Abstract
This paper describes a case scenario regarding the energy consumption in a wireless sensor network consisting of many sensors which monitor a large area, such as a forest. The sensors are distributed in environments with challenging radio conditions (with obstacles, attenuation, or perturbation) and the transmission of data from the monitoring sensor to destination is very difficult due to the large distance between the sensors. Because of the distance between the sensors are required multihop algorithms to optimize the energy consumption. Also data aggregation techniques and multiple base stations are used for optimizing the energy consumption and to increase the battery and the entire network’s lifetime.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vasar C, Prostean G, Filip I, Szeidert I (2011) Above increasing autonomy of wireless sensor networks used for windmill monitoring. Applied computational intelligence and informatics (SACI), pp 607–610, Timisoara, Romania
Al-Karaki JN, Kamal AE (2004) Routing techniques in wireless sensor networks: a survey. IEEE Wirel Commun 6–28
Zhu Y, Xie L, Yuan T (2012) Monitoring system for forest fire based on wireless sensor network. In: 10th world congress on intelligent control and automation (WCICA), pp 4245–4248. Beijing, China
Tunca C, Isik S, Donmez MY, Ersoy C (2013) Performance evaluation of heterogeneous wireless sensor networks for forest fire detection. In: 21st signal processing and communications applications conference (SIU), pp 1–4. Haspolat, Cyprus
Sabri Y, Najib EK (2012) A prototype for wireless sensor networks to the detection of forest fires in large-scale. Next generation networks and services (NGNS), pp 116–122. Faro, Portugal
Al-HabashnehA, Ahmed MH, Husain T (2009) Adaptive MAC protocols for forest fire detection using wireless sensor networks. In: Canadian conference on electrical and computer engineering (CCECE), pp 329–333. St.John’s
Rabaey JM, Ammer MJ, da Silva JL, Patel D, Roundy S (2000) PicoRadio supports ad hoc ultra-low power wireless networking. IEEE Comput 42–48
Popa M, Prostean O, Popa AS (2013) A classification of solutions for the energy limitation in wireless sensor networks. In: IEEE 9th international conference on computational cybernetics (ICCC 2013), pp 293–297. Tihany, Hungary
Shrivastava N, Buragohain C, Agrawal D, Suri S (2004) Medians and beyond: new aggregation techniques for sensor networks. In: Proceedings of the second ACM conference on embedded networked sensor systems (SenSys 2004), pp 239–249. New York, U.S.A, August 2004
Vasar C, Prostean O, Filip I, Szeidert I, Robu A (2008) Using data aggregation to prolong the lifetime of wide-area wireless sensor networks. In: 4th international conference on intelligent computer communication and processing, ICCP 2008, pp 247–252. Cluj-Napoca, Romania
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Illes, C., Vasar, C., Filip, I. (2016). Energy Performance Optimization of Wireless Sensor Networks. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-18296-4_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18295-7
Online ISBN: 978-3-319-18296-4
eBook Packages: EngineeringEngineering (R0)