The developments in hardware and wireless networks have brought humans to the brink of a new era in
which small, wire-free devices will give them access to data at any time and any location and significantly contribute to
the building of smart surroundings. Wireless Sensor Network (WSN) sensors collect data on the parameters they are used
to detect. However, the performance of these sensors is constrained due to power and bandwidth limitations. In order to
get beyond these limitations, they may use Machine Learning (ML) techniques. WSNs have witnessed a steady rise in the
use of advanced ML techniques to distribute and improve network performance over the last decade. ML enthuses a
plethora of real-world applications that maximize resource use and extend the network's life span. Furthermore, WSN
designers have agreed that ML paradigms may be used for a broad range of meaningful tasks, such as localization and
data aggregation as well as defect detection and security. This paper presents a survey of the ML models, as well as
application in wireless networking and information processing. In addition, this paper evaluates the open challenges and
future research directions of ML for WSNs.
Ö. Ertuğrul and M. Tağluk, "A novel version of k nearest neighbor: Dependent nearest neighbor", Applied Soft Computing, vol. 55, pp. 480-490, 2017. Doi : 10.1016/j.asoc.2017.02.020.
J. Li and Y. Wang, "A new fast reduction technique based on binary nearest neighbor tree", Neurocomputing, vol. 149, pp. 1647-1657, 2015. Doi : 10.1016/j.neucom.2014.08.028.
S. Satkin, M. Rashid, J. Lin and M. Hebert, "3DNN: 3D Nearest Neighbor", International Journal of Computer Vision, vol. 111, no. 1, pp. 69-97, 2014. Doi : 10.1007/s11263-014-0734-4.
P. Gupta and G. Kaur, "A Concept of A-KNN Clustering in Software Engineering", International Journal of Computer Applications, vol. 119, no. 18, pp. 25-28, 2015. Doi : 10.5120/21168-4235.
K. E and P. Jadhav, "A Study Paper on Forest Fire Detection Using Wireless Sensor Network", International Journal of Psychosocial Rehabilitation, vol. 23, no. 4, pp. 397-407, 2019. Doi : 10.37200/ijpr/v23i4/pr190199.
G. Corani, "Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning", Ecological Modelling, vol. 185, no. 2-4, pp. 513-529, 2005. Doi : 10.1016/j.ecolmodel.2005.01.008.
S. Maity and S. Hati, "Adaptive Technique for CI/MC-CDMA System using Combined Strategy of Genetic Algorithms and Neural Network",Network Protocols and Algorithms, vol. 4, no. 1, 2012. Doi : 10.5296/npa.v4i1.1330.
C. Yalkın and E. Korkmaz, "NEURAL NETWORK WORLD: A NEURAL NETWORK BASED SELECTION METHOD FOR GENETIC ALGORITHMS", Neural Network World, vol. 22, no. 6, pp. 495-510, 2012. Doi : 10.14311/nnw.2012.22.030.
G. Cervera, M. Barbeau, J. Garcia-Alfaro and E. Kranakis, "A multipath routing strategy to prevent flooding disruption attacks in link state routing protocols for MANETs", Journal of Network and Computer Applications, vol. 36, no. 2, pp. 744-755, 2013. Doi : 10.1016/j.jnca.2012.12.013.
W. Drzewiecki, "Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping", Geodesy and Cartography, vol. 66, no. 2, pp. 171-210, 2017. Doi : 10.1515/geocart-2017-0012.
Y. Sun, W. Meng, C. Li and X. Wu, "Panoramic Camera-Based Human Localization Using Automatically Generated Training Data", IEEE Access, vol. 8, pp. 48836-48845, 2020. Doi : 10.1109/access.2020.2979562.
"Gesture Movement Detection Based on IoT", JOURNAL OF XI'AN UNIVERSITY OF ARCHITECTURE & TECHNOLOGY, vol., no., 2020. Doi : 10.37896/jxat12.03/129.
