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Internet of Things (IoT) is becoming increasingly prevalent in transportation systems. The traffic system depends on safer, faster, and more intelligent vehicles. Vehicular networks [vehicle-to-vehicle (V2V) and vehicle-to- Infrastructure (V2I)] and automated driving technique are two of the cornerstone technologies enabling the construction of the future-generation highly functional and intelligent

The Internet of Things (IoT) offers new opportunities for cities to make citizens live and work in more sustainable, healthy, and safe places. Since IoT applications in smart cities are characterized by different devices, networking standards, and data management strategies, trust becomes a fundamental issue in the IoT ecosystem. The explosion of IoT devices, along with their decentralized deployment

With the emergence of the Internet of Things (IoT), billions of heterogeneous physical objects are connected through a network for collecting and sharing information, which can improve various aspects of daily lives, including smart living and transportation, and smart ambient environment, including smart city, smart home, smart agriculture, smart water, waste management, etc. The main objective of

The influence of the Internet of Things (IoT) and related produced data is destined to revolutionize economic and social society, even more incisively than the advent of digital. The creation of the IoT world would have been much more complicated if a big data structure had not been followed since the latter allows to analyze vast amounts of data. The IoT is the most significant flow of information

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Recent advances in artificial intelligence, edge computing, and big data have enabled extensive reasoning capabilities at the edge of the network. Edge servers are now capable of extracting meaningful intelligence from IoT nodes, which can benefit a very diverse set of IoT applications, including smart carrier and distribution networks (power, people, water, and food), smart agriculture and manufacturing

Internet of Multimedia Things (IoMT) applications generate huge volumes of multimedia data that are uploaded to cloud servers for storage and processing. During the uploading process, the IoMT applications face three major challenges, i.e., node management, privacy-preserving, and network protection. In this article, we propose a multilayer framework (PAAL) based on a multilevel edge computing architecture

Edge computing has enabled extensive reasoning capabilities at the edge of the network. Edge nodes have direct access to edge sources (e.g., sensors, devices) to read and process relevant data, while each sensor/device manufacturer uses their bespoke protocols in the Internet of Things (IoT) environment, hence, addressing compatibility problems to ensure seamless access has become a cumbersome task

With the rapid development of the Internet of Things (IoT), security and privacy of smart home systems based on IoT are more and more popular. As the key component of IoT, wireless communication and sensor technology are prerequisites for the security and confidentiality of smart home systems. The smart home systems integrate electronic information technology and computer control. By designing and

Stroke affects 33 million individuals worldwide every year and is one of the prime causes of paralysis. Due to partial or full paralysis, most of the patients affected by stroke depend on caregivers for the rest of their lives. Easy and efficient communication from the patient to the caregiver is a vital parameter determining the quality of life during rehabilitation. Several solutions, such as brain–computer

Android Apps have become the most important mobile applications in the evolving mobile IoT systems, whose security and privacy are confronted with ever more challenges, since such mobile devices as smartphones involve too much personal privacy information. Meanwhile, the developers prefer to put core functions (e.g., encryption function and T9 search function) of Android applications in the native

Nowadays, cloud-assisted Industrial Internet of Things (IIoT) has become pervasive in modern enterprises, because it supplies a promising way to transform the operation mode of existing industrial facilities, to enhancing the production efficiency and lowering the manufacturing cost. In order to preserve the privacy of enterprises, sensitive industrial data needs to be encrypted prior to being uploaded

The notion of aggregation of data in Industrial Internet of Things (IIoT) environment is a common practice. It shortens the data and associated signatures to reduce the bandwidth requirement. The compact aggregate signature (CAS) scheme creates a constant length aggregate signature (AS). Thus, the length of the CAS is independent of the number of messages or signatures to be aggregated. This article

Internet of Things (IoT) applications are increasingly being adopted for innovative and cost-effective services. However, the IoT devices and data are susceptible to various attacks, including cyberattacks, which emphasizes the need for pervasive security measure like trust evaluation on the fly. There exist several IoT numerical data trustworthiness measures which are based on the quality of information

Vehicular ad hoc networks (VANETs) have received considerable attention in recent years. Like any other network, privacy and anonymity are a requirement in VANETs. Classic anonymous routing protocols such as onion routing algorithm are fragile in mobile networks due to frequent link breaks. In this article, we propose a novel onion-based anonymous routing protocol for highly mobile vehicular networks

