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Issue 1 • Jan.-Feb.-2025
Showing 1-25 of 35
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A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments
Publication Year: 2025,Page(s):1 - 14
Mobile Edge Computing (MEC) can augment the capability of Internet of Things (IoT) mobile devices (MDs) through offloading the computation-intensive tasks to their adjacent servers. Synergistic computation offloading among MEC servers is one possible solution to reduce the completion time of system during peak hours. However, due to the large number of servers and the long distance between base st...Show More
A Reinforcement Learning Based Framework for Holistic Energy Optimization of Sustainable Cloud Data Centers
Publication Year: 2025,Page(s):15 - 28
The widespread adoption of cloud data centers has led to a rise in energy consumption, with the associated carbon emissions posing a further threat to the environment. Cloud providers are increasingly moving towards sustainable data centers powered by renewable energy sources (RES). The existing approaches fail to efficiently coordinate IT and cooling resources in such data centers due to the inte...Show More
A Resource-Efficient Multiple Recognition Services Framework for IoT Devices
Chuntao Ding;Zhuo Liu;Ao Zhou;Jinhui Yu;Yidong Li;Shangguang Wang
Publication Year: 2025,Page(s):29 - 42
Deploying the convolutional neural network (CNN) model on Internet of Things (IoT) devices to provide diverse recognition services has received increasing attention. Due to the limited storage, computing, and other resources of IoT devices, it has become mainstream to first train the CNN model on the edge/cloud server and then send the trained CNN to the IoT device. However, most existing related ...Show More
ADSS: An Available-but-Invisible Data Service Scheme for Fine-Grained Usage Control
Hao Wang;Jun Wang;Chunpeng Ge;Yuhang Li;Lu Zhou;Zhe Liu;Weibin Wu;Mingsheng Cao
Publication Year: 2025,Page(s):43 - 56
Cited by: Papers (1)
The demand for mobile terminals to participate in data services is increasingly vital. The General Data Protection Regulation (GDPR) has established several principled requirements for data services. Existing studies focusing on data service put emphasis on data privacy and accessibility. However, they face challenges in achieving data forgetability and portability on mobile devices under GDPR and...Show More
An Active Defense Adjudication Method Based on Adaptive Anomaly Sensing for Mimic IoT
Sisi Shao;Tiansheng Gu;Yijun Nie;Zongkai Ji;Fei Wu;Zhongjie Ba;Yimu Ji;Kui Ren;Guozi Sun
Publication Year: 2025,Page(s):57 - 71
Security issues in the Internet of Things (IoT) are inevitable. Uncertain threats, such as known vulnerabilities and backdoors exist within IoT, and traditional passive network security technologies are ineffective against uncertain threats. To address the above issues, we propose an active defense adjudication method based on adaptive anomaly sensing for mimic IoT. The method constructs a mimic I...Show More
ARAScaler: Adaptive Resource Autoscaling Scheme Using ETimeMixer for Efficient Cloud-Native Computing
Publication Year: 2025,Page(s):72 - 84
The container resource autoscaling techniques offer scalability and continuity for microservices operating in cloud-native computing environments. However, they manage resources inefficiently, causing resource waste and overload under complex workload patterns. In addition, these techniques fail to prevent oscillations caused by dynamic workloads, increasing the operational complexity. Therefore, ...Show More
Automatic Data Generation and Optimization for Digital Twin Network
Publication Year: 2025,Page(s):85 - 97
With the rise of new applications such as AR/VR, cloud gaming, and vehicular networks, traditional network management solutions are no longer cost-effective. Digital Twin Network (DTN) creates a real-time virtual twin of the physical network, which improves the network's stability, security, and operational efficiency. AI models have been used to model complex network environments in DTN, whose qu...Show More
Automatic Data Generation and Optimization for Digital Twin Network
Year: 2025 | Volume: 18 | Issue: 1
Content-Specific and Buffer-Based Migration Schemes for Fog Computing
Publication Year: 2025,Page(s):98 - 111
Fog computing and network function virtualization (NFV) technologies reduce latency and provide scalable services at the network edge. However, the limited resources of edge nodes pose a challenge in handling high traffic volumes from requests that demand high computation, extended lifetime, and low latency. To address this challenge, dynamic load migration schemes for NFV-based fog paradigms are ...Show More
Content-Specific and Buffer-Based Migration Schemes for Fog Computing
Year: 2025 | Volume: 18 | Issue: 1
Continuous Management of Machine Learning-Based Application Behavior
Marco Anisetti;Claudio A. Ardagna;Nicola Bena;Ernesto Damiani;Paolo G. Panero
Publication Year: 2025,Page(s):112 - 125
Modern applications are increasingly driven by Machine Learning (ML) models whose non-deterministic behavior is affecting the entire application life cycle from design to operation. The pervasive adoption of ML is urgently calling for approaches that guarantee a stable non-functional behavior of ML-based applications over time and across model changes. To this aim, non-functional properties of ML ...Show More
Crowdsourcing Home Healthcare Service: Matching Caretakers With Caregivers for Jointly Rostering and Routing
Publication Year: 2025,Page(s):126 - 139
In this work, we introduce crowdsourcing home healthcare service (CHHS) systems, where caregivers (including nurses, personal care attendants, and housekeepers) from different locations (rather than centralized institutions) offer diverse home healthcare services to caretakers at home. Powered by cloud computing, the CHHS system enables real-time, dynamic, and large-scale matching between caretake...Show More
Efficient Hierarchical Federated Services for Heterogeneous Mobile Edge
Shengyuan Liang;Qimei Cui;Xueqing Huang;Borui Zhao;Yanzhao Hou;Xiaofeng Tao
Publication Year: 2025,Page(s):140 - 155
As 6G networks actively advance edge intelligence, Federated Learning (FL) emerges as a key technology that enables data sharing while preserving data privacy and fostering collaboration among edge devices for intelligent service learning. However, the multi-dimensional heterogeneous and hierarchical network architecture brings many challenges to FL deployment, including selecting appropriate node...Show More
Efficient Parameter Synchronization for Peer-to-Peer Distributed Learning With Selective Multicast
Shouxi Luo;Pingzhi Fan;Ke Li;Huanlai Xing;Long Luo;Hongfang Yu
Publication Year: 2025,Page(s):156 - 168
Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided that workers will eventually participate in the synchronizations, $i)$i) the training still converges, even if only $p$p workers take part in each round of synchronization, and $ii)$ii) a larger $p$p generally leads to a faster rate of convergence. These findings shed light on elimina...Show More
Enhanced Tube-Based Sampling for Accurate Network Distance Measurement with Minimal Sampling Scheduling Overhead
Jiazheng Tian;Cheng Wang;Kun Xie;Jigang Wen;Gaogang Xie;Kenli Li;Wei Liang
Publication Year: 2025,Page(s):169 - 183
The surge in demand for latency-sensitive services has propelled network distance measurement to the forefront of networking research. Utilizing the low-rank structure of full network data, the tensor completion method can efficiently estimate network distance from partially sampled distance data measured from a small set of node pairs. However, its performance is affected by sampling algorithm li...Show More
FeDistSlice: Federated Policy Distillation for Collaborative Intelligence in Multi-Tenant RAN Slicing
Guolin Sun;Daniel Ayepah-Mensah;Huan Chen;Gordon Owusu Boateng;Guisong Liu
Publication Year: 2025,Page(s):184 - 197
Federated Deep Reinforcement Learning (FDRL) for Radio Access Network (RAN) Slicing offers a promising approach for optimizing resource allocation and network performance, while also preserving data privacy for multiple tenants. However, the inherently non-independent and identically distributed (non-IID) nature of data, stemming from the diverse services and unique characteristics of RAN slices, ...Show More
Flexible Computing: A New Framework for Improving Resource Allocation and Scheduling in Elastic Computing
Weipeng Cao;Jiongjiong Gu;Zhong Ming;Zhiyuan Cai;Yuzhao Wang;Changping Ji;Zhijiao Xiao;Yuhong Feng;Ye Liu;Liang-Jie Zhang
Publication Year: 2025,Page(s):198 - 211
Since the advent of cloud computing, Elastic Computing (EC) has become the standard architecture for resource allocation and scheduling. EC typically allocates computing resources based on predefined specifications, such as virtual machine or container flavors. However, these flavors are often constrained by fixed CPU-to-memory ratios, which frequently fail to match the actual resource needs of ap...Show More
Intent-Guided Bilateral Long and Short-Term Information Mining With Contrastive Learning for Sequential Recommendation
Junhui Niu;Wei Zhou;Fengji Luo;Yihao Zhang;Jun Zeng;Junhao Wen
Publication Year: 2025,Page(s):212 - 225
The current sequential recommendation systems mainly focus on mining information related to users to make personalized recommendations. However, there are two subjects in the user historical interaction sequence: users and items. We believe that mining sequence information only from the users’ perspective is limited, ignoring effective information from the perspective of items, which is not conduc...Show More
KA$^{2}$2SE: Key-Aggregation Authorized Searchable Encryption Scheme for Data Sharing in Wireless Sensor Networks
Publication Year: 2025,Page(s):226 - 238
As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “Key Forg...Show More
Learning-to-Adaptation for Security Service in Industrial IoT: An AI-Enabled Slice-Specific Solution
Publication Year: 2025,Page(s):239 - 252
Network slicing is the key enabler for the 5G Industrial Internet of Things (IIoT), allowing tailored services and security guarantees for vertical industries. With the advent of 5G-Advanced (5G-A) and 6G era, the number of slices will increase significantly, leading to more diverse security requirements given different slice features. To provide adaptive security management spanning multiple slic...Show More
MEC-Enabled Task Replication With Resource Allocation for Reliability-Sensitive Services in 5G mMTC Networks
Publication Year: 2025,Page(s):253 - 269
The increasing demand for connectivity in 5G networks has led to a focus on massive machine-type communication (mMTC) in mobile edge computing (MEC) for IoTs. However, the proliferation of IoT devices has resulted in densely deployed networks and led to a high volume of task offloading to the same edge servers simultaneously. As a consequence, mMTC applications may experience service congestion, n...Show More
Multi-Granularity Weighted Federated Learning for Heterogeneous Edge Computing
Yunfeng Zhao;Chao Qiu;Shangxuan Cai;Zhicheng Liu;Yu Wang;Xiaofei Wang;Qinghua Hu
Publication Year: 2025,Page(s):270 - 287
Federated learning (FL), an advanced variant of distributed machine learning, enables clients to collaboratively train a model without sharing raw data, thereby enhancing privacy, security, and reducing communication overhead. However, in edge computing scenarios, there is an increasing trend towards diversity, heterogeneity, and complexity in clients’ data and models. The fundamental challenges, ...Show More
Multi-Objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing
Yaning Yang;Xiao Du;Yutong Ye;Jiepin Ding;Ting Wang;Mingsong Chen;Keqin Li
Publication Year: 2025,Page(s):288 - 301
Function offloading problems play a crucial role in optimizing the performance of applications in serverless edge computing (SEC). Existing research has extensively explored function offloading strategies based on optimizing a single objective. However, a significant challenge arises when users expect to optimize multiple objectives according to the relative importance of these objectives. This ch...Show More
Next PoI Recommendation Based on Graph Convolutional Networks and Multiple Context-Awareness
Publication Year: 2025,Page(s):302 - 313
Next Point-of-interest recommendation involves modeling user interactions with Point-of-interests (PoIs) to analyze user behavior patterns and suggest future scenarios. Data sparsity problems in PoI recommendations can significantly impact the performance of the recommendation model. This paper introduces the Graph Convolutional Network and Multiple Context-Aware PoI Recommendation model (GMCA). F...Show More
ObliuSky: Oblivious User-Defined Skyline Query Processing in the Cloud
Yifeng Zheng;Weibo Wang;Songlei Wang;Zhongyun Hua;Yansong Gao
Publication Year: 2025,Page(s):314 - 327
The proliferation of cloud computing has spurred the popularity of storing and querying databases in the cloud. Among others, skyline queries play an important role in the database field due to its usefulness in multi-criteria decision support systems. To accommodate the tailored needs of users, user-defined skyline query has recently emerged, allowing users to define custom preferences in their s...Show More
Online Layer-Aware Joint Request Scheduling, Container Placement, and Resource Provision in Edge Computing
Zhenzheng Li;Jiong Lou;Zhiqing Tang;Jianxiong Guo;Tian Wang;Weijia Jia;Wei Zhao
Publication Year: 2025,Page(s):328 - 341
Containers have emerged as a pivotal tool for service deployment in edge computing. Before running the container, an image composed of several layers must exist locally. Recent strategies have utilized layer-sharing in images to reduce deployment delays. However, existing research only focuses on a single aspect of container orchestration, like container placement, neglecting the joint optimizatio...Show More
Participation-Dependent Privacy Preservation in Cross-Silo Federated Learning
Publication Year: 2025,Page(s):342 - 356
In cross-silo federated learning (FL), clients of common interest cooperatively train a global model without sharing local sensitive data, but they still face potential privacy leakage due to privacy threats from malicious attackers. Although some articles have proposed effective privacy-preserving mechanisms for FL (such as differential privacy (DP)), clients in cross-silo FL are usually differen...Show More
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