T. Chan, H. Ross, S. Hoverman and B. Powell, "Participatory development of a Bayesian network model for catchment-based water resource management", Water Resources Research, vol. 46, no. 7, 2010. Doi : 10.1029/2009wr008848.
M. Lampis and J. Andrews, "Bayesian belief networks for system fault diagnostics", Quality and Reliability Engineering International, vol. 25, no. 4, pp. 409-426, 2008. Doi : 10.1002/qre.978.
J. Chen, S. Li and Y. Sun, "Novel Deployment Schemes for Mobile Sensor Networks", Sensors, vol. 7, no. 11, pp. 2907-2919, 2007. Doi : 10.3390/s7112907.
T. MEI, X. WANG and J. ZHANG, "THE PROCESS AND MODEL OF INFORMATION ACQUISITION", International Journal of Information Acquisition, vol. 01, no. 02, pp. 101-108, 2004. Doi : 10.1142/s0219878904000197.
G. Walster and V. Kreinovich, "Computational complexity of optimization and crude range testing: a new approach motivated by fuzzy optimization", Fuzzy Sets and Systems, vol. 135, no. 1, pp. 179-208, 2003. Doi : 10.1016/s0165-0114(02)00254-3.
V. Novák, "Which logic is the real fuzzy logic?", Fuzzy Sets and Systems, vol. 157, no. 5, pp. 635-641, 2006. Doi : 10.1016/j.fss.2005.10.010.
J. Gustafsson and K. Polynczuk-Alenius, "Media and Communication between the Local and the Global", Media and Communication, vol. 6, no. 2, pp. 145-148, 2018. Doi : 10.17645/mac.v6i2.1637.
N. Manteghi and A. Zohrabi, "A proposed comprehensive framework for formulating strategy: a Hybrid of balanced scorecard, SWOT analysis, porter‘s generic strategies and Fuzzy quality function deployment", Procedia - Social and Behavioral Sciences, vol. 15, pp. 2068-2073, 2011. Doi : 10.1016/j.sbspro.2011.04.055.
M. Argany, F. Karimipour, F. Mafi and A. Afghantoloee, "Optimization of Wireless Sensor Networks Deployment Based on Probabilistic Sensing Models in a Complex Environment", Journal of Sensor and Actuator Networks, vol. 7, no. 2, p. 20, 2018. Doi : 10.3390/jsan7020020.
G. Zhang and H. Li, "An Efficient Configuration for Probabilistic Fuzzy Logic System", IEEE Transactions on Fuzzy Systems, vol. 20, no. 5, pp. 898-909, 2012. Doi : 10.1109/tfuzz.2012.2188897.
M. Nardini and J. Dunning, "Pulmonary nodules precision localization techniques", Future Oncology, vol. 16, no. 16, pp. 15-19, 2020. Doi : 10.2217/fon-2019-0069.
S. Babu, A. Raha and M. Naskar, "Trust Evaluation Based on Node’s Characteristics and Neighbouring Nodes’ Recommendations for WSN", Wireless Sensor Network, vol. 06, no. 08, pp. 157-172, 2014. Doi : 10.4236/wsn.2014.68016.
Sheng-Fu Liang, Shih-Mao Lu, Jyh-Yeong Chang and Chin-Teng Lin, "A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision", IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 863-873, 2008. Doi : 10.1109/tfuzz.2008.917297.
M. Gurupriya and A. Sumathi, "HOFT-MP: A Multipath Routing Algorithm Using Hybrid Optimal Fault Tolerant System for WSNs Using Optimization Techniques", Neural Processing Letters, 2022. Doi : 10.1007/s11063-022-10852-3.
X. DU, K. HUANG, S. LAN, Z. FENG and F. LIU, "LB-AGR: level-based adaptive geo-routing for underwater sensor network", The Journal of China Universities of Posts and Telecommunications, vol. 21, no. 1, pp. 54-59, 2014. Doi : 10.1016/s1005-8885(14)60268-5.
