Blockchain and IoMT against physical abuse: bullying in schools as a case study
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AbstractBy law, schools are required to protect the well-being of students against problems such as on-campus bullying and physical abuse. In the UK, a report by the Office for Education (OfE) showed 17% of young people had been bullied during 2017–2018. This problem continues to prevail with consequences including depression, anxiety, suicidal thoughts, and eating disorders. Additionally, recent evidence suggests this type of victimisation could intensify existing health complications. This study investigates the opportunities provided by Internet of Medical Things (IoMT) data towards next-generation safeguarding. A new model is developed based on blockchain technology to enable real-time intervention triggered by IoMT data that can be used to detect stressful events, e.g., when bullying takes place. The model utilises private permissioned blockchain to manage IoMT data to achieve quicker and better decision-making while revolutionising aspects related to compliance, double-entry, confidentiality, and privacy. The feasibility of the model and the interaction between the sensors and the blockchain was simulated. To facilitate a close approximation of an actual IoMT environment, we clustered and decomposed existing medical sensors to their attributes, including their function, for a variety of scenarios. Then, we demonstrated the performance and capabilities of the emulator under different loads of sensor-generated data. We argue to the suitability of this emulator for schools and medical centres to conduct feasibility studies to address sensor data with disruptive data processing and management technologies.
CitationErsotelos N, Bottarelli M, Al-Khateeb H, Epiphaniou G, Alhaboby Z, Pillai P, Aggoun A. (2020) Blockchain and IoMT against Physical Abuse: Bullying in Schools as a Case Study. Journal of Sensor and Actuator Networks. 2021; 10(1),1.
JournalJournal of Sensor and Actuator Networks
Description© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/jsan10010001
SponsorsThis research was funded by Innovate UK, grant number 133891.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/