Empirical path loss models at 433 MHz in Himalayan snow for health monitoring

Purpose The purpose of this paper is to investigate the effect of snow on the radio link performance of wireless sensor nodes in Indian Himalayan conditions and to propose empirical path loss models for radio wave propagation. Design/methodology/approach At the remote test site, one source and three listening wireless sensor nodes were deployed at frequency of 433 MHz. The path loss models are derived from experimental data collected during the period of snowfall and clear weather conditions. Linear, exponential, second and third-order polynomials path loss models have been investigated along with experimental data. Findings With the help of curve fitting and goodness-of-fit tests, it is found that path loss can be modelled through third-order polynomial equation during the snowfall period. However, if sensor is buried, the acceptable path loss model is exponential. Similarly, for unified modelling requirement, exponential path loss model over linear can be a preferred choice. Originality/value Results show that path loss can be estimated priori for deciding optimum transmission energy in wireless sensor network. Presented work is usable in extending the lifetime of health monitoring devices buried in snowy environment.

[1]  Gerhard P. Hancke,et al.  Experimental Link Quality Characterization of Wireless Sensor Networks for Underground Monitoring , 2015, IEEE Transactions on Industrial Informatics.

[2]  Michael Cheffena,et al.  Empirical Path Loss Models for Wireless Sensor Network Deployment in Snowy Environments , 2017, IEEE Antennas and Wireless Propagation Letters.

[3]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[4]  Mihaela Cardei,et al.  Delay-constrained energy-efficient routing in heterogeneous wireless sensor networks , 2010, Int. J. Sens. Networks.

[5]  Joel J. P. C. Rodrigues,et al.  Wireless Sensor Networks: a Survey on Environmental Monitoring , 2011, J. Commun..

[6]  Dimitrios D. Vergados,et al.  Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[7]  Vijay Kumar,et al.  Astrophysics inspired multi-objective approach for automatic clustering and feature selection in real-life environment , 2018, Modern Physics Letters B.

[8]  Amandeep Kaur,et al.  Design of a novel energy efficient routing framework for Wireless Nanosensor Networks , 2018, 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC).

[9]  Mukesh Soni,et al.  Advanced formal authentication protocol using smart cards for network applicants , 2018, Comput. Electr. Eng..

[10]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[11]  Elaine Lawrence,et al.  WSN Applications in Personal Healthcare Monitoring Systems: A Heterogeneous Framework , 2010, 2010 Second International Conference on eHealth, Telemedicine, and Social Medicine.

[12]  Vijay Kumar,et al.  Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems , 2018, Knowl. Based Syst..

[13]  Surender Kumar Soni,et al.  Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks , 2020, J. Ambient Intell. Humaniz. Comput..

[14]  Jianming Wei,et al.  Measurement and Analysis of Near-Ground Propagation Models under Different Terrains for Wireless Sensor Networks , 2019, Sensors.

[15]  Anthony Tzes,et al.  Power Conservation through Energy Efficient Routing in Wireless Sensor Networks , 2009, Sensors.

[16]  Jagdish W. Bakal,et al.  Smart Healthcare Monitoring System based on Wireless Sensor Networks , 2016, 2016 International Conference on Computing, Analytics and Security Trends (CAST).

[17]  Peter Ball,et al.  A ground level radio propagation model for road-based wireless sensor networks , 2014, 2014 9th International Symposium on Communication Systems, Networks & Digital Sign (CSNDSP).

[18]  Mohamed-Slim Alouini,et al.  A Survey of Channel Modeling for UAV Communications , 2018, IEEE Communications Surveys & Tutorials.

[19]  Gaurav Dhiman,et al.  Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications , 2017, Adv. Eng. Softw..

[20]  Vijay Kumar,et al.  Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems , 2019, Knowl. Based Syst..

[21]  D.P. Agrawal,et al.  APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[22]  Mukesh Soni,et al.  Blockchain-based security & privacy for biomedical and healthcare information exchange systems , 2021, Materials Today: Proceedings.

[23]  Peter I. Corke,et al.  Environmental Wireless Sensor Networks , 2010, Proceedings of the IEEE.

[24]  Samaher Al-Janabi,et al.  Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications , 2017 .

[25]  A.E. Kamal,et al.  Data aggregation in wireless sensor networks - exact and approximate algorithms , 2004, 2004 Workshop on High Performance Switching and Routing, 2004. HPSR..

[26]  Lorenzo Chiari,et al.  Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61–70 Years Old , 2019, Sensors.

[27]  Victor C. M. Leung,et al.  Reliable and energy-efficient routing protocol in dense wireless sensor networks , 2008, Int. J. Sens. Networks.

[28]  Seyed Alireza Zekavat,et al.  Snow covered forest channel modeling for near-ground Wireless Sensor Networks , 2017, 2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE).