mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks

Abstract Knowledge of the battery lifetime of the wireless sensor network is important for many situations, such as in evaluation of the location of nodes or the estimation of the connectivity, along time, between devices. However, experimental evaluation is a very time-consuming task. It depends on many factors, such as the use of the radio transceiver or the distance between nodes. Simulations reduce considerably this time. They allow the evaluation of the network behavior before its deployment. This article presents a simulation tool which helps developers to obtain information about battery state. This simulator extends the well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications using an accurate model of the battery consumption and its relation to the radio power transmission. Although an specific indoor scenario is used in testing of simulation, the simulator is not limited to this environment. It is possible to work in outdoor scenarios too. Experimental results validate the proposed model.

[1]  Anand Raghunathan,et al.  Battery discharge characteristics of wireless sensor nodes: an experimental analysis , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[2]  Salvatore Venticinque,et al.  A simulation model for localization of pervasive objects using heterogeneous wireless networks , 2011, Simul. Model. Pract. Theory.

[3]  John S. Baras,et al.  ATEMU: a fine-grained sensor network simulator , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[4]  A. Stepanov,et al.  Implementation of supercapacitors in uninterruptible power supplies , 2007, 2007 European Conference on Power Electronics and Applications.

[5]  Roger Wattenhofer,et al.  Analyzing connectivity-based multi-hop ad-hoc positioning , 2004 .

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

[8]  Carlos León,et al.  SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence , 2006, APWeb Workshops.

[9]  D. Doerffel,et al.  A critical review of using the peukert equation for determining the remaining capacity of lead-acid and lithium-ion batteries , 2006 .

[10]  Nelson Minar,et al.  The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations , 1996 .

[11]  Jae C. Oh,et al.  SWARM-eTOSSIM: A Simulator for Distributed Energy-Constrained Tiny Devices , 2011, 2011 Tenth International Symposium on Autonomous Decentralized Systems.

[12]  George Papadopoulos,et al.  A Practical RF Propagation Model for Wireless Network Sensors , 2009, 2009 Third International Conference on Sensor Technologies and Applications.

[13]  A. Joshi,et al.  GPS-less animal tracking system , 2008, 2008 Fourth International Conference on Wireless Communication and Sensor Networks.

[14]  Dharma P. Agrawal,et al.  Intrusion Detection in Homogeneous and Heterogeneous Wireless Sensor Networks , 2008, IEEE Transactions on Mobile Computing.

[15]  Philip Levis,et al.  TOSSIM: A Simulator for TinyOS Networks , 2003 .

[16]  Brian D. O. Anderson,et al.  Path loss exponent estimation for wireless sensor network localization , 2007, Comput. Networks.

[17]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[19]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[20]  Theofilos Chrysikos,et al.  Site-specific validation of ITU indoor path loss model at 2.4 GHz , 2009, 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops.

[21]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[22]  Sotiris E. Nikoletseas,et al.  Parameterized energy-latency trade-offs for data propagation in sensor networks , 2011, Simul. Model. Pract. Theory.

[23]  Joy Kuri,et al.  Simulation blocks for TOSSIM-T2 , 2008, 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE '08).

[24]  J. Galbreath,et al.  Channel allocation strategies for wireless sensors statically deployed in multipath environments , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[25]  Osman Ceylan,et al.  Comparing ray tracing, free space path loss and logarithmic distance path loss models in success of indoor localization with RSSI , 2011, 2011 19thTelecommunications Forum (TELFOR) Proceedings of Papers.

[26]  H. Sanson,et al.  Localization for emergency sensor networks , 2005, The 7th International Conference on Advanced Communication Technology, 2005, ICACT 2005..

[27]  Thanasis Loukopoulos,et al.  Algorithms for energy-driven agent placement in wireless embedded systems with memory constraints , 2011, Simul. Model. Pract. Theory.

[28]  Jie Wu,et al.  Optimal movement-assisted sensor deployment and its extensions in wireless sensor networks , 2006, 12th International Conference on Parallel and Distributed Systems - (ICPADS'06).

[29]  Randy Meyers.  X macros , 2001 .

[30]  Andrea Conti,et al.  An Overview on Wireless Sensor Networks Technology and Evolution , 2009, Sensors.

[31]  Mihail L. Sichitiu,et al.  Localization in Wireless Sensor Networks: A Probabilistic Approach , 2003, International Conference on Wireless Networks.

[32]  Vashek Matyas,et al.  Calibrating and Comparing Simulators for Wireless Sensor Networks , 2011, 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems.