Huaming Wu

Tianjin University | tju · Center for Applied Mathematics

Cloud computation offloading is a promising method that sending heavy computation to resourceful servers on cloud and then receiving the results from them. In this paper, we study the offloading techniques and further explore the tradeoff between shortening execution time and extending battery life of mobile devices. A novel adaptive offloading sch...

—Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices thus providing higher performance and saving energy. Different applications usually give different relative importance to response time and energy consumption....

Convolutional Neural Networks (CNNs) are getting deeper and wider to improve their performance and thus increase their computational complexity. We apply channel pruning methods to accelerate CNNs and reduce its computational consumption. A new pruning criterion is proposed based on the mean gradient for convolutional kernels. To significantly redu...

Application partitioning that splits the executions into local and remote parts, plays a critical role in high-performance mobile offloading systems. Mobile devices can obtain the most benefit from Mobile Cloud Computing (MCC) or Mobile Edge Computing (MEC) through optimal partitioning. Due to unstable resources at the wireless network (network dis...

Mobile cloud offloading migrates heavy computation from mobile devices to remote cloud resources or nearby cloudlets. It is a promising method to alleviate the struggle between resource-constrained mobile devices and resource-hungry mobile applications. Caused by frequently changing location mobile users often see dynamically changing network condi...

Due to numerous computation-intensive and delay-sensitive tasks in the Internet of Vehicles (IoV), Vehicular Edge Computing (VEC) is increasingly playing a crucial role as a key solution in the IoV. However, how to concurrently enhance communication quality and reduce the cost of latency and energy has emerged as a critical challenge in VEC. To tac...

DNA-based data storage is a promising solution to the challenges of large-scale data storage. However, the low throughput of the mainstream inkjet-based DNA synthesis method has hindered its widespread adoption. In contrast, high-throughput electrochemical synthesis provides higher throughput but with more nucleotide insertion, deletion, and substi...

Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge...

Heterogeneous graphs (HGs) with multiple entity and relation types are common in real-world networks. Heterogeneous graph neural networks (HGNNs) have shown promise for learning HG representations. However, most HGNNs are designed for static HGs and are not compatible with heterogeneous temporal graphs (HTGs). A few existing works have focused on H...

Yellow fever is a vigorous, phlebotomic, vector-borne disease that poses a significant public health threat in regions with high mosquito density and inadequate vaccination coverage. The disease's toxic phase is lethal, making prompt identification and control measures crucial. The emergence of the latest technologies and data analytics techniques...

The vision for the sixth-generation (6G) network involves the integration of communication and sensing capabilities in internet of everything (IoE), towards enabling broader interconnection in the devices of distributed wireless sensor networks (WSN). Moreover, the merging of SDN policies in 6G IoE-based WSNs i.e. SDN-enable WSN improves the networ...

Cloud computing is an emerging choice among businesses all over the world since it provides flexible and world wide web computer capabilities as a customizable service. Because of the dispersed nature of cloud services, security is a major problem. Since it is extremely accessible to intruders for any kind of assault, privacy and security are major...

With the increasing Quality of Service (QoS) requirements of the Internet of Things (IoT), Mobile Edge Computing (MEC) has undoubtedly become a new paradigm for locating various resources in the proximity of User Equipment (UE) to alleviate the workload of backbone IoT networks. Deep Reinforcement Learning (DRL) has gained widespread popularity as...

Kubernetes has revolutionized traditional monolithic Internet of Things (IoT) applications into lightweight, decentralized, and independent microservices, thus becoming the de facto standard in the realm of container orchestration. Intelligent and efficient container placement in Mobile Edge Computing (MEC) is challenging subjected to user mobility...

Smart resource allocation is essential for optimising cloud computing efficiency and utilisation, but it is also very challenging as traditional approaches often overprovision CPU resources, leading to financial inefficiencies. Recently developed Artificial Intelligence (AI) techniques have the potential to solve this problem efficiently; for examp...

Vehicular edge computing (VEC), which extends the computing, storage, and networking resources from the cloud center to the logical network edge through the deployment of edge servers at the road-side unit (RSU), has aroused extensive attention in recent years, by virtue of the advantages in meeting the stringent latency requirements of vehicular a...

Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses on identifying the next user to be influenced. Wh...

For collaborative tasks requiring multiple users, in Mobile Crowd Sensing (MCS), low user interest in certain tasks usually results in insufficient user re-cruitment. However, the interest of the user directly affects the quality and effi-ciency of task completion. To address this issue, we propose a multi-stage incen-tive mechanism based on the To...

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapti...

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing....

Serverless edge computing has emerged as a new paradigm that integrates the serverless and edge computing. By bringing processing power closer to the edge of the network, it provides advantages such as low latency by quickly processing data for time‐sensitive Internet of Things (IoT) applications. Additionally, serverless edge computing also brings...

Change point detection in dynamic networks aims to detect the points of sudden change or abnormal events within the network. It has garnered substantial interest from researchers due to its potential to enhance the stability and reliability of real-world networks. Most change point detection methods are based on statistical characteristics and phas...

The integration of serverless edge computing and the Industrial Internet of Things (IIoT), like Industry 4.0 applications, is seen as a promising development that can make industrial processes more efficient and faster. These two technologies can be integrated to optimize production by enabling faster adaptation in critical industries with variable...

Network alignment, which integrates multiple network resources by identifying anchor nodes that exist in different networks, is beneficial for conducting comprehensive network analysis. Although there have been many studies on network alignment, most of them are limited to static scenarios and only can achieve acceptable top- $\alpha$ ( $\alpha \g...

Edge computing plays a crucial role in the processing of Consumer Internet of Things (IoT)-enabled latency-sensitive applications. In smart homes, dynamic action strategies based on multiple IoT objects with edge processing can be the best solution for handling adverse events. To overcome these challenges, the use of Stochastic Game Net (SGN) formi...

Fetal Heart Rate (FHR) signal is widely used in doppler fetal heart monitors. However, incomplete FHR signals reduce the effectiveness of fetal heart rate monitoring. Filling missing data is a key technique to improve FHR quality, but existing filling algorithms lack consideration the correlation of FHR signals. Therefore, we focus on two correlati...

The recent advancements in integrated sensing and communications (ISAC) technology have introduced new possibilities to address the quality of communication and high-resolution positioning requirements in the next-generation wireless communication network (6G) vehicle-to-everything (V2X). Simultaneously providing high accurate positioning and high...

With the proliferation of Telematics and autonomous driving, vehicles are generating increasing volumes of data from numerous sensors. To accommodate this influx of in-vehicle data, Data Centers (DCs) play a pivotal role. These DCs must possess ample storage capacity and bandwidth to cater to the rising demand for in-vehicle data uploads. Meanwhile...

Vehicular Edge Computing (VEC) has garnered substantial attention owing to its capacity to provide ample computational resources for computation-intensive tasks. However, how to flexibly allocate computing tasks within vehicles and efficiently manage the resources consumed by tasks has emerged as a challenge. To tackle this issue, this research adv...

The massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-ef...

The unique capabilities of Unmanned Aerial Vehicles (UAVs), including their superior mobility, flexibility, and line-of-sight transmission, have made them well-suited for facilitating Aerial Edge Computing (AEC). This computing paradigm is particularly beneficial for meeting the computing demands of User Equipments (UEs) in emergency situations, as...

Modern smart city services necessitate complex technological infrastructure with heterogeneous compute servers, networks, and communication protocols. However, there are many research issues in heterogeneous computing infrastructure for Intelligent transport systems (ITS) when using the services in the network. Therefore, the main objective of this...

Network alignment is a fundamental problem in various domains since it can establish bridges for the same entity (i.e., anchor nodes) between different networks. Most existing network alignment methods are based on consistency assumption, i.e., anchor nodes exhibit similar local structures or neighbors across different networks. However, many ancho...

Containerized edge computing emerged as a preferred platform for latency-sensitive applications requiring informed and efficient decision-making accounting for the end user and edge service providers’ interests simultaneously. Edge decision engines exploit pipelined knowledge streams to enhance performance and often fall short by employing inferior...

The trend towards transitioning from monolithic applications to microservices has been widely embraced in modern distributed systems and applications. This shift has resulted in the creation of lightweight, fine-grained, and self-contained microservices. Multiple microservices can be linked together via calls and inter-dependencies to form complex...

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made...

Metaverse is a concept that aims to create a virtual-reality space where users can engage in social and communication activities. This virtual world is expected to be enabled by a number of key technologies, including edge computing. However, the specific details of how edge computing can boost the development of metaverse still require further inv...

A secondary structure in single-stranded DNA refers to its propensity to undergo self-folding, leading to functional inactivity and irreparable failures within DNA storage systems. Consequently, the property of secondary structure avoidance (SSA) becomes a crucial criterion in the design of single-stranded DNA sequences for DNA storage, as it prohi...

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs, etc. There are many approaches used for load forecasting such as the support vector regression, the autoregressive integrated moving average, and neural networks, but most of these methods...

In the beyond 5 G (B5G)/6 G era, to achieve ultra-dense and ultra-large-capacity intelligent connection of all things, an intelligent wideband spectrum sensing technology is particularly important. However, in an extremely wide frequency range, it is still a challenge to achieve high-precision and high-reconstruction-capability wideband spectrum se...

Temporal graph representation learning aims to generate low-dimensional dynamic node embeddings to capture temporal information as well as structural and property information. Current representation learning methods for temporal networks often focus on capturing fine-grained information, which may lead to the model capturing random noise instead of...

A wide variety of Mobile Devices (MDs) are adopted in Internet of Things (IoT) environments, resulting in a dramatic increase in the volume of task data and greenhouse gas emissions. However, due to the limited battery power and computing resources of MD, it is critical to process more data with less energy. This paper studies the Wireless Power Tr...

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning hea...

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the educat...

With the rapid development of Artificial Intelligence (AI) and the Internet of Vehicles (IoV), there is an increasing demand for deploying various intelligent applications on vehicles. Vehicular Edge Computing (VEC) is receiving extensive attention from both the industry and academia due to its benefits from the edge computing paradigm, which pushe...

With the rapid development of the Internet of Vehicles (IoV), smart vehicles can fulfill multiple roles in either the information-centric IoV or the task-oriented IoV. However, malicious vehicles may undermine the trustiness of vehicles towards each other, and further damage these IoV networks. Given the multiple roles undertaken by vehicles in IoV...

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recentl...

Residential energy consumption continues to climb steadily, requiring intelligent energy management strategies to reduce power system pressures and residential electricity bills. However, it is challenging to design such strategies due to the random nature of electricity pricing, appliance demand, and user behavior. This article presents a novel re...

With the increasingly humanized and intelligent operation of Industrial Internet of Things (IIoT) systems in Industry 5.0, Delay-Sensitive and Compute-Intensive (DSCI) devices have proliferated, and their demand for low latency and low power consumption has become more and more eager. In order to extend the battery life and improve the quality of u...

Modern online services often require mobile devices to convert paper-based information into its digital counterpart, e.g., passport, ownership documents, etc. This process relies on Document Localization (DL) technology to detect the outline of a document within a photograph. In recent years, increased demand for real-time DL in live video has emer...

Fraud in e-commerce fields (e.g., Amazon, Taobao, and so on) and social networks (e.g., Twitter and Weibo) has recently brought a very bad user experience. Rating fraud detection is an urgent issue for improving user experiences. However, existing methods have lots of limitations in some respects, because it is always very hard to acquire sufficien...

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreadi...

Deoxyribonucleic acid (DNA)-based data storage has grown rapidly due to its advantages with the increase in infrequently large amounts of data. However, when the maximum homopolymer runlength (RLL) of the DNA strand is large and the GC-content is either too high or too low, the DNA synthesis and sequencing processes are prone to substitution, delet...

Considering the limited computing power of Mobile Edge Computing (MEC) servers and the emergence of Vehicular Ad-Hoc Networks (VANETs), we employ the computing paradigm known as Parked Vehicle Edge Computing (PVEC) to leverage the computational capabilities of idle vehicles, thereby enhancing the overall computing performance of these vehicles. We...

