Cascaded WLAN-FWA Networking and Computing Architecture for Pervasive In-Home Healthcare

Pervasive healthcare is a promising assisted-living solution for chronic patients. However, current cutting-edge communication technologies are not able to strictly meet the requirements of these applications, especially in the case of life-threatening events. To bridge this gap, this article proposes a new architecture to support indoor healthcare monitoring, with a focus on epileptic patients. Several novel elements are introduced. The first element is the cascading of a WLAN and a cellular network, where IEEE 802.11ax is used for the wireless local area network to collect physiological and environmental data in-home and 5G-enabled Fixed Wireless Access links transfer them to a remote hospital. The second element is the extension of the network slicing concept to the WLAN, and the introduction of two new slice types to support both regular monitoring and emergency handling. Moreover, the inclusion of local computing capabilities at the WLAN router, together with a mobile edge computing resource, represents a further architectural enhancement. Local computation is required to trigger not only health-related alarms but also the network slicing change in case of emergency; in fact, proper radio resource scheduling is necessary for the cascaded networks to handle healthcare traffic together with other promiscuous everyday communication services. Numerical results demonstrate the effectiveness of the proposed approach while highlighting the performance gain achieved with respect to baseline solutions.

[1]  Josemir W Sander,et al.  Value of video monitoring for nocturnal seizure detection in a residential setting , 2016, Epilepsia.

[2]  Sandeep K. Sood,et al.  An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks , 2016, Journal of Medical Systems.

[3]  Víctor M. González Suárez,et al.  An IoT Platform for Epilepsy Monitoring and Supervising , 2017, J. Sensors.

[4]  Haoyu Wang,et al.  HealthEdge: Task scheduling for edge computing with health emergency and human behavior consideration in smart homes , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[5]  Ramón Agüero,et al.  Slicing in WiFi Networks Through Airtime-Based Resource Allocation , 2018, Journal of Network and Systems Management.

[6]  Rami Langar,et al.  Dynamic Network Slicing for 5G IoT and eMBB services: A New Design with Prototype and Implementation Results , 2018, 2018 3rd Cloudification of the Internet of Things (CIoT).

[7]  Mufti Mahmud,et al.  Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things , 2019, IEEE Internet of Things Journal.

[8]  Rui L. Aguiar,et al.  Network-Cloud Slicing Definitions for Wi-Fi Sharing Systems to Enhance 5G Ultra Dense Network Capabilities , 2019, Wirel. Commun. Mob. Comput..

[9]  Nguyen H. Tran,et al.  Network Slicing: Recent Advances, Taxonomy, Requirements, and Open Research Challenges , 2020, IEEE Access.

[10]  Dario Pompili,et al.  Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing , 2020, Artif. Intell. Medicine.

[11]  Stefano Tomasin,et al.  Requirements and Enablers of Advanced Healthcare Services over Future Cellular Systems , 2019, IEEE Communications Magazine.