Abstract
Wireless Sensor Networks (WSNs) frequently have distinguished amount of data loss, causing data integrity issues. Sensor nodes are inherently a cheap piece of hardware - due to the common need to use many of them over a large area - and usually contain a small amount of RAM and flash memory, which are insufficient in case of high degree of data sampling. An overloaded sensor can harm the data integrity, or even completely reject incoming messages. The problem gets even worse when data should be received from many nodes, as missing data becomes a more common phenomenon as deployed WSNs grow in scale. In cases of an overflow, our Distributed Adaptive Clustering algorithm (D-ACR) reconfigures the network, by adaptively and hierarchically re-clustering parts of it, based on the rate of incoming data packages in order to minimize the energy-consumption, and prevent premature death of nodes. However, the re-clustering cannot prevent data loss caused by the nature of the sensors. We suggest to address this problem by an efficient distributed backup-placement algorithm named DBP-ACR, performed on the D-ACR refined clusters. The DBP-ACR algorithm re-directs packages from overloaded sensors to more efficient placements outside of the overloaded areas in the WSN cluster, thus increasing the fault-tolerance of the network and reducing the data loss.
This work was supported by the Lynn and William Frankel Center for Computer Science, the Open University of Israel’s Research Fund, and ISF grant 724/15.
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
References
Kakamanshadi, G., et al.: A survey on fault tolerance techniques in wireless sensor networks. In: International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE (2015)
Seema, A., et al.: Towards efficient wireless video sensor networks: a survey of existing node architectures and proposal for a Flexi-WVSNP design. IEEE Commun. Surv. Tutor. 13(3), 462–486 (2011)
Mottola, L., et al.: Programming wireless sensor networks: fundamental concepts and state of the art. ACM Comput. Surv. (CSUR) 43(3), 19 (2011)
Oren, G., et al.: Adaptive distributed hierarchical sensing algorithm for reduction of wireless sensor network cluster-heads energy consumption. In: Proceedings of the 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 980-986. IEEE (2017)
Balazinska, M., et al.: Data management in the worldwide sensor web. IEEE Pervasive Comput. 6(2), 30–40 (2007)
Kong, L., et al.: Data loss and reconstruction in sensor networks. In: INFOCOM, 2013 Proceedings. IEEE (2013)
Luo, C., et al.: Compressive data gathering for large-scale wireless sensor networks. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking. ACM (2009)
Wagner, D., et al.: Algorithms for Sensor and Ad Hoc Networks: Advanced Lectures. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74991-2
Vieira, M.A.M., et al.: Survey on wireless sensor network devices. emerging technologies and factory automation, In: Proceedings of ETFA 2003. IEEE Conference. vol. 1. IEEE (2003)
Nuns, T., et al.: Evaluation of recent technologies of non-volatile RAM. radiation and its effects on components and systems. In: 9th European Conference on RADECS 2007. IEEE (2007)
Halldórsson, M.M., et al.: Distributed backup placement in networks. In: Proceedings of the 27th ACM symposium on Parallelism in Algorithms and Architectures. ACM (2015)
Barenboim, L., et al.: Distributed graph coloring: fundamentals and recent developments. Synth. Lect. Distrib. Comput. Theory 4(1), 1–171 (2013)
Wajgi, D., et al.: Load balancing based approach to improve lifetime of wireless sensor network. Int. J. Wirel. Mob. Netw. 4(4), 155 (2012)
Kakiuchi, H.: Dynamic Load Balancing in Sensor Networks. Stanford (2004)
Oren, G., Barenboim, L., Levin, H.: Load-Balancing adaptive clustering refinement algorithm for wireless sensor network clusters. In: Koucheryavy, Y., Mamatas, L., Matta, I., Ometov, A., Papadimitriou, P. (eds.) WWIC 2017. LNCS, vol. 10372, pp. 157–173. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61382-6_13
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Oren, G., Barenboim, L., Levin, H. (2019). Distributed Fault-Tolerant Backup-Placement in Overloaded Wireless Sensor Networks. In: Sucasas, V., Mantas, G., Althunibat, S. (eds) Broadband Communications, Networks, and Systems. BROADNETS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-030-05195-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-05195-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05194-5
Online ISBN: 978-3-030-05195-2
eBook Packages: Computer ScienceComputer Science (R0)