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Internet of Things (IoT) refers to an ecosystem where applications and services are driven by data collected from devices interacting with each other and the physical world. Although IoT has already brought spectacular benefits to human society, the progress is actually not as fast as expected. From network structures to control flow graphs, IoT naturally generates an unprecedented volume of graph

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Blockchain, as a constantly evolving Peer-to-Peer (P2P) distributed ledger technology with characteristics, such as decentralization, security, interoperation, and trust establishment, can potentially lower the costs of the underpinning infrastructure and maintenance compared with conventional centralized systems. Consequently, the distributed structure of blockchain is naturally suitable for the Internet

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An Analysis of many IoT deployments showed that most of them can address the sustainable development goals (SDGs) and the UN’s 2030 agenda. Interestingly, most of these projects concentrate on five SDGs: 1) industry, innovation, infrastructure; 2) smart cities and communities; 3) affordable and clean energy; 4) good health and well-being; and 5) responsible production and consumption. Examples include

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Internet of Vehicles (IoV) is one of the most promising applications of Internet of Things (IoT) in the automotive industry, which can empower moving vehicles to exchange information with neighboring cars, roadside infrastructure, remote servers, traffic control centers, and so on. IoV expects to support a wide range of vehicular services, such as road safety, path planning, infotainment, and smart

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As The trajectory of the Internet of Things (IoT) is moving at a rapid pace, most companies and organizations are awash and drowning in massive amounts of data. Determining how to profit from data deluge and unlock its value can give companies an edge in the market because data have the potential to add tremendous value to many aspects of a business [A1]. The market has already seen a level of monetization

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Programmable logic controllers (PLCs), i.e., the core of control systems, are well-known to be vulnerable to a variety of cyber attacks. To mitigate this issue, we design PLC-Sleuth, a novel noninvasive intrusion detection/localization system for PLCs, which is built on a set of control invariants—i.e., the correlations between sensor readings and the concomitantly triggered PLC commands—that exist

Emerging Internet of Things (IoT) applications, such as cashier-less shopping, mobile ads targeting, and geo-based augmented reality (AR), are expected to bring us much more convenience and infotainment. To realize this amazing future, we need to feed these applications with user locations of (sub)meter-level resolution anytime and anywhere. Unfortunately, many widely used location sources are either

Reinforcement learning (RL) is increasingly being used to optimize resource-constrained wireless Internet of Things (IoT) devices. However, existing RL algorithms that are lightweight enough to be implemented on these devices, such as $Q$ -learning, converge too slowly to effectively adapt to the experienced information source and channel dynamics, while deep RL algorithms are too complex to be implemented

Due to the inherent openness of wireless channels and the restriction of communication resources and energy supply, the privacy protection of the sensing data transmission in the security-critical Internet of Medical Things (IoMT) has become a great challenge. In order to guarantee the privacy of IoMT sensing and transmission in a wireless wiretap channel and reduce the power consumption, a privacy-aware

With accelerated advances in various technologies, drones, better known as unmanned aerial vehicles (UAVs), are increasingly commonplace and consequently have a more pronounced impact on society. For example, Internet of Drones (IoD), a new communication paradigm offering fundamental navigation assistance and access to information, has widespread applications ranging from agricultural drones in farming

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When a UAV broadcasts its location periodically during flight, a target will observe the strongest signal when the UAV is at the closest location; thus, the target can infer its location based on the observed signals and the UAV’s trajectory. We consider how to design trajectories for UAVs such that all targets can locate themselves, and the maximum length of trajectories is minimized. To make the

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computer vision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature, owing to

The paradigm of Internet of Vehicles (IoV) as an extension to the Internet of Things (IoT) concept can assist the development of smart cities. IoV relies on vehicular communications to allow vehicles to communicate in real time with other vehicles, roadside infrastructure, and pedestrians. The main goal is to enable not only road safety services but also time-constrained IoT applications. In this context

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The last two decades have seen a clear trend toward crafting intelligent vehicles based on the significant advances in communication and computing paradigms, which provide a safer, stress-free, and more enjoyable driving experience. Moreover, emerging applications and services necessitate massive volumes of data, real-time data processing, and ultrareliable and low-latency communication (URLLC). However

With the rapid increase of smart devices in the Internet of Things, more and more visible light (VL) systems, e.g., VL communication (VLC) and VL sensing (VLS), are developed on the existing light fixtures to offload the current crowded spectrum, how to guarantee the authenticity of the VL signal in these systems becomes an urgent problem. This is because almost all of today’s light fixtures are unprotected

The Internet of Things (IoT) is an information service paradigm based on the integration of smart objects, mobile devices, and computers via the Internet. IoT technologies are key enablers for a multitude of applications in diverse fields, such as digital health, smart city, industrial automation, and supply chain. This raises new security and privacy challenges that can be addressed by advanced cryptographic

Running computer vision applications, such as 3-D simultaneous localization and mapping (SLAM), on mobile devices requires low-latency responses and a massive amount of computation. Edge computing has been introduced to move Cloud features closer to end users, providing necessary computing and network resources for end devices. The heterogeneous edge devices, with different hardware architectures (e

Molecular communication and the Internet of Nanothings (IoNT) are emerging research hotspots recently, which show great potential in biomedical applications inside the human body. However, how to transmit information from inside body IoNTs to outside devices is seldomly studied. It is well known that the nervous system is responsible for perceiving the external environment and controlling the feedback

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Intelligent tires are one key element for autonomous self-driving cars and the Internet of Vehicles (IoV), especially if the battery-driven sensor systems attain the transition to be self-powered. Harvesting vibrational energy of these tire pressure monitoring systems (TPMSs) is the promising technique to make tire sensors self-sufficient and to provide additional energy for sophisticated algorithms