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

    Roonak Rezvani, Samaneh Kouchaki, Ramin Nilforooshan, David J Sharp, Payam Barnaghi (2021)Analysing behavioural changes in people with dementia using in‐home monitoring technologies, In: Alzheimer's & Dementia: The Journal of the Alzheimer's Association17(S11)e052181 Wiley

    Background Behavioural changes and neuropsychiatric symptoms such as agitation are common in people with dementia. These symptoms impact the quality of life of people with dementia and can increase the stress on caregivers. This study aims to identify the likelihood of having agitation in people affected by dementia (i.e., patients and carers) using routinely collected data from in‐home monitoring technologies. We have used a digital platform and analytical methods, developed in our previous study, to generate alerts when changes occur in the digital markers collected using in‐home sensing technologies (i.e., vital signs, environmental and activity data). A care monitoring team use the platform and interact with participants and caregivers when an alert is generated. Method We have used connected sensory devices to collect environmental markers, including Passive Infra‐Red (PIR), smart power plugs for monitoring home appliance use, motion and door sensors. The environmental marker data have been aggregated within each hour and used to train an agitation risk analysis model. We have trained a model using data collected from 88 homes (∼6 months of data from each home). The proposed model has two components: a self‐supervised transformation learning and an ensemble classification model for agitation likelihood. Ten different neural network encoders are learned to create pseudo‐labels using the samples from the unlabelled data. We use these pseudo‐labels to train a classification model with a convolutional block and a decision layer. The trained convolutional block is then used to learn a latent representation of the data for an ensemble classification block. Results Comparing with baseline models such as LSTM network, Bidirectional LSTM (BiLSTM) network, VGG, ResNet, Inception, Random Forest (RF), Support Vector Machine (SVM) and Gaussian Process (GP) classifiers, the proposed model performs better in sensitivity (recall) and area under the precision‐recall curve with at most 40% improvement. The recall measure using the 10‐fold cross‐validation technique is 61%. Conclusion This method can support early interventions and help develop new pathways to support people affected by dementia. A limitation in our current study is that the environmental and movement data is at the home level and not personalised.

    T. Elsaleh, M. Bermudez-Edo, S. Enshaeifar, S. T. Acton, R. Rezvani, P. Barnaghi (2019)IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams, In: 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.

    Roonak Rezvani, Payam Barnaghi, Shirin Enshaeifar (2019)A New Pattern Representation Method for Time-series Data, In: 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.

    Roonak Rezvani, Shirin Enshaeifar, Payam Barnaghi (2019)Lagrangian-based Pattern Extraction for Edge Computing in the Internet of Things, In: 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.

    Thorben Iggena, Eushay Bin Ilyas, Marten Fischer, Ralf Tönjes, Tarek Elsaleh, Roonak Rezvani, Narges Pourshahrokhi, Stefan Bischof, Andreas Fernbach, Josiane Xavier Parreira, Patrik Schneider, Pavel Smirnov, Martin Strohbach, Hien Truong, Aurora González-Vidal, Antonio F Skarmeta, Parwinder Singh, Michail J Beliatis, Mirko Presser, Juan A Martinez, Pedro Gonzalez-Gil, Marianne Krogbæk, Sebastian Holmgård Christophersen (2021)IoTCrawler: Challenges and Solutions for Searching the Internet of Things, In: Sensors (Basel, Switzerland)21(5)1559 MDPI

    Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

    Tarek Elsaleh, Shirin Enshaeifar, Roonak Rezvani, Sahr Thomas Acton, Valentinas Janeiko, Maria Bermudez-Edo (2020)IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services, In: Sensors20(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.

    V. Janieko, R. Rezvani, N. Pourshahrokhi, Shirin Enshaeifar, M. Krogbæk, S.H Christophersen, Tarek Elsaleh, Payam Barnaghi (2020)Enabling Context-Aware Search using Extracted Insights from IoT Data Streams, In: GIoTS 2020

    The rapid growth in collecting and sharing sensory observation form the urban environments provides opportunities to plan and manage the services in the cities better and allows citizens to also observe and understand the changes in their surrounding in a better way. The new urban sensory data also creates opportunities for further application and service development by creative industries and start-ups. However, as the size and diversity of this data increase, finding and accessing the right set of data in a timely manner is becoming more challenging. This paper describes a search engine designed for indexing, searching and accessing urban sensory data. We present the key feature and architecture of the system and demonstrate some of the functionalities that are provided by searching for raw sensory observations and also pattern search functions that are enabled by a pattern analysis algorithm, supported by monitoring of data streams for changes in quality of information and remediation.