Geyong Min

University of Exeter | UoE · College of Engineering, Mathematics and Physical Sciences

IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to...

Federated Learning (FL) has emerged as a privacy-preserving distributed Machine Learning paradigm, which collaboratively trains a shared global model across a number of end devices (clients) without exposing their raw data. However, FL typically assumes that all clients are benign and trust the coordinating central server, which is unrealistic for...

This century has been a major avenue for revo- lutionary changes in technology and industry. Industries have transitioned towards intelligent automation, relying less on hu- man intervention, resulting in the fourth industrial revolution, Industry 4.0. That is why IoT has been the researcher’s arena for quite some time. With Industry 4.0 still in m...

Mobile edge computing (MEC) relieves the latency and energy consumption of mobile applications by offloading computation-intensive tasks to nearby edges. In wireless metropolitan area networks (WMANs), edges can better provide computing services via advanced communication technologies. For improving the Quality-of-Service (QoS), edges need to be co...

In the era of the Internet of Things (IoT), it is a promising way to improve system energy utility and better meet users’ requirements for quality of service (QoS) via integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) technologies. In light of this idea, we investigate device access, sub-channel division, and transmi...

Federated learning (FL) has shown its great potential for achieving distributed intelligence in privacy-sensitive IoT. However, popular FL approaches such as FedAvg and its variants share model parameters among clients during the training process and thus cause significant communication overhead in IoT. Moreover, non-independent and identically dis...

In the real world, relationships between objects are often complex, involving multiple variables and modes. Hypergraph neural networks possess the capability to capture and represent such intricate relationships by deriving and inheriting their graph-based counterparts. Nevertheless, both graph and hypergraph neural networks suffer from the problem...

With the explosive increase in mobile data traffic generated by various application services like video-on-demand and stringent quality of experience requirements of users, mobile edge caching is a promising paradigm to reduce delivery latency and network congestions by serving content requests locally. However, how to conduct cache replacement whe...

Location recommendation is at the core of location-based service, while recommendation based on graph neural networks (GNNs) has recently flourished, and for location recommendation tasks, GNN-based approaches are equally applicable. To provide fair location recommendation services for multi-users, correlation information between non-adjacent locat...

In Federated Learning (FL) client devices connected over the internet collaboratively train a machine learning model without sharing their private data with a central server or with other clients. The seminal Federated Averaging (FedAvg) algorithm trains a single global model by performing rounds of local training on clients followed by model avera...

Greedy routing efficiently achieves routing solutions for vehicular networks due to its simplicity and reliability. However, the existing greedy routing algorithms have mainly considered simple routing metrics only, e.g., distance based on the local view of an individual vehicle. This consideration is insufficient for analysing dynamic and complica...

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC) in road scenarios. One key technology of VEC is task offloading, which allows vehicles to send their computation tasks to the surrounding Roadside Units (RSUs) or other vehicles for execution, thereby reducing computation delay and energy consumption. Howeve...

Nowadays, face recognition technology has been dramatically boosted by the advances in deep learning and big data fields. However, this also poses grand challenges in protecting personal identity information in intelligent applications of the Internet of Things (IoT). Existing methods based on the $K$ -Same algorithm have low effectiveness for pr...

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data. Extending FL beyond the supervised learning models, federated reinforcement learning (FRL) was proposed to handle sequential decision-makin...

With the mushrooming growth of data volumes, data replica placement plays a key role in promoting the energy efficiency and Quality-of-Service (QoS) of data-intensive data centers. The existing data placement strategies mainly focus on storage performance improvement or QoS enhancement in data centers, but ignore the indispensable factor-heat recir...

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes col...

Generative adversarial networks (GANs) have been advancing and gaining tremendous interests from both academia and industry. With the development of wireless technologies, a huge amount of data generated at the network edge provides an unprecedented opportunity to develop GANs applications. However, due to the constraints such as bandwidth, privacy...

With the emergence of the 5G/6G communications, edge computing has attracted increasing research interests in recent years. To provide pervasive 5G/6G edge computing services, numerous edge servers are required for service coverage, and the deployment cost can be $\gt$ 10000 times larger than the deployment cost of the 4G infrastructure. To addre...

Network Functions Virtualization (NFV), which decouples network functions from the underlying hardware, has been regarded as an emerging paradigm to provide flexible virtual resources for various applications through the ordered interconnection of Virtual Network Functions (VNFs), in the form of Service Function Chains (SFCs). In order to achieve t...

In Mobile Edge Computing (MEC) systems, Unmanned Aerial Vehicles (UAVs) facilitate Edge Service Providers (ESPs) offering flexible resource provisioning with broader communication coverage and thus improving the Quality-of-Service (QoS). However, dynamic system states and various traffic patterns seriously hinder efficient cooperation among UAVs. E...

Internet of Things (IoT) as a ubiquitous networking paradigm has been experiencing serious security and privacy challenges with the increasing data in diversified applications. Fortunately, this will be, to a great extent, alleviated with the emerging blockchain, which is a decentralized digital ledger based on cryptography and has offered potentia...

With the rapid growth of cloud computing, frequent workload bursts show an increasing influence on the Quality of Service (QoS) and energy efficiency of cloud-based data centers. Existing virtual machine placement schemes are expected to optimize either QoS or energy efficiency for cloud data centers running under bursty workload conditions. To bri...

Unmanned aerial vehicle (UAV)-assisted wireless communication systems are able to provide high-quality services and ubiquitous connectivity for massive Internet of Things (IoT) devices. In this paper, we study the Age of Information (AoI) and energy tradeoff in a system where an employed UAV performs data collection for multiple IoT nodes (INs). Be...

Low-power wireless networks (LPWNs) are of paramount importance for the pervasive deployment of Internet-of-Things (IoT). To deal with the lossy nature of LPWNs, opportunistic routing (OR) and multichannel communications (MC) have received significant research interests. In particular, coupling OR with MC has become an important way to further enha...

COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain earl...

Data deduplication has been broadly used in Cloud due to its storage space saving ability. An issue of deduplication is the contiguous data chunks in a segment may be scattered in different containers. This phenomenon is called data fragmentation. Because of data fragmentation, a restore process must reference various containers across a wide varie...

Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the learning performance. To fill this research gap, we develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks wh...

Vehicular Edge Computing (VEC) is a computing paradigm that brings Mobile Edge Computing (MEC) to the road and vehicular scenarios by providing low-latency and high-efficiency computation services. One key technology of VEC is task offloading, which allows vehicles to send their computation tasks to surrounding Roadside Units (RSUs) for execution,...

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively training models at the network edge in a privacy-preserving fashion, without training data leaving the devices where it was generated. The privacy-preserving nature of FL shows great promise for applications with sensitive data such as healthcare, financ...

Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC...

As an indispensable part of modern critical infrastructures, cameras deployed at strategic places and prime junctions in an intelligent transportation system (ITS), can help operators in observing traffic flow, identifying any emergency situation, or making decisions regarding road congestion without arriving on the scene. However, these cameras ar...

Efficient and timely dispatch of maintenance personnel for fault detection and failure recovery play a key role towards safe operation of power grid and has become a challenging issue. To address this challenge, this paper proposes a new optimal strategy, namely adaptive NSGAII (NSGAII/A), for dispatching maintenance personnel in the event of secur...

Increasing workload conditions lead to a significant surge in power consumption and computing node failures in data centers. The existing workload distribution strategies focused on either thermal awareness or failure mitigation, overlooking the impact of node failures on the energy efficiency of cloud data centers. To address this issue, a new hol...

During the past decade, Industry 4.0 has greatly promoted the improvement of industrial productivity by introducing advanced communication and network technologies in the manufacturing process. With the continuous emergence of new communication technologies and networking facilities, especially the rapid evolution of cellular networks for 5 G and b...

Collaborative machine learning, especially Federated Learning (FL), is widely used to build high-quality Machine Learning (ML) models in the Internet of Vehicles (IoV). In this paper, we study the performance evaluation problem in an inherently heterogeneous IoV, where the final models across the network are not identical and are computed on differ...

Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. As a crucial problem in MEC, service migration needs to decide how to migrate user services for maintaining the Quality-of-Service when users roam between MEC servers with...

Resource provisioning for the ever-increasing applications to host the necessary network functions necessitates the efficient and accurate prediction of required resources. However, the current efforts fail to leverage the inherent features hidden in network traffic, such as temporal stability, service correlation and periodicity, to predict the re...

