Collaboration opportunities for devices are facilitated with Federated Learning (FL). Edge computing facilitates aggregation at edge and reduces latency. To deal with model poisoning attacks, model-based outlier detection mechanisms may not operate efficiently with hetereogenous models or in recognition of complex attacks. This paper fosters the defense line against model poisoning attack by exploiting device-level traffic analysis to anticipate the reliability of participants. FL is empowered with a topology mutation strategy, as a Moving Target Defence (MTD) strategy to dynamically change the participants in learning. Based on the adoption of recurrent neural networks for time-series analysis of traffic and a 6G wireless model, optimization framework for MTD strategy is given. A deep reinforcement mechanism is provided to optimize topology mutation in adaption with the anticipated Byzantine status of devices and the communication channel capabilities at devices. For a DDoS attack detection application and under Botnet attack at devices level, results illustrate acceptable malicious models exclusion and improvement in recognition time and accuracy.

Advancements in deep learning and the Internet of Things (IoT) enable early fire detection through vision-based systems, reducing ecological, social, and economic damage. These systems necessitate lightweight, cost-effective convolutional neural networks (CNNs) for real-time operation. Effective deployment on AI-assisted edge devices is crucial for optimal performance. To mitigate this problem, we present the optimized fire attention network (OFAN) for effective and efficient fire detection. In the re-engineered attention block, we swapped the convolution layers by dilated variants and integrated additional dense layers to capture global context and refine more weight optimization. We calibrate the OFAN for real-time processing using a lightweight and efficient feature extractor backbone model. Additionally, a challenging fire dataset is a critical contribution that contains extremely diverse, blazing, and non-fire, captured in lighting and foggy environments. It advances traditional fire detection samples by considering low-light and foggy conditions. A comprehensive experiment is conducted over three widely used fire detection datasets, and our proposed OFAN outperforms state-of-the-art. The proposed OFAN achieved 96.23%, 96.54% and 94.63% accuracies over BoWFire, FD and the newly proposed DiverseFire dataset, respectively. Our research sets a standard for fire detection over edge devices, offering improved accuracy and better frames per second (FPS) performance.

This contribution quantifies the achievable physical layer security of wireless transmission over realistic composite fading conditions in the presence of multiple eavesdroppers (Eves). To this end, exact closed-form analytic expressions are derived for the achievable secure outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) for the case of$\mathcal{F}$composite fading channels. The derived expressions are tractable and their validity is justified through comparisons with respective computer simulation results. In addition, they allow for the development of useful theoretical and practical insights on the effect of the presence of numerous eavesdroppers under different multipath fading and shadowing conditions, as these are encountered in realistic wireless communication scenarios.

In this contribution we evaluate the transmission of confidential information over ${\mathcal{F}}$ composite fading channels in the presence of an eavesdropper (Eve) who also experiences ${\mathcal{F}}$ composite fading. Upon obtaining tractable closed-form expressions for the secure outage probability and the probability of strictly positive secrecy capacity, we analyze extensively the achievable physical layer security performance in the context of mm-wave communications. This is realized with the aid of extensive measurement results from realistic communication scenarios, which show the behavior of composite ${\mathcal{F}}$ fading channels in device-to-device communication scenarios. The offered results provide meaningful insights of theoretical and practical importance that are expected to be useful in the design of mm-wave based communication systems.

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 of these technologies resulting in a fundamental paradigm shift unlocking vast benefits and opportunities across vertical markets. However, there are still several barriers, such as a lack of consensus toward any reference models or best practices, hindering the full fusion of these technologies [A1]. To tackle these challenges and facilitate this promising transformation, this special issue was organized to provide a holistic multidisciplinary reference for solutions, architectures, protocols, services, and applications addressing all aspects of the future generation of IoT systems via the fusion of edge, cloud, AI, and blockchain, while considering the corresponding challenges. Thanks to the enormous support from the Editor-in-Chief, Prof. Honggang Wang, and the dedicated work of many reviewers, after a rigorous review process, 27 excellent articles out of 125 submissions were accepted for inclusion in this special issue of the IEEE Internet of Things Journal. We introduce these papers and highlight their key contributions below.

The digital transformation is characterized by the convergence of technologies—from the Internet of Things (IoT) to edge–fog–cloud computing, artificial intelligence (AI), and Blockchain—in multiple dimensions, blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next-generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of the existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus toward any reference models or best practices. This article provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this article by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

The management of remote services, such as remote surgery, remote sensing, or remote driving, has become increasingly important, especially with the emerging 5G and Beyond 5G technologies. However, the strict network requirements of these remote services represent one of the major challenges that hinder their fast and large-scale deployment in critical infrastructures. This article addresses certain issues inherent in remote and immersive control of virtual reality (VR)-based unmanned aerial vehicles (UAVs), whereby a user remotely controls UAVs, equipped with 360° cameras, using their head-mounted devices (HMD) and their respective controllers. Remote and immersive control services, using 360° video streams, require much lower latency and higher throughput for true immersion and high service reliability. To assess and analyze these requirements, this article introduces a real-life testbed system that leverages different technologies (e.g., VR, 360° video streaming over 4G/5G, and edge computing). In the performance evaluation, different latency types are considered. They are namely: 1) glass-to-glass latency between the 360° camera of a remote UAV and the HMD display; 2) user/pilot’s reaction latency; and 3) the command/execution latency. The obtained results indicate that the responsiveness (dubbed Glass-to-Reaction-to-Execution—GRE–latency) of a pilot, using our system, to a sudden event is within an acceptable range, i.e., around 900 ms.

The explosion of the number of things connected to the Internet gave birth to a new set of services. Customers now are expecting next-generation networks to satisfy vertical applications with different requirements. For instance, 5G networks can carry these various demands by logically dividing the physical network into multiple slices, each slice features specific characteristics based on the type of service. Software-Defined Network (SDN) and Network Function Virtualization (NFV) introduced the term “Network Softwarization” which is the main enabler of network slicing and 5G networks. Specifically, SDN separates the control plane from the data plane, this concept brings many benefits such as dynamism, flexibility, and innovation. However, when it comes to the assurance of Quality of Service (QoS), SDN is still behind. Not only SDN was not optimized for real-time communications, but also SDN networks cannot offer a deterministic End-to-End (E2E) delay. In this paper, we study OpenFlow-based communications with a focus on the delay. The modeling of queuing delays shows a stable linear development of the mean waiting time under a probability of 0.2 that 10 switches generate packet_in message. After that, the increase becomes exponential and thus hard to predict.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

The Internet of Medical Things (IoMT) is an extension and specialization of that original Internet of Things (IoT) concept, and applies to the interconnectedness of devices, software applications, and data which are specific to the medical industry. IoMT can add smart technologies to medical devices to monitor the progression of a disease away from the doctor’s office and learn things that could impact future care guidelines and patients. It can also provide a better way to care for our elderly by tracking vitals and heart performance, glucose and other body systems, and activity and sleeping levels. During the outbreak of pandemic (e.g., COVID-19), IoMT can even be used to detect main symptoms ubiquitously using intelligent sensors and trace the origin of the outbreak based on aggregated IoT data (e.g., geographic mobile data and purchase history). Although most of the contemporary IoMT systems can measure risks, make decisions, and take actions automatically, the lack of emotion-aware abilities will be an obstacle to more harmonious human–machine interaction and more efficient medical process. Besides, mental disorders, such as depression, schizophrenia, and anxiety, have become a more noticeable cause of suffering. The integration of emotion-aware abilities into IoMT can also contribute to monitor emotional dysregulation continuously in subjects with mental disorders or undergoing serious pandemic such as COVID-19, and give these patients personalized therapy recommendations. Research on affective computing has defined a framework to recognize, interpret, and process human affects, but more research is needed to investigate its application to biomedical applications, especially “in the wild” and over extended periods of time, and how to integrate emotion-aware abilities into IoMT organically is still an open question. This special issue aims to create a platform for researchers, developers, and practitioners from both academia and industry to disseminate the state-of-the-art results and to advance the Emotion-Aware ubiquitous computing in IoMT.

With the advancement of Internet-of-Things (IoT) technology, various domains, such as smart factories and smart cars have used this new technology to provide value-added services. In addition, technologies, such as multiaccess edge computing (MEC) and network slicing provide another opportunity for the IoT to support more advanced and real-time services that could not have been previously supported. However, the simple integration of such technologies into the IoT does not take the full advantage of MEC and network slicing or the reduction of latency and traffic prioritization, respectively. Therefore, there is a strong need for an efficient integration mechanism for IoT platforms to maximize the benefit of using such technologies. In this article, we introduce a novel architectural framework that enables the virtualization of an IoT platform with minimum functions to support specific IoT services and host the instance in an edge node, close to the end user. As the instance provides its service at the edge node, where the MEC node and network slice are located, the traffic for the end user does not need to traverse back to the cloud. This architecture guarantees not only low latency but also efficient management of IoT services at the edge node. To show the feasibility of the proposed architecture, we conducted an experimental evaluation by comparing the transmission time of both IoT services running on the central cloud and those using sliced IoT functions in the edge gateway. The results show that the proposed architecture provides twice as much transmission speed as that from the conventional cloud-based IoT platform.

