International Journal of Computer Networks and Applications

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Performance Analysis of a Dynamically Power Managed and Event-Based Wireless Sensor Node Enabled by Queue Discipline


Affiliations
1 Department of Electronics and Communications Engineering, S.G. Balekundri Institute of Technology, Shivabasavanagar, Belagavi, Karnataka, India
 

In recent years, Wireless Sensor Networks (WSNs) have attracted the attention of researchers in view of providing a system with high performance and low power consumption. The consistent challenge is to address the trade-off between performance and power consumption. Hence, many key performance metrics need to be analysed for the design of an efficient Wireless Sensor Network. Existing power management techniques, when surveyed, have addressed the issue of power and performance of the node but with the limitation on the selection of queue discipline; this motivates our study to analyse the importance on the selection of queue discipline as it majorly plays a vital role in power and performance management. Thus, we developed a Dynamically Power Managed WSN node in MATLAB Simulink depicting the stochastic behaviour of event arrival and performed the analysis on a single server. This article focuses on the study of queue discipline based on M/M/1 queuing theory with a detailed analysis of First In First Out Queue on the performance of an individual WSN node. The innovation of our work is in the detailed analysis of the behaviour of events in the queue. The parameters analysed are waiting time, average length of the event in the queue and the number of events missed the service by the server. The simulation results make it evident that the events when served based on the First Come First Serve basis performs 10% better in terms of missing events in the queue as compared to Last in First Out Discipline. It is also observed that the arrival rate of the events has an impact on the number of missed events and the utilization of the processor; hence we analysed the need to reduce the number of missed events, especially when the arrival of events is fast.

Keywords

Wireless Sensor Network, Dynamic Power Management, First In First Out Queue, Waiting Time, Utilization.
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  • A. Rahman: Novel Optical Sensor Based on Morphology-Dependent Resonances for Measuring Thermal Deformation in Microelectromechanical Systems Devices, Journal of Micro/Nanolithography, MEMS, and MOEMS, Vol. 8, No. 3, 2009, pp.033-071.

  • Y. Atsushi, T. Nakanishi: Optical Fiber Sensor for Humidity Monitoring Based on Thermal Lens Detection Technique, IEICE Electronics Express, Vol. 2, No. 14, 2005, 417–422.

  • C. Li, et al.: Experimental Investigation and Error Analysis of High Precision FBG Displacement Sensor for Structural Health Monitoring, International Journal of Structural Stability and Dynamics, Vol. 20, No.6, 2020.

  • C. Bin, J. Zhao, P. Yang, et al.: Multiobjective 3-D Topology Optimization of Next-Generation Wireless Data Center Network, IEEE Transactions on Industrial Informatics, Vol. 16, No. 5, 2020, pp. 3597– 3605.

  • C. Bin, J. Zhao, Y. Gu, et al.: Security-Aware Industrial Wireless Sensor Network Deployment Optimization, IEEE Transactions on Industrial Informatics, Vol. 16, No. 8, 2020, pp. 5309–5316.

  • A. Pughat, V. Sharma: Queue Discipline Analysis for Dynamic Power Management in Wireless Sensor Node, 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015.

  • R Kallimani, K Pai, ,K Rasane: Stochastic Model of a Sensor Node. In Next Generation Information Processing System, Vol. 1162,Springer, Singapore, 2021, pp. 242-250.

  • O. Mokrenko, S. Lesecq, W. Lombardi, D. Puschini, C. Albea, and O. Debicki.: Dynamic power management in a wireless sensor network using predictive control. In IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2014, pp. 4756-4761.

  • X. Zhang, L. Demin, Z. Yihong: Maximum Throughput under Admission Control with Unknown Queue-Length in Wireless Sensor Networks, IEEE Sensors Journal, Vol. 20, No. 19, 2020, pp. 11387– 11399.

  • T. Phung-Duc, K. Kenichi: Delay Performance of Data-Center Queue with Setup Policy and Abandonment, Annals of Operations Research, Vol. 293, No. 1, 2020, pp. 269–93.

  • E. Ever, et al.: On the Performance, Availability and Energy Consumption Modelling of Clustered IoT Systems, Computing, Vol.101, No. 12, 2019.

  • S. El Kafhali, S. Khaled: Efficient and Dynamic Scaling of Fog Nodes for IoT Devices, Journal of Supercomputing, Vol. 73, No. 12, 2017, pp. 5261–5284.

  • E. De Cuypere, K. De Turck, D. Fiems: A Queueing Model of an Energy Harvesting Sensor Node with Data Buffering, Telecommunication Systems, Vol. 67, No. 2, 2018, pp. 281–295.

