10.1145/3342428.3342702acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicpsprocConference Proceedings
research-article
Free Access

A review of frameworks on continuous data acquisition for e-Health and m-Health

ABSTRACT

There is a huge number of mobile devices which use as an e-Health and m-Health system. The main purpose of the article is to make a review of frameworks for continuous data acquisition to identify the most commonly used and better methods. We are discussing environmental monitoring, middle-tier, cross-sending, and middleware frameworks such as SeeMon, DEAMON (Distributed Energy-Aware Monitoring), PRISM (Performance of Routine Information System Management), Medusa, MOSDEN (Mobile Sensor Data Processing Engine), C-MOSDEN (Context-aware data streaming engine called Mobile Sensor Date Engine) and MECA (Mobile edge capture and analysis middleware for social sensing applications) frameworks. These results are able to develop e-Health and m-Health systems in order to improve their efficiency.

References

  1. Javad Rezazadeh Amirhossein Farahzadi, Pooyan Shams and Reza Farahbakhsh. 2018. Middleware technologies for cloud of things: a survey. Digit. Commun. Networks 4, 3 (2018), 176--188.Google ScholarGoogle Scholar
  2. Raheleh Dimaghani Keith Grueneberg an Ye, Raghu Ganti and Seraphin Calo. 2012. MECA. Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion 1 (2012), 699.Google ScholarGoogle Scholar
  3. Theo Lippeveld Anwer Aqil and Dairiku Hozumi. 2009. PRISM framework: a paradigm shift for designing, strengthening and evaluating routine health information systems. Health Policy Plan. 24, 3 (2009), 217--28.Google ScholarGoogle Scholar
  4. AT&T Laboratories Cambridge. 2019. The Medusa Applications Environment. https://www.cl.cam.ac.uk/research/dtg/attarchive/medusa.htmlGoogle ScholarGoogle Scholar
  5. Paul Y. Cao, Gang Li, Guoxing Chen, and Biao Chen. 2015. Mobile Data Collection Frameworks: A Survey. In Proceedings of the 2015 Workshop on Mobile Big Data (Mobidata '15). ACM, New York, NY, USA, 25--30. Google ScholarGoogle Scholar
  6. Chi Harold Liu Charith Perera, Dumidu S. Talagala and Julio C. Estrella. 2015. Energy-Efficient Location and Activity-Aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds. IEEE Trans Comput. Soc. Syst. 2, 4 (2015), 171--181.Google ScholarGoogle Scholar
  7. Theo Lippeveld David R Hotchkiss, Anwer Aqil and Edward Mukooyo. 2010. Evaluation of the Performance of Routine Information System Management (PRISM) framework: evidence from Uganda. BMC Health Serv. Res. 10, 1 (2010), 17.Google ScholarGoogle Scholar
  8. Ciprian Dobre, Constandinos x Mavromoustakis, Nuno Garcia, Rossitza Ivanova Goleva, and George Mastorakis. 2016. Ambient Assisted Living and Enhanced Living Environments: Principles, Technologies and Control. Butterworth-Heinemann, Butterworth-Heinemann. Google ScholarGoogle Scholar
  9. Nuno M Garcia. 2016. A Roadmap to the Design of a Personal Digital Life Coach. In ICT Innovations 2015, Suzana Loshkovska and SasoEditors Koceski (Eds.). Springer Cham, Ohrid, Macedonia, 21--27.Google ScholarGoogle Scholar
  10. Nuno M Garcia and Joel Jose P C Rodrigues. 2015. Ambient assisted living. CRC Press, Boca Ratom, FL.Google ScholarGoogle Scholar
  11. Github. 2019. Github. https://github.com/USC-NSL/MedusaGoogle ScholarGoogle Scholar
  12. Github. 2019. Github. https://github.com/opencobra/cobrapyGoogle ScholarGoogle Scholar
  13. Github. 2019. Github. https://github.com/mecafw/MECAGoogle ScholarGoogle Scholar
  14. Virginia Pilloni Giuseppe Colistra and Luigi Atzori. 2014. The problem of task allocation in the Internet of Things and the consensus-based approach. Comput. Networks 73 (2014), 98--111. Google ScholarGoogle Scholar
  15. Teena Gupta. 2019. Introduction to PRISM Framework. https://blog.e-zest.com/introduction-to-prism-framework/Google ScholarGoogle Scholar
  16. Arkady Zaslavsky Dimitrios Georgakopoulos harith Perera, Prem Prakash Jayaraman and Peter Christen. 2014. MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices. 2014 47th Hawaii International Conference on System Sciences 1 (2014), 1053--1062. Google ScholarGoogle Scholar
  17. Weblet Importer. 2019. Weblet Importer. https://ccrma.stanford.edu/guides/package/jmax/fts/etc/ftsd.txtGoogle ScholarGoogle Scholar
  18. Smart Insights. 2019. mobile marketing statistics compilation. https://www.smartinsights.com/mobile-marketing/mobile-marketing-analytics/mobile-marketing-statistics/Google ScholarGoogle Scholar
  19. Prem Prakash Jayaraman, João Bártolo Gomes, Hai Long Nguyen, Zahraa Said Abdallah, Shonali Krishnaswamy, and Arkady Zaslavsky. 2014. CARDAP: A Scalable Energy-Efficient Context Aware Distributed Mobile Data Analytics Platform for the Fog. In Advances in Databases and Information Systems, Yannis Manolopoulos, Goce Trajcevski, and Margita Kon-Popovska (Eds.). Springer International Publishing, Cham, 192--206.Google ScholarGoogle Scholar
  20. Seungwoo Kang, Jinwon Lee, Hyukjae Jang, Hyonik Lee, Youngki Lee, Souneil Park, Taiwoo Park, and Junehwa Song. 2008. SeeMon: Scalable and Energy-efficient Context Monitoring Framework for Sensor-rich Mobile Environments. In Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services (MobiSys '08). ACM, New York, NY, USA, 267--280. Google ScholarGoogle Scholar
  21. Seema Kharb and Anita Singhrova. 2019. A survey on network formation and scheduling algorithms for time slotted channel hopping in industrial networks. J. Netw. Comput. Appl. 126 (2019), 59--87.Google ScholarGoogle Scholar
  22. Marco Marengo Luca Ardito, Marco Torchiano and Paolo Falcarin. 2013. gLCB: an energy aware context broker. Sustain. Comput. Informatics Syst 3, 1 (2013), 18--26.Google ScholarGoogle Scholar
  23. Krešimir Pripužić Aleksandar Antonić Martina Marjanović, Lea Skorin-Kapov and Ivana Podnar Žarko. 2016. Energy-aware and quality-driven sensor management for green mobile crowd sensing. J. Netw. Comput. Appl. 59 (2016), 95--108. Google ScholarGoogle Scholar
  24. David Kotz Minho Shin, Patrick Tsang and Cory Cornelius. 2009. DEAMON: Energy-efficient Sensor Monitoring. 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks 1 (2009), 1--9. Google ScholarGoogle Scholar
  25. Tom F. La Porta Moo-Ryong Ra, Bin Liu and Ramesh Govindan. 2012. Medusa. Proceedings of the 10th international conference on Mobile systems, applications, and services - MobiSys '12 1 (2012), 337.Google ScholarGoogle Scholar
  26. Teh Ying Wah Muhammad Habib ur Rehman, Chee Sun Liew and Muhammad Khurram Khan. 2017. Towards next-generation heterogeneous mobile data stream mining applications: Opportunities, challenges, and future research directions. Netw. Comput. Appl. 79 (2017), 1--24. Google ScholarGoogle Scholar
  27. Guoxing Chen Paul Y. Cao, Gang Li and Biao Chen. 2015. Mobile Data Collection Frameworks. Proceedings of the 2015 Workshop on Mobile Big Data - Mobidata '15 1 (2015), 25--30. Google ScholarGoogle Scholar
  28. Fernando M. V. Ramos Pedro A. R. S. Costa, Xiao Bai and Miguel Correia. 2016. Medusa: An Efficient Cloud Fault-Tolerant MapReduce. 2016 16th IEEE/ACM International Symposium on Cluster: Cloud and Grid Computing (CCGrid) 1 (2016), 443--452. Google ScholarGoogle Scholar
  29. Ivan Miguel Pires, Nuno M Garcia, Nuno Pombo, and Francisco Flórez-revuelta. 2018. Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People. HSP 1, Ict4awe (2018), 269--275.Google ScholarGoogle Scholar
  30. Dimitrios Georgakopoulos Prem Jayaraman, Charith Perera and Arkady Zaslavsky. 2013. Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN. Proceedings of the 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing 1 (2013), 77--86.Google ScholarGoogle Scholar
  31. Dimitrios Georgakopoulos Prem Prakash Jayaraman, Charith Perera and Arkady Zaslavsky. 2014. MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications. arxiv.org/abs/1405.5867Google ScholarGoogle Scholar
  32. Hai-Long Nguyen Zahraa Said Abdallah Shonali Krishnaswamy Prem Prakash Jayaraman, Joao Bartolo Gomes and Arkady Zaslavsky. 2015. Scalable Energy-Efficient Distributed Data Analytics for Crowdsensing Applications in Mobile Environments. IEEE Trans. Comput. Soc. Syst. 2,3 2, 3 (2015), 109--123.Google ScholarGoogle Scholar
  33. Prism. 2019. Introduction to Prism. https://prismlibrary.github.io/docsGoogle ScholarGoogle Scholar
  34. Prism. 2019. Modular Application Development Using Prism Library for WPF. https://prismlibrary.github.io/docs/wpf/Modules.htmlGoogle ScholarGoogle Scholar
  35. Hyukjae Jang Youngki Lee Souneil Park Seungwoo Kang, Jinwon Lee and Junehwa Song. 2010. A Scalable and Energy-Efficient Context Monitoring Framework for Mobile Personal Sensor Networks. IEEE Trans. Mob. Comput. 9, 5 (2010), 686--702. Google ScholarGoogle Scholar
  36. P S Sousa, D Sabugueiro, V Felizardo, R Couto, I Pires, and N M Garcia. 2015. mHealth Sensors and Applications for Personal Aid BT- Mobile Health: A Technology Road Map. Springer International Publishing, Switzerland, 265--281.Google ScholarGoogle Scholar
  37. Yacine Challal Tifenn Rault, Abdelmadjid Bouabdallah and Frédéric Marin. 2017. A survey of energy-efficient context recognition systems using wearable sensors for healthcare application. s. Pervasive Mob. Comput 37 (2017), 23--44. Google ScholarGoogle Scholar

Index Terms

  1. A review of frameworks on continuous data acquisition for e-Health and m-Health

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Article Metrics

          • Downloads (Last 12 months)32
          • Downloads (Last 6 weeks)11

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!

        To help support our community working remotely during COVID-19, we are making all work published by ACM in our Digital Library freely accessible through June 30, 2020. Learn more