WiSeBat: accurate energy benchmarking of wireless sensor networks

Recent applications of Wireless Sensor Network require small yet sustainable battery-powered devices. As a consequence, it becomes crucial to accurately and efficiently compute a node's power consumption in order to estimate its lifetime. Existing wireless network simulators either implement simplistic energy consumption and battery models, or very complex and general ones that hinders scalability. In this paper, we (i) present WiSeBat, a module to estimate devices lifetime using realistic energy consumption and battery models, that has specially been optimized for wireless sensor network simulations. We then (ii) validate it through real measurements. Finally, we used it (iii) to compare wireless sensor lifetime in several realistic scenarios. Firstly, we review existing techniques to simulate a battery and discuss what behaviors are important to get realistic and fast simulations. We then propose simulator-independent models for the battery and for the energy consumption of sensors, and implement this model in the WSNet simulator. Secondly, we compare measured and simulated lifetimes of sensors. On the one hand, our experiments show that our models provide an 86 - 96% accurate lifetime estimation. On the other hand, the previous default WSNet models overestimate lifetime by more than 2600%. Once validated, we used our approach to benchmark the energy consumption of different protocol stacks of wireless sensor networks, under different scenarios. These simulations match well-known results in simple scenarios, as we demonstrate better performance of ContikiMAC over X-MAC. They also provide an accurate comparison of sensor lifetime in more complex scenarios.

[1]  Enrico Perla,et al.  PowerTOSSIM z: realistic energy modelling for wireless sensor network environments , 2008, PM2HW2N '08.

[2]  Patrick Garda,et al.  An Emulation-Based Method for Lifetime Estimation of Wireless Sensor Networks , 2014, 2014 IEEE International Conference on Distributed Computing in Sensor Systems.

[3]  Piotr Zwierzykowski,et al.  Surv ey of Simulators for Wireless Sensor Networks , 2012 .

[4]  O. P. Vyas,et al.  An Exploratory Study of Experimental Tools for Wireless Sensor Networks , 2011, Wirel. Sens. Netw..

[5]  Mani B. Srivastava,et al.  SensorSim: a simulation framework for sensor networks , 2000, MSWIM '00.

[6]  Daniel Willkomm,et al.  Energy Framework: an extensible framework for simulating battery consumption in wireless networks , 2010, SimuTools.

[7]  Eric Anderson,et al.  X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks , 2006, SenSys '06.

[8]  Adam Dunkels,et al.  The ContikiMAC Radio Duty Cycling Protocol , 2011 .

[9]  Sally Floyd,et al.  ns-3 project goals , 2006 .

[10]  Boleslaw K. Szymanski,et al.  SENSE: A WIRELESS SENSOR NETWORK SIMULATOR , 2005 .

[11]  Qing Zhao,et al.  On the lifetime of wireless sensor networks , 2005, IEEE Communications Letters.

[12]  Sébastien Tixeuil,et al.  Advanced faults patterns for WSN dependability benchmarking , 2010, MSWIM '10.

[13]  Sarma B. K. Vrudhula,et al.  Battery Modeling for Energy-Aware System Design , 2003, Computer.

[14]  Patrick Garda,et al.  A fixed frequency sampling method for wireless sensors power consumption estimation , 2013, 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS).

[15]  Prabir Bhattacharya,et al.  Wireless Sensor Network Simulators A Survey and Comparisons , 2011 .

[16]  Guillaume Chelius,et al.  Worldsens: a fast and accurate development framework for sensor network applications , 2007, SAC '07.

[17]  Sajjad Ahmad Madani,et al.  Power Aware Simulation Framework for Wireless Sensor Networks and Nodes , 2008, EURASIP J. Embed. Syst..

[18]  Laura Marie Feeney,et al.  Evaluating Battery Models in Wireless Sensor Networks , 2013, WWIC.

[19]  Sébastien Tixeuil,et al.  XS-WSNet: Extreme scale wireless sensor network simulation , 2010, 2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).