Sensor networks and personal health data management: software engineering challenges

Zhang, Xiang and Zhang, Jialu and Pike, Matthew and Mustafa, Nasser M. and Towey, Dave and Brusic, Vladimir (2020) Sensor networks and personal health data management: software engineering challenges. In: Future Technologies Conference, FTC 2020, 5 November 2020 through 6 November, San Francisco; United States.

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Abstract

The advances of 5G, sensors, and information technologies enabled proliferation of smart pervasive sensor networks. 5G mobile networks provide low-power, high-availability, high density, and high-throughput data capturing by sensor networks and continuous streaming of multiple measured variables. Rapid progress in sensors that can measure vital signs, advances in the management of medical knowledge, and improvement of algorithms for decision support, are fueling a technological disruption to health monitoring. The increase in size and complexity of wireless sensor networks and expansion into multiple areas of health monitoring creates challenges for system design and software engineering practices. In this paper, we highlight some of the key software engineering and data-processing issues, along with addressing emerging ethical issues of data management. The challenges associated with ensuring high dependability of sensor network systems can be addressed by metamorphic testing. The proposed conceptual solution combines data streaming, filtering, cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices. Integration of blockchain technologies and artificial intelligence offers a solution to the increasing needs for higher accuracy of measurements of vital signs, high-quality decision-making, and dependability, including key medical and ethical requirements of safety and security of the data.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Date of acceptance estimated
Keywords: Big data; Health data streaming; Health monitoring; Metamorphic testing; Mobile health; Smart health
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1007/978-3-030-63092-8_10
Depositing User: Yu, Tiffany
Date Deposited: 11 Dec 2020 05:51
Last Modified: 11 Dec 2020 05:51
URI: http://eprints.nottingham.ac.uk/id/eprint/64041

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