Adaptive Sleep Scheduling for Health Monitoring System Based on the IEEE 802.15.4 Standard

Nurul Fahmi, M. Udin Harun Al Rasyid, Amang Sudarsono

Abstract


In the recent years, Wireless Sensor Networks (WSNs) have become a very popular technology for research in various fields. One of the technologies which is developed using WSN is environmental health monitoring. However, there is a problem when we want to optimize the performance of the environmental health monitoring such as the limitation of the energy. In this paper, we proposed a method for the environmental health monitoring using the fuzzy logic approach according to the environmental health conditions. We use that condition to determine the sleep time in the system based on IEEE 802.15.4 standard protocol. The main purpose of this method is to extend the life and minimize the energy consumption of the battery. We implemented this system in the real hardware test-bed using temperature, humidity, CO and CO2 sensors. We compared the performance without sleep scheduling, with sleep scheduling and adaptive sleep scheduling. The power consumption spent during the process of testing without sleep scheduling is 52%, for the sleep scheduling is 13%, while using the adaptive sleep scheduling is around 7%. The users also can monitor the health condition via mobile phone or web-based application, in real-time anywhere and anytime.


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References


Sheikh Ferdoush, Xinrong Li, Wireless Sensor Network System Design using Raspberry Pi and Arduino for Environmental Monitoring Applications, The 9th International Conference on Future Networks and Communications (FNC-2014), Niagara Falls, Canada, pp. 103-110, 2014.

M.F Othmana, K. Shazalib, Wireless Sensor Network Applications: A Study in Environment Monitoring System, International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012), ScienceDirect, pp. 1204-1210, 2012.

Dunfan Ye, Daoli Gong, Wei Wang, Application of Wireless Sensor Networks in Environmental Monitoring, Power Electronics and Intelligent Transportation System (PEITS), Shenzhen, pp. 205 - 208, 2009.

S. Abraham, Xinrong Li, A Cost-Effective Wireless Sensor Network System for Indoor Air Quality Monitoring Applications, Future Networks and Communications (FNC-2014), ScienceDirect, pp. 165-171, 2014.

Octavian A. Postolache, J. M. Dias Pereira, and P. M. B. Silva Girao, Smart Sensors Network for Air Quality Monitoring Applications, IEEE Transactions on Instrumentation and Measurement, Vol. 58, pp. 3253 - 3262, 2009.

M. Dutta, S. Bhowmik, and C. Giri, Fuzzy Logic Based Implementation for Forest Fire Detection Using Wireless Sensor Network, Smart Innovation, Systems and Technologies, Switzerland, pp. 319-327, 2014

N. Fahmi, S. Huda, M.U.H Al Rasyid, A. Sudarsono, Fuzzy Logic for an Implementation Environment Health Monitoring Based on Wireless Sensor Network, Recent Advancement in Informatics, Electrical and Electronics Engineering International Conference (RAIEIC 2015), Medan, 2015.

M.R Tripathy, K. Gaur, S Sharma, and G.S. Virdi, Energy Efficient Fuzzy Logic Based Intelligent Wireless Sensor Network, Progress In Electromagnetics Research Symposium Proceedings, Cambridge, USA, pp. 91-95, 2010.

Giovanni Pau, Power Consumption Reduction for Wireless Sensor Networks Using A Fuzzy Approach, International Journal of Engineering and Technology Innovation (IJETI), Taiwan, 2015.

R. Sabitha, K.T. Bhuma, and T. Thyagarajan, Design and Analysis of Fuzzy Logic and Neural Network Based Transmission Power Control Techniques for Energy Efficient Wireless Sensor Networks, Advances in Intelligent Systems and Computing, Springer International Publishing, VOl.1, pp. 295-303, 2015.

A. Sudarsono, P. Kristalina, M.U.H Al Rasyid, R. Hermawan, An implementation of secure data sensor transmission in Wireless Sensor Network for monitoring environmental health, Computer, Control, Informatics and its Applications (IC3INA), Bandung, pp. 93 - 98, 2014.

Jang, J.S.R C.T. Sun dan E. Mizutani. 1997. Neuro-Fuzzy and Soft Computing. London : Prentice-Hall.

P. Patil, U. Kulkarni, B.L. Desai,V.I. Benagi and V.B. Naragund, Fuzzy Logic Based Irrigation Control Using Wireless Sensor Network for Precision Agriculture, AIPA, India, pp. 262-269, 2012

Waspmote Specification - Spesifikasi hardware waspmote. https://www.libelium.com/products/waspmote/hardware/ [accessed on April 2016].

Libelium - Gases 2.0 Technical Guide. https://www.libelium.com/ [accesed on April 2016].

Meshlulim – Meshlium Datasheet and documentation, http://www.libelium.com/development/meshlium/documentation/ [accesed on April 2016].

Libelium - Waspmote Power Programming Guide, http://www.libelium.com/development/waspmote/documentation/w aspmote_power_programming_guide/ [accesed on April 2016].




DOI: 10.24003/emitter.v4i1.115

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