ORCID Profile
0000-0002-2572-2355
Current Organisations
Deakin University
,
Macquaire University
,
Macquarie University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Computer Software | Distributed Computing | Decision Support and Group Support Systems | Software Engineering | Computational Logic and Formal Languages | Computer System Security | Mobile Technologies | Control Systems, Robotics and Automation
ANZSRC Socio-Economic Objective (SEO)
Road Safety | Road Passenger Movements (excl. Public Transport) | Aerospace Transport not elsewhere classified | Application Tools and System Utilities |
Related Links
Publications
Guided Activity Prediction for Minimally Invasive Surgery Safety Improvement in the Internet of Medical Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-03-2022
SG-PBFT: A secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles
Publisher: Elsevier BV
Date: 06-2022
Query-Efficient Black-Box Adversarial Attacks on Automatic Speech Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
BraceAssertion: Runtime Verification of Cyber-Physical Systems
Publisher: IEEE
Date: 10-2015
DOI: 10.1109/MASS.2015.15
Facilitating Decentralized and Opportunistic Learning in Pervasive Computing
Publisher: IEEE
Date: 21-03-2022
CSP-E2: An abuse-free contract signing protocol with low-storage TTP for energy-efficient electronic transaction ecosystems
Publisher: Elsevier BV
Date: 02-2019
Unsupervised-Learning-Based Continuous Depth and Motion Estimation with Monocular Endoscopy for Virtual Reality Minimally Invasive Surgery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Physically informed assertions for cyber physical systems development and debugging
Publisher: IEEE
Date: 03-2014
An Empirical Study on Model Pruning and Quantization
Publisher: Springer Nature Switzerland
Date: 2023
A Hybrid Method Combining Markov Prediction and Fuzzy Classification for Driving Condition Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach
Publisher: IEEE
Date: 12-2021
DOI: 10.1109/ITHINGS-GREENCOM-CPSCOM-SMARTDATA-CYBERMATICS53846.2021.00040
BAGKD: A Batch Authentication and Group Key Distribution Protocol for VANETs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
SCA-LFD
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
MFF-AMD: Multivariate Feature Fusion for Android Malware Detection
Publisher: Springer International Publishing
Date: 2021
Real-Time Simulation Support for Real-Time Systems
Publisher: Springer Singapore
Date: 2020
Practical overview of security issues in wireless sensor network applications
Publisher: Informa UK Limited
Date: 05-12-2017
Friend-as-Learner: Socially-Driven Trustworthy and Efficient Wireless Federated Edge Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
When Road Information Meets Data Mining: Precision Detection for Heading and Width of Roads
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study
Publisher: MDPI AG
Date: 05-01-2021
DOI: 10.3390/S21010312
Abstract: Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices.
BraceForce: a middleware to enable sensing integration in mobile applications for novice programmers
Publisher: ACM
Date: 02-06-2014
PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition
Publisher: IEEE
Date: 13-10-2022
HUCDO
Publisher: Association for Computing Machinery (ACM)
Date: 22-05-2020
DOI: 10.1145/3379464
Abstract: Outsourcing helps relocate data from the cyber-physical system (CPS) for efficient storage at low cost. Current server-based outsourcing mainly focuses on the benefits of servers. This cannot attract users well, as their security, efficiency, and economy are not guaranteed. To solve with this issue, a hybrid outsourcing model that exploits both cloud server and edge devices to store data is needed. Meanwhile, the requirements of security and efficiency are different under specific scenarios. There is a lack of a comprehensive solution that considers all of the above issues. In this work, we overcome the above issues by proposing the first hybrid user-centric data outsourcing (HUCDO) scheme. It allows users to outsource data securely, efficiently, and economically via different CPSs. Brielly, our contributions consist of theories, implementations, and evaluations. Our theories include the first homomorphic collision-resistant chameleon hash (HCCH) and homomorphic designated-receiver signcryption (HDRS). As implementations, we instantiate how to use our proposals to outsource small- or large-scale data through distinct CPS, respectively. Additionally, a blockchain with proof-of-discrete-logarithm (B-PoDL) is instantiated to help improve our performance. Last, as demonstrated by our evaluations, our proposals are secure, efficient, and economic for users to implement while outsourcing their data via CPSs.
