Interference-aware channel switching for use in WBAN with human-sensor interface

A Wireless Body Area Network (WBAN) consists of different medical sensors connected to an on-body coordinator through a wireless medium. The sensors are placed over a human body for continuous monitoring of physiological parameters. The information sensed by the nodes are transmitted to the on-body coordinator. Upon receiving these information from the sensor nodes, the coordinator transmits the same to the medical server for post data analysis. Any radio frequency based wireless device suffers from interference due to the existence of other wireless devices operating in the same licence-free frequency band. In this paper, we address the problem of interference when multiple WBANs come in the proximity of one another. In such a scenario, the WBAN senses the existence of other interfering WBANs, based on the Signal-to-Interference Ratio (SIR). We propose an interference-aware channel switching algorithm (InterACS) for WBANs, so that there is seamless communication with their sensors without interference among them. We simulated the proposed solution using the NS-3 network simulator and observed that there is improvement in reducing the number of interferences by using the proposed solution scheme.

[1]  Huaiyu Dai,et al.  Cochannel Interference Mitigation and Cooperative Processing in Downlink Multicell Multiuser MIMO Networks , 2004, EURASIP J. Wirel. Commun. Netw..

[2]  Andrea J. Goldsmith,et al.  Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks , 2006, IEEE Transactions on Wireless Communications.

[3]  Ingrid Moerman,et al.  A survey on wireless body area networks , 2011, Wirel. Networks.

[4]  Xuan Wang,et al.  Interference Analysis of Co-Existing Wireless Body Area Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[5]  William G. Scanlon,et al.  Analysis of the performance of IEEE 802.15.4 for medical sensor body area networking , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[6]  George F. Riley,et al.  The ns-3 Network Simulator , 2010, Modeling and Tools for Network Simulation.

[7]  Jindong Tan,et al.  Heartbeat-driven medium-access control for body sensor networks , 2010, IEEE Trans. Inf. Technol. Biomed..

[8]  Kenichi Takizawa,et al.  Channel models for wireless body area networks , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Seung-Jae Han,et al.  Interference mitigation in wireless sensor networks using dual heterogeneous radios , 2011, Wirel. Networks.

[10]  David B. Smith,et al.  Co-Channel Interference in Body Area Networks with Indoor Measurements at 2.4 GHz: Distance-to-Interferer is a Poor Estimate of Received Interference Power , 2010, Int. J. Wirel. Inf. Networks.

[11]  Teng-Sheng Moh,et al.  Privacy and security in biomedical applications of wireless sensor networks , 2008, 2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies.

[12]  Aleksandar Milenkovic,et al.  Wireless sensor networks for personal health monitoring: Issues and an implementation , 2006, Comput. Commun..

[13]  Ryuji Kohno,et al.  Channel Modeling and Performance Evaluation on UWB-Based Wireless Body Area Networks , 2009, 2009 IEEE International Conference on Communications.

[14]  Rami G. Melhem,et al.  A unified interference-collision analysis for power-aware adhoc networks , 2004, IEEE INFOCOM 2004.

[15]  Sudip Misra,et al.  Interference mitigation between WBAN equipped patients , 2012, 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN).