Regional Digest Aggregation based on Opportunities in Wireless Sensor Networks
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Abstract
It is desirable to reduce the amount of data collected in sensor networks to reduce energy consumption and to extend network lifetime while one should extract as much information as possible to allow support for heterogeneous user queries. There have been a number of aggregation proposals, aiming at reducing the amount of data communications within the sensor network, mainly focused on supporting limited and simple types of queries such as SUM, COUNT, AVG, MIN/MAX. Unfortunately, user queries are not limited by these simple types of aggregates and cannot be predicted a-priori. In this paper we propose an aggregation-framework that produces regional digests at a parameter defined granularity such that arbitrary user queries can be supported. Since the success of the aggregation policy greatly depends on the integrated routing mechanisms we evaluate the performance of our approach under alternative routing approaches. Our experimental results suggest at least 3-fold improvement in spatial accuracy at a relatively small expense of increased energy consumption.
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Aksoy D., and Leung M.S. - Pull vs Push: A Quantitative Comparison for Data Broadcast. IEEE Global Telecomunications Conference (2004) Volume 3 1464-1468
Aksoy D.- PLASMA: A PLAnetary Scale Monitoring Architecture. Proceedings of ACM international conference on Multimedia (2005) 96-102 https://doi.org/10.1145/1101149.1101164
Govindan R, Estrin D, Intanagonwiwat C- Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. Proceedings of international conference on Mobile computing and networking (2000) 56-67. https://doi.org/10.1145/345910.345920
Heinzelman WR, Chandrakasan A, Balakrishnan H Energy-Efficient Communication Protocol for Wireless Microsensor Networks, IEEE Hawai Int. Conf. on System Sciences (2000).
Lindsey S, Raghavendra CS- PEGASIS, Power Efficient Gathering in Sensor Information Systems, IEEE Aerospace Conference Proceedings (2002) 1125-1130 Vol 3.
Shrivastava N., Buragohain C., Agrawal D., Suri S.- Medians and Beyond: New Aggregation Techniques for Sensor Networks . Proceedings of international conference on Embedded networked sensor systems (2004) 239-249. https://doi.org/10.1145/1031495.1031524
Suman Nath, Phillip B. Gibbons, Srinivasan Seshan, and Zachary Anderson - Synopsis Diffusion for Robust Aggregation in Sensor Networks. ACM Transactions on Sensor Networks (2008). Volume 4, Issue 2, Article No7. https://doi.org/10.1145/1340771.1340773
Chen C., Aksoy D., and Demir T.- Processed Data Collection using Opportunistic Routing in Location-Aware Wireless Sensor Networks. Proceeding of International Conference on Mobile Data Management (2006) 150-158.
Fasolo E., Rossi M., Widmer J., Zorzi M., In-network aggregation techniques for wireless sensor networks: a survey. IEEE Wireless Communication. Vol 14, Issue 2, (2007) 70-87. https://doi.org/10.1109/MWC.2007.358967
Yu B., Li J., and Li Y., Distributed Data Aggregation Scheduling in Wireless Sensor Networks, IEEE INFOCOM 2009,(2009) 19-25. https://doi.org/10.1109/INFCOM.2009.5062140
Xiaohua Xu, Xiang-Yang Li, Xufei Mao, Shaojie Tang, Shiguang Wang - A Delay-Efficient Algorithm for Data Aggregation in Multihop Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed System, Vol 22, No1 (2011) 163- 175 https://doi.org/10.1109/TPDS.2010.80
Yujie Zhua, Ramanuja Vedanthama, Seung-Jong Parkb. Raghupathy Sivakumara - A scalable correlation aware aggregation strategy for wireless sensor networks. Information Fusion, Volume 9, Issue 3, (2008) 354-369. https://doi.org/10.1016/j.inffus.2006.09.002
Parametric Assay of Energy Adept Clustering Protocols for Het-Net Wireless Sensor Networks. (2019). In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 12, pp. 2146–2155). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijitee.l2932.1081219
Patil, Mrs. Suvarna. S., & Vidyavathi, Dr. B. M. (2022). Application o f Advanced Machine Learning and Artificial Neural Network Methods in Wireless Sensor Networks Based Applications. In International Journal of Engineering and Advanced Technology (Vol. 11, Issue 3, pp. 103–109). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijeat.c3394.0211322
P. H., S., & Kakkasageri, M. S. (2021). An Efficient Information Aggregation Scheme in Internet of Things: Multi Agent based Approach. In International Journal of Soft Computing and Engineering (Vol. 11, Issue 1, pp. 23–31). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijsce.a3525.0911121
KPELOU, M., & Kishore, K. (2019). Lightweight Security Framework for Data Outsourcing and Storage in Mobile Cloud Computing. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 2, pp. 3405–3412). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijrte.b2239.078219
Nasir, F. M., & Watabe, H. (2020). Validation of the Image Registration Technique from Functional Near Infrared Spectroscopy (fNIRS) Signal and Positron Emission Tomography (PET) Image. In International Journal of Management and Humanities (Vol. 4, Issue 9, pp. 63–69). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijmh.i0877.054920