DROPS: Dynamic Radio Protocol Selection for Energy-Constrained Wearable IoT Healthcare

We propose “DROPS”, a scheme which dynamically selects radio protocols in an energy-constrained wearable IoT healthcare system. We consider the use of multiple radio protocols, which are capable of transmitting a patient’s sensed physiological parameters to the server through Local Processing Units (LPUs). As the health parameters are non-stationary and temporally fluctuating, especially for critical patients, the selection of an appropriate radio protocol is essential to maintain the accuracy and timely delivery of data from the patient to the server. Additionally, the mobility of patients through various locations within the hospital mandates the selection of the best radio protocol among the multiple available ones for each location, to enable data to offload to the remote server. We use single-leader-multiple-follower Stackelberg non-cooperative game to map the strategic interactions between a patient’s LPU and the hospital’s server. “DROPS” dynamically selects the appropriate radio protocol, based on the criticality index of a patient, the reputation of the radio, the Euclidean distance between the radios and the LPU, and the load on the protocol. Results on real-life data and their large-scale emulation show that the data rate increases by almost 78% and throughput by approximately 7%, as compared to existing schemes.

[1]  Joel J. P. C. Rodrigues,et al.  Enabling Technologies on Cloud of Things for Smart Healthcare , 2018, IEEE Access.

[2]  Joel J. P. C. Rodrigues,et al.  A Comprehensive Review on Smart Decision Support Systems for Health Care , 2019, IEEE Systems Journal.

[3]  Audun Jøsang,et al.  AIS Electronic Library (AISeL) , 2017 .

[4]  Oliver King,et al.  A 1 V 5 mA Multimode IEEE 802.15.6/Bluetooth Low-Energy WBAN Transceiver for Biotelemetry Applications , 2013, IEEE Journal of Solid-State Circuits.

[5]  Eric van Damme,et al.  Non-Cooperative Games , 2000 .

[6]  Sudip Misra,et al.  Energy-Efficient and Distributed Network Management Cost Minimization in Opportunistic Wireless Body Area Networks , 2018, IEEE Transactions on Mobile Computing.

[7]  Jong-Tae Park,et al.  A survey on power-efficient MAC protocols for wireless body area networks , 2010, 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT).

[8]  R Rashmi,et al.  A Study on Wireless Body Area Network of Intelligent Motion Sensors for Computer Assisted Physical Rehabilitation , 2017 .

[9]  Oriol Sallent,et al.  Performance evaluation of radio access selection strategies in constrained multi-access/multi-service wireless networks , 2011, Comput. Networks.

[10]  Dmitri Botvich,et al.  Virtual Groups for Patient WBAN Monitoring in Medical Environments , 2012, IEEE Transactions on Biomedical Engineering.

[11]  Joel J. P. C. Rodrigues,et al.  Enabling Technologies for the Internet of Health Things , 2018, IEEE Access.

[12]  Joel J. P. C. Rodrigues,et al.  Cloud Centric Authentication for Wearable Healthcare Monitoring System , 2019, IEEE Transactions on Dependable and Secure Computing.

[13]  Quanyan Zhu,et al.  Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach , 2013, IEEE Transactions on Smart Grid.

[14]  Muttukrishnan Rajarajan,et al.  CARE: Criticality-Aware Data Transmission in CPS-Based Healthcare Systems , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[15]  A.A. Goulianos,et al.  Wideband Power Modeling and Time Dispersion Analysis for UWB Indoor Off-Body Communications , 2009, IEEE Transactions on Antennas and Propagation.

[16]  Sudip Misra,et al.  Random room mobility model and extra-wireless body area network communication in hospital buildings , 2015, IET Networks.

[17]  Mehmet A. Orgun,et al.  Design and deployment challenges in immersive and wearable technologies , 2017, Behav. Inf. Technol..

[18]  Twan Basten,et al.  MoBAN: a configurable mobility model for wireless body area networks , 2011, SimuTools.