My research project

University roles and responsibilities

  • PhD Candidate on "Time-series Representation and Analysis Using Machine Learning Techniques"
  • Task Leader for EU-H2020 IoTCrawler Project with focus on developing a search engine for Internet of Things devices, funded by the European Commission

My publications

Publications

Rezvani Roonak, Enshaeifar Shirin, Barnaghi Payam (2019) Lagrangian-based Pattern Extraction for Edge Computing in the Internet of Things,Proceedings of the The 5th IEEE International Conference on Edge Computing and Scalable Cloud (IEEE EdgeCom 2019) Institute of Electrical and Electronics Engineers (IEEE)
Edge computing can improve the scalability and
efficiency of IoT systems by performing some of the analysis
and operations on the nodes or on intermediary edge devices.
This will reduce the energy consumption, data transmission
load and latency by shifting some of the processes to the edge
devices. In this paper, we introduce a pattern extraction method
which uses both the Lagrangian Multiplier and the Principal
Component Analysis (PCA) to create patterns from raw sensory
data. We have evaluated our method by applying a clustering
method on constructed patterns. The results show that by using
our proposed Lagrangian-based pattern extraction method, the
existing clustering algorithms perform more accurately - by
up to 20% higher compared with the state-of-the-art methods,
especially in dealing with dynamic real-world data. We have
conducted our evaluations based on synthetic and real-world data
sets and have compared the results to the existing state-of-the-art
approaches. We also discuss how the proposed methods can be
embedded into the edge computing devices in IoT systems and
applications.
Elsaleh T., Bermudez-Edo M., Enshaeifar S., Acton S. T., Rezvani R., Barnaghi P. (2019) IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams,Proceedings of the 3rd Global IoT Summit (GIoTS 2019) Institute of Electrical and Electronics Engineers (IEEE)
In recent years, the development and deployment
of Internet of Things (IoT) devices has led to the generation of
large volumes of real world data. Analytical models can be used to
extract meaningful insights from this data. However, most of IoT
data is not fully utilised, which is mainly due to interoperability
issues and the difficulties to analyse data collected by heterogeneous
resources. To overcome this heterogeneity, semantic
technologies are used to create common models to share various
data originated from heterogeneous sources. However, semantics
add further overhead to data delivery, and the processing time
to annotate the data with the model can increase the latency and
complexity in publishing and querying the annotated data. In
this paper, we present a lightweight semantic model to annotate
IoT streams. The metadata descriptions that are provided in the
models are used for search and discovery of the data using various
attributes such as value and type. The proposed model extends
commonly used ontologies such as W3C/OGC SSN ontology
and its recent lightweight core, SOSA, and includes concepts
to describe streaming IoT data. We also show use cases, tools
and applications where the proposed model has been used.
Rezvani Roonak, Barnaghi Payam, Enshaeifar Shirin (2019) A New Pattern Representation Method for Time-series Data,IEEE Transactions on Knowledge and Data Engineering IEEE
The rapid growth of Internet of Things (IoT) and sensing technologies has led to an increasing interest in time-series data analysis. In many domains, detecting patterns of IoT data and interpreting these patterns are challenging issues. There are several methods in time-series analysis that deal with issues such as volume and velocity of IoT data streams. However, analysing the content of the data streams and extracting insights from dynamic IoT data is still a challenging task. In this paper, we propose a pattern representation method which represents time-series frames as vectors by first applying Piecewise Aggregate Approximation (PAA) and then applying Lagrangian Multipliers. This method allows representing continuous data as a series of patterns that can be used and processed by various higher-level methods. We introduce a new change point detection method which uses the constructed patterns in its analysis. We evaluate and compare our representation method with Blocks of Eigenvalues Algorithm (BEATS) and Symbolic Aggregate approXimation (SAX) methods to cluster various datasets. We have also evaluated our proposed change detection method. We have evaluated our algorithm using UCR time-series datasets and also a healthcare dataset. The evaluation results show significant improvements in analysing time-series data in our proposed method.
Elsaleh Tarek, Enshaeifar Shirin, Rezvani Roonak, Acton Sahr Thomas, Janeiko Valentinas, Bermudez-Edo Maria (2020) IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services,Sensors 20 (4) MDPI
With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.