TY - GEN
T1 - Application of Graph Theory in IoT for Optimization of Connected Healthcare System
AU - Zaman, Faisal
AU - Aloqaily, Moayad
AU - Sallabi, Farag
AU - Shuaib, Khaled
AU - Othman, Jalel Ben
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Connected healthcare is the process of integrating healthcare smart applications into smart devices. These systems can enable better patient-hospital experience, efficient time usage, reduced errors, safety and security, and ultimately improved treatments. These smart devices which form an IoT network are extremely dynamic because of the user movement. In an environment where there is a constant change in the network topology and its traffic profile, it is a challenging task to provide reliable network connectivity and to maintain the IoT network. Therefore, ensuring healthcare traffic is resilient towards change in the traffic profile is of paramount importance. This paper leverages graph theory concepts to understand the behaviour of the healthcare IoT network. The paper highlights the importance of the PN (PN) and traffic splitting (stratification). A PN is a node in the network which has enough computing resources to share with other network devices. By optimizing the selection of PN, the drastic improvement in the network performance could be achieved. Moreover, we show that splitting the traffic along with optimized PN selection minimizes the chances of healthcare traffic drop during the period of high network usage.
AB - Connected healthcare is the process of integrating healthcare smart applications into smart devices. These systems can enable better patient-hospital experience, efficient time usage, reduced errors, safety and security, and ultimately improved treatments. These smart devices which form an IoT network are extremely dynamic because of the user movement. In an environment where there is a constant change in the network topology and its traffic profile, it is a challenging task to provide reliable network connectivity and to maintain the IoT network. Therefore, ensuring healthcare traffic is resilient towards change in the traffic profile is of paramount importance. This paper leverages graph theory concepts to understand the behaviour of the healthcare IoT network. The paper highlights the importance of the PN (PN) and traffic splitting (stratification). A PN is a node in the network which has enough computing resources to share with other network devices. By optimizing the selection of PN, the drastic improvement in the network performance could be achieved. Moreover, we show that splitting the traffic along with optimized PN selection minimizes the chances of healthcare traffic drop during the period of high network usage.
KW - Graph Theory
KW - Healthcare
KW - IoT
KW - Parent Node.
KW - Traffic Classification
UR - http://www.scopus.com/inward/record.url?scp=85100374580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100374580&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9322157
DO - 10.1109/GLOBECOM42002.2020.9322157
M3 - Conference contribution
AN - SCOPUS:85100374580
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -