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Detecting Anomalies within Smart Buildings using Do-It-Yourself Internet of Things

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Yasar Majib, Mahmoud Barhamgi, Behzad Momahed Heravi, Sharadha Kariyawasam, Charith Perera

Machine Learning · Submitted 4 Oct 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies.

how it is stated · asserted 1.00 read the record →

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arXiv ID2210.01840 (version 1)
Pith IDQJAXT475, Pith's permanent record of this paper and its review
CitationBehzad Momahed Heravi, Charith Perera, Mahmoud Barhamgi, Sharadha Kariyawasam, Yasar Majib. "Detecting Anomalies within Smart Buildings using Do-It-Yourself Internet of Things." arXiv:2210.01840 (2022). https://pith.science/paper/QJAXT475
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,797 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.42 · human participants 0.07 · controlled comparison 0.04 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.06 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. background 1.00 0.05 0.05 asserted 1.00
s1 This paper discussed the various mechanisms to detect anomalies as soon as they occur. contribution 0.81 0.51 0.07 asserted 0.93
s2 We shed light on crucial considerations when building machine learning models. contribution 0.82 0.57 0.05 asserted 0.89
s3 We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. contribution 0.54 0.96 0.43 asserted 1.00
s4 We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. method 0.54 0.47 0.04 asserted 0.89
s5 This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions. contribution 0.76 0.50 0.05 asserted 0.77

What kind of paper the classifier reports this is: empirical study 0.52 · evidence from observation or experiment 1.00.

What was examined, and what was not

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  • arXiv listing abstract v1
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  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.

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