Search Results for author: Aftab Khan

Found 16 papers, 2 papers with code

Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed

no code implementations24 Jan 2024 Peizheng Li, Ioannis Mavromatis, Aftab Khan

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.

Federated Learning Intrusion Detection

Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems

no code implementations24 Jan 2024 Jikun Gao, Ioannis Mavromatis, Peizheng Li, Pietro Carnelli, Aftab Khan

We evaluate our approach within an AFL deployment consisting of 10 simulated clients with heterogeneous compute constraints and non-IID data.

Federated Learning

Federated Deep Learning for Intrusion Detection in IoT Networks

no code implementations5 Jun 2023 Othmane Belarbi, Theodoros Spyridopoulos, Eirini Anthi, Ioannis Mavromatis, Pietro Carnelli, Aftab Khan

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.

Federated Learning Intrusion Detection

FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments

no code implementations15 May 2023 Theo Chow, Usman Raza, Ioannis Mavromatis, Aftab Khan

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.

Federated Learning Scheduling

Hierarchical and Decentralised Federated Learning

no code implementations28 Apr 2023 Omer Rana, Theodoros Spyridopoulos, Nathaniel Hudson, Matt Baughman, Kyle Chard, Ian Foster, Aftab Khan

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.

energy management Federated Learning

Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework

no code implementations3 Nov 2022 Ioannis Mavromatis, Aftab Khan

This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality.

Privacy Preserving Time Series +1

A Dataset of Images of Public Streetlights with Operational Monitoring using Computer Vision Techniques

no code implementations31 Mar 2022 Ioannis Mavromatis, Aleksandar Stanoev, Pietro Carnelli, Yichao Jin, Mahesh Sooriyabandara, Aftab Khan

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

no code implementations8 Mar 2021 Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham

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.

Transfer Learning

Wireless Localisation in WiFi using Novel Deep Architectures

no code implementations16 Oct 2020 Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham

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

no code implementations30 May 2019 Aftab Khan, Tim Farnham, Roget Kou, Usman Raza, Thajanee Premalal, Aleksandar Stanoev, William Thompson

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?

1 code implementation28 Aug 2018 Shengyang Li, Usman Raza, Aftab Khan

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

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