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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 (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}

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

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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}

□▭○◯◇▱TΣ

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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

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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

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hastie2009main.tex:21

vaswani2017main.tex:28

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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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@article{lecun2015deep,
  author  = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  title   = {Deep learning},
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  pages   = {436--444},
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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

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↕ TECTONICFederated Learning Data Flow

ImageUpdated Jul 29

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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

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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	रिज़्यूमे बिल्डर
स्वामित्व और पहुँच	कीमत	मुफ़्त और ओपन सोर्स। Oleafly सब्सक्रिप्शन नहीं	ChatGPT खाते के साथ मुफ़्त	मुफ़्त टियर। सशुल्क प्लान में अतिरिक्त सुविधाएँ	मुफ़्त टियर। Pro के लिए अतिरिक्त शुल्क	मुफ़्त टियर। सशुल्क प्लान में निजी प्रोजेक्ट	मुफ़्त और ओपन सोर्स	मुफ़्त, GPL	सशुल्क। मुफ़्त डेमो	मुफ़्त। AI पर सीमाएँ या सशुल्क प्लान	मुफ़्त टियर। Prime के लिए अतिरिक्त शुल्क	मुफ़्त टियर। Premium के लिए अतिरिक्त शुल्क	अलग-अलग। अधिकतर में सब्सक्रिप्शन
	खाता आवश्यक नहीं	हाँ	नहीं	नहीं	नहीं	नहीं	हाँ	हाँ	हाँ	हाँ	नहीं	नहीं	अलग-अलग
	ऑफ़लाइन काम करता है	ऑफ़लाइन लिखें, प्रूफ़रीड करें, प्रीव्यू देखें और कंपाइल करें	नहीं, क्लाउड वर्कस्पेस	नहीं, क्लाउड सेवा	नहीं। वेब ऐप को कनेक्शन चाहिए	नहीं। एडिटर एक क्लाउड सेवा है	हाँ	हाँ	हाँ	हाँ	नहीं	नहीं	शायद ही कभी
	फ़ाइलें आपकी डिस्क पर रहती हैं	आपके स्वामित्व वाले लोकल फ़ोल्डर। सेल्फ़-होस्टेड क्लाउड सिंक	क्लाउड स्टोरेज	क्लाउड स्टोरेज	क्लाउड स्टोरेज। कंपाइलर लोकल रूप से चल सकता है	क्लाउड स्टोरेज	हाँ	हाँ	लोकल फ़ाइलें और फ़ाइल प्रोवाइडर	हाँ	क्लाउड सेवा और एडिटर इंटीग्रेशन	फ़ाइलें Word या Overleaf में रहती हैं	आमतौर पर क्लाउड स्टोरेज
	संस्करण इतिहास	डिफ़ और रीस्टोर सहित स्वचालित Git चेकपॉइंट	क्लाउड रिविज़न	सशुल्क प्लान में पूरा इतिहास	Pro में Git सिंक	बिल्ट-इन संस्करण इतिहास	Git और अन्य संस्करण नियंत्रण	मैन्युअल या SVN	फ़ाइल प्रोवाइडर या Git टूल का उपयोग	बिल्ट-इन Git, जिसे आप कॉन्फ़िगर करते हैं	दस्तावेज़ित नहीं	होस्ट ऐप के इतिहास का उपयोग	अलग-अलग
	रीयल-टाइम सहयोग	जल्द आ रहा है। असीमित सहयोग। सेल्फ़-होस्टेड	असीमित सहयोगी	बिल्ट-इन। सीमाएँ प्लान पर निर्भर	बिल्ट-इन	रीयल-टाइम संपादन और टिप्पणियाँ	लाइव सह-संपादन नहीं	लाइव सह-संपादन नहीं	लाइव सह-संपादन नहीं	Live Share के ज़रिए	Word, Google Docs या Overleaf का उपयोग	Word या Overleaf का उपयोग	अलग-अलग
	क्लाउड सिंक	जल्द आ रहा है। फ़ाइलें आपकी मशीन पर रखते हुए ब्राउज़र से पहुँच	क्लाउड वर्कस्पेस	क्लाउड वर्कस्पेस, साथ में सशुल्क Git और Dropbox सिंक	क्लाउड वर्कस्पेस, साथ में Pro Git सिंक	क्लाउड प्रोजेक्ट और कनेक्टेड पब्लिशिंग	Git या अपनी सिंक सेवा इस्तेमाल करें	Git या अपनी सिंक सेवा इस्तेमाल करें	Texifier Connect लाइव सिंक	Git या अपनी सिंक सेवा इस्तेमाल करें	क्लाउड वर्कस्पेस	Word या Overleaf के भीतर काम करता है	आमतौर पर क्लाउड स्टोरेज
	टीम स्पेस	जल्द आ रहा है। साझा टेम्पलेट, लाइब्रेरी और समीक्षा वर्कफ़्लो	असीमित सहयोगियों के साथ साझा प्रोजेक्ट	समूह और संस्थागत प्लान	साझा प्रोजेक्ट, साथ में संगठनों के लिए ऑन-प्रिमाइसेस विकल्प	टीम वर्कस्पेस और एक्सेस कंट्रोल	बिल्ट-इन टीम स्पेस नहीं	बिल्ट-इन टीम स्पेस नहीं	Texifier Connect के ज़रिए साझा प्रोजेक्ट	GitHub संगठन और Live Share	संस्थागत डैशबोर्ड और लाइसेंस प्रबंधन	संस्थागत पहुँच	अलग-अलग
लेखन और प्रकाशन	डॉक्यूमेंट इंजन	एक वर्कस्पेस में LaTeX, Typst और Markdown	केवल LaTeX	केवल LaTeX	केवल Typst	MyST Markdown और Jupyter	LaTeX और DocBook	केवल LaTeX	LaTeX और Markdown	एक्सटेंशन के ज़रिए	Word, Google Docs, वेब और Overleaf	Word और Overleaf LaTeX	केवल रिज़्यूमे फ़ॉर्मेट
	TeX सेटअप	Tectonic और Typst साथ में। TeX इंस्टॉल करने की ज़रूरत नहीं	इंस्टॉल नहीं। क्लाउड में कंपाइल	इंस्टॉल नहीं। क्लाउड में कंपाइल	TeX इंस्टॉल नहीं	TeX इंस्टॉल नहीं	TeX डिस्ट्रीब्यूशन इंस्टॉल करें	TeX डिस्ट्रीब्यूशन इंस्टॉल करें	बिल्ट-इन टाइपसेटर और पैकेज मैनेजर	एक्सटेंशन और TeX इंस्टॉल करें	LaTeX के लिए Overleaf का उपयोग	LaTeX के लिए Overleaf का उपयोग	TeX सेटअप नहीं
	कोड + विज़ुअल एडिटिंग	बिना किसी हानि के दोनों ओर रूपांतरण सहित सोर्स और विज़ुअल एडिटिंग	सोर्स और प्रीव्यू	सोर्स और रिच टेक्स्ट मोड	सोर्स और लाइव प्रीव्यू	LaTeX गणित वाला विज़ुअल एडिटर	संरचित विज़ुअल एडिटर	केवल सोर्स	सोर्स और PDF प्रीव्यू	केवल सोर्स	विज़ुअल लेखन सहायक	इनलाइन लेखन सुझाव	केवल विज़ुअल
	पूरे प्रोजेक्ट की कोड इंटेलिजेंस	ऑटोकम्प्लीट, साथ में डेफ़िनिशन, रेफ़रेंस और पूरे प्रोजेक्ट में रीनेम	एडिटर और AI सहायता	ऑटोकम्प्लीट और रेफ़रेंस खोज	त्रुटियाँ, कम्प्लीशन और Assists	संरचित रेफ़रेंस और समीकरण	लेबल, रेफ़रेंस, आउटलाइन और कम्प्लीशन	स्ट्रक्चर और रेफ़रेंस चेकर	कम्प्लीशन, आउटलाइन, रेफ़रेंस और खोज	LaTeX Workshop IntelliSense	LaTeX कोड टूल नहीं	LaTeX कोड के लिए TeXGPT	नहीं
	सोर्स और PDF सिंक	शब्द स्तर पर दोनों दिशाओं में सोर्स और PDF सिंक	तुरंत PDF प्रीव्यू	सोर्स और PDF सिंक	लाइव प्रीव्यू	सोर्स और PDF सिंक नहीं	सोर्स और आउटपुट प्रीव्यू	लगभग शब्द स्तर तक	सोर्स और PDF सिंक	डायरेक्ट और रिवर्स SyncTeX	नहीं	Overleaf प्रीव्यू का उपयोग	नहीं
	PDF पढ़ने के मोड	बिल्ट-इन व्यूअर, साथ में अलग विंडो, स्प्रेड, डार्क और रीडर मोड	बिल्ट-इन प्रीव्यू	बिल्ट-इन प्रीव्यू	बिल्ट-इन प्रीव्यू	डॉक्यूमेंट प्रीव्यू	PDF व्यूअर का उपयोग	इंटीग्रेटेड व्यूअर	इंटीग्रेटेड PDF व्यूअर	इनवर्ट मोड वाला एक्सटेंशन व्यूअर	PDF के साथ चैट	PDF रीडर नहीं	डॉक्यूमेंट प्रीव्यू
	डॉक्यूमेंट टेम्पलेट	100+ स्टार्टर और दोबारा इस्तेमाल योग्य कस्टम टेम्पलेट	दस्तावेज़ित नहीं	प्रकाशक टेम्पलेट की बड़ी गैलरी	Typst Universe और निजी Pro टेम्पलेट	सशुल्क प्लान में प्रोफ़ेशनल टेम्पलेट	बिल्ट-इन डॉक्यूमेंट क्लास	बिल्ट-इन और यूज़र टेम्पलेट	दस्तावेज़ित नहीं	स्निपेट और कम्युनिटी टेम्पलेट	दस्तावेज़ित नहीं	नहीं	मुख्य सुविधा
	प्रोजेक्ट और डॉक्यूमेंट इम्पोर्ट	फ़ाइलें और ZIP, साथ में PDF, DOCX, Markdown, इमेज और फ़ोल्डर	फ़ोल्डर, ZIP या ड्रैग-एंड-ड्रॉप	फ़ाइलें, ZIP और सशुल्क इंटीग्रेशन	LaTeX, Word और Markdown को कन्वर्ट करता है	Jupyter सामग्री और रेफ़रेंस इम्पोर्ट	LaTeX और अन्य टेक्स्ट फ़ॉर्मेट	लोकल सोर्स फ़ाइलें खोलें	लोकल फ़ोल्डर और क्लाउड फ़ाइलें	लोकल सोर्स फ़ाइलें खोलें	PDF और डॉक्यूमेंट अपलोड	Word और Overleaf डॉक्यूमेंट	आमतौर पर PDF या DOCX
	एक्सपोर्ट फ़ॉर्मेट	PDF और सोर्स ZIP, साथ में DOCX, HTML और Markdown	PDF और प्रोजेक्ट फ़ाइलें	PDF और सोर्स ZIP	PDF, PNG और SVG	PDF, Word, LaTeX, Typst और MyST	PDF, HTML, Word, RTF, ePub और अन्य	PDF और सोर्स फ़ाइलें	PDF और सोर्स फ़ाइलें	लोकल टूल पर निर्भर	Word वर्कफ़्लो	ट्रैक किए गए Word बदलाव	आमतौर पर PDF और DOCX
शोध और AI	AI मॉडल का चुनाव	मॉडल-निरपेक्ष। होस्टेड प्रोवाइडर, कस्टम एंडपॉइंट या लोकल Ollama	OpenAI मॉडल बिल्ट-इन	बिल्ट-इन शोध AI। दैनिक सीमा प्लान के अनुसार बदलती है	कंपाइलर Assists	दस्तावेज़ित नहीं	कोई नहीं	कोई नहीं	कोई नहीं	Copilot, BYOK या एक्सटेंशन	Paperpal AI	Writefull मॉडल और GPT	बिल्ट-इन। मॉडल का चुनाव अलग-अलग
	लोकल AI	लोकल Ollama। होस्टेड प्रोवाइडर की आवश्यकता नहीं	नहीं	नहीं	नहीं	नहीं	नहीं	बिल्ट-इन AI नहीं	नहीं	BYOK या एक्सटेंशन	नहीं	नहीं	नहीं
	लागू करने से पहले AI संपादन की समीक्षा	समीक्षा योग्य डिफ़ और हर संपादन के लिए अलग अनुमोदन नियंत्रण	रखें या पूर्ववत करें	AI सुझावों की समीक्षा	हर Assist की समीक्षा	दस्तावेज़ित नहीं	बिल्ट-इन AI नहीं	बिल्ट-इन AI नहीं	बिल्ट-इन AI नहीं	बदलावों की समीक्षा	लेखन सुझावों की समीक्षा	Track Changes	अलग-अलग
	AI रेंडर किए गए परिणाम की जाँच करता है	सोर्स संपादित करता है, कंपाइल करता है, फिर रेंडर किए गए PDF की जाँच करता है	पुष्टि के लिए आपको दोबारा कंपाइल करना होगा	पुष्टि के लिए आपको दोबारा कंपाइल करना होगा	कंपाइल प्रीव्यू	नहीं	बिल्ट-इन AI नहीं	बिल्ट-इन AI नहीं	बिल्ट-इन AI नहीं	PDF समीक्षा के बिना बिल्ड जाँच	रेंडर किए गए PDF की समीक्षा नहीं	रेंडर किए गए PDF की समीक्षा नहीं	नहीं
	साहित्य और उद्धरण खोज	Crossref सत्यापन के साथ alphaXiv और OpenAlex में साहित्य खोज	बिल्ट-इन साहित्य खोज	रेफ़रेंस खोज और AI उद्धरण सुझाव	बिल्ट-इन खोज नहीं	DOI उद्धरण खोज	बिल्ट-इन खोज नहीं	नहीं	बिल्ट-इन खोज नहीं	AI या एक्सटेंशन के ज़रिए	25 करोड़+ लेखों में खोज	Writefull Cite छूटे हुए उद्धरण खोजता है	नहीं
	स्वायत्त शोध एजेंट	जल्द आ रहा है। एजेंट साहित्य खोजते हैं, उद्धरण सत्यापित करते हैं, मसौदा बनाते हैं और आपको डिफ़ सौंपते हैं	दस्तावेज़ित नहीं	दस्तावेज़ित नहीं	नहीं	दस्तावेज़ित नहीं	नहीं	नहीं	नहीं	AI एक्सटेंशन के ज़रिए	नहीं	नहीं	नहीं
	रेफ़रेंस लाइब्रेरी इम्पोर्ट	इम्पोर्ट से पहले डुप्लिकेट हटाने के साथ BibTeX, RIS, EndNote और Zotero	Zotero सिंक	सशुल्क प्लान में रेफ़रेंस इंटीग्रेशन	Pro में Zotero और Mendeley सिंक	Zotero, Mendeley और EndNote	BibTeX फ़ाइलें	BibTeX फ़ाइलों के साथ काम करता है	BibTeX फ़ाइलें	एक्सटेंशन के ज़रिए	उद्धरण जनरेटर	लाइब्रेरी इम्पोर्ट नहीं	नहीं
	बाहरी AI एजेंट के लिए MCP	Claude Code, Cursor और अन्य एजेंट के लिए बिल्ट-इन MCP सर्वर	नहीं	नहीं	नहीं	नहीं	नहीं	नहीं	नहीं	हाँ	नहीं	नहीं	नहीं
	वर्तनी और व्याकरण जाँच	LaTeX-सचेत गद्य जाँच के साथ निजी ऑफ़लाइन वर्तनी और व्याकरण जाँच	AI प्रूफ़रीडिंग	बिल्ट-इन वर्तनी जाँच और शोध-अनुकूल AI फ़ीडबैक	दस्तावेज़ित नहीं	दस्तावेज़ित नहीं	Hunspell, Aspell और थिसॉरस	इंटरैक्टिव वर्तनी और व्याकरण जाँच	वर्तनी जाँच	एक्सटेंशन या AI के ज़रिए	अकादमिक व्याकरण और शैली	अकादमिक भाषा पर फ़ीडबैक	बुनियादी जाँच अलग-अलग
	विज़ुअल और AI डायग्राम टूल	विज़ुअल कैनवस और रेंडर जाँच सहित AI-जनरेटेड TikZ	इमेज और आवाज़ से कोड	मैन्युअल TikZ और AI टूल	Typst पैकेज और नेटिव ड्रॉइंग	इंटरैक्टिव चार्ट और आकृतियाँ	विज़ुअल टेबल और इमेज टूल	एडिटर सहायक टूल के साथ मैन्युअल TikZ	मैन्युअल TikZ	मैन्युअल या एक्सटेंशन	नहीं	AI टेबल, समीकरण और आकृतियाँ	नहीं
गुणवत्ता और सबमिशन	सबमिशन प्रीफ़्लाइट	कंपाइल जाँच, साथ में प्रकाशन-स्थल, ATS, एक्सेसिबिलिटी, रेफ़रेंस और गोपनीयता जाँच	सबमिशन सुइट के बिना AI समीक्षा	कंपाइल जाँच	कंपाइलर त्रुटियाँ और Assists	SCMS अनुपालन टूल	कंपाइलर लॉग और वैकल्पिक ChkTeX	त्रुटियाँ और चेतावनियाँ	स्पष्ट कंपाइलर त्रुटियाँ	एक्सटेंशन के ज़रिए लिंटिंग	30+ सबमिशन जाँच	भाषा गुणवत्ता समीक्षा	केवल रिज़्यूमे जाँच
	ATS रिज़्यूमे जाँच	स्कोर सहित ATS जाँच और निकाले गए टेक्स्ट का प्रीव्यू	नहीं	नहीं	नहीं	नहीं	नहीं	नहीं	नहीं	बिल्ट-इन जाँच नहीं	नहीं	नहीं	अलग-अलग। स्कोरिंग अक्सर अस्पष्ट होती है
	एक्सेसिबिलिटी प्रीफ़्लाइट	एक्सेसिबिलिटी जाँच और टैग किए गए एक्सपोर्ट की तैयारी	नहीं	नहीं	दस्तावेज़ित नहीं	संरचित वेब प्रकाशन	संरचित डॉक्यूमेंट। प्रीफ़्लाइट नहीं	नहीं	दस्तावेज़ित नहीं	बिल्ट-इन जाँच नहीं	नहीं	नहीं	अलग-अलग

