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While this is effective, it is rather wasteful in terms of energy expended and temporal latency. In this article, the endeavour is to develop a technique that facilitates classification, an important learning algorithm, within the extremely resource constrained environments of IoT nodes. The approach comprises selecting a small number of representative data points, called prototypes, from a large dataset and deploying these prototypes over IoT nodes. The prototypes are selected in a manner that they appropriately represent the complete dataset and are able to correctly classify new, incoming data. The novelty lies in the manner of prototype selection for a cluster that not only considers the location of datapoints of its own cluster but also that of datapoints in neighboring clusters. The efficacy of the approach is validated using standard datasets and compared with state-of-the-art classification techniques used in constrained environments. A real world deployment of the technique is done over an Arduino Uno-based IoT node and shown to be effective.<\/jats:p>","DOI":"10.1145\/3549552","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T12:12:47Z","timestamp":1658232767000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Approach for Classification in Resource-Constrained Environments"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1403-9777","authenticated-orcid":false,"given":"Arun","family":"Kumar","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Indore, Indore, Madhya Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6443-7055","authenticated-orcid":false,"given":"Zhijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Invision AI, Toronto, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6338-5476","authenticated-orcid":false,"given":"Abhishek","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Indore, Indore, Madhya Pradesh, India"}]}],"member":"320","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Peter Wlodarczak Mustafa Ally and Jeffrey Soar. 2017. Data mining in IoT: Data analysis for a new paradigm on the internet. In Proceedings of the International Conference on Web Intelligence . 1100\u20131103.","DOI":"10.1145\/3106426.3115866"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3402444"},{"key":"e_1_3_1_4_2","unstructured":"Matt Kusner Stephen Tyree Kilian Weinberger and Kunal Agrawal. 2014. Stochastic neighbor compression. In Proceedings of the 31st International Conference on Machine Learning . 622\u2013630."},{"key":"e_1_3_1_5_2","unstructured":"Feng Nan Joseph Wang and Venkatesh Saligrama. 2015. Feature-budgeted random forest. In Proceedings of the 32 nd International Conference on Machine Learning ."},{"key":"e_1_3_1_6_2","unstructured":"Song Han Huizi Mao and William J. Dally. 2016. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. In Proceedings of ICLR ."},{"key":"e_1_3_1_7_2","unstructured":"J. Shlens. A tutorial of principal component analysis. Retrieved Feb 9 2019 from https:\/\/www.cs.princeton.edu\/picasso\/mats\/PCA-Tutorial-Intuition_jp.pdf. Accessed August 2019."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3341105.3373904"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Shibing Zhou Zhenyuan Xu and Fei Liu. 2017. Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Transactions on Neural Networks and Learning Systems 28 12 (2017) 3007\u20133017.","DOI":"10.1109\/TNNLS.2016.2608001"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Prasad Bandyopadhyay Chiranjeet Dey and Paramita Biswas. 2020. A comparative analysis approach of unsupervised techniques to explore their potentiality in microarray data. In Proceedings of the 5th IEEE International Conference on Computing Communication and Automation . 565\u2013570.","DOI":"10.1109\/ICCCA49541.2020.9250833"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Susheela Devi and Narasimha Murty. 2002. An incremental prototype set building technique. Pattern Recognition 35 2 (2002) 505\u2013513.","DOI":"10.1016\/S0031-3203(00)00184-9"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Thomas Cover and Peter E. Hart. 2006. Nearest neighbor pattern classification. IEEE Transaction Information Theory 13 1 (2006) 21\u201327.","DOI":"10.1109\/TIT.1967.1053964"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Jon L. Bentley. 1975. Multidimensional binary search trees used for associative searching. Communication of ACM 18 (1975) 509\u2013517.","DOI":"10.1145\/361002.361007"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Alina Beygelzimer Sham Kakade and John Langford. 2006. Cover trees for nearest neighbor. In Proceedings of the 23 rd International Conference on Machine Learning .","DOI":"10.1145\/1143844.1143857"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Piotr Indyk and Rajeev Motwani. 1998. Approximate nearest neighbors towards removing the curse of dimensionality. In Proceedings of the ACM Symposium on Theory of Computing 604\u2013613.","DOI":"10.1145\/276698.276876"},{"key":"e_1_3_1_16_2","unstructured":"Mohammad Norouzi David J. Fleet and Ruslan Salakhutdinov. Hamming distance metric learning. In Proceedings of the Advances in Neural Information Processing Systems . 1061\u20131069."},{"key":"e_1_3_1_17_2","unstructured":"Felix Yu Sanjiv Kumar Yunchao Gong and Shih-Fu Chang. 2014. Circulant binary embedding. In Proceedings of the 31st International Conference on Machine Learning . 946\u2013954."},{"key":"e_1_3_1_18_2","unstructured":"Aristides Gionis Piotr Indyk and Rajeev Motwani. 1999. Similarity search in high dimensions via hashing. In Proceedings of the 25th International Conference on Very Large Data Bases . 518\u2013529."},{"key":"e_1_3_1_19_2","unstructured":"Yair Weiss Antonio Torralba and Rob Fergus. 2008. Spectral hashing. In Proceedings of the 21st International Conference on Neural Information Processing Systems (NIPS\u201908) . Curran Associates Inc. Red Hook NY USA 1753\u20131760."},{"key":"e_1_3_1_20_2","unstructured":"Brian Kulis and Trevor Darrell. 2009. Learning to hash with binary reconstructive embeddings. In Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS\u201909) . Curran Associates Inc. Red Hook NY USA 1042\u20131050."},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Wei Liu Jun Wang Rongrong Ji Yu-Gang Jiang and Shih-Fu Chang. 2012. Supervised hashing with kernels. In Proceedings of Computer Vision and Pattern Recognition . 2074\u20132081.","DOI":"10.1109\/CVPR.2012.6247912"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Fabrizio Angiulli. 2005. Fast condensed nearest neighbor rule. In Proceedings of the 22nd International Conference on Machine learning .","DOI":"10.1145\/1102351.1102355"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Xinzheng Xu ShanLi Tianming Liang and Tongfeng Sun. 2020. Sample selection-based hierarchical extreme learning machine. Neurocomputing 377 (2020) 95\u2013102.","DOI":"10.1016\/j.neucom.2019.10.013"},{"key":"e_1_3_1_24_2","unstructured":"Liyanaarachchi L. C. Kasun Hongming Zhou Guangbin Huang and Chi-Man Vong. 2013. 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Prototype learning algorithms for nearest neighbor classifier with application to handwritten character recognition. In Proceedings of the 5th International Conference on Document Analysis and Recognition . 378\u2013381.","DOI":"10.1109\/ICDAR.1999.791803"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"Christine Decaestecker. 1997. Finding prototypes for nearest neighbour classification by means of gradient descent and deterministic annealing. Pattern Recognition 30 2 (1997) 281\u2013288.","DOI":"10.1016\/S0031-3203(96)00072-6"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","unstructured":"Charles T. Zahn. 1971. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers 20 1 (1971) 68\u201386.","DOI":"10.1109\/T-C.1971.223083"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1973.223640"},{"key":"e_1_3_1_35_2","unstructured":"Kai Zhong Ruiqi Guo Sanjiv Kumar Bowei Yan David Simcha and Inderjit Dhillon. 2017. Fast classification with binary prototypes. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 1255\u20131263."},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","unstructured":"Wenlin Wang Changyou Chen Wenlin Chen Piyush Rai and Lawrence Carin. 2016. Deep metric learning with data summarization. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases . 777\u2013794.","DOI":"10.1007\/978-3-319-46128-1_49"},{"key":"e_1_3_1_37_2","unstructured":"Chirag Gupta Arun S. Suggala Ankit Goyal Harsha V. Simhadri Bhargavi Paranjape Ashish Kumar Saurabh Goyal Raghavendra Udupa Manik Varma and Prateek Jain. 2017. ProtoNN: Compressed and accurate kNN for resource-scarce devices. In Proceedings of the 34th International Conference on Machine Learning ."},{"key":"e_1_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Elena I. Gaura James Brusey Michael Allen Ross Wilkins Dan Goldsmith and Ramona Rednic. 2013. Edge mining the Internet of Things. IEEE Sensors Journal 13 10 (2013) 3816\u20133825.","DOI":"10.1109\/JSEN.2013.2266895"},{"key":"e_1_3_1_39_2","unstructured":"Kilian Q. Weinberger and Lawrence K. Saul. 2009. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10 2 (2009) 207\u2013244."},{"key":"e_1_3_1_40_2","unstructured":"Richard O. Duda Peter E. Hart and David G. Stork. 2001. Pattern Classification . Wiley New York."},{"key":"e_1_3_1_41_2","unstructured":"20 Dec 2018. 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