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main.texmain.tex:19:1

Introductionmain.tex:19:1

System designmain.tex:26:1

fig:architecturemain.tex:43:8

System designmain.tex:26:1

Deployment and evaluationmain.tex:45:1

tab:resultsmain.tex:56:69

Deployment and evaluationmain.tex:45:1

equationmain.tex:50:1

Resultsmain.tex:65:1

figuremain.tex:71:1

Ablations, safety, and limitat…main.tex:81:1

Conclusionmain.tex:87:1

fairmain.tex:92:1

tinymlmain.tex:93:1

main.tex

1\documentclass[conference]{IEEEtran}

2\usepackage{amsmath,amssymb,booktabs,graphicx,xcolor,tikz}

3\usepackage[hidelinks]{hyperref}

4\usetikzlibrary{positioning,arrows.meta,shapes.geometric}

5\title{EdgeSense: Energy-Aware Wildfire Detection with Collaborative Sensor Networks}

6\author{\IEEEauthorblockN{Nadia Flores, Min-jun Lee, and Caleb Morgan}

7\IEEEauthorblockA{Center for Resilient Computing, Cascadia Institute of Technology\\

8Seattle, WA, USA \quad \{nflores,mjlee,cmorgan\}@example.edu}}

9\begin{document}\maketitle

10\begin{abstract}

11We present EdgeSense, a low-power wildfire early-warning system that fuses thermal,

12particulate, humidity, and wind observations across a mesh of battery-powered

13nodes. A gated temporal model runs locally and transmits only uncertain windows~\cite{lecun2015deep}.

14In a 16-week controlled field deployment, EdgeSense detected 94.1\% of burn events

15at 0.7 false alarms per node-week while reducing radio energy by 63\% relative to

16periodic streaming. Results use a synthetic but realistic demo dataset.

17\end{abstract}

18\begin{IEEEkeywords}edge computing, environmental sensing, wildfire, TinyML, sensor networks\end{IEEEkeywords}

19\section{Introduction}

20Wildfire ignition can develop faster than sparse satellite revisit cycles and

21manual reports~\cite{hastie2009}. Dense ground networks provide local measurements but create a

22difficult systems trade-off: radios consume most of a node's energy, yet delaying

23communication can hide the earliest evidence~\cite{wilkinson2016}. EdgeSense moves triage to the node

24and treats transmission as a decision under uncertainty.

26\section{System design}

27Each node samples temperature, relative humidity, particulate matter, and wind

28once per minute~\cite{vaswani2017}. A 12\,kB quantized temporal convolutional network scores

29five-minute windows~\cite{gelman2013}. Scores below 0.25 are stored locally; scores between 0.25 and

300.70 trigger peer confirmation; scores above 0.70 trigger an immediate gateway

31packet.

32\begin{figure}[t]\centering

33\begin{tikzpicture}[font=\scriptsize,node distance=4mm,

34n/.style={draw,rounded corners,fill=blue!6,minimum width=18mm,minimum height=7mm},

35a/.style={-{Stealth[length=1.7mm]},thick}]

36\node[n] (s) {Sensors}; \node[n,right=of s] (f) {Feature buffer};

37\node[n,right=of f] (m) {Tiny model}; \node[n,below=of m] (p) {Peer vote};

38\node[n,left=of p] (g) {Gateway};

39\draw[a](s)--(f);\draw[a](f)--(m);\draw[a](m)--(p);\draw[a](p)--(g);

40\draw[a,bend left=25](m) to node[above]{high confidence}(g);

41\end{tikzpicture}

42\caption{EdgeSense performs local triage and requests peer confirmation only for ambiguous windows.}

43\label{fig:architecture}\end{figure}

45\section{Deployment and evaluation}

46We deployed 48 nodes across three ecological zones~\cite{wilkinson2016}. Controlled burns and heated

47aerosol challenges generated 186 positive windows; 11,420 background windows

48captured fog, dust, vehicle exhaust, and sensor maintenance~\cite{lundberg2017}. Splits were grouped

49by day to prevent temporal leakage.

50\begin{equation}

51\mathcal{L}=-w_+y\log p-w_-(1-y)\log(1-p)+\lambda\lVert\theta\rVert_2^2.

52\end{equation}

53We compare periodic streaming, fixed thresholds, an ungated neural baseline, and

54EdgeSense. The primary metrics are event recall, false alarms per node-week,

55median alert latency, and measured radio energy.

56\begin{table}[t]\centering\caption{Field evaluation results.}\label{tab:results}

57\begin{tabular}{@{}lrrrr@{}}\toprule

58Method & Recall & FA/wk & Latency & Energy\\\midrule

59Periodic stream & .957 & 1.8 & 72 s & 100\%\\

60Fixed threshold & .876 & 3.4 & 61 s & 41\%\\

61Ungated TCN & .946 & 1.1 & 58 s & 52\%\\

62\textbf{EdgeSense} & \textbf{.941} & \textbf{0.7} & \textbf{54 s} & \textbf{37\%}\\\bottomrule

63\end{tabular}\end{table}

65\section{Results}

66EdgeSense retains 98.3\% of the streaming baseline's recall while using 37\% of

67its radio energy~\cite{gelman2013}. Peer voting removes 31\% of fog-related false alarms~\cite{hastie2009}. At the

68median observed solar input, the modeled service interval rises from 11 to 29

69months. Performance degrades gracefully when one sensing channel is unavailable.

71\begin{figure}[t]\centering

72\begin{tikzpicture}[x=.72cm,y=.55cm,font=\scriptsize]

73\draw[->](0,0)--(7,0)node[right]{week};\draw[->](0,0)--(0,5)node[above]{recall};

74\draw[blue,very thick] plot coordinates{(0,.8)(1,1.6)(2,2.2)(3,2.8)(4,3.5)(5,4.0)(6,4.25)};

75\draw[orange,very thick,dashed] plot coordinates{(0,.7)(1,1.3)(2,1.9)(3,2.4)(4,2.8)(5,3.1)(6,3.25)};

76\node[blue] at (5.2,4.5){EdgeSense};\node[orange] at (5,2.6){threshold};

77\end{tikzpicture}

78\caption{Cumulative detection performance remained stable across the deployment.}

79\end{figure}

81\section{Ablations, safety, and limitations}

82Removing peer voting increases false alarms by 44\%; removing wind direction

83reduces recall by 3.8 points~\cite{lundberg2017}. EdgeSense is advisory: alerts require confirmation

84by the incident-management system, and nodes never autonomously dispatch a public

85warning~\cite{vaswani2017}. The deployment did not cover crown fires or winter conditions.

87\section{Conclusion}

88Selective communication can extend unattended sensing without hiding early

89signals~\cite{hastie2009}. The next deployment will test transfer across biomes and signed

90over-the-air model updates.

91\begin{thebibliography}{2}

92\bibitem{fair} M~\cite{wilkinson2016}. Wilkinson et al., \emph{The FAIR Guiding Principles}, \emph{Scientific Data}, 2016.

93\bibitem{tinyml} P. Warden and D. Situnayake, \emph{TinyML}. O'Reilly, 2019.

94\end{thebibliography}

95\end{document}

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EdgeSense: Energy-Aware Wildfire Detection with
Collaborative Sensor Networks

Nadia Flores, Min-jun Lee, and Caleb Morgan
Center for Resilient Computing, Cascadia Institute of Technology
Seattle, WA, USA {nflores,mjlee,cmorgan}@example.edu

Fig. 1. EdgeSense performs local triage and requests peer confirmation only for ambiguous windows.

Abstract—We present EdgeSense, a low-power wildfire early-warning system that fuses thermal, particulate, humidity, and wind observations across a mesh of battery-powered nodes. A gated temporal model runs locally and transmits only uncertain windows. In a 16-week controlled field deployment, EdgeSense detected 94.1% of burn events at 0.7 false alarms per node-week while reducing radio energy by 63% relative to periodic streaming. Results use a synthetic but realistic demo dataset.

Index Terms—edge computing, environmental sensing, wildfire, TinyML, sensor networks

I. INTRODUCTION

Wildfire ignition can develop faster than sparse satellite revisit cycles and manual reports . Dense ground networks provide local measurements but create a difficult systems trade-off: radios consume most of a node's energy, yet delaying communication can hide the earliest evidence . EdgeSense moves triage to the node and treats transmission as a decision under uncertainty.

II. SYSTEM DESIGN

Each node samples temperature, relative humidity, particulate matter, and wind once per minute . A 12 kB quantized temporal convolutional network scores five-minute windows . Scores below 0.25 are stored locally; scores between 0.25 and 0.70 trigger peer confirmation; scores above 0.70 trigger an immediate gateway packet .

III. DEPLOYMENT AND EVALUATION

We deployed 48 nodes across three ecological zones . Controlled burns and heated aerosol challenges generated 186 positive windows; 11,420 background windows captured fog, dust, vehicle exhaust, and sensor maintenance . Splits were grouped by day to prevent temporal leakage.

ℒ = −w₊y log p − w₋(1−y) log(1−p) + λ∥θ∥²₂.(1)

TABLE I
Field evaluation results.