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Investigating the Prospect of Leveraging Blockchain and Machine Learning to Secure Vehicular Networks: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
SmartVM: A Multi-Layer Microservice-Based Platform for Deploying SaaS
Publisher: IEEE
Date: 12-2017
Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Detection of Smoking Events from Confounding Activities of Daily Living
Publisher: ACM
Date: 29-01-2019
Sparse Trust Data Mining
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Leveraging ICN With Network Sensing for Intelligent Transportation Systems: A Dynamic Naming Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2021
Perceptions on the State of the Art in Verification and Validation in Cyber-Physical Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
An Intelligent Dynamic Offloading From Cloud to Edge for Smart IoT Systems With Big Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Dynamic clustering method for imbalanced learning based on AdaBoost
Publisher: Springer Science and Business Media LLC
Date: 02-03-2020
Detecting Difference Between Process Models Using Edge Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
A survey of security and privacy issues in the Internet of Things from the layered context
Publisher: Wiley
Date: 16-03-2022
DOI: 10.1002/ETT.3935
Abstract: Internet of Things (IoT) is a novel paradigm, which not only facilitates a large number of devices to be ubiquitously connected over the Internet but also provides a mechanism to remotely control these devices. The IoT is pervasive and is almost an integral part of our daily life. These connected devices often obtain user's personal data and store it online. The security of collected data is a big concern in recent times. As devices are becoming increasingly connected, privacy and security issues become more and more critical and these need to be addressed on an urgent basis. IoT implementations and devices are eminently prone to threats that could compromise the security and privacy of the consumers, which, in turn, could influence its practical deployment. In recent past, some research has been carried out to secure IoT devices with an intention to alleviate the security concerns of users. There have been research on blockchain technologies to tackle the privacy and security issues of the collected data in IoT. The purpose of this paper is to highlight the security and privacy issues in IoT systems. To this effect, the paper examines the security issues at each layer in the IoT protocol stack, identifies the under‐lying challenges and key security requirements and provides a brief overview of existing security solutions to safeguard the IoT from the layered context.
Using AI to Attack VA: A Stealthy Spyware Against Voice Assistances in Smart Phones
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Scenario-based test reduction and prioritization for multi-module autonomous driving systems
Publisher: ACM
Date: 07-11-2022
TDD4Fog: A Test-Driven Software Development Platform for Fog Computing Systems
Publisher: IEEE
Date: 05-2020
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications
Publisher: IEEE
Date: 22-03-2021
EIDLS: An Edge-Intelligence-Based Distributed Learning System Over Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
ASQ-FastBM3D: An Adaptive Denoising Framework for Defending Adversarial Attacks in Machine Learning Enabled Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Leveraging Multi-view Learning for Human Anomaly Detection in Industrial Internet of Things
Publisher: IEEE
Date: 11-2020
DOI: 10.1109/ITHINGS-GREENCOM-CPSCOM-SMARTDATA-CYBERMATICS50389.2020.00095
Attacks and defences on intelligent connected vehicles: a survey
Publisher: Elsevier BV
Date: 11-2020
Energy absorption of bio-inspired multi-layered graded foam-filled structures under axial crushing
Publisher: Elsevier BV
Date: 10-2020
Crowdsourcing Mechanism for Trust Evaluation in CPCS Based on Intelligent Mobile Edge Computing
Publisher: Association for Computing Machinery (ACM)
Date: 24-10-2019
DOI: 10.1145/3324926
Abstract: Both academia and industry have directed tremendous interest toward the combination of Cyber Physical Systems and Cloud Computing, which enables a new breed of applications and services. However, due to the relative long distance between remote cloud and end nodes, Cloud Computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this article, we first propose a novel trust evaluation mechanism using crowdsourcing and Intelligent Mobile Edge Computing. The mobile edge users with relatively strong computation and storage ability are exploited to provide direct management for end nodes. Through close access to end nodes, mobile edge users can obtain various information of the end nodes and determine whether the node is trustworthy. Then, two incentive mechanisms, i.e., Trustworthy Incentive and Quality-Aware Trustworthy Incentive Mechanisms, are proposed for motivating mobile edge users to conduct trust evaluation. The first one aims to motivate edge users to upload their real information about their capability and costs. The purpose of the second one is to motivate edge users to make trustworthy effort to conduct tasks and report results. Detailed theoretical analysis demonstrates the validity of Quality-Aware Trustworthy Incentive Mechanism from data trustfulness, effort trustfulness, and quality trustfulness, respectively. Extensive experiments are carried out to validate the proposed trust evaluation and incentive mechanisms. The results corroborate that the proposed mechanisms can efficiently stimulate mobile edge users to perform evaluation task and improve the accuracy of trust evaluation.
Cyber-physical systems in transportation
Publisher: Springer International Publishing
Date: 15-12-2022
Service-Mediated On-Road Situation-Awareness for Group Activity Safety
Publisher: ACM
Date: 07-11-2017
A Security-Enhanced Certificateless Aggregate Signature Authentication Protocol for InVANETs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Efficient Human Activity Recognition Using a Single Wearable Sensor
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
TT-SVD: An Efficient Sparse Decision-Making Model With Two-Way Trust Recommendation in the AI-Enabled IoT Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-06-2021
Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
MFE-HAR: Multiscale feature engineering for human activity recognition using wearable sensors
Publisher: ACM
Date: 12-11-2019
Service2vec: A Vector Representation for Web Services
Publisher: IEEE
Date: 06-2017
Edge-Based Communication Optimization for Distributed Federated Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Security analysis of modern mission critical android mobile applications
Publisher: ACM
Date: 31-01-2017
Am I eclipsed? A smart detector of eclipse attacks for Ethereum
Publisher: Elsevier BV
Date: 2020
Who spread to whom? Inferring online social networks with user features
Publisher: IEEE
Date: 05-2018
Cloud-Edge Orchestrated Power Dispatching for Smart Grid With Distributed Energy Resources
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Privacy-preserving time-series medical images analysis using a hybrid deep learning framework
Publisher: Association for Computing Machinery (ACM)
Date: 16-06-2021
DOI: 10.1145/3383779
Abstract: Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
Publisher: IEEE
Date: 03-2020
A Privacy-Preserving Data Inference Framework for Internet of Health Things Networks
Publisher: IEEE
Date: 12-2020
Real-Time Simulation Support for Runtime Verification of Cyber-Physical Systems
Publisher: Association for Computing Machinery (ACM)
Date: 11-05-2017
DOI: 10.1145/3063382
Abstract: In Cyber-Physical Systems (CPS), cyber and physical components must work seamlessly in tandem. Runtime verification of CPS is essential yet very difficult, due to deployment environments that are expensive, dangerous, or simply impossible to use for verification tasks. A key enabling factor of runtime verification of CPS is the ability to integrate real-time simulations of portions of the CPS into live running systems. We propose a verification approach that allows CPS application developers to opportunistically leverage real-time simulation to support runtime verification. Our approach, termed B race B ind , allows selecting, at runtime, between actual physical processes or simulations of them to support a running CPS application. To build B race B ind , we create a real-time simulation architecture to generate and manage multiple real-time simulation environments based on existing simulation models in a manner that ensures sufficient accuracy for verifying a CPS application. Specifically, B race B ind aims to both improve simulation speed and minimize latency, thereby making it feasible to integrate simulations of physical processes into the running CPS application. B race B ind then integrates this real-time simulation architecture with an existing runtime verification approach that has low computational overhead and high accuracy. This integration uses an aspect-oriented adapter architecture that connects the variables in the cyber portion of the CPS application with either sensors and actuators in the physical world or the automatically generated real-time simulation. Our experimental results show that, with a negligible performance penalty, our approach is both efficient and effective in detecting program errors that are otherwise only detectable in a physical deployment.
