Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best case scenario regarding the participating clients and the communication channels. However, these assumptions are often not met in real-world applications. Asynchronous settings can reflect a more realistic environment

In this article, we propose a multi-functional reconfigurable intelligent surface (MF-RIS) to address the half-space coverage and double-fading attenuation issues faced by existing RISs. By simultaneously reflecting, refracting, and amplifying the incident signal, the proposed MF-RIS is capable of realizing full-space coverage with the mitigated signal degradation. The operation principle of the MF-RIS

Three-dimensional (3-D) understanding or inference has received increasing attention, where 3-D convolutional neural networks (3D-CNNs) have demonstrated superior performance compared to 2D-CNNs, since 3D-CNNs learn features from all three dimensions. However, 3D-CNNs suffer from intensive computation and data movement. In this article, Sagitta, an energy-efficient low-latency on-chip 3D-CNN accelerator

Integrated sensing and communication (ISAC) has been treated as a key technology for providing high-quality performance on both communication and sensing with higher spectrum efficiency and lower hardware cost. Among the research on ISAC, joint radar and communication (JRC) is one of the typical scenarios. Due to remaining challenges on the design for full-duplex hardware and interference depression

Multisource navigation involves using multiple available sensors to achieve high-precision location services, but navigation devices and data are susceptible to various environments and attacks, emphasizing the need for pervasive security measures like data reliability evaluation. Common reliability estimation methods for Internet of Things data are weak for highly dynamic navigation data, which are

Recent advances in the Internet of Things are leading to a proliferation of smart devices in our daily life. Having so many connected devices around us potentially introduces new witnesses that can be a reference for forensic investigations. For these reasons, IoT Forensics has become a popular research area with the goal of extracting information from IoT devices to be used as potential evidence.

With the rapid expansion of the Internet of Things, a vast number of microcontroller-based (MCU) IoT devices are now susceptible to attacks through the Internet. Vulnerabilities within the firmware are one of the most important attack surfaces. Fuzzing has emerged as one of the most effective techniques for identifying such vulnerabilities. However, when applied to IoT firmware, several challenges

This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment

Industrial wireless sensor networks (IWSNs) play a key role in the Industry 4.0 revolution. The network infrastructure is critical to interconnect sensors and actuators and needs to respect key performance indicators. IEEE 802.15.4-TSCH is a candidate technology for IWSN since it relies on scheduled transmissions and frequency hopping to make the network more reliable. However, distributed scheduling

Thanks to their high-flexibility and low-operational cost, unmanned aerial vehicles (UAVs) can be used to support mission-critical applications in the Internet of Things (IoT). However, due to the limited onboard energy, it is difficult for UAVs to provide continuous data collection. In this article, we study the problem of rechargeable-UAV-aided timely data collection in IoT networks, where the UAV

Abundant real-time applications over Internet of Things (IoT) have imperative demands on timely information. Compared to average Age of Information (AoI), distribution of AoI characterizes the timeliness in more details. This article studies the timeliness of an IoT-based multisource status update system. By modeling the system as a multisource M/G/1/1 bufferless preemptive queue, general formulas

As an emerging technology, mobile edge computing (MEC) network paradigm provides great computing potential for edge services, which has been widely applied in friendly city environment. However, there are still many challenges to deploy MEC technology in harsh tactical communication environment due to poor communication conditions, limited computational resources, and hostile malicious interference

As electric vehicle (EV) demand increases, so does the demand for efficient smart charging (SC) applications. However, SC is only acceptable if the EV user’s mobility requirements and risk preferences are fulfilled, i.e., their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration

Internet of Underwater Things (IoUT) needs to maintain effective communication even under the circumstances of severe environments and limited energy. Named data networking (NDN), a future network architecture, is starting to be used for IoUT as an effective architecture implementation. Despite having a good performance of data transmission, Underwater named data networking (UNDN) nevertheless faces

Malware traffic classification (MTC) plays an important role for securing the Internet of Things (IoT). Many machine learning (ML) and deep learning (DL)-based MTC methods have been proposed in recent years. However, the former still requires human intervention, while the latter incurs considerable computation overheads. To address these problems, we propose a broad learning (BL)-aided MTC method (BL-MTC)

Multiple-input–multiple-output (MIMO) and long range (LoRa) have been synergistically combined to better support a variety of Internet of Things (IoT) applications. This letter investigates the problem of demodulation for a MIMO-LoRa system. The existing demodulation strategy for MIMO-LoRa is based on the maximum-likelihood criterion, which, however, suffers from its high computational complexity.

Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions (CSTs) can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded

The standardization of the fifth-generation (5G) communications has been completed, and the visioning and planning of the sixth-generation (6G) communications have begun, with an objective of casting the high technical standard of new spectrum, high time and phase synchronization accuracy, and 100% geographical coverage to flexibly and efficiently connect upper trillion-level devices in the future

Unmanned aerial vehicles (UAVs) are well known for violating citizen’s privacy either inadvertently or deliberately. However, UAVs could be victims of privacy violations themselves in the sense that an adversary observing a UAV can infer its destination. This article proposes several privacy-preserving mechanisms (PPMs) for protecting a UAV’s location privacy. In particular, we address the privacy

Along with the ever-increasing amount of data generated from industrial devices, the cross domain [also known as autonomous systems (ASs)] data transmission problem has attracted more and more attention in the Industrial Internet of Things (IIoT). As mature and widely used interdomain routing protocols, border gateway protocol-based solutions often take the number of domains (i.e., AS hops) of each

The presence of a jammer in an Internet of Things (IoT) network severely degrades all communication efforts between adjacent wireless devices. The situation is getting worse due to retransmission attempts made by affected devices. Therefore, jammers must be detected or localized quickly to activate a series of corrective countermeasures so as to ensure the robust operation of the IoT network. This

The employment of broadband/dual-band ultrahigh frequency (UHF) radio-frequency identification (RFID) tag antennas contributes to the growth of RFID technology, with many potential implications, such as the increase of international trade, and reducing costs thereof. This study presents all reported articles on RFID tags for metal objects that can work seamlessly across different countries. Moreover

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This article proposes

To provide a dependency-aware application, multiple unmanned aerial vehicles (UAVs) are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV-enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we

Unmanned aerial vehicles (UAVs) will be widely deployed due to their flexibility, mobility, and miniaturization, providing the necessary support for spectrum sharing between different communication systems in the space–air–ground-integrated IoT network (SAGIN). However, there are potential security threats to spectrum sharing among different communication systems due to the openness of a wireless network

The unmanned aerial vehicles (UAVs) are envisioned as promising aerial facilities for providing advanced communication services as well as sensing functionalities in the next-generation wireless system. This article considers a UAV-enabled integrated sensing and communications (ISACs) system, where a moving ground user (GU) is simultaneously tracked by multiple UAVs and receives the downlink communication

Facing the explosive growth of data, the introduction of cloud computing in the Space-Air-Ground Integrated Internet of Things (SAGIIoT) can solve the problem of limited computing power of the terminals. At the same time, data security on the cloud is also a focus that cannot be ignored. Secure outsourcing computing is helpful in improving privacy preserving. Due to the wide applicability of $K $ -means

We investigate a computing task scheduling problem in space–air–ground integrated network (SAGIN) for Internet of Remote Things (IoRT). In the considered scenario, the unmanned aerial vehicles (UAVs) collect computing tasks from IoRT devices and make offloading decisions, in which the tasks can be computed at the UAVs or offloaded to the low Earth orbital (LEO) satellite. The optimization objective

Recently, buoyed by advances in the space industry, low Earth orbit (LEO) satellites have become an important part of the Internet of Things (IoT). LEO satellites have entered the era of a big data link with IoT, how to deal with the data from the satellite IoT is a problem worthy of consideration. Conventional object detection method in optical remote sensing simply transmits the raw data to the ground

Artificial intelligence (AI) and machine learning (ML) are widely employed to make the solutions more accurate and autonomous in many smart and intelligent applications in the Internet of Things (IoT). In these IoT applications, the performance and accuracy of AI/ML models are the main concerns; however, the transparency, interpretability, and responsibility of the models’ decisions are often neglected

Marine Internet of Things (IoT) systems have grown substantially with the development of nonterrestrial networks (NTNs) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance, and sea transportation. Due to the unpredictable climate changes and the extreme channel conditions of maritime networks, however, it is challenging

Millimeter wave (mm-wave) antenna modules with switchable beam radiation provide a potential solution for enabling flexible wireless links and high data rate in Internet of Things (IoT) networks. Aiming to simultaneously fulfill a wide coverage and follow the cost-effective principle of IoT devices, a novel mm-wave wideband antenna module with switchable fan-beam radiation is proposed based on an air-filled