Method	Recall	FA/wk	Latency	Energy
Periodic stream	.957	1.8	72 s	100%
Fixed threshold	.876	3.4	61 s	41%
Ungated TCN	.946	1.1	58 s	52%
EdgeSense	.941	0.7	54 s	37%

Fig. 2. Cumulative detection performance remained stable across the deployment.

We compare periodic streaming, fixed thresholds, an ungated neural baseline, and EdgeSense. The primary metrics are event recall, false alarms per node-week, median alert latency, and measured radio energy.

IV. RESULTS

EdgeSense retains 98.3% of the streaming baseline's recall while using 37% of its radio energy . Peer voting removes 31% of fog-related false alarms . At the median observed solar input, the modeled service interval rises from 11 to 29 months. Performance degrades gracefully when one sensing channel is unavailable.

V. ABLATIONS, SAFETY, AND LIMITATIONS

Removing peer voting increases false alarms by 44%; removing wind direction reduces recall by 3.8 points . EdgeSense is advisory: alerts require confirmation by the incident-management system, and nodes never autonomously dispatch a public warning . The deployment did not cover crown fires or winter conditions.

VI. CONCLUSION

Selective communication can extend unattended sensing without hiding early signals . The next deployment will test transfer across biomes and signed over-the-air model updates.

REFERENCES

[1]L. Giglio, J. Descloitres, C. O. Justice, and Y. J. Kaufman, “An enhanced contextual fire detection algorithm for MODIS,” Remote Sensing of Environment, vol. 87, no. 2–3, pp. 273–282, 2003.

[2]W. Schroeder, P. Oliva, L. Giglio, and I. A. Csiszar, “The new VIIRS 375 m active fire detection data product,” Remote Sensing of Environment, vol. 143, pp. 85–96, 2014.

[3]U. Dampage, L. Bandaranayake, R. Wanasinghe, K. Kottahachchi, and B. Jayasanka, “Forest fire detection system using wireless sensor networks and machine learning,” Scientific Reports, vol. 12, no. 1, p. 46, 2022.

[4]K. S. Adu-Manu, N. Adam, C. Tapparello, H. Ayatollahi, and W. Heinzelman, “Energy-harvesting wireless sensor networks: A review,” ACM Transactions on Sensor Networks, vol. 14, no. 2, pp. 1–50, 2018.

[5]F. Adelantado, X. Vilajosana, P. Tuset-Peiro, B. Martinez, J. Melia-Segui, and T. Watteyne, “Understanding the limits of LoRaWAN,” IEEE Communications Magazine, vol. 55, no. 9, pp. 34–40, 2017.

[6]S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv:1803.01271, 2018.

[7]B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, “Quantization and training of neural networks for efficient integer-arithmetic-only inference,” in Proc. IEEE/CVF CVPR, 2018, pp. 2704–2713.

[8]P. P. Ray, “A review on TinyML: State-of-the-art and prospects,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1595–1623, 2022.

[9]Y. Geifman and R. El-Yaniv, “Selective classification for deep neural networks,” arXiv:1705.08500, 2017.

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1@article{open_science2015,

2 title = {Estimating the Reproducibility of Psychological Science},

1@article{giglio2003,

2 author = {Giglio, Louis and Descloitres, Jacques and Justice, Christopher O.},

3 title = {An Enhanced Contextual Fire Detection Algorithm for {MODIS}},

4 journal = {Remote Sensing of Environment},

5 year = {2003}, doi = {10.1016/S0034-4257(03)00184-6}

6}

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20Wildfire ignition can develop faster than sparse satellite revisit cycles and

21manual reports~\cite{hastie2009}. Dense ground networks provide local measurements

21manual reports~\cite{giglio2003,schroeder2014}. Dense ground networks provide local measurements

22but create a difficult systems trade-off: radios consume most of a node's energy,

23yet delayed communication can hide the earliest evidence~\cite{wilkinson2016}.

23yet delayed communication can hide the earliest evidence~\cite{dampage2022,adumanu2018}.

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EdgeSense: Energy-Aware Wildfire Detection with
Collaborative Sensor Networks

Nadia Flores, Min-jun Lee, and Caleb Morgan
Center for Resilient Computing, Cascadia Institute of Technology
Seattle, WA, USA {nflores,mjlee,cmorgan}@example.edu

Fig. 1. EdgeSense performs local triage and requests peer confirmation only for ambiguous windows.

Index Terms—edge computing, environmental sensing, wildfire, TinyML, sensor networks

I. INTRODUCTION

II. SYSTEM DESIGN

III. DEPLOYMENT AND EVALUATION

ℒ = −w₊y log p − w₋(1−y) log(1−p) + λ∥θ∥²₂.(1)

TABLE I
Field evaluation results.

Method	Recall	FA/wk	Latency	Energy
Periodic stream	.957	1.8	72 s	100%
Fixed threshold	.876	3.4	61 s	41%
Ungated TCN	.946	1.1	58 s	52%
EdgeSense	.941	0.7	54 s	37%

Fig. 2. Cumulative detection performance remained stable across the deployment.

IV. RESULTS

V. ABLATIONS, SAFETY, AND LIMITATIONS

VI. CONCLUSION

Selective communication can extend unattended sensing without hiding early signals . The next deployment will test transfer across biomes and signed over-the-air model updates.

REFERENCES

[2]W. Schroeder, P. Oliva, L. Giglio, and I. A. Csiszar, “The new VIIRS 375 m active fire detection data product,” Remote Sensing of Environment, vol. 143, pp. 85–96, 2014.

[6]S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv:1803.01271, 2018.

[8]P. P. Ray, “A review on TinyML: State-of-the-art and prospects,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1595–1623, 2022.

[9]Y. Geifman and R. El-Yaniv, “Selective classification for deep neural networks,” arXiv:1705.08500, 2017.

Stale

references.bib

1@article{giglio2003,

2 title = {An Enhanced Contextual Fire Detection Algorithm for MODIS},

3 year = {2003},

4}

6@article{schroeder2014,

7 title = {The New VIIRS 375 m Active Fire Detection Data Product},

8 year = {2014},

9}

11@article{dampage2022,

12 title = {Forest Fire Detection System Using Wireless Sensor Networks},

13 year = {2022},

14}

16@article{adumanu2018,

17 title = {Energy-Harvesting Wireless Sensor Networks: A Review},

18 year = {2018},

19}

21@article{adelantado2017,

22 title = {Understanding the Limits of LoRaWAN},

23 year = {2017},

24}

26@article{bai2018,

27 title = {An Empirical Evaluation of Generic Convolutional Networks},

28 year = {2018},

29}

31@article{jacob2018,

32 title = {Quantization and Training of Neural Networks},

33 year = {2018},

34}

36@article{ray2022,

37 title = {A Review on TinyML},

38 year = {2022},

39}

41@article{geifman2017,

42 title = {Selective Classification for Deep Neural Networks},

43 year = {2017},

44}

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  \node (n22_hapki) at (16.25,-7.075) [draw=c1E293B, line width=0.025cm, fill=cEEF2C3, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Add \& Norm};

  \node (n24_qwbl3) at (19.375,-11.825) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=1.25cm, minimum height=0.75cm] {N×};

  \node (n25_isnp1) at (16.25,-4.8) [draw=c1E293B, line width=0.025cm, fill=cDAD2F0, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.1cm] {Linear};

  \node (n26_ggail) at (16.25,-2.8) [draw=c1E293B, line width=0.025cm, fill=cCDEDD0, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.1cm] {Softmax};

  \node (n27_76g5v) at (16.25,-0.875) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.5cm, minimum height=1.25cm] {Output Probabilities};

38 \begin{scope}[on background layer]

    \node (n10_ma9el) at (6.25,-13.575) [draw=cC7C7CC, line width=0.025cm, fill=cE9E9EC, rounded corners=0.4cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=5.5cm, minimum height=7.65cm] {};

    \node (n23_egiii) at (16.25,-11.7) [draw=cC7C7CC, line width=0.025cm, fill=cE9E9EC, rounded corners=0.4cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=5.5cm, minimum height=11.4cm] {};

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-21.25) -- (6.25,-20.75) -- (6.25,-21.15) -- (6.25,-20.65);

42 \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-19.5) -- (6.25,-19) -- (6.25,-19.1) -- (6.25,-18.6);

43 \draw[->, line width=0.05cm] (n3_x9vjf.east) to[out=0, in=180] (n4_aa0b6.west);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-17.75) -- (6.25,-17.25) -- (6.25,-17.4) -- (6.25,-16.9);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-15.75) -- (6.25,-15.25) -- (6.25,-15.65) -- (6.25,-15.15);

46 \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-14) -- (6.25,-13.5) -- (6.25,-13.65) -- (6.25,-13.15);

47 \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-12) -- (6.25,-11.5) -- (6.25,-11.9) -- (6.25,-11.4);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-21.25) -- (16.25,-20.75) -- (16.25,-21.15) -- (16.25,-20.65);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-19.5) -- (16.25,-19) -- (16.25,-19.1) -- (16.25,-18.6);

50 \draw[->, line width=0.05cm] (n15_te7fn.west) to[out=180, in=0] (n14_e6m10.east);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-17.75) -- (16.25,-17.25) -- (16.25,-17.4) -- (16.25,-16.9);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-15.75) -- (16.25,-15.25) -- (16.25,-15.65) -- (16.25,-15.15);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-14) -- (16.25,-13.5) -- (16.25,-13.65) -- (16.25,-13.15);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-12) -- (16.25,-11.5) -- (16.25,-11.9) -- (16.25,-11.4);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-10.25) -- (16.25,-9.75) -- (16.25,-9.9) -- (16.25,-9.4);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-8.25) -- (16.25,-7.75) -- (16.25,-8.15) -- (16.25,-7.65);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-6.5) -- (16.25,-6) -- (16.25,-5.925) -- (16.25,-5.85) -- (16.25,-5.35);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-4.25) -- (16.25,-3.75) -- (16.25,-3.85) -- (16.25,-3.35);

59 \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-2.25) -- (16.25,-1.75) -- (16.25,-2) -- (16.25,-1.5);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (8.375,-10.825) -- (8.875,-10.825) -- (11.25,-10.825) -- (11.25,-12.575) -- (13.625,-12.575) -- (14.125,-12.575);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (5.825,-18.175) -- (5.325,-18.175) -- (3.625,-18.175) -- (3.625,-14.575) -- (4.125,-14.575);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (4.125,-14.575) -- (4.1,-14.575) -- (3.625,-14.575) -- (3.625,-10.825) -- (4.125,-10.825);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.675,-18.175) -- (17.175,-18.175) -- (18.875,-18.175) -- (18.875,-14.575) -- (18.375,-14.575);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (18.375,-14.575) -- (18.875,-14.575) -- (18.875,-10.825) -- (18.4,-10.825) -- (18.375,-10.825);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (18.375,-10.825) -- (18.875,-10.825) -- (18.875,-7.075) -- (18.4,-7.075) -- (18.375,-7.075);

66 \end{scope}

67\end{tikzpicture}

PREVIEWPNG scale2x　⌄Background

Output Probabilities

Softmax

Linear

Add & Norm

Feed Forward

Add & Norm

Multi-Head Attention

N×

Add & Norm

Feed Forward

Add & Norm

Multi-Head Attention

Add & Norm

Masked Multi-Head
Attention

N×

Positional Encoding

Input Embedding

Inputs

Positional Encoding

Output Embedding

Outputs (shifted right)

Compiling...

Import 6

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Oleafly›diagram

◉　prajwal-svm

main.tex　×

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Snippets　□　○　→　—　{ }

1\definecolor{c0F172A}{HTML}{0F172A}

2\definecolor{c1E293B}{HTML}{1E293B}

3\definecolor{cC7C7CC}{HTML}{C7C7CC}

4\definecolor{cCDEDD0}{HTML}{CDEDD0}

5\definecolor{cCFE8F8}{HTML}{CFE8F8}

6\definecolor{cDAD2F0}{HTML}{DAD2F0}

7\definecolor{cE9E9EC}{HTML}{E9E9EC}

8\definecolor{cEEF2C3}{HTML}{EEF2C3}

9\definecolor{cF9D7D9}{HTML}{F9D7D9}

10\definecolor{cFDE3C7}{HTML}{FDE3C7}

11\definecolor{cFFFFFF}{HTML}{FFFFFF}

12\begin{tikzpicture}[>={Triangle[length=0.313cm,width=0.313cm]}]

  \node (n1_mds37) at (6.25,-21.75) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1cm] {Inputs};

  \node (n2_hnw3j) at (6.25,-20.075) [draw=c1E293B, line width=0.025cm, fill=cF9D7D9, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Input Embedding};

  \node (n3_x9vjf) at (4.25,-18.175) [circle, draw=c1E293B, line width=0.025cm, fill=cFFFFFF, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=0.85cm, minimum height=0.85cm] {};

  \node (n4_aa0b6) at (6.25,-18.175) [circle, draw=c1E293B, line width=0.025cm, fill=cFFFFFF, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=0.85cm, minimum height=0.85cm] {+};

  \node (n5_lbeum) at (2.625,-19.375) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=3.5cm, minimum height=1cm] {Positional Encoding};

  \node (n6_gr85r) at (6.25,-16.325) [draw=c1E293B, line width=0.025cm, fill=cFDE3C7, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Multi-Head Attention};

  \node (n7_it3d0) at (6.25,-14.575) [draw=c1E293B, line width=0.025cm, fill=cEEF2C3, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Add \& Norm};

  \node (n8_0ae9p) at (6.25,-12.575) [draw=c1E293B, line width=0.025cm, fill=cCFE8F8, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Feed Forward};

  \node (n9_31jhg) at (6.25,-10.825) [draw=c1E293B, line width=0.025cm, fill=cEEF2C3, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Add \& Norm};

  \node (n11_3mv5b) at (1.875,-13.575) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=1.25cm, minimum height=0.75cm] {N×};

  \node (n12_y6hil) at (16.25,-21.75) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1cm] {Outputs (shifted right)};

  \node (n13_oukbf) at (16.25,-20.075) [draw=c1E293B, line width=0.025cm, fill=cF9D7D9, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Output Embedding};

  \node (n14_e6m10) at (16.25,-18.175) [circle, draw=c1E293B, line width=0.025cm, fill=cFFFFFF, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=0.85cm, minimum height=0.85cm] {+};

  \node (n15_te7fn) at (18.25,-18.175) [circle, draw=c1E293B, line width=0.025cm, fill=cFFFFFF, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=0.85cm, minimum height=0.85cm] {};

  \node (n16_otz7n) at (19.875,-19.375) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=3.5cm, minimum height=1cm] {Positional Encoding};

  \node (n17_nw4kz) at (16.25,-16.325) [draw=c1E293B, line width=0.025cm, fill=cFDE3C7, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Masked Multi-Head Attention};

  \node (n18_feep8) at (16.25,-14.575) [draw=c1E293B, line width=0.025cm, fill=cEEF2C3, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Add \& Norm};

  \node (n19_9lw0g) at (16.25,-12.575) [draw=c1E293B, line width=0.025cm, fill=cFDE3C7, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Multi-Head Attention};

  \node (n20_lz6h6) at (16.25,-10.825) [draw=c1E293B, line width=0.025cm, fill=cEEF2C3, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Add \& Norm};

  \node (n21_iyi8d) at (16.25,-8.825) [draw=c1E293B, line width=0.025cm, fill=cCFE8F8, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Feed Forward};

  \node (n22_hapki) at (16.25,-7.075) [draw=c1E293B, line width=0.025cm, fill=cEEF2C3, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Add \& Norm};

  \node (n24_qwbl3) at (19.375,-11.825) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=1.25cm, minimum height=0.75cm] {N×};

  \node (n25_isnp1) at (16.25,-4.8) [draw=c1E293B, line width=0.025cm, fill=cDAD2F0, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.1cm] {Linear};

  \node (n26_ggail) at (16.25,-2.8) [draw=c1E293B, line width=0.025cm, fill=cCDEDD0, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.1cm] {Softmax};

  \node (n27_76g5v) at (16.25,-0.875) [text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.5cm, minimum height=1.25cm] {Output Probabilities};

38 \begin{scope}[on background layer]

    \node (n10_ma9el) at (6.25,-13.575) [draw=cC7C7CC, line width=0.025cm, fill=cE9E9EC, rounded corners=0.4cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=5.5cm, minimum height=7.65cm] {};

    \node (n23_egiii) at (16.25,-11.7) [draw=cC7C7CC, line width=0.025cm, fill=cE9E9EC, rounded corners=0.4cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=5.5cm, minimum height=11.4cm] {};

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-21.25) -- (6.25,-20.75) -- (6.25,-21.15) -- (6.25,-20.65);

42 \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-19.5) -- (6.25,-19) -- (6.25,-19.1) -- (6.25,-18.6);

43 \draw[->, line width=0.05cm] (n3_x9vjf.east) to[out=0, in=180] (n4_aa0b6.west);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-17.75) -- (6.25,-17.25) -- (6.25,-17.4) -- (6.25,-16.9);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-15.75) -- (6.25,-15.25) -- (6.25,-15.65) -- (6.25,-15.15);

46 \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-14) -- (6.25,-13.5) -- (6.25,-13.65) -- (6.25,-13.15);

47 \draw[->, line width=0.05cm, rounded corners=0.125cm] (6.25,-12) -- (6.25,-11.5) -- (6.25,-11.9) -- (6.25,-11.4);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-21.25) -- (16.25,-20.75) -- (16.25,-21.15) -- (16.25,-20.65);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-19.5) -- (16.25,-19) -- (16.25,-19.1) -- (16.25,-18.6);

50 \draw[->, line width=0.05cm] (n15_te7fn.west) to[out=180, in=0] (n14_e6m10.east);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-17.75) -- (16.25,-17.25) -- (16.25,-17.4) -- (16.25,-16.9);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-15.75) -- (16.25,-15.25) -- (16.25,-15.65) -- (16.25,-15.15);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-14) -- (16.25,-13.5) -- (16.25,-13.65) -- (16.25,-13.15);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-12) -- (16.25,-11.5) -- (16.25,-11.9) -- (16.25,-11.4);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-10.25) -- (16.25,-9.75) -- (16.25,-9.9) -- (16.25,-9.4);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-8.25) -- (16.25,-7.75) -- (16.25,-8.15) -- (16.25,-7.65);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-6.5) -- (16.25,-6) -- (16.25,-5.925) -- (16.25,-5.85) -- (16.25,-5.35);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-4.25) -- (16.25,-3.75) -- (16.25,-3.85) -- (16.25,-3.35);

59 \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.25,-2.25) -- (16.25,-1.75) -- (16.25,-2) -- (16.25,-1.5);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (8.375,-10.825) -- (8.875,-10.825) -- (11.25,-10.825) -- (11.25,-12.575) -- (13.625,-12.575) -- (14.125,-12.575);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (5.825,-18.175) -- (5.325,-18.175) -- (3.625,-18.175) -- (3.625,-14.575) -- (4.125,-14.575);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (4.125,-14.575) -- (4.1,-14.575) -- (3.625,-14.575) -- (3.625,-10.825) -- (4.125,-10.825);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (16.675,-18.175) -- (17.175,-18.175) -- (18.875,-18.175) -- (18.875,-14.575) -- (18.375,-14.575);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (18.375,-14.575) -- (18.875,-14.575) -- (18.875,-10.825) -- (18.4,-10.825) -- (18.375,-10.825);

    \draw[->, line width=0.05cm, rounded corners=0.125cm] (18.375,-10.825) -- (18.875,-10.825) -- (18.875,-7.075) -- (18.4,-7.075) -- (18.375,-7.075);

66 \end{scope}

67\end{tikzpicture}

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Output Probabilities

Softmax

Linear

Add & Norm

Feed Forward

Add & Norm

Multi-Head Attention

N×

Add & Norm

Feed Forward

Add & Norm

Multi-Head Attention

Add & Norm

Masked Multi-Head
Attention

N×

Positional Encoding

Input Embedding

Inputs

Positional Encoding

Output Embedding

Outputs (shifted right)

▤ Logs　✓ 1.4s⌗　−　131%⌄　＋　◐　↻　

Output Probabilities

Softmax

Linear

Add & Norm

Feed Forward

Add & Norm

Multi-Head Attention

N×

Add & Norm

Feed Forward

Add & Norm

Multi-Head Attention

Add & Norm

Masked Multi-Head
Attention

N×

Positional Encoding

Input Embedding

Inputs

Positional Encoding

Output Embedding

Outputs (shifted right)

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diagram.png

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Structure 38

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main.texmain.tex:19:1

Introductionmain.tex:19:1

System designmain.tex:26:1

fig:architecturemain.tex:43:8

System designmain.tex:26:1

Deployment and evaluationmain.tex:45:1

tab:resultsmain.tex:56:69

Deployment and evaluationmain.tex:45:1

equationmain.tex:50:1

Resultsmain.tex:65:1

figuremain.tex:71:1

Ablations, safety, and limitat…main.tex:81:1

Conclusionmain.tex:87:1

fairmain.tex:92:1

tinymlmain.tex:93:1

References 4

References · 1213Citations · 8Symbols · 4

Filter…

Unresolved citations 4

hastie2009main.tex:21

vaswani2017main.tex:28

Bibliography 1213

references.bib

lecun2015deep2015

Deep learning — Nature 521, 436–444

wilkinson20162016

The FAIR Guiding Principles for scientific…

warden20192019

TinyML: Machine Learning with Ultra-Low…

Citations in main.tex 8

hastie200921 · 67 · 89

wilkinson201623 · 46

vaswani201728 · 85

gelman201329 · 67

lundberg201748 · 83

Labels 4

fig:architecturemain.tex:43

tab:resultsmain.tex:56

equationmain.tex:50

figuremain.tex:71

main.tex

1\documentclass[conference]{IEEEtran}

2\usepackage{amsmath,amssymb,booktabs,graphicx,xcolor,tikz}

3\usepackage[hidelinks]{hyperref}

4\usetikzlibrary{positioning,arrows.meta,shapes.geometric}

5\title{EdgeSense: Energy-Aware Wildfire Detection with Collaborative Sensor Networks}

6\author{\IEEEauthorblockN{Nadia Flores, Min-jun Lee, and Caleb Morgan}

7\IEEEauthorblockA{Center for Resilient Computing, Cascadia Institute of Technology\\

8Seattle, WA, USA \quad \{nflores,mjlee,cmorgan\}@example.edu}}

9\begin{document}\maketitle

10\begin{abstract}

11We present EdgeSense, a low-power wildfire early-warning system that fuses thermal,

12particulate, humidity, and wind observations across a mesh of battery-powered

13nodes. A gated temporal model runs locally and transmits only uncertain windows~\cite{lecun2015deep}.

14In a 16-week controlled field deployment, EdgeSense detected 94.1\% of burn events

15at 0.7 false alarms per node-week while reducing radio energy by 63\% relative to

16periodic streaming. Results use a synthetic but realistic demo dataset.

17\end{abstract}

18\begin{IEEEkeywords}edge computing, environmental sensing, wildfire, TinyML, sensor networks\end{IEEEkeywords}

19\section{Introduction}

20Wildfire ignition can develop faster than sparse satellite revisit cycles and

21manual reports~\cite{hastie2009}. Dense ground networks provide local measurements but create a

22difficult systems trade-off: radios consume most of a node's energy, yet delaying

23communication can hide the earliest evidence~\cite{wilkinson2016}. EdgeSense moves triage to the node

24and treats transmission as a decision under uncertainty.

26\section{System design}

27Each node samples temperature, relative humidity, particulate matter, and wind

28once per minute~\cite{vaswani2017}. A 12\,kB quantized temporal convolutional network scores

29five-minute windows~\cite{gelman2013}. Scores below 0.25 are stored locally; scores between 0.25 and

300.70 trigger peer confirmation; scores above 0.70 trigger an immediate gateway

31packet.

32\begin{figure}[t]\centering

33\begin{tikzpicture}[font=\scriptsize,node distance=4mm,

34n/.style={draw,rounded corners,fill=blue!6,minimum width=18mm,minimum height=7mm},

35a/.style={-{Stealth[length=1.7mm]},thick}]

36\node[n] (s) {Sensors}; \node[n,right=of s] (f) {Feature buffer};

37\node[n,right=of f] (m) {Tiny model}; \node[n,below=of m] (p) {Peer vote};

38\node[n,left=of p] (g) {Gateway};

39\draw[a](s)--(f);\draw[a](f)--(m);\draw[a](m)--(p);\draw[a](p)--(g);

40\draw[a,bend left=25](m) to node[above]{high confidence}(g);

41\end{tikzpicture}

42\caption{EdgeSense performs local triage and requests peer confirmation only for ambiguous windows.}

43\label{fig:architecture}\end{figure}

45\section{Deployment and evaluation}

46We deployed 48 nodes across three ecological zones~\cite{wilkinson2016}. Controlled burns and heated

47aerosol challenges generated 186 positive windows; 11,420 background windows

48captured fog, dust, vehicle exhaust, and sensor maintenance~\cite{lundberg2017}. Splits were grouped

49by day to prevent temporal leakage.

50\begin{equation}

51\mathcal{L}=-w_+y\log p-w_-(1-y)\log(1-p)+\lambda\lVert\theta\rVert_2^2.

52\end{equation}

53We compare periodic streaming, fixed thresholds, an ungated neural baseline, and

54EdgeSense. The primary metrics are event recall, false alarms per node-week,

55median alert latency, and measured radio energy.

56\begin{table}[t]\centering\caption{Field evaluation results.}\label{tab:results}

57\begin{tabular}{@{}lrrrr@{}}\toprule

58Method & Recall & FA/wk & Latency & Energy\\\midrule

59Periodic stream & .957 & 1.8 & 72 s & 100\%\\

60Fixed threshold & .876 & 3.4 & 61 s & 41\%\\

61Ungated TCN & .946 & 1.1 & 58 s & 52\%\\

62\textbf{EdgeSense} & \textbf{.941} & \textbf{0.7} & \textbf{54 s} & \textbf{37\%}\\\bottomrule

63\end{tabular}\end{table}

65\section{Results}

66EdgeSense retains 98.3\% of the streaming baseline's recall while using 37\% of

67its radio energy~\cite{gelman2013}. Peer voting removes 31\% of fog-related false alarms~\cite{hastie2009}. At the

68median observed solar input, the modeled service interval rises from 11 to 29

69months. Performance degrades gracefully when one sensing channel is unavailable.

71\begin{figure}[t]\centering

72\begin{tikzpicture}[x=.72cm,y=.55cm,font=\scriptsize]

73\draw[->](0,0)--(7,0)node[right]{week};\draw[->](0,0)--(0,5)node[above]{recall};

74\draw[blue,very thick] plot coordinates{(0,.8)(1,1.6)(2,2.2)(3,2.8)(4,3.5)(5,4.0)(6,4.25)};

75\draw[orange,very thick,dashed] plot coordinates{(0,.7)(1,1.3)(2,1.9)(3,2.4)(4,2.8)(5,3.1)(6,3.25)};

76\node[blue] at (5.2,4.5){EdgeSense};\node[orange] at (5,2.6){threshold};

77\end{tikzpicture}

78\caption{Cumulative detection performance remained stable across the deployment.}

79\end{figure}

81\section{Ablations, safety, and limitations}

82Removing peer voting increases false alarms by 44\%; removing wind direction

83reduces recall by 3.8 points~\cite{lundberg2017}. EdgeSense is advisory: alerts require confirmation

84by the incident-management system, and nodes never autonomously dispatch a public

85warning~\cite{vaswani2017}. The deployment did not cover crown fires or winter conditions.

