Secure non-public health enterprise networks

Increasing demand for secure remote operation in industry and technology advancements to support delivering efficient services and tele-mentoring have opened a new market in healthcare sector and emergency services based on 5G and Tactile internet capabilities. In a connected world, hospitals would benefit from providing the on-time availability either for continuous health monitoring or critical services to the citizens in need. In this paper, we propose a secure non-public health enterprise network concept to enable an end-to-end secure and location-agnostic communication between a patient and a healthcare service provider, and other contacts with patient’s consent either in case of an emergency or to be stored in the medical records. We present how applying non-public enterprise networks can address market demand in health care sector for improved end-to-end security and privacy when dealing with personal and critical information. We present the three-tier architecture model describing continuous authentication mechanisms based on biometric collection as well as the dynamic network solutions in the healthcare domain. The biometric collection can be done using ambient/IoT sensors as well as wearable/implantable devices to monitor the patient unobtrusively. Furthermore, end-to-end security solutions should adapt dynamically based on the user profile and situation awareness to address the required level of security at the network side. We discuss the related research challenges for developing the presented non-public health enterprise platform and provide suggestions for future work based on the healthcare sector requirements and opportunities

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. Nature, 412(6846), 543-545. doi:10.1038/35087589United Nations, Department of Economic and Social Affairs, World Population Prospoects 2019 https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdfAtzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122-140. doi:10.1016/j.adhoc.2016.12.004Cook, D. J., Duncan, G., Sprint, G., & Fritz, R. L. (2018). Using Smart City Technology to Make Healthcare Smarter. Proceedings of the IEEE, 106(4), 708-722. doi:10.1109/jproc.2017.2787688Cook, D. J., & Krishnan, N. (2014). Mining the home environment. Journal of Intelligent Information Systems, 43(3), 503-519. doi:10.1007/s10844-014-0341-4Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. Journal of Network and Computer Applications, 97, 48-65. doi:10.1016/j.jnca.2017.08.017Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localisation for indoor applications. Ad Hoc Networks, 64, 80-98. doi:10.1016/j.adhoc.2017.06.007Chen, G., Wang, A., Zhao, S., Liu, L., & Chang, C.-Y. (2017). Latent feature learning for activity recognition using simple sensors in smart homes. Multimedia Tools and Applications, 77(12), 15201-15219. doi:10.1007/s11042-017-5100-4Tewell, J., O’Sullivan, D., Maiden, N., Lockerbie, J., & Stumpf, S. (2019). Monitoring meaningful activities using small low-cost devices in a smart home. Personal and Ubiquitous Computing, 23(2), 339-357. doi:10.1007/s00779-019-01223-2Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003Wang, A., Chen, G., Wu, X., Liu, L., An, N., & Chang, C.-Y. (2018). Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18(11), 3629. doi:10.3390/s18113629Liu, Y., Wang, X., Zhai, Z., Chen, R., Zhang, B., & Jiang, Y. (2019). Timely daily activity recognition from headmost sensor events. ISA Transactions, 94, 379-390. doi:10.1016/j.isatra.2019.04.026Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., & Massa, A. (2013). Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE, 101(11), 2381-2396. doi:10.1109/jproc.2013.2266858Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011). Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering, 23(4), 527-539. doi:10.1109/tkde.2010.148Samsung SmartThings http://www.smartthings.com/Apple HomeKit https://www.apple.com/ios/home/Vera3 Advanced Smart Home Controller http://getvera.com/controllers/vera3/AndroidThings https://developer.android.com/things/index.htmlTeleAlarm Assisted Living http://www.telealarm.com/en/products/assisted-livingBirdie—Connected Sensors around the Home https://birdie.care/AllJoyn Framework https://identity.allseenalliance.org/developersCook, D. J., Crandall, A. S., Thomas, B. L., & Krishnan, N. C. (2013). CASAS: A Smart Home in a Box. Computer, 46(7), 62-69. doi:10.1109/mc.2012.328Skubic, M., Alexander, G., Popescu, M., Rantz, M., & Keller, J. (2009). A smart home application to eldercare: Current status and lessons learned. Technology and Health Care, 17(3), 183-201. doi:10.3233/thc-2009-0551Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005). The Gator Tech Smart House: a programmable pervasive space. Computer, 38(3), 50-60. doi:10.1109/mc.2005.107Doctor, F., Hagras, H., & Callaghan, V. (2005). A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 55-65. doi:10.1109/tsmca.2004.838488Abowd, G. D., & Mynatt, E. D. (2005). Designing for the Human Experience in Smart Environments. Smart Environments, 151-174. doi:10.1002/047168659x.ch7Technology Integrated Health Management (TIHM) Project https://www.sabp.nhs.uk/tihmAhvar, E., Daneshgar-Moghaddam, N., Ortiz, A. M., Lee, G. M., & Crespi, N. (2016). On analyzing user location discovery methods in smart homes: A taxonomy and survey. Journal of Network and Computer Applications, 76, 75-86. doi:10.1016/j.jnca.2016.09.012Milenkovic, M., & Amft, O. (2013). Recognizing Energy-related Activities Using Sensors Commonly Installed in Office Buildings. Procedia Computer Science, 19, 669-677. doi:10.1016/j.procs.2013.06.089Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Dogan, O., Bayo-Monton, J.-L., Fernandez-Llatas, C., & Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors, 19(3), 557. doi:10.3390/s19030557Schmitter-Edgecombe, M., & Cook, D. J. (2009). Assessing the Quality of Activities in a Smart Environment. Methods of Information in Medicine, 48(05), 480-485. doi:10.3414/me0592Alberdi Aramendi, A., Weakley, A., Aztiria Goenaga, A., Schmitter-Edgecombe, M., & Cook, D. J. (2018). Automatic assessment of functional health decline in older adults based on smart home data. Journal of Biomedical Informatics, 81, 119-130. doi:10.1016/j.jbi.2018.03.009Dawadi, P. N., Cook, D. J., & Schmitter-Edgecombe, M. (2016). Automated Cognitive Health Assessment From Smart Home-Based Behavior Data. IEEE Journal of Biomedical and Health Informatics, 20(4), 1188-1194. doi:10.1109/jbhi.2015.2445754Sprint, G., Cook, D. J., & Schmitter-Edgecombe, M. (2017). Unsupervised Detection and Analysis of Changes in Everyday Physical Activity Data. Intelligent Systems Reference Library, 97-122. doi:10.1007/978-3-319-67513-8_6Taheri Tanjanai, P., Moradinazar, M., & Najafi, F. (2016). Prevalence of depression and related social and physical factors amongst the Iranian elderly population in 2012. Geriatrics & Gerontology International, 17(1), 126-131. doi:10.1111/ggi.12680Zhao, Z., Zhang, M., Yang, C., Fang, J., & Huang, G. Q. (2018). Distributed and collaborative proactive tandem location tracking of vehicle products for warehouse operations. Computers & Industrial Engineering, 125, 637-648. doi:10.1016/j.cie.2018.05.00

Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardization and pre-standardization landscape

Performance evaluation of IP-based multihop access networks

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An investigation of the impact of mobility on the protocol performance in wireless sensor networks

Mobility has introduced a new dimension to the wireless research areas such as IP mobility management protocols and wireless ad hoc networks. In this paper we take a closer look at the issues related to the mobility in wireless sensor networks (WSNs). The aim of this work is to identify improvements that can be obtained considering mobility, recognize its research challenges. We describe different levels of mobility in WSNs and highlight the effect of the mobility on the performance of WSN protocols

Mobility status as dynamic context for behaviour optimisation in self-organised networks

A novel technique by which wireless devices such as sensor nodes can deduce their own mobility status,based on analysis of patterns in their local neighbourhood, is described.For many systems in which a neighbour table is maintained through regular status messages or other interaction, the technique incurs no additional communication overhead. The technique does not require that nodes have additional information such as absolute or relative locations, or neighbourhood node density.The work considers systems with heterogeneous time-variant mobility models, in which a subset of nodes follows a random walk mobility model, another subset follows a random waypoint mobility model (i.e.have intermittent movement), some nodes have group mobility and there is a static subset.We simulate these heterogeneous mobility systems and evaluate the performance of the Self-Detection of Mobility Status algorithm (SDMS) against a number of metrics and in a wide variety of system configurations

A cross-layer optimisation solution to improve routing protocol performance for dense wireless sensor environment

In unreliable communication environments, traditional routing protocols designed for static wireless sensor networks may fail to deliver data timely since link/node failures can be found out only after trying multiple transmissions. The main goal of the proposed protocol is to find more stable paths and to control the flooding overhead which will lead to higher level of scalability. This mechanism considers link stability for the route selection, which makes routing data more reliable and decreases the probability of retransmission, thus saves the energy and prolongs the lifetime of the whole network. In this work, we propose a cross-layer solution which utilises MAC layer information to improve routing mechanisms reliability and scalability in network layer