Gain Without Pain: Offsetting DP-Injected Noises Stealthily in Cross-Device Federated Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2022
Privacy-Preserving Distributed Transfer Learning and its Application in Intelligent Transportation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
DeCoP
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Object localisation through clustering unreliable ultrasonic range sensors
Publisher: Inderscience Publishers
Date: 2018
WiP abstract: BraceForce: Software engineering support for sensing in CPS applications
Publisher: IEEE
Date: 04-2014
Leveraging Energy Function Virtualization with Game Theory for Fault-Tolerant Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Generalized Centered 2-D Principal Component Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Efficient and Scalable Runtime Monitoring for Cyber–Physical System
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-02-2021
CPFL: An Effective Secure Cognitive Personalized Federated Learning Mechanism for Industry 4.0
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Integrating NFV and ICN for Advanced Driver-Assistance Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Prototyping Opportunistic Learning in Resource Constrained Mobile Devices
Publisher: IEEE
Date: 21-03-2022
Fog-based Secure Service Discovery for Internet of Multimedia Things
Publisher: Association for Computing Machinery (ACM)
Date: 31-10-2020
DOI: 10.1145/3415151
Abstract: The Internet of Multimedia Things (IoMT) has become the backbone of innumerable multimedia applications in various fields. The wide application of IoMT not only makes our life convenient but also brings challenges to service discovery. Service discovery aims to leverage location information and trust evidence scattered in a variety of multimedia applications to find trusted IoMT devices that can provide specific service in target areas. However, the eavesdropping and t ering to these sensitive IoMT data during the trust propagation process invalidate the service discovery process. To address these challenges, we propose Secure Service Discovery (SSD) for IoMT using cross-blockchain-enabled fog computing. To resist the t ering and eavesdropping during the trust propagation process, a scalable cross-blockchain structure consisting of multiple parallel blockchains is first proposed based on fog, in which different parallel blockchains can be orchestrated to propagate encrypted location information and trust evidence of different applications. Moreover, to enable a cross-blockchain structure to leverage encrypted location information and trust evidence to find trusted IoMT devices in preset areas, a novel privacy-preserving range query is proposed to query and aggregate trust evidence. Security analysis and simulations are carried out to demonstrate the effectiveness and security of the proposed SSD.
JOSP: Joint Optimization of Flow Path Scheduling and Virtual Network Function Placement for Delay-Sensitive Applications
Publisher: Springer Science and Business Media LLC
Date: 04-02-2022
Verification of Microservices Using Metamorphic Testing
Publisher: Springer International Publishing
Date: 2020
Who Activated My Voice Assistant? A Stealthy Attack on Android Phones Without Users’ Awareness
Publisher: Springer International Publishing
Date: 2019
Efficient and Lightweight Data Streaming Authentication in Industrial Control and Automation Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
A Testbed for Security Analysis of Modern Vehicle Systems
Publisher: IEEE
Date: 08-2017
Security analysis of indistinguishable obfuscation for internet of medical things applications
Publisher: Elsevier BV
Date: 09-2020
ESDA: An Energy-Saving Data Analytics Fog Service Platform
Publisher: Springer International Publishing
Date: 2019
Rate-Adaptive Fog Service Platform for Heterogeneous IoT Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Real-Time Diagnosis of Configuration Errors for Software of AI Server Infrastructure
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
APDP: Attack-Proof Personalized Differential Privacy Model for a Smart Home
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Roundtable Gossip Algorithm: A Novel Sparse Trust Mining Method for Large-Scale Recommendation Systems
Publisher: Springer International Publishing
Date: 2018
A Privacy-Preserving Medical Data Sharing Scheme Based on Blockchain
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Swarm: Playground for Large-scale Decentralized Learning Simulations
Publisher: IEEE
Date: 21-03-2022
Can Steering Wheel Detect Your Driving Fatigue?
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
FengHuoLun: A Federated Learning based Edge Computing Platform for Cyber-Physical Systems
Publisher: IEEE
Date: 03-2020
BMT: Behavior Driven Development-based Metamorphic Testing for Autonomous Driving Models
Publisher: IEEE
Date: 06-2021
Edge-based differential privacy computing for sensor–cloud systems
Publisher: Elsevier BV
Date: 02-2020
DynaComm: Accelerating Distributed CNN Training Between Edges and Clouds Through Dynamic Communication Scheduling
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Cyber security attacks to modern vehicular systems
Publisher: Elsevier BV
Date: 10-2017
SmartVM: a SLA-aware microservice deployment framework
Publisher: Springer Science and Business Media LLC
Date: 05-05-2019
Non-invasive sensor based automated smoking activity detection
Publisher: IEEE
Date: 07-2017
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
BigVM: A Multi-Layer-Microservice-Based Platform for Deploying SaaS
Publisher: IEEE
Date: 08-2017
DOI: 10.1109/CBD.2017.16
A survey on security issues in services communication of Microservices‐enabled fog applications
Publisher: Wiley
Date: 14-02-2018
DOI: 10.1002/CPE.4436
Collaborative Three-Tier Architecture Noncontact Respiratory Rate Monitoring Using Target Tracking and False Peaks Eliminating Algorithms
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Privacy-Preserving Data Scheduling in Incentive-Driven Vehicular Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2022
Testing of autonomous driving systems: where are we and where should we go?
Publisher: ACM
Date: 07-11-2022
Investigating Security Vulnerabilities in Modern Vehicle Systems
Publisher: Springer Singapore
Date: 2016
DAliM: Machine Learning Based Intelligent Lucky Money Determination for Large-Scale E-Commerce Businesses
Publisher: Springer International Publishing
Date: 2018
OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Big data storage and management in SaaS applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
SSL-SVD
Publisher: Association for Computing Machinery (ACM)
Date: 29-01-2020
DOI: 10.1145/3369390
Abstract: Recommendation systems have been widely used in large e-commerce websites, but cold start and data sparsity seriously affect the accuracy of recommendation. To solve these problems, we propose SSL-SVD, which works to mine the sparse trust between users and improve the performance of the recommendation system. Specifically, we mine sparse trust relationships by decomposing trust impact into fine-grained factors and employing the Transductive Support Vector Machine algorithm to combine these factors. Then, we incorporate both social trust and sparse trust information into the SVD++ model, which can effectively utilize the explicit and implicit influence of trust for rating prediction in the recommendation system. Experiments show that our SSL-SVD increases the trust density degree of each dataset by more than 65% and improves the recommendation accuracy by up to 4.3%.
Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Differential Privacy and IRS Empowered Intelligent Energy Harvesting for 6G Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2022
Verification and Validation in Cyber Physical Systems: Research Challenges and a Way Forward
Publisher: IEEE
Date: 05-2015
Trust2Privacy: A Novel Fuzzy Trust-to-Privacy Mechanism for Mobile Social Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
PaaS - Black or white: An investigation into software development model for building retail industry SaaS
Publisher: IEEE
Date: 05-2017
Privacy-preserving federated learning framework based on chained secure multi-party computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-04-2021
EIHDP: Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Leveraging CNN and Transfer Learning for Vision-based Human Activity Recognition
Publisher: IEEE
Date: 11-2019
Mobile edge-enabled trust evaluation for the Internet of Things
Publisher: Elsevier BV
Date: 11-2021
Non-Contact Negative Mood State Detection Using Reliability-Focused Multi-Modal Fusion Model
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
PDAAA: Progressive Defense Against Adversarial Attacks for Deep Learning-as-a-Service in Internet of Things
Publisher: IEEE
Date: 10-2021
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
SoProtector: Securing Native C/C++ Libraries for Mobile Applications
Publisher: Springer International Publishing
Date: 2018
SSL-STR: Semi-Supervised Learning for Sparse Trust Recommendation
Publisher: IEEE
Date: 12-2019
A Declarative Metamorphic Testing Framework for Autonomous Driving
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Data Dissemination With Trajectory Privacy Protection for 6G-Oriented Vehicular Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses
Publisher: Springer International Publishing
Date: 2020
Using Sparse Representation to Detect Anomalies in Complex WSNs
Publisher: Association for Computing Machinery (ACM)
Date: 30-10-2019
DOI: 10.1145/3331147
Abstract: In recent years, wireless sensor networks (WSNs) have become an active area of research for monitoring physical and environmental conditions. Due to the interdependence of sensors, a functional anomaly in one sensor can cause a functional anomaly in another sensor, which can further lead to the malfunctioning of the entire sensor network. Existing research work has analysed faulty sensor anomalies but fails to show the effectiveness throughout the entire interdependent network system. In this article, a dictionary learning algorithm based on a non-negative constraint is developed, and a sparse representation anomaly node detection method for sensor networks is proposed based on the dictionary learning. Through experiment on a specific thermal power plant in China, we verify the robustness of our proposed method in detecting abnormal nodes against four state of the art approaches and proved our method is more robust. Furthermore, the experiments are conducted on the obtained abnormal nodes to prove the interdependence of multi-layer sensor networks and reveal the conditions and causes of a system crash.
