Yasmin Fathy

PhD Candidate



I am a PhD student at Institute of Communication Systems (ICS) at the University of Surrey, Surrey in the UK. My supervisors are Dr. Payam Barnaghi and Prof. Rahim Tafazolli. Before joining ICS, I studied MSc at Artificial Intelligence (AI) Lab at Vrije Universiteit Brussel (VUB) in Belgium. My MSc dissertation entitled "A Mixed Task Scheduling Policy for Work-stealing Schedulers" was under supervision of Prof. Ann Nowé and Prof. Tom Van Cutsem.

Research interests

Internet of Things (IoT), Big Data, Machine Learning, Streaming Processing, and Information Retrieval.

My publications


Abbas Fathy Abbas Y, Barnaghi P, Tafazolli R (2017) Large-Scale Indexing, Discovery and Ranking for the Internet of Things (IoT), ACM Computing Surveys Association for Computing Machinery (ACM)
Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber
world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies
that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous
amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant
streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated
with different granularities and have diverse structures, types and qualities. Dealing with the data deluge
from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking
mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data.
However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders
their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on
indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop,
implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and
Abbas Fathy Abbas Y, Barnaghi P, Enshaeifar S, Tafazolli R (2017) A Distributed In-network Indexing Mechanism for the Internet of Things, IEEE World Forum on Internet of Things pp. 585-590 IEEE
The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system; however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.
Abbas Fathy Abbas Y, Barnaghi P, Tafazolli R (2017) Distributed Spatial Indexing for the Internet of Things Data Management, Proceedings of IM 2017 pp. 1246-1251 IEEE
The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts).
Ahrabian A, Elsaleh T, Abbas Fathy Abbas Y, Barnaghi P (2017) Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC, Proceedings of IEEE Sensors 2017 IEEE
An important field in exploratory sensory data
analysis is the segmentation of time-series data to identify
activities of interest. In this work, we analyse the performance
of univariate and multi-sensor Bayesian change detection
algorithms in segmenting accelerometer data. In particular, we
provide theoretical analysis and also performance evaluation on
synthetic data and real-world data. The results illustrate the
advantages of using multi-sensory variance change detection in
the segmentation of dynamic data (e.g. accelerometer data).
Abbas Fathy Abbas Y, Barnaghi P, Tafazolli R (2018) An Adaptive Method for Data Reduction in the Internet of Things, Proceedings of IEEE 4th World Forum on Internet of Things IEEE
Enormous amounts of dynamic observation and
measurement data are collected from sensors in Wireless
Sensor Networks (WSNs) for the Internet of Things (IoT)
applications such as environmental monitoring. However, continuous
transmission of the sensed data requires high energy
consumption. Data transmission between sensor nodes and
cluster heads (sink nodes) consumes much higher energy than
data sensing in WSNs. One way of reducing such energy
consumption is to minimise the number of data transmissions.
In this paper, we propose an Adaptive Method for Data Reduction
(AM-DR). Our method is based on a convex combination
of two decoupled Least-Mean-Square (LMS) windowed filters
with differing sizes for estimating the next measured values
both at the source and the sink node such that sensor nodes
have to transmit only their immediate sensed values that
deviate significantly (with a pre-defined threshold) from the
predicted values. The conducted experiments on a real-world
data show that our approach has been able to achieve up to
95% communication reduction while retaining a high accuracy
(i.e. predicted values have a deviation of ý+0:5 from real data