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Data Summarization in Internet of Things

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Abstract

With recent advances in the field of Internet of Things (IoT), the quantity of data being generated by various sensors and Internet users has increased dramatically which in turn has skyrocketed the need for efficient data compression methods. Data summarization is an efficient and effective technique for data compression that can generate a brief and succinct summary from typically larger quantities of data in an intelligent and highly useful manner, which can be done at various levels of abstraction. The impact of using such a technique in large IoT networks can be significantly advantageous in terms of reduction in the processing time, overall computation, data storage-transmission requirements, energy consumption, and possible workload on IoT users. In this work, a review of existing methods for data summarization techniques at various levels of abstraction of typical IoT networks are discussed. The levels of categorization that are considered are Low-level and High-level. Under each abstraction level, various techniques are further classified while briefly describing their essential characters.

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References

  1. Vermesan O, Friess P. Internet of things: converging technologies for smart environments and integrated ecosystems. Denmark: River Publishers; 2013.

    Google Scholar 

  2. Nithyakalyani S, Kumar SS, Barmawi A. Data aggregation in wireless sensor network using node clustering algorithms-a comparative study. In: Barmawi A, editors. 2013 IEEE conference on information and communication technologies, pp 508–513 (IEEE, 2013).

  3. Agarwal R, Motwani M. Survey of clustering algorithms for manet. ArXiv (2009) .

  4. Bezdek JC, Ehrlich R, Full W. Fcm: the fuzzy c-means clustering algorithm. Comput Geosci. 1984. https://doi.org/10.1016/0098-3004(84)90020-7.

    Article  Google Scholar 

  5. Nithyakalyani S, Kumar SS. An approach to data aggregation in wireless sensor network using voronoi fuzzy clustering algorithm. J Sci Ind Res. 2013;20:20.

    Google Scholar 

  6. Nithyakalyani S, Kumar S. Data relay clustering algorithm for wireless sensor networks: a data mining approach. J Comput Sci. 2012;20:20.

    Google Scholar 

  7. Maschi LF, Pinto AS, Meneguette RI, Baldassin A. Data summarization in the node by parameters (DSNP): local data fusion in an IoT environment. Sensors. 2018;20:799.

    Article  Google Scholar 

  8. Ji Y-K, Kim Y-I, Park S. Big data summarization using semantic feature for iot on cloud. Contemp Eng Sci. 2014;20:1095–103.

    Article  Google Scholar 

  9. Muhammad K, Hussain T, Tanveer M, Sannino G, de Albuquerque VHC. Cost-effective video summarization using deep cnn with hierarchical weighted fusion for iot surveillance networks. IEEE Internet Things J. 2019;20:20.

    Google Scholar 

  10. Gong Y, Liu X. Generic text summarization using relevance measure and latent semantic analysis. SIGIR ’01 . Association for Computing Machinery, New York, NY, USA; 2001. https://doi.org/10.1145/383952.383955.

  11. Yeh J-Y, Ke H-R, Yang W-P, Meng I-H. Text summarization using a trainable summarizer and latent semantic analysis. Inf Process Manage. 2005;20:20.

    Google Scholar 

  12. Zha H. Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering, SIGIR ’02. Association for Computing Machinery, New York, NY, USA; 2002. https://doi.org/10.1145/564376.564398.

  13. Li W, Li B, Wu M, Dang HT. Query focus guided sentence selection strategy for duc 2006. In: Dang HT, editor. Proceedings of Document Understanding Conferences; 2006.

  14. Sammut C, Webb GI, editors. TF-IDF. Boston: Springer US; 2010. p. 986–7. https://doi.org/10.1007/978-0-387-30164-8_832.

    Book  Google Scholar 

  15. Li P, Lam W, Bing L, Guo W, Li H, Martha Palmer SR, Rebecca Hwa. Cascaded attention based unsupervised information distillation for compressive summarization. In: Martha Palmer SR., Rebecca H, editor. Proceedings of the 2017 conference on empirical methods in natural language processing, pp. 2081–2090. Association for Computational Linguistics, Copenhagen, Denmark; 2017. https://aclanthology.org/D17-1221.

  16. Liu Y, Lapata M, Preslav Nakov AP. Hierarchical transformers for multi-document summarization. In: Preslav Nakov AP, editor. Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5070–5081. Association for Computational Linguistics, Florence, Italy; 2019. https://aclanthology.org/P19-1500.

  17. Ma C, Zhang W, Guo M, Wang H, Sheng QZ. Multi-document summarization via deep learning techniques: a survey. ArXiv (2020).

  18. Kikuchi Y, Naofumi A, Dobashi Y. IEEE-Staff A study on automatic music genre classification based on the summarization of music data. In: .IEEE-Staff, editor. 2020 international conference on artificial intelligence in information and communication (ICAIIC), pp. 705–708 (IEEE, 2020).

  19. Xu C, Maddage NC, Shao X. Automatic music classification and summarization. IEEE Trans Speech Audio Process. 2005;20:20.

    Google Scholar 

  20. Shao X, Xu C, Wang Y, Kankanhalli MS. Automatic music summarization in compressed domain. In: IEEE-Staff, editor. 2004 IEEE international conference on acoustics, speech, and signal processing. IEEE; 2004.

  21. Liu C-C, Yao P-C. Automatic summarization of mp3 music objects. In: IEEE-Staff, editor2004 IEEE international conference on acoustics, speech, and signal processing. IEEE; 2004.

  22. Kamaladas MD, Dialin MM. Fingerprint extraction of audio signal using wavelet transform. In: IEEE-Staff, editor. 2013 international conference on signal processing, image processing and pattern recognition, pp 308–312. IEEE; 2013.

  23. Damm D, von Zeddelmann D, Oispuu M, Häge M, Kurth F. A system for audio summarization in acoustic monitoring scenarios. In: IEEE-Staff, editor. 2012 proceedings of the 20th European signal processing conference (EUSIPCO), pp. 1279–1283. IEEE; 2012.

  24. Zlatintsi A, Maragos P, Potamianos A, Evangelopoulos G. A saliency-based approach to audio event detection and summarization. In: IEEE-Staff, editor. 2012 proceedings of the 20th European signal processing conference (EUSIPCO), pp 1294–1298. IEEE; 2012.

  25. Ziaei A, Sangwan A, Kaushik L, Hansen JH. Prof-life-log: analysis and classification of activities in daily audio streams. In: IEEE-Staff, editor. 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4719–4723. IEEE; 2015.

  26. Vinciarelli A. Sociometry based multiparty audio recordings summarization. In: IEEE-Staff, editor. 18th international conference on pattern recognition (ICPR’06). IEEE; 2006.

  27. Kimber D, Wilcox L, et al. Acoustic segmentation for audio browsers. Comput Sci Stat. 1997;20:20.

    Google Scholar 

  28. Zeng D, Yu Y Oyama K, Dick CA, Bulterman MMPC-YSJJT, Mohan Kankanhalli. Audio-visual embedding for cross-modal music video retrieval through supervised deep cca. In: Dick CA, Bulterman MMPC-YSJJT, Mohan K, editor. 2018 IEEE international symposium on multimedia (ISM). IEEE; 2018.

  29. Allawadi S, Rana V, Jain M. et al. Multimedia data summarization using joint integer linear programming. In: Baraneetharan E, editor. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE; 2021.

  30. Li H, Zhu J, Ma C, Zhang J, Zong C. Read, watch, listen, and summarize: Multi-modal summarization for asynchronous text, image, audio and video. IEEE Trans Knowl Data Eng. 2018;20:20.

    Google Scholar 

  31. Mehmood I, Sajjad M, Baik SW, Rho S. Audio-visual and eeg-based attention modeling for extraction of affective video content. In: IEEE-Staff, editor. 2015 International conference on platform technology and service. IEEE; 2015.

  32. Evangelopoulos G., et al. Video event detection and summarization using audio, visual and text saliency. In: IEEE-Staff, editor. 2009 IEEE international conference on acoustics, speech and signal processing, pp. 3553–3556. IEEE; 2009.

  33. Wang D, Li T, Ogihara M, Hoffmann J, Selman B. Generating pictorial storylines via minimum-weight connected dominating set approximation in multi-view graphs. In: Hoffmann J, Selman B, editor. Twenty-sixth AAAI conference on artificial intelligence; 2012.

  34. Wang WY, Mehdad Y, Radev D, Stent A, Kevin Knight OR, Ani Nenkova. A low-rank approximation approach to learning joint embeddings of news stories and images for timeline summarization. In: Kevin Knight OR, Ani N , editor. Proceedings of the 2016 conference of the north American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2016.

  35. Bian J, Yang Y, Chua T-S, Mathieu d’Aquin SD Multimedia summarization for trending topics in microblogs. In: Mathieu d’Aquin SD, editor. Proceedings of the 22nd ACM international Conference on information and knowledge management; 2013.

  36. Bian J, Yang Y, Zhang H, Chua T-S. Multimedia summarization for social events in microblog stream. IEEE Trans Multimed. 2014;20:20.

    Google Scholar 

  37. Park S, Cha BR, Kwon J. Personalized document summarization using pseudo relevance feedback and semantic feature. IETE J Res. 2012. https://doi.org/10.4103/0377-2063.96184.

    Article  Google Scholar 

  38. Díaz A, Gervás P. User-model based personalized summarization. Inf Process Manage. 2007;20:20.

    Google Scholar 

  39. Park S, Cha BR, Kwon J. Personalized document summarization using pseudo relevance feedback and semantic feature. IETE J Res. 2012;20:20.

    Google Scholar 

  40. Lee J-H, Park S, Ahn C-M, Kim D. Automatic generic document summarization based on non-negative matrix factorization. Inf Process Manage. 2009;20:20.

    Article  Google Scholar 

  41. Han K-S, Baek D-H, Rim H-C, Kam-FaiWong J-HL, Dik LL. Automatic text summarization based on relevance feedback with query splitting. In: Kam-Fai Wong J-HL, Dik LL, editor. Proceedings of the fifth international workshop on on Information retrieval with Asian languages; 2000.

  42. Sharghi A, Gong B, Shah M, Theo Gevers AS. Query-focused extractive video summarization. In: Theo Gevers AS, editor. European conference on computer vision. Berlin: Springer; 2016.

    Google Scholar 

  43. Sharghi A, Laurel JS. Gong B, Rama Chellappa ZZ, Anthony Hoogs. Query-focused video summarization: Dataset, evaluation, and a memory network based approach. In: Rama Chellappa ZZ, Anthony H, editor. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR); 2017.

  44. Li Y, Merialdo B. Multi-video summarization based on video-mmr. In: IEEE-Staff, editor. 11th international workshop on image analysis for multimedia interactive services WIAMIS 10. IEEE; 2010.

  45. Wu J, Zhong S-H, Liu Y. Dynamic graph convolutional network for multi-video summarization. Pattern Recogn. 2020;20:20.

    Google Scholar 

  46. Park S, Cha B, An DU. Automatic multi-document summarization based on clustering and nonnegative matrix factorization. IETE Tech Rev. 2010;20:20.

    Google Scholar 

  47. Liu T, Zhang H-J, Qi FA. novel video key-frame-extraction algorithm based on perceived motion energy model. IEEE Trans Circ Syst Video Technol. 2003;20:20.

    Google Scholar 

  48. He Y, Gao C, Sang N, Qu Z, Han J. Graph coloring based surveillance video synopsis. Neurocomputing. 2017;20:20.

    Google Scholar 

  49. Liu Y, Titov I, Lapata M, Jill Burstein TS, Christy Doran. Single document summarization as tree induction. In: Jill Burstein TS, Christy D, editor. Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1745–1755 (2019).

