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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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		Oleafly	OpenAI Prism	Overleaf	Typst Web App	Curvenote	LyX	TeXstudio	Texifier	VS Code + LaTeX	Paperpal	Writefull	Criadores de currículos
Propriedade e acesso	Preço	Gratuito e de código aberto. Sem assinatura do Oleafly	Gratuito com uma conta do ChatGPT	Plano gratuito. Os planos pagos adicionam recursos	Plano gratuito. O Pro tem custo adicional	Plano gratuito. Os planos pagos adicionam projetos privados	Gratuito e de código aberto	Gratuito, GPL	Pago. Demonstração gratuita	Gratuito. A IA tem limites ou planos pagos	Plano gratuito. O Prime tem custo adicional	Plano gratuito. O Premium tem custo adicional	Varia. A maioria usa assinatura
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	Funciona offline	Escreva, revise, visualize e compile offline	Não, espaço de trabalho na nuvem	Não, serviço na nuvem	Não. O aplicativo web precisa de conexão	Não. O editor é um serviço na nuvem	Sim	Sim	Sim	Sim	Não	Não	Raramente
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	Histórico de versões	Checkpoints automáticos do Git, com comparação e restauração	Revisões na nuvem	Histórico completo nos planos pagos	Sincronização com Git no Pro	Histórico de versões integrado	Git e outros sistemas de controle de versão	Manual ou SVN	Usa provedores de arquivos ou ferramentas Git	Git integrado, configurado por você	Não documentado	Usa o histórico do aplicativo host	Varia
	Colaboração em tempo real	Em breve. Colaboração ilimitada. Auto-hospedada	Colaboradores ilimitados	Integrada. Os limites dependem do plano	Integrada	Edição e comentários em tempo real	Sem coedição em tempo real	Sem coedição em tempo real	Sem coedição em tempo real	Por meio do Live Share	Usa Word, Google Docs ou Overleaf	Usa Word ou Overleaf	Varia
	Sincronização na nuvem	Em breve. Acesso pelo navegador enquanto os arquivos permanecem na sua máquina	Espaço de trabalho na nuvem	Espaço de trabalho na nuvem, além de sincronização paga com Git e Dropbox	Espaço de trabalho na nuvem, além de sincronização com Git no Pro	Projetos na nuvem e publicação integrada	Use Git ou seu próprio serviço de sincronização	Use Git ou seu próprio serviço de sincronização	Sincronização em tempo real pelo Texifier Connect	Use Git ou seu próprio serviço de sincronização	Espaço de trabalho na nuvem	Funciona dentro do Word ou Overleaf	Geralmente, armazenamento na nuvem
	Espaços de equipe	Em breve. Modelos e bibliotecas compartilhados, além de fluxos de revisão	Projetos compartilhados com colaboradores ilimitados	Planos para grupos e instituições	Projetos compartilhados e implantação local para organizações	Espaços de trabalho em equipe e controles de acesso	Sem espaço de equipe integrado	Sem espaço de equipe integrado	Projetos compartilhados pelo Texifier Connect	Organizações do GitHub e Live Share	Painéis institucionais e gerenciamento de licenças	Acesso institucional	Varia
Redação e publicação	Mecanismos de documentos	LaTeX, Typst e Markdown em um único espaço de trabalho	Somente LaTeX	Somente LaTeX	Somente Typst	MyST Markdown e Jupyter	LaTeX e DocBook	Somente LaTeX	LaTeX e Markdown	Por meio de extensões	Word, Google Docs, web e Overleaf	Word e LaTeX no Overleaf	Somente formatos de currículo
	Configuração do TeX	Tectonic e Typst incluídos. Não é preciso instalar o TeX	Sem instalação. Compila na nuvem	Sem instalação. Compila na nuvem	Não é preciso instalar o TeX	Não é preciso instalar o TeX	Instale uma distribuição TeX	Instale uma distribuição TeX	Compositor tipográfico e gerenciador de pacotes integrados	Instale extensões e o TeX	Usa Overleaf para LaTeX	Usa Overleaf para LaTeX	Sem configuração do TeX
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Pesquisa e IA	Opções de modelos de IA	Independente de modelo. Provedores hospedados, endpoints personalizados ou Ollama local	Modelos da OpenAI integrados	IA de pesquisa integrada. A franquia diária varia conforme o plano	Assists do compilador	Não documentado	Nenhuma	Nenhuma	Nenhuma	Copilot, BYOK ou extensões	IA do Paperpal	Modelos do Writefull e GPT	Integrada. As opções de modelos variam
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	Ferramentas visuais e de IA para diagramas	Tela visual e TikZ gerado por IA, com verificações de renderização	Imagem e voz convertidas em código	TikZ manual e ferramentas de IA	Pacotes do Typst e desenho nativo	Gráficos e figuras interativos	Tabelas visuais e ferramentas de imagem	TikZ manual, com recursos auxiliares no editor	TikZ manual	Manual ou por extensões	Não	Tabelas, equações e figuras geradas por IA	Não
Qualidade e submissão	Verificação prévia à submissão	Verificações de compilação, local de publicação, ATS, acessibilidade, referências e privacidade	Revisão por IA sem um conjunto de ferramentas para submissão	Verificações de compilação	Erros do compilador e Assists	Ferramentas de conformidade com SCMS	Logs do compilador e ChkTeX opcional	Erros e avisos	Erros claros do compilador	Linting por meio de extensões	Mais de 30 verificações para submissão	Revisão da qualidade linguística	Somente verificações de currículo
	Verificações de currículo para ATS	Verificações de ATS com pontuação e visualização do texto extraído	Não	Não	Não	Não	Não	Não	Não	Sem verificação integrada	Não	Não	Varia. A pontuação costuma ser pouco transparente
	Verificação prévia de acessibilidade	Verificações de acessibilidade e preparação para exportação com marcação	Não	Não	Não documentado	Publicação estruturada na web	Documentos estruturados. Sem verificação prévia	Não	Não documentado	Sem verificação integrada	Não	Não	Varia