This article presents real-time edge video analytics for multicamera privacy-aware pedestrian tracking (REVAMP 2 T), as an integrated end-to-end Internet of Things (IoT) system for privacy built-in decentralized situational awareness. REVAMP 2 T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e., video cameras). On the algorithm side

Recently, the use of industrial synthetic aperture radar (SAR) sensors for unmanned aerial vehicles (UAVs) has received more attention. Despite the recent growth in the use of SAR, the connection bandwidth and the low data rate communications among networked UAVs in an Internet of Things (IoT) environment is a challenge, specifically, for exchanging big data such as remote sensing videos. To this end

We describe a new generative algorithm called trajectory generative mechanism (TGM) for publishing trajectory datasets with $\varepsilon $ -differential privacy guarantee, which achieves substantially higher computational efficiency and utility (practical) than the state-of-the-art algorithms. Our algorithm first encodes (models) the data as a graphical generative model and accurately captures the

Currently, edge computing (EC), emerging as a burgeoning paradigm, is powerful in handling real-time resource provision for Internet of Things (IoT) applications. However, due to the spatial distribution of geographically sparse IoT devices and the resource limitations of EC units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient and the execution performance

With the advancement of the Internet of Things (IoT) in recent years, there is a bigger potential to use online services than ever before. The use of the IoT brings numerous opportunities for both service providers and end users, however, it faces critical questions of security and privacy. Toward this, access control is one of the significant security challenges for the IoT, in particular, considering

Internet of Things and the related computing paradigms, such as cloud computing and fog computing, provide solutions for various applications and services with massive and high-dimensional data, while producing threats to the personal privacy. Differential privacy is a promising privacy-preserving definition for various applications and is enforced by injecting random noise into each query result such

Outlier detection is widely used in practice to track the anomaly on incremental data sets, such as network traffic and system logs. However, these data sets often involve sensitive information, and sharing the data to third parties for anomaly detection raises privacy concerns. In this article, we present a privacy-preserving outlier detection (PPOD) protocol for incremental data sets. The protocol

Machine learning, particularly the neural network (NN), is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited

Internet of Things (IoT) devices and the edge jointly broaden the IoT’s sensing capability and the monitoring scope for various applications. Though accessing sensing data and making decisions through IoT smart devices turns out to be commonplace, it is challenging to guarantee user privacy and preserve the accuracy (integrity) of the collected data. The IoT smart devices frequently lose either IoT

The Internet of Things (IoT) is the network of connected computing devices that have the ability to transfer valued data between each other via the Internet without requiring human intervention. In such a connected environment, the social IoT (SIoT) has become an emerging trend where multiple IoT devices owned by users support communication within a social circle. Trust management in the SIoT network

This article aims to improve spectrum efficiency (SE) for the unmanned aerial vehicle (UAV)-relayed cellular uplinks, through distinguishing both line-of-sight (LoS) and non-LoS (NLoS) links. Meanwhile, aiming to accommodate the air-to-ground (A2G) cooperative nonorthogonal multiple access (NOMA)-based cellular users (CUs) with a high energy efficiency (EE), a joint resource allocation (RA) problem

To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input–multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster

In this article, a hierarchical online distributed algorithm (HODA) is developed to achieve optimal energy management in interconnected microgrids (IMG). The energy management objectives include maximizing users’ utility, optimizing the output power of controllable generators, and keeping the system operating in an economic manner. We formulate the problem as an online least absolute shrinkage and

In this article, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price), and temporally coupled operational

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation-intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending the data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have

Space information network (SIN), which has the characteristics of large capacity, high reliability, and wide coverage, can be effectively applied to the Internet of Things (IoT) business. However, SIN is vulnerable to various attacks due to its highly exposed links, and the power and processing capacity of satellites and IoT devices (IoTDs) are usually limited. Meanwhile, massive IoTDs connecting to

Low-power wide-area network technologies allow sending sensor information through long distances with low data rates. Long Range (LoRa) is one of these technologies, which is less susceptible to noise and interference. This technology operates in unlicensed bands, increasing the chances of having interfering devices in the communication link. The issue of LoRa jamming with external interference and

Since underwater acoustic channels are shared by multiple heterogeneous entities and can suffer from severe interference, underwater acoustic communication networks (UACNs) are faced with the challenge of mitigating interference and improving communication quality by implementing distributed resource allocation approaches. In this article, we introduce the concept of reinforced learning in intelligent

