Buffer-Aware Resource Allocation Scheme With Energy Efficiency and QoS Effectiveness in Wireless Body Area Networks

Wireless body area network (WBAN) has attracted more and more attention to automatically and intelligently sense the health data of one person for supporting various health applications in smart cities. In the energy-constrained and heterogeneous WBAN system, there are three main issues: 1) the dynamic link characteristics due to the time-varying postures and environments; 2) the high energy efficiency requirement with considering the limited sensor battery; and 3) the high quality-of-service (QoS) requirement due to the importance of health data. To provide long service with high quality, the resource allocation scheme becomes indispensable with considering all these issues. In this paper, a mix-cost parameter is designed to evaluate the energy efficiency and QoS effectiveness, and a resource allocation problem is formulated to minimize the total mix-cost with optimizing the transmission rate, the transmission power, and the allocated time slots for each sensor. Then, a buffer-aware sensor evaluation method with low complexity is introduced to the resource allocation scheme to evaluate the sensor state in real time and then decide when applying for the resource re-allocation by the hub for further improving both the short-term and the long-term QoS performance. Finally, a greedy sub-optimal resource allocation scheme is designed to reduce the time complexity of the resource allocation scheme. Simulation results are presented to demonstrate the effectiveness of the proposed optimal buffer-aware resource allocation scheme as well as the greedy sub-optimal resource allocation scheme with low complexity.

[1]  Raffaele D'Errico,et al.  A Statistical Model for On-Body Dynamic Channels , 2010, Int. J. Wirel. Inf. Networks.

[2]  Zhiqiang Liu,et al.  Energy-Efficient Resource Allocation with QoS Support in Wireless Body Area Networks , 2014, GLOBECOM 2014.

[3]  Aristides Lopes da Silva,et al.  Health and emergency-care platform for the elderly and disabled people in the Smart City , 2015, J. Syst. Softw..

[4]  David B. Smith,et al.  Challenges in body area networks for healthcare: the MAC , 2012, IEEE Communications Magazine.

[5]  Ali Hassan Sodhro,et al.  Energy-efficient adaptive transmission power control for wireless body area networks , 2016, IET Commun..

[6]  Kai Juan Wong,et al.  Asymmetric Multihop Networks for Multi-capsule Communications within the Gastrointestinal Tract , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[7]  Vijay Sivaraman,et al.  Transmission Power Control in Body Area Sensor Networks for Healthcare Monitoring , 2009, IEEE Journal on Selected Areas in Communications.

[8]  K. S. Deepak,et al.  Optimal packet size for energy efficient WBAN under m-periodic scheduled access mode , 2014, 2014 Twentieth National Conference on Communications (NCC).

[9]  Marwa Salayma,et al.  Wireless Body Area Network (WBAN) , 2017, ACM Comput. Surv..

[10]  Muhannad Quwaider,et al.  Body-posture-based dynamic link power control in wearable sensor networks , 2010, IEEE Communications Magazine.

[11]  Honggang Wang,et al.  Power control and localization of wireless body area networks using semidefinite programming , 2015, 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech).

[12]  Bin Liu,et al.  Buffer-aware and QoS-effective resource allocation scheme in WBANs , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[13]  Xiaoli Zhou,et al.  Energy Efficiency Optimization by Resource Allocation in Wireless Body Area Networks , 2014, 2014 IEEE 79th Vehicular Technology Conference (VTC Spring).

[14]  Francis Minhthang Bui,et al.  Optimal Relay Selection and Power Control With Quality-of-Service Provisioning in Wireless Body Area Networks , 2016, IEEE Transactions on Wireless Communications.

[15]  Hao Yan,et al.  An Energy Efficient MAC Protocol for Multi-Hop Swallowable Body Sensor Networks , 2014, Sensors.

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

[17]  David B. Smith,et al.  Transmit power control for wireless body area networks using novel channel prediction , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[18]  Virtual Bridged,et al.  IEEE Standards for Local and Metropolitan Area Networks: Specification for 802.3 Full Duplex Operation , 1997, IEEE Std 802.3x-1997 and IEEE Std 802.3y-1997 (Supplement to ISO/IEC 8802-3: 1996/ANSI/IEEE Std 802.3, 1996 Edition).

[19]  Bin Liu,et al.  An energy-efficient and QoS-effective resource allocation scheme in WBANs , 2016, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[20]  Bin Liu,et al.  A two-layer and multi-strategy framework for human activity recognition using smartphone , 2016, 2016 IEEE International Conference on Communications (ICC).

[21]  Danilo De Donno,et al.  An IoT-Aware Architecture for Smart Healthcare Systems , 2015, IEEE Internet of Things Journal.

[22]  Doo Seop Eom,et al.  Link-State-Estimation-Based Transmission Power Control in Wireless Body Area Networks , 2014, IEEE Journal of Biomedical and Health Informatics.

[23]  Ling Guan,et al.  Optimal Resource Allocation for Pervasive Health Monitoring Systems with Body Sensor Networks , 2011, IEEE Transactions on Mobile Computing.

[24]  Ingrid Moerman,et al.  Characterization of On-Body Communication Channel and Energy Efficient Topology Design for Wireless Body Area Networks , 2009, IEEE Transactions on Information Technology in Biomedicine.

[25]  Bin Liu,et al.  Medium Access Control for Wireless Body Area Networks with QoS Provisioning and Energy Efficient Design , 2017, IEEE Transactions on Mobile Computing.

[26]  Sudip Misra,et al.  Link-Quality-Aware Resource Allocation With Load Balance in Wireless Body Area Networks , 2018, IEEE Systems Journal.