  50. Hamza R, Muhammad K, Lv Z, Titouna F. Secure video summarization framework for personalized wireless capsule endoscopy. Pervasive Mobile Comput. 2017;20:20.

    Google Scholar 

  51. Marujo L. et al. Privacy-preserving multi-document summarization. ArXiv (2015) .

  52. Radev DR, Jing H, Styś M, Tam D. Centroid-based summarization of multiple documents. Inf Process Manage. 2004;20:20.

    MATH  Google Scholar 

  53. Marujo L, et al. Martha Palmer PR, Gemma B. Extending a single-document summarizer to multi-document: a hierarchical approach. In: Martha Palmer PR, Gemma B, editors. Proceedings of the fourth joint conference on lexical and computational semantics, pp. 176–181. Association for Computational Linguistics, Denver, Colorado; 2015. https://aclanthology.org/S15-1020.

  54. Angelidis S, Lapata M, Ellen Riloff JHJT, David C. Summarizing opinions: Aspect extraction meets sentiment prediction and they are both weakly supervised. In: Ellen Riloff JHJT, David C, editors. Proceedings of the 2018 conference on empirical methods in natural language processing. Association for Computational Linguistics,Brussels, Belgium; 2018. https://aclanthology.org/D18-1403.

  55. Fabbri A, Li I, She T, Li S, Radev D, Anna Korhonen LM, David T. Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model. In: Anna KLM, David T, editors. Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics,Florence, Italy; 2019. https://aclanthology.org/P19-1102.

  56. Liu PJ, et al. Generating wikipedia by summarizing long sequences. ArXiv (2018) .

  57. Li P, Bing L, Lam W, Lu Wang GCFL, Jackie CKC. Reader-aware multi-document summarization: an enhanced model and the first dataset. In: Lu Wang GCFL, Jackie CKC, editors. Proceedings of the workshop on new frontiers in . Association for Computational Linguistics,Copenhagen, Denmark; 2017. https://aclanthology.org/W17-4512.

  58. Yasunaga M, et al. Scisummnet: A large annotated corpus and content-impact models for scientific paper summarization with citation networks. In: Stone P, editors. Proceedings of the AAAI conference on artificial intelligence, 01, pp. 7386–7393 (2019).

  59. Ganesan K, Zhai C, Han J, Chu-RHDJ. Opinosis: a graph based approach to abstractive summarization of highly redundant opinions. In: Chu-Ren Huang DJ, editors. Proceedings of the 23rd international conference on computational linguistics (Coling 2010). Coling 2010 Organizing Committee,Beijing, China; 2010. https://aclanthology.org/C10-1039.

  60. Yasunaga M, et al. Scisummnet: A large annotated corpus and content-impact models for scientific paper summarization with citation networks. In: Stone, P, editors. Proceedings of the AAAI conference on artificial intelligence (2019).

  61. Gholipour Ghalandari D, Hokamp C, Pham NT, Glover J, Ifrim G, Dan Jurafsky NSJT, Joyce C. A large-scale multi-document summarization dataset from the Wikipedia current events portal. In: Dan Jurafsky NSJT, Joyce C, editors. Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics,Online; 2020. https://aclanthology.org/2020.acl-main.120.

  62. Chu E, Liu P, Chaudhuri K, Salakhutdinov R. MeanSum: A neural model for unsupervised multi-document abstractive summarization. In: Chaudhuri K, Salakhutdinov R, editors. Proceedings of the 36th international conference on machine learning, vol. 97 of Proceedings of machine learning research, pp. 1223–1232. PMLR; 2019. https://proceedings.mlr.press/v97/chu19b.html.

  63. Wang L, Ling W, Kevin Knight OR, Ani N. Neural network-based abstract generation for opinions and arguments. In: Kevin KOR, Ani N, editors. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics,San Diego, California; 2016. https://aclanthology.org/N16-1007.

