@conference {758,
	title = {Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption},
	booktitle = {2nd International Conference on Sensors, Signal and Image Processing (SSIP 19)},
	year = {2019},
	publisher = {ACM},
	organization = {ACM},
	address = {Prague, Czech Republic},
	abstract = {<p>People are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of\&nbsp; early illness\&nbsp; detection\&nbsp; and\&nbsp; for\&nbsp; monitoring\&nbsp; the well-being of those with known illnesses.</p>
},
	keywords = {Ambient Assisted Living, Home Monitoring, Internet of Things, Sensor Applications, Sensor Networks},
	author = {Quinn, Se{\'a}n and Murphy, Noel and Smeaton, Alan F.}
}
@article {890,
	title = {Machine learning for internet of things data analysis: a survey},
	journal = {Digital Communications and Networks},
	volume = {4},
	year = {2018},
	pages = {161-175},
	abstract = {<p>Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.</p>
},
	keywords = {Internet of Things, Machine learning, Smart City, Smart data},
	issn = {2352-8648},
	doi = {https://doi.org/10.1016/j.dcan.2017.10.002},
	url = {https://www.sciencedirect.com/science/article/pii/S235286481730247X},
	author = {Mohammad Saeid Mahdavinejad and Mohammadreza Rezvan},
	editor = {Mohammadamin Barekatain and Peyman Adibi and Payam Barnaghi and Amit P. Sheth}
}
