An Admission Control Method Based on QoS Constraint of BSN Traffic Aggregation

In the body sensor networks (BSN), sensor nodes transmit data to remote receiving terminal via sink node. During the process of data aggregation, interference and self-similar character of wireless business have significant influence on traffic queue performance of sink node. To balance traffic quality of service (QoS) requirement and system present available resource and decide optimum resource distribution project. Hence, a complex function of sink node cache length, distributed channel rate and packet loss probability, and queue delay is built in this paper. Based on the function, by combining multiple target optimizing method and considering joint constraint of queue delay and packet loss probability, we proposed an admission control method based on traffic aggregation QoS constraint. In a BSN consisting of a large amount of sensor nodes, by admission control of sink node, reasonable acceptance of new nodes, and distribution sink node resource, the method we proposed can meet needs of important parameters in WSN design.

[1]  Matthias Grossglauser,et al.  On the relevance of long-range dependence in network traffic , 1999, TNET.

[2]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.

[3]  Gang Zhou,et al.  BodyQoS: Adaptive and Radio-Agnostic QoS for Body Sensor Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[4]  Weizhen Mao,et al.  BodyT2: Throughput and time delay performance assurance for heterogeneous BSNs , 2011, 2011 Proceedings IEEE INFOCOM.

[5]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Ahmed Mehaoua,et al.  Energy-aware topology design for wireless body area networks , 2012, 2012 IEEE International Conference on Communications (ICC).

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

[8]  Gang Zhou,et al.  Throughput Assurance for Multiple Body Sensor Networks , 2016, IEEE Transactions on Parallel and Distributed Systems.

[9]  Nicolas D. Georganas,et al.  Analysis of an ATM buffer with self-similar ("fractal") input traffic , 1995, Proceedings of INFOCOM'95.

[10]  Grisha Spasov,et al.  Modeling and analysis of the gateway node in body sensor networks , 2012, 2012 Proceedings of the 35th International Convention MIPRO.

[11]  Shaohan Hu,et al.  NeuroPhone: brain-mobile phone interface using a wireless EEG headset , 2010, MobiHeld '10.

[12]  Walter Willinger,et al.  Proof of a fundamental result in self-similar traffic modeling , 1997, CCRV.

[13]  Geyong Min,et al.  Analytical Modelling and Optimization of Congestion Control for Prioritized Multi-Class Self-Similar Traffic , 2013, IEEE Transactions on Communications.

[14]  Guoliang Xing,et al.  PBN: towards practical activity recognition using smartphone-based body sensor networks , 2011, SenSys.

[15]  Y. Li,et al.  A Wireless Sensor , AdHoc and Delay Tolerant Network System for Disaster Response , 2011 .

[16]  Heng Liu,et al.  A novel packet scheduling algorithm based on self-similar traffic in WSN , 2010 .

[17]  Arnold L. Neidhardt,et al.  The concept of relevant time scales and its application to queuing analysis of self-similar traffic (or is Hurst naughty or nice?) , 1998, SIGMETRICS '98/PERFORMANCE '98.

[18]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.

[19]  Guang-Zhong Yang,et al.  Body Sensor Networks for Sport, Wellbeing and Health , 2010 .

[20]  Lang Tong,et al.  A Measurement-Based Model for Dynamic Spectrum Access in WLAN Channels , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.