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Local Load Migration in High-Capacity Fog Computing
Fog computing brings storage and computational capabilities closer to the data source, which reduces latency and enhances efficiency in processing data. However, these capabilities are resource-constrained at the fog nodes as compared to the cloud core. ...
Exposing Stealthy Wash Trading on Automated Market Maker Exchanges
Decentralized Finance (DeFi), a pivotal component of the emerging Web3 landscape, is gaining popularity but remains vulnerable to market manipulations, such as wash trading. Wash trading is an illegal practice, where traders buy and sell assets to ...
Conscious Task Recommendation via Cognitive Reasoning Computing in Mobile Crowd Sensing
Mobile Crowd Sensing is a human-based data collection model, and the approach taken to recommend data collection tasks to users in order to maximize task acceptance rates is an important part of this research. Existing task recommendation methods are ...
Malicious Participants and Fake Task Detection Incorporating Gaussian Bias
Mobile crowdsensing (MCS) is a combination of crowdsourcing ideas and mobile sensing devices, designed to enable rational allocation of resources at scale. However, the MCS platform is highly vulnerable to injection attacks from malicious participants and ...
Enhancing Alexa Skill Testing Through Improved Utterance Discovery
Extracting skill utterances is a crucial step in testing and evaluating smart speaker skills. Previous works have proposed various techniques for extracting utterances from the skill web page to test and evaluate smart speaker skills. In this article, we ...
A Network Calculus Model for SFC Realization and Traffic Bounds Estimation in Data Centers
Network Function Virtualization (NFV) is a promising technology that can transform how internet service providers deliver their services. However, recent studies have identified several challenges in adopting NFV. Two key challenges are central to the ...
Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones
This work aims to develop a surveillance mission system using unmanned aerial vehicles (UAVs) or drones when Denial-of-Service (DoS) attacks are present to disrupt normal operations for mission systems. In particular, we introduce the concept of cyber ...
Safeguarding User-Centric Privacy in Smart Homes
Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint ...
FedGK: Communication-Efficient Federated Learning through Group-Guided Knowledge Distillation
Federated learning (FL) empowers a cohort of participating devices to contribute collaboratively to a global neural network model, ensuring that their training data remains private and stored locally. Despite its advantages in computational efficiency and ...
Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach
In the rapidly evolving digital world, blockchain technology is becoming the foundation for numerous applications, ranging from financial services to supply chain management. As the usage of blockchain is becoming more prevalent, the energy-intensive ...
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, improved latency, ...
Model-Driven Development Towards Distributed Intelligent Systems
A Distributed Intelligent System (DIS) encompasses a set of intelligent subsystems and components that collaborate to perform tasks and solve problems. Given the advancements of paradigms such as the Internet of Things, along with the advancements of ...
Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic Analysis
The expanding use of Internet-of-Things (IoT) has driven machine learning (ML)-based traffic analysis. 5G networks’ standards, requiring low-latency communications for time-critical services, pose new challenges to traffic analysis. They necessitate fast ...
RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates
Distributed intelligence enables the widespread deployment of AI technology, greatly promoting the development of AI. Federated learning is a widely used distributed intelligence technology that allows iterative optimization of global model while ...
Hybrid Algorithm Selection and Hyperparameter Tuning on Distribute Machine Learning Resources: Hierarchical Agent-based Approach
Algorithm selection and hyperparameter tuning are critical steps in both academic and applied machine learning (ML). These steps are becoming increasingly delicate due to the extensive rise in the number, diversity, and distributed nature of ML resources. ...
