Techniques For Reducing Energy And Delay For Data Aggregation In Wireless Sensor Networks

Authors

DOI:

https://doi.org/10.4108/eetct.v9i3.2140

Keywords:

WSN, QOS, Data Aggregation

Abstract

WSN have many applications in different fields like medical, military, health and agriculture, etc., due to its data sensing and gathering abilities to Base station. The main issue in wireless sensor network is energy efficiency under consideration of QOS parameter like delay and security. Many of techniques have been proposed in literature but few work on energy efficient network with QOS. Due to lack of prior research in this area of study, this research will optimize the existing result in manner of giving efficient energy mechanisms and will also provide QOS as well as reduction of delay. It is very important to calculate energy efficiency and data transmission rate in wireless sensor networks because it is widely used in every field of life, especially when we are talking about medical, military and navigation system. After identifying main issues, this research will focus on energy efficiency and delay reduction. But other factors like security and average transmission time is not fully focused.

References

I. D. Filip, A. V. Postoaca, R. D. Stochitoiu, D. F. Neatu, C. Negru, and F. Pop, “Data Capsule: Representation of Heterogeneous Data in Cloud-Edge Computing,” IEEE Access, vol. 7, pp. 49558–49567, 2019, doi: 10.1109/ACCESS.2019.2910584.

A. Nespoli, E. Ogliari, S. Pretto, M. Gavazzeni, S. Vigani, and F. Paccanelli, “Electrical Load Forecast by Means of LSTM: The Impact of Data Quality,” Forecasting, vol. 3, no. 1, pp. 91–101, 2021, doi: 10.3390/forecast3010006.

T. K. Dao, T. T. Nguyen, J. S. Pan, Y. Qiao, and Q. A. Lai, “Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM,” IEEE Access, vol. 8, pp. 61070–61084, 2020, doi: 10.1109/ACCESS.2020.2983219.

S. Koziel and A. Pietrenko-Dabrowska, “Fast multi-objective optimization of antenna structures by means of data-driven surrogates and dimensionality reduction,” IEEE Access, vol. 8, pp. 183300–183311, 2020, doi: 10.1109/ACCESS.2020.3028911.

S. Bhushan, M. Kumar, P. Kumar, T. Stephan, A. Shankar, and P. Liu, “FAJIT: a fuzzy-based data aggregation technique for energy efficiency in wireless sensor network,” Complex Intell. Syst., vol. 7, no. 2, pp. 997–1007, 2021, doi: 10.1007/s40747-020-00258-w.

K. Singh, S. Rajora, D. K. Vishwakarma, G. Tripathi, S. Kumar, and G. S. Walia, “Crowd anomaly detection using Aggregation of Ensembles of fine-tuned ConvNets,” Neurocomputing, vol. 371, pp. 188–198, 2020, doi: 10.1016/j.neucom.2019.08.059.

Y. Feng and S. Zhao, “Analysis of Edge Computing Model for Real-Time Self-Organizing Push of Data in Wireless LAN Environment,” IEEE Access, vol. 7, pp. 178033–178046, 2019, doi: 10.1109/ACCESS.2019.2957025.

J. Ponocko and J. V. Milanovic, “Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5446–5455, 2018, doi: 10.1109/TPWRS.2018.2799903.

J. Song, Q. Zhong, W. Wang, C. Su, Z. Tan, and Y. Liu, “FPDP: Flexible Privacy-Preserving Data Publishing Scheme for Smart Agriculture,” IEEE Sens. J., vol. 21, no. 16, pp. 17430–17438, 2021, doi: 10.1109/JSEN.2020.3017695.

Y. Lu et al., “Data Collection Study Based on Spatio-Temporal Correlation in Event-Driven Sensor Networks,” IEEE Access, vol. 7, pp. 175857–175864, 2019, doi: 10.1109/ACCESS.2019.2957450.

P. Movva and P. T. Rao, “Novel Two-Fold Data Aggregation and MAC Scheduling to Support Energy Efficient Routing in Wireless Sensor Network,” IEEE Access, vol. 7, pp. 1260–1274, 2019, doi: 10.1109/ACCESS.2018.2888484.

M. Peng, S. Garg, X. Wang, A. Bradai, H. Lin, and M. S. Hossain, “Learning-Based IoT Data Aggregation for Disaster Scenarios,” IEEE Access, vol. 8, pp. 128490–128497, 2020, doi: 10.1109/ACCESS.2020.3008289.

V. J. Jariwala and D. C. Jinwala, AdaptableSDA: secure data aggregation framework in wireless body area networks. Elsevier Inc., 2020.

N. Yukinawa, S. Oba, K. Kato, and S. Ishii, “Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 6, no. 2, pp. 333–343, 2009, doi: 10.1109/TCBB.2007.70239.

H. Zhang and Z. Li, “Energy-aware data gathering mechanism for mobile sink in wireless sensor networks using particle swarm optimization,” IEEE Access, vol. 8, pp. 177219–177227, 2020, doi: 10.1109/ACCESS.2020.3026113.