Privacidade, precisamente

Local por padrão. Rede por escolha.

Sem promessas vagas: aqui está exatamente o que fica na sua máquina e exatamente quando o Oleafly entra em contato com a rede. Nenhuma conta, nenhuma telemetria e relatórios de falhas abertos para sua revisão antes que qualquer coisa seja enviada. Privacidade.

Sempre locais

Escrevendo, editando e seus arquivos de projeto
Compilando com pacotes em cache (Tectonic & Typst são enviados no aplicativo)
Histórico, diferenças e restauração do Git

Rede, somente quando você age

Os provedores de IA hospedados recebem o contexto que você lhes envia
Publicação, push e pull do GitHub
Pesquisa de citações (DOI, arXiv, Crossref) e conectores de pesquisa (OpenAlex, alphaXiv)

Filosofia

Os documentos devem durar mais que os serviços.

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 aberto, construído seriamente

Um projeto independente com disciplina de produção.

Oleafly é desenvolvido abertamente por um mantenedor independente, e as partes nada glamorosas também são tratadas como recursos do produto: cancelamento de compilação, segurança de caminho, corridas de resultados obsoletos, recuperação de Git, fallback de PDF e testes de plataforma cruzada em macOS, Windows e Linux.

Licença AGPL-3.0. Leia a fonte, registre um problema ou envie um PR em 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.

Ortografia e gramática offline

Hunspell e Harper são executados como WASM em sua máquina, mascarando comandos e matemática para que apenas a prosa seja verificada.

Download

Livre. Código aberto. Seu.

Obtenha um instalador pré-construído da versão mais recente ou construir a partir da fonte. Sem conta, sem telemetria.

macOS Windows Linux

As compilações ainda não foram assinadas por código, então seu sistema operacional avisa na primeira inicialização. O guia de instalação tem desbloqueio único.

Escrever (()=>{var e=async t=>{await(await t())()};(self.Astro||(self.Astro={})).load=e;window.dispatchEvent(new Event("astro:load"));})();artigos de pesquisacurrículostesespropostas de subvençãoartigos de pesquisacomo você escreve código.

artigos de pesquisa

Tudo o mais que você esperaria. Na sua máquina.

Importe o que você tem

Exporte para onde você está indo

Faça com que seja seu

Escreva em código ou visual

SyncTeX, nível de palavra

Inteligência de código

Comandos de barra

Citações por DOI

Traga toda a sua biblioteca

Paleta de comandos

Traga sua própria IA

Pesquisa disponível

Ortografia e gramática offline

Um agente que se lembra

De uma página a seiscentas

Um visualizador de PDF para leitura real

Compositor de Diagramas

Três motores, um espaço de trabalho

The LaTeX editor

The AI assistant

The reference manager

The resume builder

The version control

The PDF toolkit

Pesquise, escreva, verifique, publique.

Pesquisar

Escrever

Verificar

Publicar

O único espaço de trabalho para pesquisa criado para substituir todos os outros.

Local por padrão. Rede por escolha.

Sempre locais

Rede, somente quando você age

Os documentos devem durar mais que os serviços.Os documentos devem durar mais que os serviços.

Um projeto independente com disciplina de produção.

What to know before you switch.

Livre. Código aberto. Seu.

Escrever
artigos de pesquisa
como você escreve código.

Os documentos devem durar mais que os serviços.