Sensor Networks and Data Management in Healthcare: Emerging Technologies and New Challenges

Smart pervasive sensor networks are becoming an important part of our daily lives. Low-power, high-availability and high-throughput 5G mobile networks provide the necessary communication means for highly pervasive sensor networks, introducing a technological disruption to health monitoring. The meaningful use of large concurrent sensor networks in healthcare requires multi-level health knowledge integration with sensor data streams. In this paper, we highlight some software engineering and data-processing issues that can be addressed by metamorphic testing. The proposed solution combines data streaming with filtering and cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices.

[1]  Glenford J. Myers,et al.  Art of Software Testing , 1979 .

[2]  Algirdas Avizienis,et al.  The N-Version Approach to Fault-Tolerant Software , 1985, IEEE Transactions on Software Engineering.

[3]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[4]  ABBAS JAMALIPOUR,et al.  Network selection in an integrated wireless LAN and UMTS environment using mathematical modeling and computing techniques , 2005, IEEE Wireless Communications.

[5]  John F. Hurdle,et al.  Measuring diagnoses: ICD code accuracy. , 2005, Health services research.

[6]  H. Quan,et al.  Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data , 2005, Medical care.

[7]  K. Kinsella,et al.  Global aging : the challenge of success , 2005 .

[8]  Bernhard Walke,et al.  IEEE 802.11 Wireless Local Area Networks , 2006 .

[9]  Mohamed Medhat Gaber,et al.  Learning from Data Streams: Processing Techniques in Sensor Networks , 2007 .

[10]  Huai Liu,et al.  An innovative approach for testing bioinformatics programs using metamorphic testing , 2009, BMC Bioinformatics.

[11]  Yang Hao,et al.  Wireless body sensor networks for health-monitoring applications , 2008, Physiological measurement.

[12]  J. Glaser,et al.  Health Care Information Systems: A Practical Approach for Health Care Management , 2009 .

[13]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[14]  Youzhi Xu,et al.  An Elderly Health Care System Using Wireless Sensor Networks at Home , 2009, 2009 Third International Conference on Sensor Technologies and Applications.

[15]  Angus K. Y. Wong.  The Near-Me Area Network , 2010, IEEE Internet Computing.

[16]  Alcides Montoya,et al.  Artificial Intelligence for Wireless Sensor Networks Enhancement , 2010 .

[17]  Ian F. Akyildiz,et al.  Nanonetworks: A new frontier in communications , 2012, 2010 International Conference on Security and Cryptography (SECRYPT).

[18]  Insup Lee,et al.  On effective testing of health care simulation software , 2011, SEHC '11.

[19]  Roy Want,et al.  Near field communication , 2011, IEEE Pervasive Computing.

[20]  Ibm Redbooks.  Introduction to Storage Area Networks and System Networking , 2012 .

[21]  R. Dodge,et al.  The challenge of defining wellbeing , 2012 .

[22]  Gregorio López,et al.  A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems , 2012, Sensors.

[23]  Joel J. P. C. Rodrigues,et al.  Real-time data management on wireless sensor networks: A survey , 2012, J. Netw. Comput. Appl..

[24]  Avita Katal,et al.  Big data: Issues, challenges, tools and Good practices , 2013, 2013 Sixth International Conference on Contemporary Computing (IC3).

[25]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[26]  Daniel E. O'Leary,et al.  Artificial Intelligence and Big Data , 2013, IEEE Intelligent Systems.

[27]  Murray E. Jennex,et al.  A Revised Knowledge Pyramid , 2013, Int. J. Knowl. Manag..

[28]  Jung-Min Lee,et al.  TRACK IT: Validity and Utility of Consumer-Based Physical Activity Monitors , 2014 .

[29]  Junyi Li,et al.  Network densification: the dominant theme for wireless evolution into 5G , 2014, IEEE Communications Magazine.

[30]  I. Kohane,et al.  Finding the missing link for big biomedical data. , 2014, JAMA.

[31]  Huai Liu,et al.  How Effectively Does Metamorphic Testing Alleviate the Oracle Problem? , 2014, IEEE Transactions on Software Engineering.

[32]  Annalisa Cocchia.  Smart and Digital City: A Systematic Literature Review , 2014 .

[33]  K. Volpp,et al.  Accuracy of smartphone applications and wearable devices for tracking physical activity data. , 2015, JAMA.

[34]  Mark Harman,et al.  The Oracle Problem in Software Testing: A Survey , 2015, IEEE Transactions on Software Engineering.

[35]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[36]  Mikael Lindvall,et al.  Metamorphic Model-Based Testing Applied on NASA DAT -- An Experience Report , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[37]  Kevin Hung,et al.  Ubiquitous Health Monitoring: Integration of Wearable Sensors, Novel Sensing Techniques, and Body Sensor Networks , 2015 .

[38]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.

[39]  Albert Y. Zomaya,et al.  Remote sensing big data computing: Challenges and opportunities , 2015, Future Gener. Comput. Syst..

[40]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[41]  E. Chiauzzi,et al.  Patient-centered activity monitoring in the self-management of chronic health conditions , 2015, BMC Medicine.

[42]  Xin Zhang,et al.  End to End Learning for Self-Driving Cars , 2016, ArXiv.

[43]  Sergio Segura,et al.  A Survey on Metamorphic Testing , 2016, IEEE Transactions on Software Engineering.

[44]  Z. Obermeyer,et al.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.

[45]  James N. Gilmore,et al.  Everywear: The quantified self and wearable fitness technologies , 2016, New Media Soc..

[46]  Andrew McRae,et al.  Limitations of pulmonary embolism ICD-10 codes in emergency department administrative data: let the buyer beware , 2017, BMC Medical Research Methodology.

[47]  Filipe Portela,et al.  Interoperability in Healthcare , 2017 .

[48]  Huai Liu,et al.  Metamorphic Testing , 2018, ACM Comput. Surv..

[49]  Sergio Segura,et al.  Metamorphic Testing of RESTful Web APIs , 2018, IEEE Transactions on Software Engineering.

[50]  Russell Beale,et al.  Digital Behaviour Change Interventions to Break and Form Habits , 2018, ACM Trans. Comput. Hum. Interact..

[51]  Sergio Segura,et al.  Metamorphic Testing: Testing the Untestable , 2020, IEEE Software.

[52]  Tsong Yueh Chen,et al.  Metamorphic Testing: A New Approach for Generating Next Test Cases , 2020, ArXiv.