SPRNN: A spatial–temporal recurrent neural network for crowd flow prediction
Publisher: Elsevier BV
Date: 10-2022
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles
Publisher: IEEE
Date: 10-2021
A Democratically Collaborative Learning Scheme for Fog-enabled Pervasive Environments
Publisher: IEEE
Date: 03-2020
Metamorphic Testing on Multi-module UAV Systems
Publisher: IEEE
Date: 11-2021
Guest Editorial: Special Section on Emerging Privacy and Security Issues Brought by Artificial Intelligence in Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
RICA-MD
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2021
DOI: 10.1145/3416492
Abstract: With the rapid development of various computing technologies, the constraints of data processing capabilities gradually disappeared, and more data can be simultaneously processed to obtain better performance compared to conventional methods. As a standard statistical analysis method that has been widely used in many fields, Independent Component Analysis (ICA) provides a new way for motion detection by extracting the foreground without precisely modeling the background. However, most existing ICA-based motion detection algorithms use only two-channel data for source separation and simply generate the observation vectors by decomposing and reconstructing the images by row, hence they cannot obtain an integrated and accurate shape of the moving objects in complex scenes. In this article, we propose a refined ICA algorithm for motion detection (RICA-MD), which fuses a larger number of channels than conventional ICA-based motion detection algorithms to provide more effective information for foreground extraction. Meanwhile, we propose four novel methods for generating observation vectors to further cover the erse motion styles of the moving objects. These improvements enable RICA-MD to effectively deal with slowly moving objects, which are difficult to detect using conventional methods. Our quantitative evaluation in multiple scenes shows that our proposed method is able to achieve a better performance at an acceptable cost of false alarms.
E-AUA: An Efficient Anonymous User Authentication Protocol for Mobile IoT
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Privacy-Aware Key Task Scheduling in Vehicular Networks Based on Incentive Mechanism
Publisher: IEEE
Date: 12-2020
Artifact: Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications
Publisher: IEEE
Date: 22-03-2021
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data
Publisher: IEEE
Date: 12-2019
Cluster-Enabled Cooperative Scheduling Based on Reinforcement Learning for High-Mobility Vehicular Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Achieving Privacy-Preserving Iris Identification Via El Gamal
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2019
When Sensor-Cloud Meets Mobile Edge Computing
Publisher: MDPI AG
Date: 03-12-2019
DOI: 10.3390/S19235324
Abstract: Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds.
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Edge Computing and Sensor-Cloud: Overview, Solutions, and Directions
Publisher: Association for Computing Machinery (ACM)
Date: 13-07-2023
DOI: 10.1145/3582270
Abstract: Sensor-cloud originates from extensive recent applications of wireless sensor networks and cloud computing. To draw a roadmap of the current research activities of the sensor-cloud community, we first investigate the state-of-the-art sensor-cloud literature reviews published since the late 2010s and discovered that these surveys have primarily studied the sensor-cloud in specific aspects, security-enabled solutions, efficient management mechanisms, and architectural challenges. While the existing surveys have reviewed the sensor-cloud from various perspectives, they are inadequate for the three key issues that require urgent attention in the sensor-cloud: reliability , energy , and heterogeneity . To fill this gap, we perform a thorough survey by examining the origins of the sensor-cloud and providing an in-depth and comprehensive discussion of these three key challenges. We summarize initial designs of the new edge-based schemes to address these challenges and identify several open issues and promising future research directions.
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