With the advancement of the Internet of Things (IoT), different use cases, such as smart factories, smart cities, and smart cars are being developed. Technologies, such as multiaccess edge computing (MEC) and network slicing are being researched to support various vertical use cases. However, simply utilizing network slicing and MEC is insufficient when providing a specific IoT service. Accordingly, there is a need for a method of arranging and operating common service functions (CSFs) used in the IoT platform at the edge of the network. In addition, it is necessary not only to deliver virtual CSFs but also to implement autoscaling policies of these virtual common service resources. This study extends the idea of resource slicing technologies for the IoT service layer. The IoT service layer exposes the virtual IoT CSFs as an IoT slice service to be provided in the form of virtual network functions (VNFs) with MEC applications on top of the network function virtualization infrastructure (NFVI) at the edge of the network. We propose a framework architecture for virtual IoT slice service orchestration, and illustrate how to instantiate the virtual IoT service functions in the existing centralized cloud to the MEC platform. We also propose an elastic computing algorithm of virtual IoT slice services functions(vIoT-SSFs) resources at the NFVI so that the IoT applications and underlying IoT resources can access vIoT-SSFs at the edge of the network.

The Digital economy is based on confidence in its trustworthiness. Blockchain distributed consensus provides a reliable and trustful network for financial and non-financial transactions. Blockchain-based electric vehicles (EVs) charging applications benefit blockchain features to provide automated and verifiable services for EV charging market. Requirements for feasible charging operation and privacy concerns are challenging issues with blockchain-based EV charging approaches. To provide a feasible charging ability and preserve EV owner's privacy, we introduce EVChain. The EVChain is a trustful and decentralized platform based on blockchain technology to share charging credits in the EV charging market. To share credits, the main blockchain in EVChain is connected to one or more subnetwork blockchains. We introduce an interconnection position to preserve EV owners' privacy with k-anonymity protection. We simulate and evaluate the privacy protection it provides, based on an example EV charging scenario.

Since the Internet-of-Things (IoT) has been introduced, it is considered as one of the emerging technologies providing great opportunities to many vertical industries. One of the major IoT application areas that gets significant attention is smart city. Since it is unrealistic to expect full convergence toward a single IoT platform in the near future, it is mandatory to enable interworking between different platforms based on multiple standards, coexisting in the emerging smart cities. In this paper, we take the example of two global IoT standards, FIWARE and oneM2M, which are actively used in many smart city projects, and analyze them to show the feasibility of IoT platforms interworking. Based on the analysis, we design and implement a novel IoT interworking architecture providing a semantic driven integration framework suitable for smart city. The core idea behind our approach is to introduce interworking proxies that: 1) conduct a static mapping of sensor information between IoT platforms and 2) perform semantic interoperability using semantically annotated resources via a semantic interworking proxy that dynamically discovers new kinds of information and adapts itself to enable automatic translation of semantic data between given source and target IoT platforms while it is running. We present the system based on these proxies and evaluate it in Santander smart city. The results demonstrate that it is able to discover and manage IoT sensors connected to both oneM2M and FIWARE. It appears that the semantic approach provides the flexibility and dynamic adaptivity needed for fast growing and rapidly changing urban environments.

This paper presents a mechanism for steering connections to different mobile networks for UAV-based reliable communications. This connection steering mechanism works by selecting the best Radio Signal Strength Indicator (RSSI) quality among the available networks in order to ensure the highest availability. In this work, we developed a test-bed to evaluate the performance of the steering mechanism. In addition, to mimic the mobility of UAVs, we analyze our work by applying Discrete Time Markov Chain (DTMC) to evaluate the performance of the testbed results. The results obtained from our analysis and testbed-based evaluation show the efficiency of the proposed connection steering mechanism. These results demonstrate the efficiency of the proposed connection steering mechanism in terms of data packet transmission rate and energy consumption saving.

