Wei Yu

Shandong University | SDU · School of Translation and interpretion

When network coding is used in wireless mesh networks (WMNs), the epidemic effect of pollution attacks can reduce network throughput dramatically. Nevertheless, little attention has been directed toward the performance gain of network coding versus traditional routing in adversarial wireless mesh networks. To address this critical issue, in this pa...

To date, Unmanned Aerial Vehicles (UAVs) have been widely used for numerous applications. UAVs can directly connect to ground stations or satellites to transfer data. Multiple UAVs can communicate and cooperate with each other and then construct an ad-hoc network. Multi-UAV systems have the potential to provide reliable and timely services for end...

The Smart Grid is a new type of power grid that will use advanced communication network technologies to support more efficient energy transmission and distribution. The grid infrastructure was designed for reliability; but security, especially against cyber threats, is also a critical need. In particular, an adversary can inject false data to disru...

It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain...

With the development of modern mobile operating systems, computing and communication technologies, smart mobile devices have been widely used to support rich applications and have been integrated to enterprise networks for various organizations. With accessing sensitive personal and business information, the security of smart mobile devices has bec...

Cyber-Physical Systems (CPS) are new systems designed to support and synthesize sensing, communication, and computing components that interact with physical objects so that the system can sense, monitor, control, and respond to changes occurring in their operating environments. With developing Internet of Things (IoT), edge/cloud computing, and Art...

Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has pr...

Internet of Things (IoT) devices generate massive amounts of data from local devices, making Federated Learning (FL) a viable distributed machine learning paradigm to learn a global model while keeping private data locally in various IoT systems. However, recent studies show that FL’s decentralized nature makes it susceptible to backdoor attacks. E...

Federated learning (FL) has made possible the collaborative training of machine learning models between aggregation server and clients without sharing their private data. With the massive volume of heterogeneous data from various clients, the server faces challenges such as data unbalance, data corruption, and/or data irrelevancy. As a result, the...

Artificial Intelligence (AI) has been widely adopted in numerous fields and enabled various smart systems because of its strong ability to perform tasks, including prediction, event detection, and status estimation, among others. As one of the typical smart systems empowered by AI and Internet of Things (IoT) technologies, the smart transportation...

In this paper, we address the issue of automating network configurations for dynamic network environments such as the Internet of Vehicles (IoV). Configuring network settings in IoV environments has proven difficult due to their dynamic and self-organizing nature. To address this issue, we propose a deep reinforcement learning-based approach to con...

The Internet of Things (IoT) constitutes a vast network comprising various components such as physical devices, vehicles, buildings, and other items equipped with sensors, actuators, and software. These components are interconnected, facilitating the collection and exchange of copious data across networked communications. IoT empowers extensive mon...

The rise of deep learning and the Internet of Things (IoT) has driven a number of smart-world applications, which are mostly deployed in distributed environments. Federated learning, a privacy-preserving collaborative learning paradigm, has shown considerable potential to leverage the rich distributed data at network edges. Nonetheless, the heterog...

The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities...

Federated learning (FL) allows the collaborative training of machine learning (ML) models between an aggregation server and different clients without sharing their private data. However, the FL archetype is mostly vulnerable to malicious model updates from various clients because of the privacy feature that makes the server see clients as a black-b...

With the proliferation of computationally powerful edge devices, edge computing has been widely adopted for wide-ranging computational tasks. Among these, edge artificial intelligence (AI) has become a new trend, allowing local devices to work cooperatively and build deep learning models. Federated learning is one of the representative frameworks i...

The fast development of the Internet of Things (IoT) and deep learning enables learning useful patterns from the massive amount of collected data with sporadic nodes in IoT systems. Federated learning has received increasing attention in distributed machine learning where only intermediate parameters are exchanged with training samples that resided...

Machine learning, as a viable way of conducting data analytics, has been successfully applied to a number of areas. Nonetheless, the lack of sufficient data is one critical issue for applying machine learning in Industrial Internet of Things (IIoT) systems. Insufficient data raises could negatively affect the accuracy of machine learning models. To...

Hurricanes, or Tropical Cyclones (TC), are dangerous, natural phenomena that can wreak major havoc to human lives, properties, and can last from a couple of hours to a couple of days. Studies have been conducted to analyze TCs and make predictions in order to help people make further preparations such as estimating intensity and tracking the storm...

The Internet of Things (IoT) encompasses a near-incalculable collection of dispersed and embedded computing devices acting as sensors and actuators, generating data at an incredible scale. However, a lack of coherency and cross-compatibility in IoT deployments has lead to increasing redundancy and waste of resources. To combat this, various concept...

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has been crucial for distinguishing normal users from malicious requests. However, DNNs (Deep Learning Models) have been exploited to recognize symbols and objects in CAPTCHA challenges. To combat the threat, a number of works have been conducted on enhancing the r...

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin...

The advance of deep learning has shown great potential in applications (speech, image, and video classification). In these applications, deep learning models are trained by datasets in Euclidean space with fixed dimensions and sequences. Nonetheless, the rapidly increasing demands on analyzing datasets in non-Euclidean space require additional rese...

Distributed machine learning paradigms have benefited from the concurrent advancement of deep learning and the Internet of Things (IoT), among which federated learning is one of the most promising frameworks, where a central server collaborates with local learners to train a global model. The inherent heterogeneity of IoT devices, i.e., non-indepen...

In this paper, we address the issue of disaster damage assessments using deep learning (DL) techniques. Specifically, we propose integrating DL techniques into the Internet of Things Search Engine (IoTSE) system to carry out disaster damage assessment. Our approach is to design two scenarios, Single and Complex Event Settings, to complete performan...

The advance of internet of things (IoT) techniques enables a variety of smart-world systems in energy, transportation, home, and city infrastructure, among others. To provide cost-effective data-oriented service, internet of things search engines (IoTSE) have received growing attention as a platform to support efficient data analytics. There are a...

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting features for classifying EEG signals. However, neural responses are naturally heterogeneous by show...

Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To add...

With the increasing adoption of Industrial Internet-of-Things (IIoT) devices, infrastructures, and supporting applications, it is critical to design schemes to effectively allocate resources (e.g., networking, computing, and energy) in IIoT systems, generally formalized as optimization problems. Nonetheless, because the system is highly complex, op...

The increasing popularity and widespread use of Internet of Things (IoT) and Cyber-Physical Systems (CPS) technologies have produced a significant need for the integration of cloud and edge computing with distributed detection solutions to handle the growing volume of distributed security threats. While deep learning-based approaches have been used...

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-time data and deploying machine learning models. Nonetheless, an individual IoT device may not have adequate computing resource to train and deploy an entire learning model. At the same time, transmitting continuous real-time data to a central server that h...

Industrial Internet of Things (IIoT) envisions the tight coupling of numerous critical industrial manufacturing subsystems, such as control, networking, and computing through the ubiquitous Internet of Things technologies. Nonetheless, such interconnectivity poses significant challenges to the successful management and operation of massively distri...

The federated learning framework builds a deep learning model collaboratively by a group of connected devices via only sharing local parameter updates to the central parameter server. Nonetheless, the lack of transparency in the local data resource makes it prone to adversarial federated attacks, which have shown increasing ability to reduce learni...

Machine learning techniques have been widely adopted to assist in data analysis in a variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models, one viable solution is to leverage all categories of data from different applications to train a general model, which can be further tuned for applications through tunin...

The Industrial Internet of Things (IIoT), also known as Industry 4.0, empowers manufacturing and production processes by leveraging automation and Internet of Things (IoT) technologies. In IIoT, the information communication technologies enabled by IoT could greatly improve the efficiency and timeliness of information exchanges between both vertica...

In this article, the authors implement a deep learning environment and fine-tune parameters to determine the optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, the authors demonstrate the potential performance of a deep learning environment for Android malware detection. S...

The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines t...

The Industrial Internet of Things (IIoT) is a critically important implementation of the Internet of Things (IoT), connecting IoT devices ubiquitously in an industrial environment. Based on the interconnection of IoT devices, IIoT applications can collect and analyze sensing data, which help operators to control and manage manufacturing systems, le...

The increasing volume of network-connected devices comprising Internet of Things and the variety of heterogeneous network architectures across these devices pose significant challenges to effective deployment and routing. In this article, we consider the adoption of probabilistic data structures to develop a novel Bloom Filter-based dual-layer inte...

As a key smart transportation service, public vehicle systems are intended to improve traffic efficiency and vehicle occupancy ratios, and to reduce the number of vehicles on roads, by inducing travelers to share rides with others. Despite the clear logic behind this service, achieving a viable model for matching multiple riders to vehicles with lo...

The densified deployment of heterogeneous networks coexisting with cells with overlapping coverage has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or m...

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destinat...

In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time coll...

In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time coll...

The simultaneous development of deep learning techniques and Internet of Things (IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for improving distributed computing, sensing, and data analysis. Among these technologies, federated learning has received increased attention as a privacy-preserving collaborative learnin...

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIo...

Industrial Internet-of-Things (IIoT), also known as Industry 4.0, is the integration of Internet of Things (IoT) technology into the industrial manufacturing system so that the connectivity, efficiency, and intelligence of factories and plants can be improved. From a cyber physical system (CPS) perspective, multiple systems (e.g., control, networki...

The rapid and wide adoption of microgrids (MGs) and the increasing popularity of electric vehicles (EVs) have created a unique opportunity for the integration of these technologies. In this article, we address the issue of demand response of EVs during MG outages by leveraging Vehicle-to-Grid (V2G) technology. Particularly, we investigate an auctio...

Delay Tolerant Networking (DTN) is designed to achieve reliable data transmission in resource constrained networks. When it is applied to dynamic networks, there is a lack of research on the evaluation of its performance with the thorough consideration for the impacts of key factors of network components, as well as the configuration of the DTN imp...

A massive number of Internet-of-Things (IoT) devices are deployed to monitor and control a variety of physical objects as well as support a body of smart-world applications. How to efficiently allocate network resources becomes a challenging issue with the rapidly growing connected IoT devices. Depending on applications, the burst of IoT traffic co...

With the immensity of distributed Internet of Things (IoT) devices and the exponential increase in data generated from a variety of IoT-driven smart-world applications, how to effectively provide data driven service supported by IoT has become a critical issue. While the state-of-the-art technologies have been developed and network infrastructures...

With the increasing popularity of crowdsourcing services, high-dimensional crowdsourced data provides a wealth of knowledge. Nonetheless, unprecedented privacy threats to participants have emerged, due to complex correlations among multiple attributes and the vulnerabilities of untrusted crowdsourcing servers. Differential privacy-based paradigms h...

Developments in the Internet of Things and cyber–physical systems have enabled intelligent solutions to alleviate peak loads, and transmission and storage concerns in MicroGrids (MGs) through the deployment of Electrical Vehicles (EVs), as well as provide a platform for EV energy exchange. Nonetheless, EV participation depends upon the willingness...

The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and d...

In this article, the authors implement a deep learning environment and fine-tune parameters to determine the optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, the authors demonstrate the potential performance of a deep learning environment for Android malware detection. S...

In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows Electric Vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designi...