, based on which the indoor

Since 2020, the coronavirus disease (COVID-19) pandemic has had a substantial impact on all community sectors worldwide, particularly the healthcare sector. Healthcare workers (HCWs) are at risk of COVID-19 infection due to occupational exposure to infectious patients, visitors, and staff. Contact tracing of close physical interaction is an essential control measure, especially in hospitals, to prevent

Indoor positioning system plays a key role in location-based services since the widely used global navigation satellite system (GNSS) is denied in indoor scenarios. Crowdsensing or walking-surveying-based indoor positioning is proposed aiming at providing low-cost and high-efficient 3-D location. This article proposes a crowdsensing/walking-surveying 3-D indoor positioning system by fusing the crowdsensed

Recent advances in wearable devices and Internet of Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging. In addition, data generated by different users bear various personal attributes and edge heterogeneity, rendering it impractical to develop a global model that adapts well to all users

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

As my term as the Editor-in-Chief (EiC) of IEEE Internet of Things Journal (IoT-J) has ended, I would like to share my appreciation for all of you, including authors and IoT-J team. I have been serving as the EiC since 1 January 2020, just before the pandemic. I am thankful that the pandemic is mostly behind us, and I am happy that our editorial board members have been able to work on making the Journal

Device-to-device (D2D) caching is becoming prevalent in relieving network congestion. However, there remain challenges in exploring efficient D2D caching strategies due to the diverse user requirements. In this article, we propose a social-aware D2D caching scheme that integrates the concept of social incentive and recommendation with D2D caching decision making. First, we investigate federated learning

Keeping sensor data fresh is desired for Industrial Internet of Things (IIoT), especially, in real-time monitoring applications. However, this may require sensors always in active mode and, thus, incur low energy efficiency. In this article, we consider that a wireless sensor monitors a dynamical system and reports real-time measurements to a processing center through an unreliable wireless channel