With the ubiquitous demand for wireless communications, researchers have studied heterogeneous networks (HetNets) for years. Often the HetNets include a macrocell base station (MBS), several sets of macrocell users (MUs), a large number of femtocell base stations (FBSs), and femtocell users (secondary users), where the femtocells help the macrocell system relay the uplink or downlink traffic between

One prominent security threat that targets unmanned aerial vehicles (UAVs) is the capture via global positioning system (GPS) spoofing in which an attacker manipulates a UAV’s GPS signals in order to capture it. Given the anticipated widespread deployment of UAVs for various purposes, it is imperative to develop new security solutions against such attacks. In this article, a mathematical framework

Machine learning has played a significant role in building intelligent systems in the history of data science. In the recent paradigm where objects in the world will be connected with each other, commonly referred to as the Internet of Things (IoT), people begin to consider the challenges and opportunities to utilize the huge data sets generated, also referred to as Big data. One of the active research

Network virtualization enables cloud service providers sharing various substrate resources and serving end users with heterogeneous virtual network (VN) applications. As more and more Internet applications migrate to the cloud, the survivability of VNs, particularly for disaster survivability, has become a crucial issue. In this article, we study the VN survivability problem and propose an early-warning-time-based

Passive indoor localization for mobile Wi-Fi devices, e.g., smartphones, has attracted increasing attention from research communities recently. Existing passive localization techniques leverage received signal strength (RSS) of packets transmitted by target Wi-Fi devices and do not require a dedicated software installed on the devices. However, RSS-based passive localization techniques: 1) are device

Duties are segregated within a team by using the role-based access control (RBAC) in the Azure Internet of Things (IoT) framework, and only an appropriate level of access is granted to users to perform specific tasks, depending on a given situation. However, the same authentication and authorization mechanism is used for “sort of user,” which increases the operation overload on the cloud server. Moreover

Text sentiment classification is of critical importance to improve the autonomous decision making and communication ability among object peers in the Social Internet of Things (SIoT). To classify sentiment polarity on a fine-grained level, aspect-level sentiment classification has become a promising direction in recent years. However, the existing solutions typically ignore the mutual information between

Data access control in a cloud storage system is regarded as a promising technique for enhanced efficiency and security utilizing a ciphertext-policy attribute-based encryption (CP-ABE) approach. However, due to a large number of data users as well as limited resources and heterogeneity of data devices in Internet of Things (IoT), existing access control schemes for the cloud storage are not effectively

This article aims to put forward the utilization of onshore narrowband IoT (NB-IoT) infrastructure for tracking of containers transported by marine cargo vessels while operating near the coastline. We introduce and evaluate three connectivity strategies, including direct sensor-to-onshore base station (BS) transmission as well as two relay-aided schemes using dedicated vessel-BS or unmanned-aerial-vehicle

Object reidentification with the goal of matching pedestrian or vehicle images captured from different camera viewpoints is of considerable significance to public security. Quadruple directional deep learning features (QD-DLFs) can comprehensively describe object images. However, the correlation among QD-DLFs is an unavoidable problem, since QD-DLFs are learned with quadruple independent directional

Graphical Internet of Things (IoT) device management platforms, such as IoTtalk, make it easy to describe interactions between IoT devices. Applications are defined by dragging-and-dropping devices and specifying how they are connected, e.g., a door sensor controlling a light. While this allows simple and rapid development, it remains possible to specify unwanted device configurations, such as using

Solving quadratic congruences is a widely applied operation in cryptographic protocols to ensure the data secrecy in the Internet of Things (IoT). Yet it requires unaffordable computation resource for resource-constrained IoT devices when bulk of this type of operations need to be performed. How to efficiently and effectively solve quadratic congruences on IoT devices becomes a challenging issue. To

In this article, an online truthful mechanism is designed for mobile crowdsensing systems. Traditionally, the scenario where participants arrived at the platform in an online manner has been widely discussed in existing works. On the contrary, we focus on random task arrival case to design an online truthful mechanism by jointly considering the cost budget and the requirement of sensed data of each

In this article, we propose a novel simultaneous wireless information and power transfer (SWIPT) scheme for the Internet of Things (IoT). Different from the conventional power splitting (PS) and time switching (TS) schemes, the proposed scheme sends the wireless power via the unmodulated high-power continuous wave (CW) and transmits information by using a small modulated signal in order to reduce the