X. Hu, D. Dang, Y. Yao, and L. Ye, “Natural language aggregate query over RDF data,” Inf. Sci. (Ny)., vol. 454–455, pp. 363–381, 2018, doi: 10.1016/j.ins.2018.04.042.

N. M. Durrani, N. Kafi, J. Shamsi, W. Haider, and A. M. Abbsi, “Secure multi-hop routing protocols in Wireless Sensor Networks: Requirements, challenges and solutions,” 8th Int. Conf. Digit. Inf. Manag. ICDIM 2013, no. April 2015, pp. 41–48, 2013, doi: 10.1109/ICDIM.2013.6694001.

S. M. Murshed, A. M. Al-Hyari, J. Wendel, and L. Ansart, “Design and implementation of a 4D web application for analytical visualization of smart city applications,” ISPRS Int. J. Geo-Information, vol. 7, no. 7, 2018, doi: 10.3390/ijgi7070276.

U. M. Aïvodji, S. Gambs, and A. Martin, “IOTFLA : AA secured and privacy-preserving smart home architecture implementing federated learning,” Proc. - 2019 IEEE Symp. Secur. Priv. Work. SPW 2019, pp. 175–180, 2019, doi: 10.1109/SPW.2019.00041.

K. Kucuk, C. Bayilmis, A. F. Sonmez, and S. Kacar, “Crowd sensing aware disaster framework design with IoT technologies,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 4, pp. 1709–1725, 2020, doi: 10.1007/s12652-019-01384-1.

Z. H. Wei and B. J. Hu, “A Fair Multi-Channel Assignment Algorithm with Practical Implementation in Distributed Cognitive Radio Networks,” IEEE Access, vol. 6, pp. 14255–14267, 2018, doi: 10.1109/ACCESS.2018.2808479.

L. Y. B et al., Exploring graph mining approaches for dynamic heterogeneous networks, vol. 1. Springer International Publishing, 2020.

Z. Sun et al., “An Energy-Efficient Cross-Layer-Sensing Clustering Method Based on Intelligent Fog Computing in WSNs,” IEEE Access, vol. 7, pp. 144165–144177, 2019, doi: 10.1109/ACCESS.2019.2944858.

S. Pervez and G. Alandjani, “Role of Internet of Things (Iot) in Higher Education,” Proc. ADVED 2018- 4th Int. Conf. Adv. Educ. Soc. Sci., no. October, 2018.

K. Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT Scenarios,” IEEE Access, vol. 8, pp. 23022–23040, 2020, doi: 10.1109/ACCESS.2020.2970118.

M. K, K. P, and M. S. M, “Energy Efficient Algorithms for Wireless Sensor Network,” Ijarcce, vol. 4, no. 1, pp. 342–346, 2015, doi: 10.17148/IJARCCE.2015.4178.

M. Abdullah, A. Al-Thobaity, A. Bawazir, and N. Al-Harbe, “Energy Efficient Ensemble K-means and SVM for Wireless Sensor Network,” Int. J. Comput. Technol., vol. 11, no. 9, pp. 3034–3042, 2013, doi: 10.24297/ijct.v11i9.3409.

Z. Ahmed, K. Mohamed, S. Zeeshan, and X. Q. Dong, “Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine,” Database, vol. 2020, 2020, doi: 10.1093/DATABASE/BAAA010.

G. Desolda, C. Ardito, M. F. Costabile, and M. Matera, “End-user composition of interactive applications through actionable UI components,” J. Vis. Lang. Comput., vol. 42, pp. 46–59, 2017, doi: 10.1016/j.jvlc.2017.08.004.

M. R. Anawar, S. Wang, M. Azam Zia, A. K. Jadoon, U. Akram, and S. Raza, “Fog Computing: An Overview of Big IoT Data Analytics,” Wirel. Commun. Mob. Comput., vol. 2018, 2018, doi: 10.1155/2018/7157192.

L. Li, L. Sun, Y. Xue, S. Li, X. Huang, and R. F. Mansour, “Fuzzy Multilevel Image Thresholding Based on Improved Coyote Optimization Algorithm,” IEEE Access, vol. 9, pp. 33595–33607, 2021, doi: 10.1109/ACCESS.2021.3060749.

T. K. Dao, J. Yu, T. T. Nguyen, and T. G. Ngo, “A Hybrid Improved MVO and FNN for Identifying Collected Data Failure in Cluster Heads in WSN,” IEEE Access, vol. 8, pp. 124311–124322, 2020, doi: 10.1109/ACCESS.2020.3005247.

Downloads

Published

11-10-2022

How to Cite

1.
Akhtar MS, Feng T. Techniques For Reducing Energy And Delay For Data Aggregation In Wireless Sensor Networks . EAI Endorsed Trans Creat Tech [Internet]. 2022 Oct. 11 [cited 2022 Dec. 3];9(3):e4. Available from: https://publications.eai.eu/index.php/ct/article/view/2140