In the Internet of Things (IoT) environment, a wide variety of mobile devices (MDs) have become part of it, leading to a dramatic increase in the amount of task data. However, due to the limited battery capacity and computing resources of MDs, a lot of effort is required to be taken on how to process more data with less energy. In this paper, we ta...

The advent of Intelligent Cyber-Physical Transportation Systems (ICTS) has not only accelerated the reformation and evolvement of smart transportation, but also ushered in a new era of vehicular applications. These applications typically impose stringent latency requirements and demand substantial computing resources. Vehicular edge computing (VEC)...

The rapid integration of Internet of Things (IoT) services and applications across various sectors is primarily driven by their ability to process real-time data and create intelligent environments through artificial intelligence for service consumers. However, the security and privacy of data have emerged as significant threats to consumers within...

Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications...

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three ch...

As a powerful tool for storing digital information in chemically synthesized molecules, DNA-based data storage has undergone continuous development and received increasingly more attention. Efficiently recovering information from large-scale DNA strands that suffer from insertions, deletions, and substitution errors (collectively referred to as edi...

In order to meet people’s demands for intelligent and user-friendly Internet of Things (IoT) services, the amount of computation is increasing rapidly and the requirements of task delay are becoming increasingly more stringent. However, the constrained battery capacity of IoT devices greatly limits the user experience. Energy harvesting technologie...

Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order gene interactions detection, not directly identifying individual site, but based on Deep Learning (DL)...

Deoxyribonucleic acid (DNA) is an attractive medium for long-term digital data storage due to its extremely high storage density, low maintenance cost and longevity. However, during the process of synthesis, amplification and sequencing of DNA sequences with homopolymers of large run-length, three different types of errors, namely, insertion, delet...

The startling rise in smart vehicles stimulates the rapid development of new paradigms such as Social Internet of Vehicle (SIoV) and Vehicular Fog Computing (VFC). Trustworthiness has been regarded as a dominating issue in all the have-to-be-addressed issues in SIoV, and many reputation-based countermeasures have been adopted to solve the trustines...

Object detection, as a fundamental problem in computer vision, has been widely used in many industrial applications, such as intelligent manufacturing and intelligent video surveillance. In this work, we find that classification and regression have different sensitivities to the object translation, from the investigation about the availability of h...

The application of DNA as a powerful tool for storing digital information in chemically synthesized molecules has undergone continuous development. To explore its potential and limitations, we model the DNA storage channel as a cascade of a series of parallel and independent DNA noisy synchronization error channels and a shuffling-sampling channel,...

Autonomic computing investigates how systems can achieve (user) specified “control” outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and...

Deoxyribonucleic acid (DNA)-based data storage is a promising new storage technology which has the advantage of high storage capacity and long storage time compared with traditional storage media. However, the synthesis and sequencing process of DNA can randomly generate many types of errors, which makes it more difficult to cluster DNA sequences t...

While Identity Document Verification (IDV) technology on mobile devices becomes ubiquitous in modern business operations, the risk of identity theft and fraud is increasing. The identity document holder is normally required to participate in an online video interview to circumvent impostors. However, the current IDV process depends on an additional...

Network embedding is an important class of link prediction methods, which can use the distance between learned low-dimensional node representations to characterize the similarity between nodes. Traditional network embedding methods focus on single-layer networks, while in reality, a large part of complex networks are not isolated, but interdependen...

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoT), we have to perform increasingly more resource-hungry and compute-intensive applications on IoT devices, where the available computing resources are insufficient. With the assistance of Mobile Edge Computing (MEC), offloading partial complex tasks from mobile de...

Big data frameworks such as Apache Spark are becoming prominent to perform large-scale data analytics jobs. However, local or on-premise computing resources are often not sufficient to run these jobs. Therefore, public cloud resources can be hired on a pay-per-use basis from the cloud service providers to deploy a Spark cluster entirely on the clou...

As a distributed computing paradigm, edge computing has become a key technology for providing timely services to mobile devices by connecting Internet of Things (IoT), cloud centers and other facilities. By offloading compute-intensive tasks from IoT devices to edge/cloud servers, the communication and computation pressure caused by the massive dat...