  64. Peyrard M, Anna Korhonen LM, David T. A simple theoretical model of importance for summarization. In: Anna KLM, David T, editors. Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, Italy; 2019. https://aclanthology.org/P19-1101.

  65. Lin C-Y. ROUGE: a package for automatic evaluation of summaries, 74–81 (Association for Computational Linguistics,Barcelona, Spain, 2004). https://aclanthology.org/W04-1013.

  66. Jelinek F, Mercer RL, Bahl LR, Baker JK. Perplexity-a measure of the difficulty of speech recognition tasks. J Acoust Soc Am. 1977;20:20.

    Google Scholar 

  67. Zopf M, Marilyn Walker AS, Heng J. Estimating summary quality with pairwise preferences. In:Marilyn WAS, Heng J , editors. Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (Long Papers) (2018).

  68. Mathkunti NM, D PB Mapreduce: Summarization design patterns for processing kernel functions. In: DPB , editors. 2018 International conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT) (IEEE, 2018).

  69. Amplayo RK, Lapata M, Paola Merlo RT, Jorg T. Informative and controllable opinion summarization. In: Paola Merlo RT, Jorg T, editors. Proceedings of the 16th conference of the European chapter of the association for computational linguistics: main volume. Association for Computational Linguistics, Online; 2021.

  70. Antognini D, Faltings B. Learning to create sentence semantic relation graphs for multi-document summarization. ArXiv (2019) .

  71. Cao Z, Wei F, Dong L, Li S, Zhou M, Robert-M. Ranking with recursive neural networks and its application to multi-document summarization. In: Robert-M , editors. Proceedings of the AAAI conference on artificial intelligence (2015).

  72. Cho S, Lebanoff L, Foroosh H, Liu F, Anna Korhonen LM, David T. Improving the similarity measure of determinantal point processes for extractive multi-document summarization. In: Anna KLM, David T, editors. Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics,Florence, Italy; 2019. https://aclanthology.org/P19-1098.

  73. Li P, Lam W, Bing L, Guo W, Li H, Martha Palmer SR, Rebecca H. Cascaded attention based unsupervised information distillation for compressive summarization. In: Martha Palmer SR, Rebecca H, editors. Proceedings of the 2017 conference on empirical methods in natural language processing (2017).

  74. Zheng X, Sun A, Li J, Muthuswamy K, Sebastian Padó RH. Subtopic-driven multi-document summarization. In: Sebastian Padó RH, editors. Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (2019).

  75. Zhong M, et al. Dan Jurafsky NSJT, Joyce C. Extractive summarization as text matching. In:Dan Jurafsky NSJT, Joyce C , editors. Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics,Online; 2020. https://aclanthology.org/2020.acl-main.552.

  76. Cao Z, Li W, Li S, Wei F, Satinder Singh SM. Improving multi-document summarization via text classification. In: Satinder Singh SM, editors. Proceedings of the AAAI conference on artificial intelligence (2017).

  77. Yin W, Pei Y, Qiang Yang, MW. Optimizing sentence modeling and selection for document summarization. In: Qiang Yang MW, editors. Twenty-fourth international joint conference on artificial intelligence (2015).

  78. Zhang Y, Er MJ, Zhao R, Pratama M. Multiview convolutional neural networks for multidocument extractive summarization. IEEE Trans Cybern. 2016;20:20.

    Google Scholar 

  79. Nallapati R, Zhai F, Zhou B, Satinder Singh SM. Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. In: Satinder Singh SM, editors. Thirty-first AAAI conference on artificial intelligence (2017).

  80. Cao Z, Wei F, Dong L, Li S, Zhou M, Morris R. Ranking with recursive neural networks and its application to multi-document summarization. In: Morris R, editors. Proceedings of the AAAI conference on artificial intelligence (2015).