87\section{Conclusion}

88Selective communication can extend unattended sensing without hiding early

89signals~\cite{hastie2009}. The next deployment will test transfer across biomes and signed

90over-the-air model updates.

91\begin{thebibliography}{2}

92\bibitem{fair} M~\cite{wilkinson2016}. Wilkinson et al., \emph{The FAIR Guiding Principles}, \emph{Scientific Data}, 2016.

93\bibitem{tinyml} P. Warden and D. Situnayake, \emph{TinyML}. O'Reilly, 2019.

94\end{thebibliography}

95\end{document}

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  author  = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
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| 8 unchanged lines |

9\begin{document}\maketitle

10\begin{abstract}

11We present EdgeSense, a low-power wildfire early-warning system that fuses thermal,

12particulate, humidity, and wind observations across a mesh of battery-powered

13nodes. A gated temporal model runs locally and transmits only uncertain windows.

14In a 16-week controlled field deployment, EdgeSense detected 94.1\% of burn events

15at 0.7 false alarms per node-week while reducing radio energy by 63\% relative to

16periodic streaming. Results use a synthetic but realistic demo dataset.

17\end{abstract}

| 78 unchanged lines |

| 8 unchanged lines |

9\begin{document}\maketitle

10\begin{abstract}

11We present EdgeSense, a low-power wildfire early-warning system that fuses thermal,

12particulate, humidity, and wind observations across a mesh of battery-powered

13nodes. A gated temporal model runs locally and transmits only uncertain windows~\cite{lecun2015deep}.

14In a 16-week controlled field deployment, EdgeSense detected 94.1\% of burn events

15at 0.7 false alarms per node-week while reducing radio energy by 63\% relative to

16periodic streaming. Results use a synthetic but realistic demo dataset.

17\end{abstract}

| 78 unchanged lines |

1\documentclass[conference]{IEEEtran}

2\usepackage{amsmath,amssymb,booktabs,graphicx,xcolor,tikz}

3\usepackage[hidelinks]{hyperref}

4\usetikzlibrary{positioning,arrows.meta,shapes.geometric}

5\title{EdgeSense: Energy-Aware Wildfire Detection with Collaborative Sensor Networks}

6\author{\IEEEauthorblockN{Nadia Flores, Min-jun Lee, and Caleb Morgan}

7\IEEEauthorblockA{Center for Resilient Computing, Cascadia Institute of Technology\\

8Seattle, WA, USA \quad \{nflores,mjlee,cmorgan\}@example.edu}}

9\begin{document}\maketitle

10\begin{abstract}

11We present EdgeSense, a low-power wildfire early-warning system that fuses thermal,

12particulate, humidity, and wind observations across a mesh of battery-powered

13nodes. A gated temporal model runs locally and transmits only uncertain windows~\cite{lecun2015deep}.

14In a 16-week controlled field deployment, EdgeSense detected 94.1\% of burn events

15at 0.7 false alarms per node-week while reducing radio energy by 63\% relative to

16periodic streaming. Results use a synthetic but realistic demo dataset.

17\end{abstract}

18\begin{IEEEkeywords}edge computing, environmental sensing, wildfire, TinyML, sensor networks\end{IEEEkeywords}

19\section{Introduction}

20Wildfire ignition can develop faster than sparse satellite revisit cycles and

21manual reports~\cite{hastie2009}. Dense ground networks provide local measurements but create a

22difficult systems trade-off: radios consume most of a node's energy, yet delaying

23communication can hide the earliest evidence~\cite{wilkinson2016}. EdgeSense moves triage to the node

24and treats transmission as a decision under uncertainty.

26\section{System design}

27Each node samples temperature, relative humidity, particulate matter, and wind

28once per minute~\cite{vaswani2017}. A 12\,kB quantized temporal convolutional network scores

29five-minute windows~\cite{gelman2013}. Scores below 0.25 are stored locally; scores between 0.25 and

300.70 trigger peer confirmation; scores above 0.70 trigger an immediate gateway

31packet.

32\begin{figure}[t]\centering

33\begin{tikzpicture}[font=\scriptsize,node distance=4mm,

34n/.style={draw,rounded corners,fill=blue!6,minimum width=18mm,minimum height=7mm},

35a/.style={-{Stealth[length=1.7mm]},thick}]

36\node[n] (s) {Sensors}; \node[n,right=of s] (f) {Feature buffer};

37\node[n,right=of f] (m) {Tiny model}; \node[n,below=of m] (p) {Peer vote};

38\node[n,left=of p] (g) {Gateway};

39\draw[a](s)--(f);\draw[a](f)--(m);\draw[a](m)--(p);\draw[a](p)--(g);

40\draw[a,bend left=25](m) to node[above]{high confidence}(g);

41\end{tikzpicture}

42\caption{EdgeSense performs local triage and requests peer confirmation only for ambiguous windows.}

43\label{fig:architecture}\end{figure}

45\section{Deployment and evaluation}

46We deployed 48 nodes across three ecological zones~\cite{wilkinson2016}. Controlled burns and heated

47aerosol challenges generated 186 positive windows; 11,420 background windows

48captured fog, dust, vehicle exhaust, and sensor maintenance~\cite{lundberg2017}. Splits were grouped

49by day to prevent temporal leakage.

50\begin{equation}

51\mathcal{L}=-w_+y\log p-w_-(1-y)\log(1-p)+\lambda\lVert\theta\rVert_2^2.

52\end{equation}

53We compare periodic streaming, fixed thresholds, an ungated neural baseline, and

54EdgeSense. The primary metrics are event recall, false alarms per node-week,

55median alert latency, and measured radio energy.

56\begin{table}[t]\centering\caption{Field evaluation results.}\label{tab:results}

57\begin{tabular}{@{}lrrrr@{}}\toprule

58Method & Recall & FA/wk & Latency & Energy\\\midrule

59Periodic stream & .957 & 1.8 & 72 s & 100\%\\

60Fixed threshold & .876 & 3.4 & 61 s & 41\%\\

61Ungated TCN & .946 & 1.1 & 58 s & 52\%\\

62\textbf{EdgeSense} & \textbf{.941} & \textbf{0.7} & \textbf{54 s} & \textbf{37\%}\\\bottomrule

63\end{tabular}\end{table}

65\section{Results}

66EdgeSense retains 98.3\% of the streaming baseline's recall while using 37\% of

67its radio energy~\cite{gelman2013}. Peer voting removes 31\% of fog-related false alarms~\cite{hastie2009}. At the

68median observed solar input, the modeled service interval rises from 11 to 29

69months. Performance degrades gracefully when one sensing channel is unavailable.

71\begin{figure}[t]\centering

72\begin{tikzpicture}[x=.72cm,y=.55cm,font=\scriptsize]

73\draw[->](0,0)--(7,0)node[right]{week};\draw[->](0,0)--(0,5)node[above]{recall};

74\draw[blue,very thick] plot coordinates{(0,.8)(1,1.6)(2,2.2)(3,2.8)(4,3.5)(5,4.0)(6,4.25)};

75\draw[orange,very thick,dashed] plot coordinates{(0,.7)(1,1.3)(2,1.9)(3,2.4)(4,2.8)(5,3.1)(6,3.25)};

76\node[blue] at (5.2,4.5){EdgeSense};\node[orange] at (5,2.6){threshold};

77\end{tikzpicture}

78\caption{Cumulative detection performance remained stable across the deployment.}

79\end{figure}

81\section{Ablations, safety, and limitations}

82Removing peer voting increases false alarms by 44\%; removing wind direction

83reduces recall by 3.8 points~\cite{lundberg2017}. EdgeSense is advisory: alerts require confirmation

84by the incident-management system, and nodes never autonomously dispatch a public

85warning~\cite{vaswani2017}. The deployment did not cover crown fires or winter conditions.

87\section{Conclusion}

88Selective communication can extend unattended sensing without hiding early

89signals~\cite{hastie2009}. The next deployment will test transfer across biomes and signed

90over-the-air model updates.

91\begin{thebibliography}{2}

92\bibitem{fair} M~\cite{wilkinson2016}. Wilkinson et al., \emph{The FAIR Guiding Principles}, \emph{Scientific Data}, 2016.

93\bibitem{tinyml} P. Warden and D. Situnayake, \emph{TinyML}. O'Reilly, 2019.

94\end{thebibliography}

95\end{document}

Import 6

↕ MARKDOWNAlpine eDNA Pilot — Lab Notebook

DocumentUpdated Jul 31

↕ TECTONICMapping Pollinator Corridors — Research Poster

DocumentUpdated Jul 29

↕ TECTONICFederated Learning Data Flow

ImageUpdated Jul 29

↕ TECTONICCatalyst Screening Data Report

DocumentUpdated Jul 29

↕ TECTONICMangrove Canopy Drone Survey

DocumentUpdated Jul 28

Alex Chen

Senior Software Engineer

[email protected] · (650) 555-0142 · Mountain View, CA · alexchen.dev · github.com/alexchen

SUMMARY

Senior Software Engineer (L5) with 9+ years designing and operating large-scale distributed systems. Deep experience in backend infrastructure, performance, and reliability.

EXPERIENCE

Senior Software Engineer (L5)Google

Mountain View, CAMar 2020 — Present

Tech lead for a Search serving component handling 2M+ queries/second; cut p99 latency 38%.
Designed a globally replicated feature store on Spanner backing 40+ ML models.
Led migration of a 300-service fleet to a new RPC framework; reduced on-call pages 45%.
Mentored six engineers and ran the team’s design-review program.

Software Engineer (L4)Google

Mountain View, CAJul 2017 — Mar 2020

Built a real-time aggregation pipeline processing 8B events/day.
Cut batch-job cost 30% with sharding and incremental computation.

Software EngineerStripe

San Francisco, CAAug 2015 — Jun 2017

Shipped ledger and reconciliation services processing $60B+/year.
Reduced a reconciliation run from six hours to 25 minutes.

SELECTED PROJECTS

Ratelgithub.com/alexchen/ratel

Go, Raft, gRPC — distributed rate limiter doing 4M+ decisions/sec/node.

tqlgithub.com/alexchen/tql

Rust — typed query builder for Postgres with compile-time-checked SQL.

EDUCATION

M.S. Computer ScienceStanford University

B.S. Computer ScienceUniversity of Illinois Urbana-Champaign

SKILLS

Languages: C++, Go, Rust, Python, Java, SQL
Systems: Spanner, Bigtable, Kubernetes, Kafka, gRPC / Protobuf, Redis

↕ TECTONICSleep and Memory Consolidation — Evidence Map

DocumentUpdated Jul 28

↕ TECTONICUrban Heat Transect Field Protocol

DocumentUpdated Jul 27

↕ TECTONICNSF Progress Report — Year 2

DocumentUpdated Jul 27

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↕ TECTONICCivic Shade and Pedestrian Heat Exposure

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↕ TECTONICCS 271 — Approximation Algorithms PS3

DocumentUpdated Jul 5

↕ TECTONICWeak-Lensing Mass Reconstruction of NCJ 1427

DocumentUpdated Jul 2

↕ TECTONICStatistical Consulting Report

DocumentUpdated Jun 25

OleaflyAlex Chen - Google Resume

prajwal-svm

main.tex

T⌄

1\documentclass[11pt,letterpaper]{article}

2\usepackage[T1]{fontenc}

3\usepackage[margin=0.5in]{geometry}

4\usepackage{titlesec}

5\usepackage{enumitem}

6\usepackage{hyperref}

8% ATS-friendly: single column, real selectable text, no tables-for-layout,

9% embedded subset fonts. Linear reading order so parsers extract cleanly.

10\setlength{\parindent}{0pt}

11\pagenumbering{gobble}

12\hypersetup{colorlinks=true, urlcolor=black, linkcolor=black}

14\titleformat{\section}{\large\bfseries\uppercase}{}{0em}{}[\vspace{2pt}\titlerule]

15\titlespacing*{\section}{0pt}{8pt}{4pt}

16\setlist[itemize]{leftmargin=14pt, itemsep=1pt, topsep=2pt, parsep=0pt}

18% role{Title}{Company}{Location}{Dates}

19\newcommand{\role}[4]{%

20 \textbf{#1} \hfill #2 \\

21 \textit{#3} \hfill \textit{#4}%

22}

24\begin{document}

26\begin{center}

27 {\Huge \textbf{Alex Chen}}\\[3pt]

28 \small

29 Senior Software Engineer\\[2pt]

30 \href{mailto:[email protected]}{[email protected]}\,$\cdot$\,

31 (650) 555-0142\,$\cdot$\,

32 Mountain View, CA\,$\cdot$\,

33 \href{https://alexchen.dev}{alexchen.dev}\,$\cdot$\,

34 \href{https://github.com/alexchen}{github.com/alexchen}\,$\cdot$\,

35 \href{https://linkedin.com/in/alexchen}{linkedin.com/in/alexchen}

36\end{center}

37\vspace{2pt}

39\section*{Summary}

40Senior Software Engineer (L5) with 9+ years designing and operating large-scale

41distributed systems. Deep experience in backend infrastructure, performance, and

42reliability, with a track record of shipping products used by hundreds of

43millions of users and mentoring engineers toward senior roles.

45\section*{Experience}

46\role{Senior Software Engineer (L5)}{Google}{Mountain View, CA}{Mar 2020 -- Present}

47\begin{itemize}

  \item Tech lead for a Search serving component handling 2M+ queries/second; drove a redesign that cut p99 latency 38\% and saved an estimated \$14M/year in compute.

  \item Designed and launched a globally-replicated feature store on Spanner backing 40+ ML models, adopted by 12 teams across Search and Ads.

  \item Led migration of a 300-service fleet to a new RPC framework, improving tail latency and reducing on-call pages by 45\%.

51 \item Mentored 6 engineers (2 promoted to senior); ran the team's design-review and readability programs.

52\end{itemize}

53\vspace{3pt}

54\role{Software Engineer (L4)}{Google}{Mountain View, CA}{Jul 2017 -- Mar 2020}

55\begin{itemize}

  \item Built a real-time aggregation pipeline (C++, Flume) processing 8B events/day for a Search-quality dashboard used org-wide.

57 \item Cut batch-job cost 30\% by reworking sharding and introducing incremental recomputation.

58\end{itemize}

59\vspace{3pt}

60\role{Software Engineer}{Stripe}{San Francisco, CA}{Aug 2015 -- Jun 2017}

61\begin{itemize}

62 \item Shipped core ledger and reconciliation services for a payments platform processing \$60B+/year.

63 \item Reduced a reconciliation run from 6 hours to 25 minutes via parallelization and a columnar store.

64\end{itemize}

66\section*{Selected Projects}

67\textbf{Ratel} \hfill \href{https://github.com/alexchen/ratel}{github.com/alexchen/ratel} \\

\textit{Go, Raft, gRPC} --- open-source distributed rate limiter doing 4M+ decisions/sec/node; 1.5k GitHub stars.\par\vspace{4pt}

69\textbf{tql} \hfill \href{https://github.com/alexchen/tql}{github.com/alexchen/tql} \\

70\textit{Rust} --- a typed query builder for Postgres with compile-time-checked SQL.

72\section*{Education}

73\textbf{M.S. Computer Science} \hfill Stanford University \\

74\textit{2013 -- 2015} \hfill \textit{Focus: Distributed Systems}\par\vspace{4pt}

75\textbf{B.S. Computer Science} \hfill University of Illinois Urbana-Champaign \\

76\textit{2009 -- 2013} \hfill \textit{GPA: 3.9 / 4.0}

78\section*{Skills}

79\textbf{Languages:} C++, Go, Rust, Python, Java, SQL \\

80\textbf{Systems:} Spanner, Bigtable, Kubernetes, Kafka, gRPC / Protobuf, Redis \\

81\textbf{Focus:} Distributed systems, performance, reliability (SLOs), system design, mentoring

83\end{document}

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Alex Chen

Senior Software Engineer

[email protected] · (650) 555-0142 · Mountain View, CA · alexchen.dev · github.com/alexchen

SUMMARY

Senior Software Engineer (L5) with 9+ years designing and operating large-scale distributed systems. Deep experience in backend infrastructure, performance, and reliability.

EXPERIENCE

Senior Software Engineer (L5)Google

Mountain View, CAMar 2020 — Present

Tech lead for a Search serving component handling 2M+ queries/second; cut p99 latency 38%.
Designed a globally replicated feature store on Spanner backing 40+ ML models.
Led migration of a 300-service fleet to a new RPC framework; reduced on-call pages 45%.
Mentored six engineers and ran the team’s design-review program.

Software Engineer (L4)Google

Mountain View, CAJul 2017 — Mar 2020

Built a real-time aggregation pipeline processing 8B events/day.
Cut batch-job cost 30% with sharding and incremental computation.

Software EngineerStripe

San Francisco, CAAug 2015 — Jun 2017

Shipped ledger and reconciliation services processing $60B+/year.
Reduced a reconciliation run from six hours to 25 minutes.

SELECTED PROJECTS

Ratelgithub.com/alexchen/ratel

Go, Raft, gRPC — distributed rate limiter doing 4M+ decisions/sec/node.

tqlgithub.com/alexchen/tql

Rust — typed query builder for Postgres with compile-time-checked SQL.

EDUCATION

M.S. Computer ScienceStanford University

B.S. Computer ScienceUniversity of Illinois Urbana-Champaign

SKILLS

Languages: C++, Go, Rust, Python, Java, SQL
Systems: Spanner, Bigtable, Kubernetes, Kafka, gRPC / Protobuf, Redis

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main.tex

CodeVisual

Results

EdgeSense retains 98.3% of the streaming baseline’s recall while using 37% of its radio energy gelman2014. Peer voting removes 31% of fog-related false alarms hastie2009. At the median observed solar input, the modeled service interval rises from 11 to 29 months. Performance degrades gracefully when one sensing channel is unavailable.

Ablations, safety, and limitations

Removing peer voting increases false alarms by 44%; removing wind direction reduces recall by 3.8 points lundberg2017. EdgeSense is advisory: alerts require confirmation by the incident-management system.

1\documentclass[conference]{IEEEtran}

2\usepackage{amsmath,amssymb,booktabs,graphicx,xcolor,tikz}

3\usepackage[hidelinks]{hyperref}

4\usetikzlibrary{positioning,arrows.meta,shapes.geometric}

5\title{EdgeSense: Energy-Aware Wildfire Detection with Collaborative Sensor Networks}

6\author{\IEEEauthorblockN{Nadia Flores, Min-jun Lee, and Caleb Morgan}

7\IEEEauthorblockA{Center for Resilient Computing, Cascadia Institute of Technology\\

8Seattle, WA, USA \quad \{nflores,mjlee,cmorgan\}@example.edu}}

9\begin{document}\maketitle

10\begin{abstract}

11We present EdgeSense, a low-power wildfire early-warning system that fuses thermal,

12particulate, humidity, and wind observations across a mesh of battery-powered

13nodes. A gated temporal model runs locally and transmits only uncertain windows~\cite{lecun2015deep}.

14In a 16-week controlled field deployment, EdgeSense detected 94.1\% of burn events

15at 0.7 false alarms per node-week while reducing radio energy by 63\% relative to

16periodic streaming. Results use a synthetic but realistic demo dataset.

17\end{abstract}

18\begin{IEEEkeywords}edge computing, environmental sensing, wildfire, TinyML, sensor networks\end{IEEEkeywords}

19\section{Introduction}

20Wildfire ignition can develop faster than sparse satellite revisit cycles and

21manual reports~\cite{hastie2009}. Dense ground networks provide local measurements but create a

22difficult systems trade-off: radios consume most of a node's energy, yet delaying

23communication can hide the earliest evidence~\cite{wilkinson2016}. EdgeSense moves triage to the node

24and treats transmission as a decision under uncertainty.

26\section{System design}

27Each node samples temperature, relative humidity, particulate matter, and wind

28once per minute~\cite{vaswani2017}. A 12\,kB quantized temporal convolutional network scores

29five-minute windows~\cite{gelman2013}. Scores below 0.25 are stored locally; scores between 0.25 and

300.70 trigger peer confirmation; scores above 0.70 trigger an immediate gateway

31packet.

32\begin{figure}[t]\centering

33\begin{tikzpicture}[font=\scriptsize,node distance=4mm,

34n/.style={draw,rounded corners,fill=blue!6,minimum width=18mm,minimum height=7mm},

35a/.style={-{Stealth[length=1.7mm]},thick}]

36\node[n] (s) {Sensors}; \node[n,right=of s] (f) {Feature buffer};

37\node[n,right=of f] (m) {Tiny model}; \node[n,below=of m] (p) {Peer vote};

38\node[n,left=of p] (g) {Gateway};

39\draw[a](s)--(f);\draw[a](f)--(m);\draw[a](m)--(p);\draw[a](p)--(g);

40\draw[a,bend left=25](m) to node[above]{high confidence}(g);

41\end{tikzpicture}

42\caption{EdgeSense performs local triage and requests peer confirmation only for ambiguous windows.}

43\label{fig:architecture}\end{figure}

45\section{Deployment and evaluation}

46We deployed 48 nodes across three ecological zones~\cite{wilkinson2016}. Controlled burns and heated

47aerosol challenges generated 186 positive windows; 11,420 background windows

48captured fog, dust, vehicle exhaust, and sensor maintenance~\cite{lundberg2017}. Splits were grouped

49by day to prevent temporal leakage.

50\begin{equation}

51\mathcal{L}=-w_+y\log p-w_-(1-y)\log(1-p)+\lambda\lVert\theta\rVert_2^2.

52\end{equation}

53We compare periodic streaming, fixed thresholds, an ungated neural baseline, and

54EdgeSense. The primary metrics are event recall, false alarms per node-week,

55median alert latency, and measured radio energy.

56\begin{table}[t]\centering\caption{Field evaluation results.}\label{tab:results}

57\begin{tabular}{@{}lrrrr@{}}\toprule

58Method & Recall & FA/wk & Latency & Energy\\\midrule

59Periodic stream & .957 & 1.8 & 72 s & 100\%\\

60Fixed threshold & .876 & 3.4 & 61 s & 41\%\\

61Ungated TCN & .946 & 1.1 & 58 s & 52\%\\

62\textbf{EdgeSense} & \textbf{.941} & \textbf{0.7} & \textbf{54 s} & \textbf{37\%}\\\bottomrule

63\end{tabular}\end{table}

65\section{Results}

66EdgeSense retains 98.3\% of the streaming baseline's recall while using 37\% of

67its radio energy~\cite{gelman2013}. Peer voting removes 31\% of fog-related false alarms~\cite{hastie2009}. At the

68median observed solar input, the modeled service interval rises from 11 to 29

69months. Performance degrades gracefully when one sensing channel is unavailable.

71\begin{figure}[t]\centering

72\begin{tikzpicture}[x=.72cm,y=.55cm,font=\scriptsize]

73\draw[->](0,0)--(7,0)node[right]{week};\draw[->](0,0)--(0,5)node[above]{recall};

74\draw[blue,very thick] plot coordinates{(0,.8)(1,1.6)(2,2.2)(3,2.8)(4,3.5)(5,4.0)(6,4.25)};

75\draw[orange,very thick,dashed] plot coordinates{(0,.7)(1,1.3)(2,1.9)(3,2.4)(4,2.8)(5,3.1)(6,3.25)};

76\node[blue] at (5.2,4.5){EdgeSense};\node[orange] at (5,2.6){threshold};

77\end{tikzpicture}

78\caption{Cumulative detection performance remained stable across the deployment.}

79\end{figure}

81\section{Ablations, safety, and limitations}

82Removing peer voting increases false alarms by 44\%; removing wind direction

83reduces recall by 3.8 points~\cite{lundberg2017}. EdgeSense is advisory: alerts require confirmation

84by the incident-management system, and nodes never autonomously dispatch a public

85warning~\cite{vaswani2017}. The deployment did not cover crown fires or winter conditions.

87\section{Conclusion}

88Selective communication can extend unattended sensing without hiding early

89signals~\cite{hastie2009}. The next deployment will test transfer across biomes and signed

90over-the-air model updates.

91\begin{thebibliography}{2}

92\bibitem{fair} M~\cite{wilkinson2016}. Wilkinson et al., \emph{The FAIR Guiding Principles}, \emph{Scientific Data}, 2016.

93\bibitem{tinyml} P. Warden and D. Situnayake, \emph{TinyML}. O'Reilly, 2019.

94\end{thebibliography}

95\end{document}

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EdgeSense: Energy-Aware Wildfire Detection with
Collaborative Sensor Networks

Nadia Flores, Min-jun Lee, and Caleb Morgan
Center for Resilient Computing, Cascadia Institute of Technology
Seattle, WA, USA {nflores,mjlee,cmorgan}@example.edu

Fig. 1. EdgeSense performs local triage and requests peer confirmation only for ambiguous windows.

Index Terms—edge computing, environmental sensing, wildfire, TinyML, sensor networks

I. INTRODUCTION

II. SYSTEM DESIGN

III. DEPLOYMENT AND EVALUATION

ℒ = −w₊y log p − w₋(1−y) log(1−p) + λ∥θ∥²₂.(1)

TABLE I
Field evaluation results.

Method	Recall	FA/wk	Latency	Energy
Periodic stream	.957	1.8	72 s	100%
Fixed threshold	.876	3.4	61 s	41%
Ungated TCN	.946	1.1	58 s	52%
EdgeSense	.941	0.7	54 s	37%

Fig. 2. Cumulative detection performance remained stable across the deployment.

IV. RESULTS

V. ABLATIONS, SAFETY, AND LIMITATIONS

VI. CONCLUSION

Selective communication can extend unattended sensing without hiding early signals . The next deployment will test transfer across biomes and signed over-the-air model updates.

REFERENCES

[2]W. Schroeder, P. Oliva, L. Giglio, and I. A. Csiszar, “The new VIIRS 375 m active fire detection data product,” Remote Sensing of Environment, vol. 143, pp. 85–96, 2014.

[6]S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv:1803.01271, 2018.

[8]P. P. Ray, “A review on TinyML: State-of-the-art and prospects,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1595–1623, 2022.

[9]Y. Geifman and R. El-Yaniv, “Selective classification for deep neural networks,” arXiv:1705.08500, 2017.

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Propiedad y acceso	Precio	Gratis y de código abierto. Sin suscripción a Oleafly	Gratis con una cuenta de ChatGPT	Plan gratuito. Los planes de pago añaden funciones	Plan gratuito. El plan Pro tiene un coste adicional	Plan gratuito. Los planes de pago añaden proyectos privados	Gratis y de código abierto	Gratis, GPL	De pago. Demostración gratuita	Gratis. La IA tiene límites o planes de pago	Plan gratuito. Prime tiene un coste adicional	Plan gratuito. Premium tiene un coste adicional	Varía. La mayoría requiere una suscripción
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	Funciona sin conexión	Redacta, revisa, previsualiza y compila sin conexión	No, es un espacio de trabajo en la nube	No, es un servicio en la nube	No. La aplicación web necesita conexión	No. El editor es un servicio en la nube	Sí	Sí	Sí	Sí	No	No	Casi nunca
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Redacción y publicación	Motores de documentos	LaTeX, Typst y Markdown en un único espacio de trabajo	Solo LaTeX	Solo LaTeX	Solo Typst	MyST Markdown y Jupyter	LaTeX y DocBook	Solo LaTeX	LaTeX y Markdown	Mediante extensiones	Word, Google Docs, web y Overleaf	Word y LaTeX en Overleaf	Solo formatos de currículum
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Investigación e IA	Elección del modelo de IA	Independiente del modelo. Proveedores alojados, endpoints personalizados u Ollama local	Modelos de OpenAI integrados	IA de investigación integrada. La cuota diaria varía según el plan	Assists del compilador	No está documentado	Ninguna	Ninguno	Ninguna	Copilot, BYOK o extensiones	Paperpal AI	Modelos de Writefull y GPT	Integrada. La elección del modelo varía
	IA local	Ollama local. No requiere un proveedor alojado	No	No	No	No	No	Sin IA integrada	No	BYOK o extensiones	No	No	No
	Revisión de los cambios de la IA antes de aplicarlos	Diferencias revisables y controles para aprobar cada cambio	Conservar o deshacer	Revisión de las sugerencias de la IA	Revisión de cada Assist	No está documentado	Sin IA integrada	Sin IA integrada	Sin IA integrada	Revisión de los cambios	Revisión de sugerencias de redacción	Control de cambios	Varía
	La IA comprueba el resultado renderizado	Edita el código fuente, compila y después comprueba el PDF renderizado	Debes volver a compilar para confirmarlo	Debes volver a compilar para confirmarlo	Vista previa de la compilación	No	Sin IA integrada	Sin IA integrada	Sin IA integrada	Comprobaciones de compilación sin revisar el PDF	Sin revisión del PDF renderizado	Sin revisión del PDF renderizado	No
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	Importación de bibliotecas de referencias	BibTeX, RIS, EndNote y Zotero, con eliminación de duplicados antes de importar	Sincronización con Zotero	Integraciones de referencias en los planes de pago	Sincronización con Zotero y Mendeley en el plan Pro	Zotero, Mendeley y EndNote	Archivos BibTeX	Funciona con archivos BibTeX	Archivos BibTeX	Mediante extensiones	Generador de citas	Sin importación de bibliotecas	No
	MCP para agentes de IA externos	Servidor MCP integrado para Claude Code, Cursor y otros agentes	No	No	No	No	No	No	No	Sí	No	No	No
	Revisión ortográfica y gramatical	Ortografía y gramática privadas sin conexión, con comprobaciones de prosa compatibles con LaTeX	Corrección mediante IA	Corrector ortográfico integrado y observaciones de IA adaptadas a la investigación	No está documentado	No está documentado	Hunspell, Aspell y diccionario de sinónimos	Comprobaciones interactivas de ortografía y gramática	Corrector ortográfico	Mediante extensiones o IA	Gramática y estilo académicos	Observaciones sobre el lenguaje académico	Las comprobaciones básicas varían
	Herramientas visuales y de IA para diagramas	Lienzo visual y TikZ generado por IA con comprobaciones de renderizado	Conversión de imágenes y voz en código	TikZ manual y herramientas de IA	Paquetes de Typst y herramientas de dibujo nativas	Gráficos y figuras interactivos	Tablas visuales y herramientas de imagen	TikZ manual con ayudas del editor	TikZ manual	Manuales o mediante extensiones	No	Tablas, ecuaciones y figuras mediante IA	No
Calidad y envío	Comprobaciones previas al envío	Comprobaciones de compilación, publicación de destino, ATS, accesibilidad, referencias y privacidad	Revisión mediante IA sin un conjunto de herramientas para el envío	Comprobaciones de compilación	Errores del compilador y Assists	Herramientas de cumplimiento con SCMS	Registros del compilador y ChkTeX opcional	Errores y advertencias	Errores claros del compilador	Análisis estático mediante extensiones	Más de 30 comprobaciones para el envío	Revisión de la calidad del lenguaje	Solo comprobaciones de currículums
	Comprobaciones ATS para currículums	Comprobaciones ATS con puntuación y vista previa del texto extraído	No	No	No	No	No	No	No	Sin comprobación integrada	No	No	Varía. La puntuación suele ser poco clara
	Comprobaciones previas de accesibilidad	Comprobaciones de accesibilidad y preparación para la exportación etiquetada	No	No	No está documentado	Publicación web estructurada	Documentos estructurados. Sin comprobaciones previas	No	No está documentado	Sin comprobación integrada	No	No	Varía

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Yourthesisshouldn'tneedfivesubscriptions.YourAIshouldn'tguessateditsitneverchecked.Yourreferencesshouldn'ttakeanafternoon.YourfirstPDFshouldn'twaitinabuildqueue.Yourtoolsshouldn'tcostmorethanyourresearch.Onefreeapp.Allofit.

The Oleafly Club: an illustrated celebration of researchers at work, complete with a robot dog, math jokes, and a thesis crown

Código abierto, en serio

Un proyecto independiente con disciplina de producción.

Oleafly se construye en abierto por un mantenedor independiente, y las partes poco glamurosas también son producto: cancelación de compilación, seguridad de rutas, carreras de resultados obsoletos, recuperación Git, fallback de PDF y tests multiplataforma en macOS, Windows y Linux.

Licencia AGPL-3.0. Lee el código, abre un issue o envía un PR en GitHub.

FAQ

What to know before you switch.

What is Oleafly?

Oleafly is a local-first workspace for writing and shipping research. Draft in LaTeX, Typst, or Markdown, compile with bundled engines, proofread spelling and grammar locally, preview PDFs, manage citations, review AI edits, track versions in Git, and check resumes for ATS readability without moving between apps.

Is it really free?

Yes. Oleafly is free and open source. The desktop app does not add a subscription. If you connect a paid AI provider, that provider may charge for usage. Local models through Ollama do not require a paid API.

Which AI models does it work with?

Oleafly is model-agnostic. Use models from hosted providers, run local models through Ollama, or connect a custom OpenAI-compatible endpoint. You choose the model and supply credentials only when the provider requires them.

Do I need an account?

No. The desktop app has no Oleafly account or login. Your projects stay in ordinary local folders. Connected AI and Git hosting services may require their own credentials.

Which tools can it replace?

For many workflows, Oleafly can replace separate writing, proofreading, compilation, citation, PDF, resume, AI, and Git clients. It still works with ordinary files and GitHub, so you can adopt it without locking your work into a proprietary format.

Can I import an existing paper or resume?

Yes. Open an existing folder or import a ZIP, including exports from online LaTeX editors. Oleafly detects the main document. It can convert text-based PDFs to LaTeX locally, route DOCX through Pandoc, and use a vision model to transcribe scans or photos.

Is the PDF output ATS-friendly?

ATS readiness depends on the template and content, so Oleafly does not promise a score. Its resume templates use selectable Unicode text, embedded fonts, single-column layouts, and linear reading order. Preflight shows what a parser actually extracts before you send the PDF.

What about collaboration and sync?

Today, collaboration uses Git. Publish to GitHub, then push and pull from inside Oleafly. Real-time co-editing, comments, and managed sync are planned, but they are not in the app yet.

Ortografía y gramática offline

Hunspell y Harper en WASM en tu máquina, enmascarando comandos y matemáticas para revisar solo la prosa.

Descargar

Gratis. Código abierto. Tuyo.

Descarga un instalador precompilado de la última release, o compilar desde el código fuente. Sin cuenta, sin telemetría.

macOS Windows Linux

Las builds aún no están firmadas, así que el SO avisa en el primer arranque. La guía de instalación tiene el desbloqueo de una sola vez.

Escribe (()=>{var e=async t=>{await(await t())()};(self.Astro||(self.Astro={})).load=e;window.dispatchEvent(new Event("astro:load"));})();artículos de investigacióncurrículumstesispropuestas de subvenciónartículos de investigacióncomo escribes código.

artículos de investigación

Todo lo que esperarías. En tu máquina.

Importa lo que ya tienes

Exporta a donde vas

Hazlo a tu medida

Escribe en código o visual

SyncTeX a nivel de palabra

Inteligencia de código

Comandos slash

Citas por DOI

Trae toda tu biblioteca

Paleta de comandos

Trae tu propia IA

Investigación a mano

Ortografía y gramática offline

Un agente que recuerda

De una página a seiscientas

Un visor PDF para leer de verdad

Compositor de diagramas

Tres motores, un espacio

The LaTeX editor

The AI assistant

The reference manager

The resume builder

The version control

The PDF toolkit

Investiga, escribe, verifica, publica.

Investigar

Escribir

Verificar

Publicar

El único espacio de trabajo de investigación creado para sustituir a todos los demás.

Local por defecto. Red solo si eliges.

Siempre local

Red, solo cuando actúas

Los documentos deberían sobrevivir a los servicios.Los documentos deberían sobrevivir a los servicios.

Un proyecto independiente con disciplina de producción.

What to know before you switch.

Gratis. Código abierto. Tuyo.

Escribe
artículos de investigación
como escribes código.

Los documentos deberían sobrevivir a los servicios.