Inference of Personal Sensors in the Internet of Things

Main Article Content

James Jin Won Kang
Henry Larkin

Keywords

Inference, Data Mining, mHealth, Personal Sensor Devices, WBAN, Sensor Networks, IoT, Big Data, Cloud Computing

Abstract

Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.

Abstract 1159 | PDF Downloads 3

References

Adibi, S. (2014). mHealth Multidisciplinary Verticals. In S. Adibi (Ed.), mHealth Multidisciplinary Verticals (pp. 1-10): CRC Press.

Alaya, M. B., Banouar, Y., Monteil, T., Chassot, C., & Drira, K. (2014). OM2M: Extensible ETSI-compliant M2M Service Platform with Self-configuration Capability. Procedia Computer Science, 32(0), 1079-1086. doi:http://dx.doi.org/10.1016/j.procs.2014.05.536

Bayesian inference. (2015). Retrieved from http://en.wikipedia.org/wiki/Bayesian_inference [Accessed: 25 January 2016]

Belle, V., & Levesque, H. (2013). Reasoning about probabilities in dynamic systems using goal regression. arXiv preprint arXiv:1309.6816.

Brain, M. (2015). A Typical Mote - How Motes Work. Retrieved from http://computer.howstuffworks.com/mote4.htm [Accessed: 25 January 2016]

Choy, D. (2015). Apple Watch Vs. Samsung Gear 2 Vs. Moto 360: Technical Specs Compared [REPORT]. Retrieved from http://www.latintimes.com/apple-watch-vs-samsung-gear-2-vs-moto-360-technical-specs-compared-report-301430 [Accessed: 25 January 2016]

Engel, V. J. L., & Supangkat, S. H. (2014, 24-25 Sept. 2014). Context-aware inference model for cold-chain logistics monitoring. Paper presented at the ICT For Smart Society (ICISS), 2014 International Conference on.

ETSI. (2010). E TSI TS 102.689 v1.1.1. Machine-to-Machine communications (M2M); M2M service requirements. ETSI TS 102 689 V1.1.1 (2010-08) Technical Specification. Retrieved from http://www.etsi.org/deliver/etsi_ts/102600_102699/102689/01.01.01_60/ts_102689v010101p.pdf [Accessed: 25 January 2016]

Fanshel, S., & Bush, J. W. (1970). A Health-Status Index and Application to Health-Services Outcomes. Operations Research, 18(6), 1021. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=8990405&site=eds-live&scope=site [Accessed: 25 January 2016]

Gartner Says 4.9 Billion Connected. (2015). Retrieved from http://www.gartner.com/newsroom/id/2905717 [Accessed: 25 January 2016]

Gatterbauer, W., & Suciu, D. (2013). Dissociation and propagation for efficient query evaluation over probabilistic databases. arXiv preprint arXiv:1310.6257.

Gogate, V., & Domingos, P. (2012). Probabilistic theorem proving. arXiv preprint arXiv:1202.3724.

Harahap, E., Wijekoon, J., Tennekoon, R., Yamaguchi, F., Ishida, S., & Nishi, H. (2014, 14-16 March 2014). A router-based management system for prediction of network congestion. Paper presented at the Advanced Motion Control (AMC),2014 IEEE 13th International Workshop on.

IEEE. (2014). Health informatics--Personal health device communication - Part 20601: Application profile- Optimized Exchange Protocol. IEEE Std 11073-20601-2014 (Revision of ISO/IEEE 11073-20601:2010), 1-253. doi:http://dx.doi.org/10.1109/IEEESTD.2014.6919989

Jara, A. J., Zamora-Izquierdo, M. A., & Skarmeta, A. F. (2013). Interconnection Framework for mHealth and Remote Monitoring Based on the Internet of Things. Selected Areas in Communications, IEEE Journal on, 31(9), 47-65. doi:http://dx.doi.org/10.1109/JSAC.2013.SUP.0513005

Kartsakli, E., Lalos, A., Antonopoulos, A., Tennina, S., Renzo, M., Alonso, L., & Verikoukis, C. (2014). A Survey on M2M Systems for mHealth: A Wireless Communications Perspective. Sensors, 14(10), 18009. Retrieved from http://www.mdpi.com/1424-8220/14/10/18009 [Accessed: 25 January 2016]

Lee, J.-Y., Rampersaud, G. S., & Brown, M. G. (2008). An Index to Measure Health Status. AfEcon research, Research Papers 2008(Research Papers
2008-3), 21. Retrieved from http://purl.umn.edu/36819 [Accessed: 25 January 2016]

Lin, Y.-B., Lin, Y.-W., Chih, C.-Y., Li, T.-Y., Tai, C.-C., Wang, Y., . . . Hsu, S.-C. (2015). EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art. Internet of Things Journal, IEEE, PP(99), 1-1. doi:http://dx.doi.org/10.1109/JIOT.2015.2423286

OHP-M2M. (2015). Retrieved from http://openhealth.knu.ac.kr/ [Accessed: 25 January 2016]

Poorani, V. D., Ganapathy, K., & Vaidehi, V. (2012, 19-21 April 2012). Sensor based decision making inference system for remote health monitoring. Paper presented at the Recent Trends In Information Technology (ICRTIT), 2012 International Conference on.

Raftery, A., Chunn, J., Gerland, P., & Ševčíková, H. (2013). Bayesian Probabilistic Projections of Life Expectancy for All Countries. Demography, 50(3), 777-801. doi:http://dx.doi.org/10.1007/s13524-012-0193-x

Reilly, C. (2015). NBN Co promises gigabit speeds by 2017 with new cable technology. CNET. Retrieved from http://www.cnet.com/au/news/nbn-co-gigabit-speeds-by-2017-with-new-cable-docsis-technology/ [Accessed: 25 January 2016]

Sasan, A., Nilmini, W., & Caroline, C. CCmH: The Cloud Computing Paradigm for Mobile Health (mHealth). International Journal of Soft Computing and Software Engineering [JSCSE], 403-410. doi:http://dx.doi.org/10.7321/jscse.v3.n3.61

TH, D. (2013). Analytics 3.0. Harvard Bus Review(December ), 65-71.
Vorvick, L. J. (2015). Vital signs. U.S. National Library of Medicine. Retrieved from http://www.nlm.nih.gov/medlineplus/ency/article/002341.htm [Accessed: 25 January 2016]

Weisstein, E. W. (2015). Standard Deviation. MathWorld, A Wolfram Web Resource. Retrieved from http://mathworld.wolfram.com/StandardDeviation.html [Accessed: 25 January 2016]

Xiang, Y., & Liu, Y. (2011). Application of inverse first-order reliability method for probabilistic fatigue life prediction. Probabilistic Engineering Mechanics, 26(2), 148-156. doi:http://dx.doi.org/10.1016/j.probengmech.2010.11.001 [Accessed: 25 January 2016]

Zhengguo, S., Mahapatra, C., Chunsheng, Z., & Leung, V. C. M. (2015). Recent Advances in Industrial Wireless Sensor Networks Toward Efficient Management in IoT. Access, IEEE, 3, 622-637. doi:http://dx.doi.org/10.1109/ACCESS.2015.2435000

Zhu, X., Fang, K., & Yongheng, W. (2013). Predictive Analytics by Using Bayesian Model Averaging for Large-Scale Internet of Things. International Journal of Distributed Sensor Networks, 1-10. doi:http://dx.doi.org/10.1155/2013/723260