Federated Learning (FL) is a recent development in distributed machine learning that collaboratively trains models without training data leaving client devices, preserving data privacy. In real-world FL, the training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, harming model convergence...

In online social networks, information diffusion presents a complicated dynamic process that accompanies users' lives, in which the link between multi-information symbiosis and conflict is frequently overlooked. We investigate two aspects that influence the process of diffusion by examining the phenomena of information dissemination between individ...

Considering that low-cost and resource-constrained sensors coupled inherently could be vulnerable to growing numbers of intrusion threats, industrial Internet-of-Things (IIoT) systems are faced with severe security concerns. Data sharing for building high-performance intrusion detection models is also prohibited due to the sensitivity, privacy, and...

Existing recommendations based on machine learning are mainly based on supervised learning. However, these methods affected by historical behavior often bring great difficulties on mining high-quality long-tail items, achieving cold-start recommendations, and causing response inability to real-time environment changes. To this end, this paper propo...

The use of multi-source remote sensing data to obtain urban impervious surface has become a popular research topic. Multi-source remote sensing data fusion techniques can provide object interpretation with a higher accuracy. However, most decision-level fusion methods make insufficient use of the complementary information and degree of association...

Mobile CrowdSensing (MCS) is an emerging paradigm that employs massive mobile devices (MDs) to complete sensing tasks cooperatively. To provide ubiquitous MCS services, Unmanned Aerial Vehicle (UAV), featured by high agility and flexibility, becomes increasingly attractive as a powerful assistant for MCS to collect sensing data in hard-to-reach and...

LoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the...

Instantiated containers of an application are distributed across multiple Physical Machines (PMs) to achieve high parallel performance. Container placement plays a vital role in network traffic and the performance of containerized data centers. Existing container placement techniques are inadequate due to the ignorance of container traffic patterns...

In this paper, we propose a learning-based cooperative edge caching approach to improve the caching performance. We formulate the cooperative edge caching problem as a NP-hard knapsack problem with the goal of minimizing the average content delivery latency. To solve the problem, we firstly establish a TCNCP model to predict the popularity of futur...

The ever-expanding scale of cloud datacenters necessitates automated resource provisioning to best meet the requirements of low latency and high energy-efficiency. However, due to the dynamic system states and various user demands, efficient resource allocation in cloud faces huge challenges. Most of the existing solutions for cloud resource alloca...

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, hence significantly reducing the service latency and network traffic. In edge computing, many applications are composed of dependent tasks where the outputs of some are the inputs of others. How to offload these tasks to the...

Spatial keyword query has attracted wide-spread academic and industrial concerns due to the popularity of location-based services and Internet of Things. To efficiently support the online query processing, the data owners need to outsource their data to cloud platforms. However, the outsourcing services may raise privacy leaking issues. Moreover, a...

Most existing routing schemes in Software-Defined Vehicular Networks (SDVNs) consider the networks as a sequence of static graphs. However, the vehicular network is a de facto temporal graph, and routing in the static graph could be inefficient. The main reason could be the temporal information that would play a vital role in the vehicular network....

As customers take virtual machines (VMs) as their demands, high-efficient placement of VMs is required to reduce the energy consumption in data centers. Existing Virtual Machine placement (VMP) strategies can minimize energy consumption of data centers by optimizing resource allocation in terms of multiple physical resources (e.g., memory, bandwidt...

Blockchain has recently been regarded as an important enabler for building secure energy trading in microgrid systems due to its inherent features of distributively providing immutable data record, storage and sharing across networks in a Peer-to-Peer (P2P) manner. However, designing highly-efficient and scalable blockchain-enabled energy trading m...

Greedy routing schemes are considered the most efficient routing solutions in vehicular communications. However , most existing greedy routing schemes only consider simple routing metrics for complicated communication scenarios. This shortcoming can inevitably degrade the exhibited overall performance. In such a scheme, a vehicle only selects a rel...

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous users without gathering their data. Extending FL beyond the conventional supervised learning paradigm, federated Reinforcement Learning (RL) was proposed to handle sequential decision-makin...

Identifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studie...

Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the learning performance. To fill this research gap, firstly, we develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless ne...

Federated Learning (FL) is a recent development in the field of machine learning that collaboratively trains models without the training data leaving client devices, in order to preserve data-privacy. In realistic settings, the total training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion,...