Unmanned Aerial Vehicles (UAVs) are expected to be used everywhere to provision different services and applications, impacting different aspects of our daily lives. Basically, UAVs are characterized by their high mobility. Some may remain motionless for a specific time to perform pre-programmed missions. Whilst UAVs would be used for specific applications, they could additionally offer numerous IoT (Internet of Things) value-added services (VAS) when they are equipped with suitable IoT devices. Many IoT VAS applications require high amount of resources and/or diverse IoT devices that cannot be offered by a single UAV. In order to overcome this limitation, this paper aims to explore, i) the diversity of IoT devices on-board UAVs, and ii) the mobility of UAVs for offering UAVs-based IoT VAS. Two solutions are proposed for carrying out different IoT VAS. Both solutions are modeled using linear integer programming. While the first solution aims to reduce the energy consumption, the second one aims to shorten the response time. The simulation results demonstrate the efficiency of both solutions in achieving their design goals.

Security protocols are notoriously difficult to get right, and most go through several iterations before their hidden security vulnerabilities, which are hard to detect, are triggered. To help protocol designers and developers efficiently find non-trivial bugs, we introduce SYMCONF, a practical conformance testing tool that generates high-coverage test input packets using a conformance test suite and symbolic execution. Our approach can be viewed as the combination of conformance testing and symbolic execution: 1) it first selects symbolic inputs from an existing conformance test suite; 2) it then symbolically executes a network protocol implementation with the symbolic inputs; and 3) it finally generates high-coverage test input packets using a conformance test suite. We demonstrate the feasibility of this methodology by applying SYMCONF to the generation of a stream of high quality test input packets for multiple implementations of two network protocols, the Kerberos Telnet protocol and Dynamic Host Configuration Protocol (DHCP), and discovering non-trivial security bugs in the protocols.

Recent advances in 3GPP for device-to-device (D2D) communications underlying the cellular infrastructure have revealed several benefits regarding network resource management and increased coverage. Notwithstanding these advantages, peer discovery may significant amount of device battery if devices need to constantly sense and notify each other irrespective of the application subscriptions, and the potential of establishing D2D communications. In this paper, we consider a novel social application-based discovery mechanism that enables D2D UEs to achieve significant energy consumption savings, using the concept of social cloud region. Such a cloud-based region helps UE to activate D2D discovery features only when they are close enough to other, while having same application-based interests. The engagement of cloud applications provides the means of offloading the discovery process from not only UEs but also the LTE core network. In this paper we analyze the energy consumption profiles of various discovery mechanisms using simulations. Our simulation results validate that our proposed discovery mechanism results in significant energy savings by reducing the frequency of performing discovery procedures.

Implementations of network protocols, such as DNS, DHCP and Zeroconf, are prone to flaws, security vulnerabilities and interoperability issues caused by developer mistakes and ambiguous requirements in protocol specifications. Detecting such problems is not easy because (i) many bugs manifest themselves only after prolonged operation; (ii) reasoning about semantic errors requires a machine-readable specification; and (iii) the state space of complex protocol implementations is large. This article presents a novel approach that combines symbolic execution and rule-based specifications to detect various types of flaws in network protocol implementations. The core idea behind our approach is to (1) automatically generate high-coverage test input packets for a network protocol implementation using single- and multi-packet exchange symbolic execution (targeting stateless and stateful protocols, respectively) and then (2) use these packets to detect potential violations of manual rules derived from the protocol specification, and check the interoperability of different implementations of the same network protocol. We present a system based on these techniques, SymbexNet, and evaluate it on multiple implementations of two network protocols: Zeroconf, a service discovery protocol, and DHCP, a network configuration protocol. SymbexNet is able to discover non-trivial bugs as well as interoperability problems, most of which have been confirmed by the developers.

The secure and correct implementation of network protocols for resource discovery, device configuration and network management is complex and error-prone. Protocol specifications contain ambiguities, leading to implementation flaws and security vulnerabilities in network daemons. Such problems are hard to detect because they are often triggered by complex sequences of packets that occur only after prolonged operation. The goal of this work is to find semantic bugs in network daemons. Our approach is to replay a set of input packets that result in high source code coverage of the daemon and observe potential violations of rules derived from the protocol specification. We describe SYMNV, a practical verification tool that first symbolically executes a network daemon to generate high coverage input packets and then checks a set of rules constraining permitted input and output packets. We have applied SYMNV to three different implementations of the Zeroconf protocol and show that it is able to discover non-trivial bugs.