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GPSPiChain-Blockchain and AI based Self-Contained Anomaly Detection Family Security System in Smart Home

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Abstract

With advancements in technology, personal computing devices are better adapted for and further integrated into people’s lives and homes. The integration of technology into society also results in an increasing desire to control who and what has access to sensitive information, especially for vulnerable people including children and the elderly. With blockchain rise as a technology that can revolutionize the world, it is now possible to have an immutable audit trail of locational data over time. By controlling the process through inexpensive equipment in the home, it is possible to control whom has access to such personal data. This paper presents a block-chain based family security system for outdoor tracking and in-house monitoring of users’ activities via sensors to detect anomalies in users’ daily activities with the integration of Artificial Intelligence (AI). For outdoor tracking the locations of the consenting family members’ smart phones are logged and stored in a private blockchain which can be accessed through a node installed in the family home on a computer. The data for the whereabouts and daily activities of family members stays securely within the family unit and does not go to any third-party organizations. A Self-Organizing Maps (SOM) based smart contract is used for anomaly detection in users’ daily activities in a smart home, which notifies emergency contact or other family members in case of anomaly detection. The approach described in this paper contributes to the development of in-house data processing for outdoor tracking, and daily activities monitoring and prediction without any third-party hardware or software. The system is implemented at a small scale with one miner, two user nodes and several device nodes, as a proof of concept; the technical feasibility is discussed along with the limitations of the system. Further research will cover the integration of the system into a smart-home environment with additional sensors and multiple users, and ethical implementations of tracking, especially of vulnerable people, via the immutability of blockchain.

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References

  • Allan B M, Arnould J P, Martin J K, Ritchie E G (2013). A cost-effective and informative method of GPS tracking wildlife. Wildlife Research 40: 345–348.

    Article  Google Scholar 

  • Arrington B, Barnett L, Rufus R, Esterline A (2016). Behavioral modeling intrusion detection system (BMIDS) using internet of things (IoT) behavior-based anomaly detection via immunity-inspired algorithms. 25th International Conference on Computer Communication and Networks (ICCCN), IEEE.

    Google Scholar 

  • Bajaj R, Ranaweera S L, Agrawal D P (2002). GPS: location-tracking technology. Computer 35: 92–94.

    Article  Google Scholar 

  • Castro M, Liskov B (1999). Practical Byzantine fault tolerance. OSDI: 173–186.

    Google Scholar 

  • Collins A (2016). Contemporary Security Studies, Oxford University Press.

    Google Scholar 

  • Crosby M, Pattanayak P, Verma S, Kalyanaraman V (2016). Blockchain technology: Beyond bitcoin. Applied Innovation 2: 71.

    Google Scholar 

  • Divya M, Biradar N B (2018). IOTA-next generation blockchain. International Journal of Engineering Computer Science 7: 23823–23826.

    Google Scholar 

  • Eisses J, Verspeek L, Dawe C, Dijkstra S (2018). Effect Network: Decentralized Network for Artificial Intelligence.

  • Hagel J, Brown J S, Samoylova T, Lui M (2013). From exponential technologies to exponential innovation. Deloitte Center for the Edge, San Jose, California. Available at: http://www2.deloitte.com/content/dam/Deloitte/es/Documents/sector-publico/Deloitte_ES_Sector-Publico_From-exponentialtechnologies-to-exponential-innovation.pdf.

    Google Scholar 

  • Hanke T, Movahedi M, Williams D (2018). Dfinity technology overview series, consensus system. arXiv preprint 1805.04548.

  • Hay J (2006). Designing homes to be the first line of defense: Safe households, mobilization, and the new mobile privatization. Cultural Studies 20: 349–377.

    Article  MathSciNet  Google Scholar 

  • Kiayias A, Russell A, David B, Oliynykov R (2017). Ouroboros: A provably secure proof-of-stake blockchain protocol. Annual International Cryptology Conference, Springer.