Identifying the optimal groups of users that are closely connected and satisfy some ranking criteria from an attributed social network attracts significant attention from both academia and industry. Skyline query processing, a multicriteria decision-making optimized technique, is recently embedded into cohesive subgraphs mining in graphs/social net...

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the comp...

The statistical values of the latencies between two sets of hosts over a given period, which is referred as to statistical latency, can benefit many applications in the next-generation networks, such as Network in a Box (NIB) based resource provisioning. However, existing methods can hardly achieve low measurement cost and high prediction accuracy...

Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous works have shown that non-Independent and Identically Distributed (non-IID) user data harms the convergence speed of the FL algorithms. Furthermore, most existing work on...

The cloud computing paradigm provides numerous tempting advantages, enabling users to store and share their data conveniently. However, users are naturally resistant to directly outsourcing their data to the cloud since the data often contain sensitive information. Although numerous fine-grained access control schemes for cloud-data sharing have be...

Three-way concept analysis (3WCA), a combination of three-way decision and formal concept analysis, is widely used in the field of knowledge discovery. Generally, constructing three-way concept lattices requires the original formal context and its complement context simultaneously. Additionally, the existing three-way concept lattice construction a...

Flying Ad hoc Network (FANET) has drawn significant consideration due to its rapid advancements and extensive use in civil applications. However, the characteristics of FANET including high mobility, limited resources, and distributed nature, have posed a new challenge to develop a secure and efficient routing scheme for FANET. To overcome these ch...

Network function virtualization (NFV) is critical to the scalability and flexibility of various network services in the form of service function chains (SFCs), which refer to a set of Virtual Network Functions (VNFs) chained in a specific order. However, the NFV performance is hard to fulfill the ever-increasing requirements of network services mai...

Data centers are critical to the commercial and social activities of modern society but are also major electricity consumers. To minimize their environmental impact, it is imperative to make data centers more energy efficient while maintaining a high quality of service (QoS). Bearing this consideration in mind, we develop an analytical model using...

Wireless power transfer technologies such as simultaneous wireless information and power transfer (SWIPT) have shown significant potentials to revolutionise the design of future wireless communication systems. When the only energy source is the wireless signals that are mainly intended for information communications, the sustainability and outage p...

Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. Wi...

Notice that it is difficult and expensive to implement global network measurements to obtain network distance, a feasible idea is to predict unknown distances by introducing network coordinates with limited network measurements. The existing solutions always represent the unknown network distances in a rather unique number. However, research and ap...

The quantitative performance analysis plays a critical role in assessing the capability of Vehicular Edge Computing (VEC) systems to meet the requirements of vehicular applications. However, developing accurate analytical models for VEC systems is extremely challenging due to the unique features of intelligent vehicular applications. Specifically,...

Mobile social networks (MSNs) provide real-time information services to individuals in social communities through mobile devices. However, due to their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, and other types of misuses. To mitigate such threats, it is urgent to control the spread of fraud inf...

Three-way concept analysis (3WCA) has been an emerging and important methodology for knowledge discovery and data analysis. Particularly, 3WCA can efficiently characterize the information of “jointly possessed” and “jointly not possessed” compared to the classical formal concept only can describe common attributes owned by objects. This property, t...

With the rapid development of the fifth-generation (5G) wireless communications, the number of users is increasing dramatically and Ultra-Dense Networks (UDN) are becoming more important for supporting numerous users and emerging mission-critical applications. In order to conquer the communication restrictions caused by natural disasters, an emerge...

Over the past few years, Fog Radio Access Networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities towards the edge of networks, closer to users. In F-RANs, distributed edge caching among Fog Access Points (F-APs) can effectively redu...

The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types ge...

The rapid development of the Industrial Internet of Things (IIoT) enables IIoT devices to offload their computation-intensive tasks to nearby edges via wireless Base Stations (BSs) and thus relieve their resource constraints. To better guarantee Quality-of-Service (QoS), it has become necessary to cooperate multiple edges instead of letting them wo...

Federated Learning (FL) has been employed for tremendous privacy-sensitive applications, where distributed devices collaboratively train a global model. In Industrial Internet-of-Things (IIoT) systems, training latency is the key performance metric as the automated manufacture usually requires timely processing. The existing works increase the numb...<