Recent years, the rapid development of Internet of Things (IoT) technologies and applications have been witnessed. Three important features are characterized in modern IoT applications: 1) device heterogeneity; 2) long-range communication; and 3) cloud/edge-device integration. Difficulties are raised by the above features toward IoT application developers, e.g., predicting and evaluating the performance

This work proposes an energy-adaptive monitoring system for a smart farm using solar sensors attached to cows. The proposed system aims to achieve a high monitoring quality in the smart farm under fluctuating energy and cyber attacks disrupting the collection of sensed data from solar sensors, such as protocol noncompliance, false data injection, denial-of-service, and state manipulation. We adopt

We investigate in this article joint trajectory design and resource allocation for secure air–ground integrated Internet of Things (IoT) networks with unmanned aerial vehicle (UAV) jamming and device-to-device (D2D) enhancement. By jointly optimizing ground user (GU) scheduling, UAV flight trajectory, and transmit power, we are able to maximize the minimum system secrecy rate of UAV and D2D communications

Space–air–ground-integrated networks (SAGINs) are receiving a lot of attention as a candidate for an extension to nonterrestrial networks beyond the limit of terrestrial networks. SAGIN can be a means to satisfy various Quality of Service (QoS) by providing several communication links in different characteristics. To fully utilize the advantage of SAGIN, a protocol for informing user equipment (UE)

This article analyzes existing smart contract-based public data integrity verification schemes and identifies certain weaknesses. First, the fair arbitration mechanism deployed in these schemes fails to meet the users’ requirements as it may not promptly notify users of data corruption or loss. Second, to ensure outsourced data confidentiality, existing data integrity schemes use a conventional encrypted

Motivated by ever-increasing computational resources at edge devices and increasing privacy concerns, a new machine learning (ML) framework called federated learning (FL) has been proposed. FL enables user devices, such as mobile and Internet of Things (IoT) devices, to collaboratively train an ML model by only sending the model parameters instead of raw data. FL is considered the key enabling approach

Optimized polar-coded cooperation with an approximate prior probability-based threshold decision is proposed to improve the reliability and complexity in selective decode-and-forward (SDF) cooperation of multiple satellite constellation systems. First, an SDF scheme with polar coding is adopted in cooperative communications for efficient code construction and relay forwarding. Second, in the logarithmic

Vehicular ad hoc network (VANET) enables a more efficient and secure traffic environment by integrating several emerging technologies. The technology of certificateless aggregate signature (CLAS) is widely utilized to guarantee secure communications in VANET. However, the legitimation of vehicles’ public keys cannot be verified effectively in existing CLAS protocols for VANET. Thus, a malicious key

In the age of Internet of Things where information is explosively growing, people pay more attention on personal privacy. In the real-world task-incremental scenario for biometrics, every edge device faces continuous task flows of private data without communication with others. security and performance are the primary concerns in identity authentication, and federated continual learning (FCL) is a

In this article, we investigate sequential power allocation over fast varying channels for mission-critical applications, aiming to minimize the expected sum power while guaranteeing the transmission success probability. In particular, a reinforcement learning framework is constructed with appropriate reward design so that the optimal policy maximizes the Lagrangian of the primal problem, where the

Federated learning (FL) could provide a promising privacy-preserving intelligent learning paradigm for space–air–ground-integrated Internet of Things (SAGI-IoT) by breaking down data islands and solving the dilemma between data privacy and data sharing. Currently, adaptivity, communication efficiency and model security are the three main challenges faced by FL, and they are rarely considered by existing

In this article, an intelligent reflecting surface (IRS)-assisted integrated satellite-unmanned aerial vehicle (UAV)-terrestrial (SUT) Internet of Things (IoT) network faced with a smart jammer under imperfect channel state information (CSI) conditions is considered. We propose a Stackelberg game model to describe the adversarial relationship between the satellite, UAV, and IRS and the jammer, which

In unmanned aerial vehicle (UAV) performing tasks, the UAV often faces electricity shortages. The traditional scheme to charge a UAV needs to return to the ground. Using the charging UAV (CUAV) can avoid the waste of electricity caused by the return. However, the existing works only consider a fixed charging location for electricity replenishment. Moreover, fewer works focus on the matching relationship

The general data protection regulation (GDPR) is a European Union (EU) data protection and privacy law. According to the GDPR, the data on a hosting platform must meet semantic consistency and data integrity requirements. Semantic consistency means that the data operation should comply with the GDPR, while data integrity is meant to ensure that the outsourcing data should be intact. The two terms are

We study the deployment of an unmanned aerial vehicle (UAV) network to provide urgent communications to people trapped in a disaster zone, where each UAV is an aerial base station in the air. Unlike most existing studies that assumed that each user communicates with a UAV directly, we introduce Device-to-Device (D2D) communications, in which a user within the communication range of a UAV can serve

With the application of the Internet of Things (IoT), smart charging piles, which are important facilities for new energy electric vehicles (NEVs), have become an important part of the smart grid. Since the smart charging piles are generally deployed in complex environments and prone to failure, it is significant to perform efficient fault diagnosis and timely maintenance for them. One of the key problems

Recently, with the rapid improvement of e-health technology, a large amount of precious medical data has been accumulated in different entities, such as hospitals, clinics, and medical institutions, promoting the development of data sharing in e-health services. However, most of them lacks fine-grained functionalities: selective linkability and traceability, which are critical in an e-health environment

With the rise of the Internet of Things (IoT), tiny devices capable of computation and data transmission are being deployed across various technological domains. Due to the wide deployment of these devices, manual setup and management are infeasible and inefficient. To address this inefficiency, intelligent procedures must be established to enable autonomy that allows devices and networks to operate

To face the challenges in emergency scenarios, a multitask and multiobjective optimization algorithm for computation offloading and relay communication is investigated for the air-ground integrated networks, composed of unmanned aerial vehicles (UAVs), emergency vehicle users (EVUs) and ground sensor nodes (GSNs). We propose an HFL-DDQN algorithm, which combines horizontal federated learning (HFL)

Specific emitter identification (SEI) is crucial for attacking and defending Internet of Things (IoT) devices in untrusted scenarios or battlefield environments. However, existing SEI methods usually require annotation information, which is often unavailable in noncooperative communications and untrusted scenarios. In this article, we propose a signal contrastive self-supervised clustering (SCSC) method

This article presents an authentication and key agreement protocol for users who want to have access to constrained sensor nodes deployed in the field, e.g., doctor with healthcare nodes of patient. Both sensor and user device provide direct multifactor authentication relying on physical unclonable functions and biometrics, respectively. In addition, our scheme offers protection against the presence

With the emerging concept of satellite mega-constellations (SatCons), connections are about to become faster and broader, building an engine to boost innovation and productivity for every industry. In the following decades, over a hundred of SatCons with enormous application potentials are on the cusp of deployment, and their long-term success hinges on reliable information exchanges. We identify the

Unmanned aerial vehicles (UAVs)-assisted multiaccess edge computing (MEC) has emerged as a promising solution in B5G/6G networks. The high flexibility and seamless connectivity of UAVs make them well suited for providing enhanced communications coverage and efficient computing support. Particularly, in situations where ground facilities may be compromised or communication is unreliable. In this article

Transforming physical surfaces into virtual interfaces can extend the interaction capability of many exciting metaverse applications in the future. Recent advances in vibration-based tap sensing show promise for this vision using passive vibration signals. However, current approaches based on Time-Difference-of-Arrival (TDoA) triangulation suffer the impact of fluctuant wave velocity due to the dispersive

Under the outbreak of the COVID-19 pandemic, respiration state monitoring plays an important role in assisting respiratory disease diagnosis and treatment. Thanks to the nonintrusive nature and low deployment cost, Wi-Fi-based wireless respiration state monitoring methods have gained increasing popularity. By analyzing the variation of channel state information (CSI) of Wi-Fi signals, the respiration

Multicarrier-based frequency splitting simultaneous wireless information and power transmission (FS-SWIPT) signals are very prone to nonlinear effects due to the combination of high-power energy harvesting (EH) subcarriers with low-power data subcarriers. In this article, we study the impact of nonlinear effects in FS-SWIPT signals, as well as ways to minimize these effects on the data transmission

Human gesture recognition with radio frequency (RF) signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of RF signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when

In this article, the fully distributed neural learning algorithms by neural network approximation for networked multiagent reinforcement learning (NMARL) are studied. To tackle the convergence analysis of methods in NMARL with tremendous state-action space, most of the existing distributed algorithms are designed by linear function approximation, which however would fall into a situation of poor expression