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Found 17 papers, 2 papers with code
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study
With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge.
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed
UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices.
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
We evaluate our approach within an AFL deployment consisting of 10 simulated clients with heterogeneous compute constraints and non-IID data.
FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation
The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years.
Federated Deep Learning for Intrusion Detection in IoT Networks
The comparison shows that the heterogeneous nature of the data has a considerable negative impact on the model's performance when trained in a distributed manner.
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments
In order to simultaneously reduce communication traffic and maintain the integrity of inference models, we introduce FLARE, a novel lightweight dual-scheduler FL framework that conditionally transfers training data, and deploys models between edge and sensor endpoints based on observing the model's training behaviour and inference statistics, respectively.
Hierarchical and Decentralised Federated Learning
Hierarchical Federated Learning is likely to be a key enabler for a wide range of applications, such as smart farming and smart energy management, as it can improve performance and reduce costs, whilst also enabling FL workflows to be deployed in environments that are not well-suited to traditional FL.
Toward Multi-Service Edge-Intelligence Paradigm: Temporal-Adaptive Prediction for Time-Critical Control over Wireless
The paper articulates some of the key system design aspects of multi-service edge-intelligence.
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration.
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality.
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications.
An Intrusion Detection System based on Deep Belief Networks
The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach.
Ranked #3 on
Network Intrusion Detection
on CICIDS2017
A Dataset of Images of Public Streetlights with Operational Monitoring using Computer Vision Techniques
Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb.
Deep Transfer Learning for WiFi Localization
Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers of the base model, only 55% of the training data is required to obtain the models' accuracy similar to the base models.
Wireless Localisation in WiFi using Novel Deep Architectures
Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates.
Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies
High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people.
How Agile is the Adaptive Data Rate Mechanism of LoRaWAN?
The LoRaWAN based Low Power Wide Area networks aim to provide long-range connectivity to a large number of devices by exploiting limited radio resources.
Networking and Internet Architecture