S. Ren, X. Gu, P. Yuan and H. Xu, "An iterative paradigm of joint feature extraction and labeling for semi-supervised discriminant analysis", Neurocomputing, vol. 273, pp. 466-480, 2018. Doi : 10.1016/j.neucom.2017.08.003.
"Reawakening the learner: creating learner-centric, standards-driven schools", Choice Reviews Online, vol. 50, no. 05, pp. 50-2799-50-2799, 2013. Doi : 10.5860/choice.50-2799.
S. Soni and M. Shrivastava, "Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network", Wireless Communications and Mobile Computing, vol. 2018, pp. 1-13, 2018. Doi : 10.1155/2018/7560167.
M. Khanafer, M. Guennoun and H. Mouftah, "Priority-Based CCA Periods for Efficient and Reliable Communications in Wireless Sensor Networks", Wireless Sensor Network, vol. 04, no. 02, pp. 45-51, 2012. Doi : 10.4236/wsn.2012.42007.
D. Penkin, G. Janssen and A. Yarovoy, "Impact of a half-space interface on the wireless link between tiny sensor nodes", Radio Science, vol. 49, no. 9, pp. 798-811, 2014. Doi : 10.1002/2013rs005365.
K. K.M.MAKWANA, D. Dr.B.R.PAREKH and S. SHINKHEDE, "Fuzzy Logic Controller Vs Pi Controller for Induction Motor Drive", Indian Journal of Applied Research, vol. 3, no. 7, pp. 315-318, 2011. Doi : 10.15373/2249555x/july2013/97.
M. Hassan and I. Hassan, "Improving Artificial Neural Network Based Streamflow Forecasting Models through Data Preprocessing", KSCE Journal of Civil Engineering, vol. 25, no. 9, pp. 3583-3595, 2021. Doi : 10.1007/s12205-021-1859-y.
L. Meng, H. Wang and S. Xia, "TOA Estimation Method in Frequency Domain for Acoustic Ranging of WSN Node", Journal of Electronics & Information Technology, vol. 32, no. 4, pp. 993-997, 2010. Doi : 10.3724/sp.j.1146.2009.00127.
Y. Kang, B. Xue and R. Zeng, "Self-Adaptive Path Tracking Control for Mobile Robots under Slippage Conditions Based on an RBF Neural Network", Algorithms, vol. 14, no. 7, p. 196, 2021. Doi : 10.3390/a14070196.
I. Kim, M. Khan, T. Awan and Y. Soh, "Multi-Target Tracking Using Color Information", International Journal of Computer and Communication Engineering, vol. 3, no. 1, pp. 11-15, 2014. Doi : 10.7763/ijcce.2014.v3.283.
H. BI, H. LIANG and J. WANG, "Resampling Methods and Machine Learning", Chinese Journal of Computers, vol. 32, no. 5, pp. 862-877, 2009. Doi : 10.3724/sp.j.1016.2009.00862.
M. S.V, "Precision Agriculture Using Wireless Sensor Network System: Opportunities and Challenges", International Journal Of Engineering And Computer Science, 2016. Doi : 10.18535/ijecs/v5i11.70.
Raghavendra, AVIvizienis and Ercegovac, "Fault Tolerance in Binary Tree Architectures", IEEE Transactions on Computers, vol. -33, no. 6, pp. 568-572, 1984. Doi : 10.1109/tc.1984.1676483.
The author(s) received no financial support for the research, authorship, and/or publication of this article.
No funding was received to assist with the preparation of this manuscript.
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Faculty of Science, University of Amsterdam, Amsterdam, Netherlands.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Anna Recchi, “A Survey of Machine Learning for Information Processing and Networking”, Journal of Machine and Computing, vol.2, no.4, pp. 188-198, October 2022. doi: 10.53759/7669/jmc202202023.