  81. Paulus R, Xiong C, Socher R. A deep reinforced model for abstractive summarization. ArXiv (2018) .

  82. Cao Z, et al. Chengqing Zong MS. Learning summary prior representation for extractive summarization. In: Chengqing Zong MS, editors. Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 2: Short Papers) (2015).

  83. Cho S, Lebanoff L, Foroosh H, Liu F, Preslav Nakov AP. Improving the similarity measure of determinantal point processes for extractive multi-document summarization. In: Preslav Nakov AP, editors. Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics,Florence, Italy; 2019. https://aclanthology.org/P19-1098.

  84. Yasunaga M. et al. Graph-based neural multi-document summarization. ArXiv (2017) .

  85. Kipf T, Welling M. Semi-supervised classification with graph convolutional networks. ArXiv (2017).

  86. Jin H, Wang T, Wan X, Dan Jurafsky NSJT, Joyce C. Multi-granularity interaction network for extractive and abstractive multi-document summarization. In: Dan Jurafsky NSJT, Joyce C, editors. Proceedings of the 58th annual meeting of the association for computational linguistics, pp 6244–6254 (2020).

  87. Liu Y, Lapata M, Anna Korhonen LM, David T. Hierarchical transformers for multi-document summarization. In: Anna Korhonen LM, David T, editors. Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics,Florence, Italy; 2019. https://aclanthology.org/P19-1500.

  88. Brazinskas A, Lapata M, Titov I. Unsupervised multi-document opinion summarization as copycat-review generation. ArXiv (2019).

  89. Li P, Wang Z, Lam W, Ren Z, Bing L, Satinder Singh SM. Salience estimation via variational auto-encoders for multi-document summarization. In: Satinder Singh SM, editors. Thirty-first AAAI conference on artificial intelligence (2017).

  90. Lebanoff L. et al. Scoring sentence singletons and pairs for abstractive summarization. arXiv:1906.00077 (2019) .

  91. Singh A, Gupta M, Varma V, Zilberstein S. Unity in diversity: Learning distributed heterogeneous sentence representation for extractive summarization. In: Zilberstein S, editors. Proceedings of the AAAI conference on artificial intelligence (2018).

  92. Fabbri A, Li I, She T, Li S, Radev D, Anna Korhonen LM, David T. Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model. In: Anna Korhonen LM, David T, editors. Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, Italy; 2019.

  93. Nithyakalyani S, Kumar SS. Energy efficient data aggregation using voronoi based genetic clustering algorithm in wsn. Int J Comput Appl. 2012;20:20.

    Google Scholar 

  94. Durga P, Pathinarupothi RK, Rangan ES, Ishwar P, Kostkova P. When less is better: a summarization technique that enhances clinical effectiveness of data. In: Kostkova P, editors. Proceedings of the 2018 international conference on digital health. Association for Computing Machinery, New York, NY, USA; 2018. https://doi.org/10.1145/3194658.3194674.

  95. Pathinarupothi RK. Clinically aware data summarization at the edge for internet of medical things. In: IEEE-Staff, editors. 2019 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops).

  96. Tayebi H, Krishnaswamy S, Waluyo AB, Sinha A, Gaber MM, Arkady Zaslavsky DLL, Panos KC. Ra-sax: resource-aware symbolic aggregate approximation for mobile ECG analysis. In: Arkady Zaslavsky DLL, Panos KC, editors. 2011 IEEE 12th international conference on mobile data management.

  97. Kulahcioglu B, Ozdemir S, Kumova B, Wamkeue R. Application of symbolic piecewise aggregate approximation (PAA) analysis to ECG signals. In: Wamkeue R, editors. 17th IASTED international conference on applied simulation and modelling (Citeseer, 2008).

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  1. Department of Avionics, Indian Institute of Space science and Technology, Thiruvananthapuram, India

    Sri Aditya Deevi & B. S. Manoj

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Deevi, S.A., Manoj, B.S. Data Summarization in Internet of Things. SN COMPUT. SCI. 3, 304 (2022). https://doi.org/10.1007/s42979-022-01188-9

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