गोपनीयता, बिल्कुल

डिफ़ॉल्ट रूप से स्थानीय. पसंद से नेटवर्क.

कोई अस्पष्ट वादा नहीं: यहां वही है जो आपकी मशीन पर रहता है और ठीक उसी समय जब ओलेफली नेटवर्क को छूता है। कुछ भी भेजने से पहले आपकी समीक्षा के लिए कोई खाता, कोई टेलीमेट्री और क्रैश रिपोर्ट नहीं खुलती है। गोपनीयता.

हमेशा स्थानीय

लेखन, संपादन और आपकी प्रोजेक्ट फ़ाइलें
कैश्ड पैकेज के साथ संकलन (ऐप में टेक्टोनिक और टाइपस्ट शिप)
गिट इतिहास, अंतर, और पुनर्स्थापित करें

नेटवर्क, केवल तभी जब आप कार्य करते हैं

होस्ट किए गए AI प्रदाता आपके द्वारा भेजे गए संदर्भ को प्राप्त करते हैं
GitHub प्रकाशित करें, पुश करें और खींचें
उद्धरण लुकअप (DOI, arXiv, क्रॉसरेफ़) और अनुसंधान कनेक्टर (OpenAlex, alphaXiv)

दर्शन

दस्तावेज़ों को सेवाओं से अधिक जीवित रहना चाहिए।

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

खुला स्रोत, गंभीरता से निर्मित

उत्पादन अनुशासन के साथ एक स्वतंत्र परियोजना।

ओलेफ़ली को एक स्वतंत्र अनुरक्षक द्वारा खुले में बनाया गया है, और अनग्लैमरस भागों को उत्पाद सुविधाओं के रूप में भी माना जाता है: संकलन रद्दीकरण, पथ सुरक्षा, बासी-परिणाम दौड़, गिट पुनर्प्राप्ति, पीडीएफ फ़ॉलबैक, और मैकओएस, विंडोज और लिनक्स पर क्रॉस-प्लेटफ़ॉर्म परीक्षण।

एजीपीएल-3.0 लाइसेंस प्राप्त। स्रोत पढ़ें, समस्या दर्ज करें, या पीआर भेजें 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.

ऑफ़लाइन वर्तनी और व्याकरण

डाउनलोड करना

मुक्त। खुला स्त्रोत। आपका.

नवीनतम रिलीज़ से प्रीबिल्ट इंस्टॉलर लें, या स्रोत से निर्माण करें. कोई खाता नहीं, कोई टेलीमेट्री नहीं.

macOS Windows Linux

बिल्ड अभी तक कोड-हस्ताक्षरित नहीं हैं, इसलिए आपका OS पहले लॉन्च पर चेतावनी देता है। {लिंक} में एक बार का अनलॉक है।गाइड स्थापित करें

शोध पत्र

बाकी सब कुछ जिसकी आप अपेक्षा करेंगे। आपकी मशीन पर.

जो आपके पास है उसे आयात करें

आप जहां जा रहे हैं वहां निर्यात करें

इसे अपना बनाएं

कोड या विज़ुअल में लिखें

SyncTeX, शब्द-स्तर

कोड इंटेलिजेंस

स्लैश आदेश

डीओआई द्वारा उद्धरण

अपनी पूरी लाइब्रेरी ले आओ

कमांड पैलेट

अपना खुद का एआई लाओ

नल पर अनुसंधान

ऑफ़लाइन वर्तनी और व्याकरण

एक एजेंट जो याद रखता है

एक पेज से छह सौ तक

वास्तविक पढ़ने के लिए एक पीडीएफ दर्शक

आरेख संगीतकार

तीन इंजन, एक कार्यक्षेत्र

The LaTeX editor

The AI assistant

The reference manager

The resume builder

The version control

The PDF toolkit

शोध करें, लिखें, सत्यापित करें, प्रकाशित करें।

अनुसंधान