    Google Scholar 

  • Kohonen T, Honkela T (2007). Kohonen network. Available: http://www.scholarpedia.org/article/Kohonennetwork

  • Komninos N, Philippou E, Pitsillides A (2014). Survey in smart grid and smart home security: Issues, challenges and countermeasures. IEEE Communications Surveys & Tutorials 16(4):1933–1954.

    Article  Google Scholar 

  • Koshima H, Hoshen J (2000). Personal locator services emerge. IEEE Spectrum 37: 41–48.

    Article  Google Scholar 

  • Kremer S, Ryan M, Smyth B (2010). Election verifiability in electronic voting protocols. European Symposium on Research in Computer Security. Springer, 389–404.

    Google Scholar 

  • Liu Z, Zhang A, Li S (2013). Vehicle anti-theft tracking system based on Internet of things. Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety. IEEE, 48–52.

    Google Scholar 

  • Lyu M, Sherratt D, Sivanathan A, Gharakheili H H, Radford A, Sivaraman V (2017). Quantifying the reflective DDoS attack capability of household IoT devices. In Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks: 46–51.

    Google Scholar 

  • Mascheroni G, Ólafsson K (2014). Net children go mobile: Risks and opportunities. Lse Research Online Documents on Economics.

    Google Scholar 

  • Melgosa A, Scott R (2013). School Internet Safety More than ‘Block It to Stop It’. The Education Digest 79: 46.

    Google Scholar 

  • Nakamoto S (2008). Bitcoin: A peer-to-peer electronic cash system. Available: https://bitcoin.org/bitcoin.pdf

  • Noury N, Hadidi T, Laila M, Fleury A, Villemazet C, Rialle V, Franco A (2008). Level of activity, night and day alternation, and well being measured in a smart hospital suite. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.

    Google Scholar 

  • Philips Hue Tap Wireless Smart Switch. Available: https://www.harveynorman.com.au/philips-hue-tap-wireless-smart-switch.html.

  • Poushter J (2016). Smartphone ownership and internet usage continues to climb in emerging economies. Pew Research Center 22: 1–44.

    Google Scholar 

  • Sagiroglu S, Sinanc D (2013). Big data: A review. International Conference on Collaboration Technologies and Systems (CTS), IEEE.

    Google Scholar 

  • Sha L, Gopalakrishnan S, Liu X, Wang Q (2008). Cyberphysical systems: A new frontier. IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2008), IEEE.

    Google Scholar 

  • Shin D.-H (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with Computers 22: 428–438.

    Article  Google Scholar 

  • SIM900 Datasheet. Available: https://components101.com/sites/default/files/component_datasheet/SIM900A%20Datasheet.pdf

  • Soria-Comas J, Domingo-Ferrer J (2016). Big data privacy: Challenges to privacy principles and models. Data Science and Engineering 1: 21–28.

    Article  Google Scholar 

  • Starosta P, Brzeziński K (2014). The structure of social trust in post-industrial cities of Central and Eastern Europe. Przegląd Socjologiczny 63: 49–79.

    Google Scholar 

  • Subbalakshmi S, Madhavi K (2018). Security challenges of Big Data storage in Cloud environment: A survey. International Journal of Applied Engineering Research 13: 13237–13244.

    Google Scholar 

  • Urwyler P, Rampa L, Stucki R, Büchler M, Müri R, Mosimann U P, Nef T (2015). Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers. Biomedical Engineering Online 14: 54.

    Article  Google Scholar 

  • Varadharajan V (2010). Internet filtering-issues and challenges. IEEE Security, Privacy 8: 62–65.

    Article  Google Scholar 

  • Wang X, Wong A K S, Kong Y (2012). Mobility tracking using GPS, Wi-Fi and cell ID. The International Conference on Information Network, IEEE.

    Google Scholar 

  • Yang W, Garg S, Raza A, Herbert D, Kang B (2018). Blockchain: Trends and Future. Pacific Rim Knowledge Acquisition Workshop, Springer.

    Google Scholar 

  • Yeh S C, Hsu W H, Su M Y, Chen C H, Liu K H (2009). A study on outdoor positioning technology using GPS and WiFi networks. International Conference on Networking, Sensing and Control, IEEE.

    Google Scholar 

  • Zhang T, Luo Z, Zhou F, Yang X (2008). Developing a trusted system for tracking asset on the move. The 9th International Conference for Young Computer Scientists, IEEE.

    Google Scholar 

Download references

Acknowledgments

This research was supported by School of ICT, University of Tasmania, Sandy Bay. We thank the anonymous reviewers whose comments/suggestions helped improve the quality of this manuscript.

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Correspondence to Ali Raza.

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Ali Raza received the B.Sc. degree (Hons.) in communications systems engineering from the Institute of Space Technology Islamabad, Pakistan, in 2014. He is currently pursuing the Ph.D. degree in information technology (IT) with the University of Tasmania, Australia. He was a satellite engineer for a few years with Pakistan’s national space agency SUPARCO and a developer support engineer with Facebook. His research interests include blockchain, expert systems, IoT, embedded systems and robotics.

Lachlan Hardy is an associate teaching fellow in cyber security at the University of Tasmania where he teaches IT security management and data analysis for cyber security. Lachlan is also completing his PhD in investigating a technology supported framework for industry independent self-managed technical support which was spurred by his interest in human-computer interaction and applied technology. Lachlan is also interested in the research areas of digital and e-Learning, information systems and knowledge transfer, and industry 4.0 including the use of blockchain. Lachlan’s PhD and interest in these areas is to aid in human development and use of technology for the benefit of society at the local, national, and global levels.

Erin Roehrer is a senior lecturer in ICT in the School of ICT, Syndicate of Technology, Environments and Design, University of Tasmania. Erin is currently the Learning and Teaching Lead for the School of ICT and Program Leader for the TNE program delivered by the University of Tasmania and the AIEN Institute — Shanghai Ocean University (SHOU). Her research interests include in the socio-technical factors and processes with technology supporting chronic disease self-management, Transnational Learning and Teaching, digital adoption and person centred interaction, and the placement of technology in information seeking behaviour and authentic learning and teaching. Erin’s research interests span both the use of technologies to support individual and organisational information seeking behaviour and decision-making, as well as the scholarship of learning and teaching.

Soonja Yeom (Ph.D, MComp) has been an academic staff member at the School of Computing, Hobart, University of Tasmania since 1994. Her research interests include big data, cyber security, affective computing, and educational technology. Applying digital technologies including virtual reality and augmented reality is also her interest. She has been awarded a couple of best paper awards. She is a member of ACM, IEEE, ACS (Australian Computer Society), Working Group 3.3 (research into educational applications of information technologies) for IFIP/UNESCO.

Byeong Ho Kang received the Ph.D. degree from the University of New South Wales, Sydney, in 1996. He was a Visiting Researcher with the Advanced Research Laboratory, Hitachi, Japan. In addition, he has played a role in the foundation of several spinoff companies. He is currently a Professor with the School of Engineering and ICT, University of Tasmania, Australia, where he is also the Head of the Discipline of ICT, CoSE. He leads the Smart Services and Systems Research Group of postdoctoral scientists, which has carried out fundamental and applied research in expert systems, Web services, SNS analysis, and smart industry areas. He has been involved in the development of several commercial and Internet-based applications, including AI products, expert system development tools, intelligent help desk systems, and Web-based information monitoring and classification systems. His current research interests include basic knowledge acquisition methods and applied research in Internet systems and medical expert systems. He has served as a steering committee member and the chair in many international organizations and conferences.

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Raza, A., Hardy, L., Roehrer, E. et al. GPSPiChain-Blockchain and AI based Self-Contained Anomaly Detection Family Security System in Smart Home. J. Syst. Sci. Syst. Eng. 30, 433–449 (2021). https://doi.org/10.1007/s11518-021-5496-2

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