  • Y. Sun, Z. Haiwei, C. Yan, W. Bin: Queueing and Channel Access Delay Analysis in In-Band Full-Duplex Wireless Networks, International Journal of Distributed Sensor Networks, Vol. 15, No. 4, 2019.

  • R. Fang, W. Jianping, S. Wei, L. Qiyue: QoS Model of WSNs Communication in Smart Distribution Grid, International Journal of Distributed Sensor Networks, Vol. 12, No. 1, 2016.

  • X. Fu, Y. Yongsheng: Modeling and Analysis of Cascading Node-Link Failures in Multi-Sink Wireless Sensor Networks, Reliability Engineering and System Safety, Vol. 197, 2020.

  • X. Fu, et al.: Topology Optimization against Cascading Failures on Wireless Sensor Networks Using a Memetic Algorithm, Computer Networks, Vol. 177, 2020.

  • J. Hu, et al.: A Survey on Multi-Sensor Fusion Based Obstacle Detection for Intelligent Ground Vehicles in off-Road Environments, Frontiers of Information Technology and Electronic Engineering, Vol. 21, No. 5, 2020, pp. 675–692.

  • L. Sun, et al.: Early Monitoring of Rebar Corrosion Evolution Based on FBG Sensor, International Journal of Structural Stability and Dynamics, Vol. 18, No. 8, 2018, pp. 1–11.

  • MathWorks. 2020: SimEvents - MATLAB & Simulink,https://uk.mathworks.com/products/simevents.html..

  • P Levis, S Madden, J Polastre, R Szewczyk, K Whitehouse, A Woo, D Gay et al. :TinyOS: An operating system for sensor networks. In Ambient intelligence, Springer, Berlin, Heidelberg, 2005, pp. 115-148.

  • H. Gillert, G. F. Newell: Applications of Queuing Theory, Monographs on Applied Probability and Statistics. New Fetter. Lane. Chapman and Hall Ltd. Vol. 53, No. 3, 1973, pp. 209–209.

  • A. Ivo, J. Resing: AR01 Queueing Systems: Queuing Systems, Department of Mathematics and Computing Science Eindhoven University of Technology, Course Hero.https://www.coursehero.com/file/22722521/AR01-Queueing-Systems.

  • M. Zukerman: Introduction to Queueing Theory and Stochastic Teletraffic Models, 2013. http://arxiv.org/abs/1307.2968,.

  • M/M/1 Queuing System: MATLAB & Simulink – MathWorks, India.https://in.mathworks.com/help/simevents/ug/m-m-1-queuing-system.html, < January 16, 2021>.


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  • Performance Analysis of a Dynamically Power Managed and Event-Based Wireless Sensor Node Enabled by Queue Discipline

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Authors

Rakhee Kallimani
Department of Electronics and Communications Engineering, S.G. Balekundri Institute of Technology, Shivabasavanagar, Belagavi, Karnataka, India
Sridhar Iyer
Department of Electronics and Communications Engineering, S.G. Balekundri Institute of Technology, Shivabasavanagar, Belagavi, Karnataka, India

Abstract


In recent years, Wireless Sensor Networks (WSNs) have attracted the attention of researchers in view of providing a system with high performance and low power consumption. The consistent challenge is to address the trade-off between performance and power consumption. Hence, many key performance metrics need to be analysed for the design of an efficient Wireless Sensor Network. Existing power management techniques, when surveyed, have addressed the issue of power and performance of the node but with the limitation on the selection of queue discipline; this motivates our study to analyse the importance on the selection of queue discipline as it majorly plays a vital role in power and performance management. Thus, we developed a Dynamically Power Managed WSN node in MATLAB Simulink depicting the stochastic behaviour of event arrival and performed the analysis on a single server. This article focuses on the study of queue discipline based on M/M/1 queuing theory with a detailed analysis of First In First Out Queue on the performance of an individual WSN node. The innovation of our work is in the detailed analysis of the behaviour of events in the queue. The parameters analysed are waiting time, average length of the event in the queue and the number of events missed the service by the server. The simulation results make it evident that the events when served based on the First Come First Serve basis performs 10% better in terms of missing events in the queue as compared to Last in First Out Discipline. It is also observed that the arrival rate of the events has an impact on the number of missed events and the utilization of the processor; hence we analysed the need to reduce the number of missed events, especially when the arrival of events is fast.

Keywords


Wireless Sensor Network, Dynamic Power Management, First In First Out Queue, Waiting Time, Utilization.

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DOI: https://doi.org/10.22247/ijcna%2F2021%2F208893