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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 (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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ResumeUpdated Jul 12

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DocumentUpdated Jul 12

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}

□▭○◯◇▱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

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)

✓Saved to figures/diagram.png

Save As:

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

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hastie2009main.tex:21

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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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  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

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

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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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Araştırma ve yapay zekâ	Yapay zekâ modeli seçimi	Modelden bağımsız. Barındırılan sağlayıcılar, özel uç noktalar veya yerel Ollama	Yerleşik OpenAI modelleri	Yerleşik araştırma yapay zekâsı. Günlük kullanım hakkı plana göre değişir	Derleyici Assists	Belgelendirilmemiş	Yok	Yok	Yok	Copilot, BYOK veya eklentiler	Paperpal AI	Writefull modelleri ve GPT	Yerleşik. Model seçimi değişir
	Yerel yapay zekâ	Yerel Ollama. Barındırılan sağlayıcı gerekmez	Hayır	Hayır	Hayır	Hayır	Hayır	Yerleşik yapay zekâ yok	Hayır	BYOK veya eklentiler	Hayır	Hayır	Hayır
	Yapay zekâ düzenlemelerini uygulamadan önce inceleme	İncelenebilir farklar ve her düzenleme için ayrı onay denetimleri	Koru veya Geri Al	Yapay zekâ önerilerini inceleme	Her Assist'i inceleme	Belgelendirilmemiş	Yerleşik yapay zekâ yok	Yerleşik yapay zekâ yok	Yerleşik yapay zekâ yok	Değişiklikleri inceleme	Yazım önerilerini inceleme	Değişiklikleri İzle	Değişir
	Yapay zekânın oluşturulan çıktıyı denetlemesi	Kaynağı düzenler, derler ve ardından oluşturulan PDF'yi denetler	Doğrulamak için yeniden derlersiniz	Doğrulamak için yeniden derlersiniz	Derleme önizlemesi	Hayır	Yerleşik yapay zekâ yok	Yerleşik yapay zekâ yok	Yerleşik yapay zekâ yok	PDF incelemesi olmadan derleme denetimleri	Oluşturulan PDF'yi incelemez	Oluşturulan PDF'yi incelemez	Hayır
	Literatür ve atıf arama	Crossref doğrulamasıyla alphaXiv ve OpenAlex genelinde literatür arama	Yerleşik literatür arama	Kaynak arama ve yapay zekâ destekli atıf önerileri	Yerleşik arama yok	DOI ile atıf arama	Yerleşik arama yok	Hayır	Yerleşik arama yok	Yapay zekâ veya eklentiler aracılığıyla	250 milyondan fazla makalede arama	Writefull Cite eksik atıfları bulur	Hayır
	Otonom araştırma ajanları	Yakında. Ajanlar literatürü tarar, atıfları doğrular, taslak hazırlar ve farkları size sunar	Belgelendirilmemiş	Belgelendirilmemiş	Hayır	Belgelendirilmemiş	Hayır	Hayır	Hayır	Yapay zekâ eklentileri aracılığıyla	Hayır	Hayır	Hayır
	Kaynakça kitaplığını içe aktarma	İçe aktarma öncesi yinelenen kayıtları ayıklayarak BibTeX, RIS, EndNote ve Zotero	Zotero eşitleme	Ücretli planlarda kaynakça entegrasyonları	Pro'da Zotero ve Mendeley eşitleme	Zotero, Mendeley ve EndNote	BibTeX dosyaları	BibTeX dosyalarıyla çalışır	BibTeX dosyaları	Eklentiler aracılığıyla	Atıf oluşturucu	Kitaplık içe aktarma yok	Hayır
	Harici yapay zekâ ajanları için MCP	Claude Code, Cursor ve diğer ajanlar için yerleşik MCP sunucusu	Hayır	Hayır	Hayır	Hayır	Hayır	Hayır	Hayır	Evet	Hayır	Hayır	Hayır
	Yazım ve dil bilgisi denetimleri	LaTeX'e duyarlı düzyazı denetimleriyle gizli ve çevrimdışı yazım ve dil bilgisi denetimi	Yapay zekâ destekli metin denetimi	Yerleşik yazım denetiminin yanı sıra araştırmaya özel yapay zekâ geri bildirimi	Belgelendirilmemiş	Belgelendirilmemiş	Hunspell, Aspell ve eş anlamlılar sözlüğü	Etkileşimli yazım ve dil bilgisi denetimleri	Yazım denetimi	Eklentiler veya yapay zekâ aracılığıyla	Akademik dil bilgisi ve üslup	Akademik dil geri bildirimi	Temel denetimler değişir
	Görsel ve yapay zekâ destekli diyagram araçları	Görsel tuval ve oluşturma denetimleriyle yapay zekânın ürettiği TikZ	Görsel ve sesten koda	Elle TikZ ve yapay zekâ araçları	Typst paketleri ve yerel çizim	Etkileşimli grafikler ve şekiller	Görsel tablo ve görsel araçları	Düzenleyici yardımcılarıyla elle TikZ	Elle TikZ	Elle veya eklentiler aracılığıyla	Hayır	Yapay zekâ destekli tablolar, denklemler ve şekiller	Hayır
Kalite ve gönderim	Gönderim öncesi denetim	Derleme denetimlerinin yanı sıra yayın yeri, ATS, erişilebilirlik, kaynakça ve gizlilik denetimleri	Gönderim araç seti olmadan yapay zekâ incelemesi	Derleme denetimleri	Derleyici hataları ve Assists	SCMS uyumluluk araçları	Derleyici günlükleri ve isteğe bağlı ChkTeX	Hatalar ve uyarılar	Anlaşılır derleyici hataları	Eklentiler aracılığıyla kod denetimi	30'dan fazla gönderim denetimi	Dil kalitesi incelemesi	Yalnızca özgeçmiş denetimleri
	ATS özgeçmiş denetimleri	Puanlı ATS denetimleri ve ayıklanan metin önizlemesi	Hayır	Hayır	Hayır	Hayır	Hayır	Hayır	Hayır	Yerleşik denetim yok	Hayır	Hayır	Değişir. Puanlama çoğu zaman belirsizdir
	Erişilebilirlik ön denetimi	Erişilebilirlik denetimleri ve etiketli dışa aktarıma hazırlık	Hayır	Hayır	Belgelendirilmemiş	Yapılandırılmış web yayıncılığı	Yapılandırılmış belgeler. Ön denetim yok	Hayır	Belgelendirilmemiş	Yerleşik denetim yok	Hayır	Hayır	Değişir

Gizlilik, tam olarak

Varsayılan olarak yerel. Seçime göre ağ.

Belirsiz vaatler yok: İşte makinenizde tam olarak ne kalacak ve Oleafly ağa tam olarak ne zaman dokunacak. Hiçbir hesap, telemetri yok ve herhangi bir şey gönderilmeden önce incelemeniz için kilitlenme raporları açık değil. Mahremiyet.

Her zaman yerel

Proje dosyalarınızı yazma, düzenleme ve
Önbelleğe alınmış paketlerle derleme (uygulamada Tectonic ve Typst gönderilir)
Git geçmişi, farklar ve geri yükleme

Ağ, yalnızca harekete geçtiğinizde

Barındırılan yapay zeka sağlayıcıları, onlara gönderdiğiniz içeriği alır
GitHub yayınlama, aktarma ve çekme
Alıntı arama (DOI, arXiv, Crossref) ve araştırma bağlayıcıları (OpenAlex, alphaXiv)

Felsefe

Belgeler hizmetlerden daha uzun süre dayanmalıdır.

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

Açık kaynak, ciddi şekilde oluşturulmuş

Üretim disiplini ile bağımsız bir proje.

Oleafly, bağımsız bir bakımcı tarafından açıkta oluşturulmuştur ve göze çarpmayan kısımlar da ürün özellikleri olarak ele alınır: derleme iptali, yol güvenliği, eski sonuç yarışları, Git kurtarma, PDF geri dönüşü ve macOS, Windows ve Linux'ta platformlar arası testler.

AGPL-3.0 lisanslıdır. Kaynağı okuyun, bir sorun bildirin veya PR gönderin 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.

Çevrimdışı yazım ve dil bilgisi

Hunspell ve Harper makinenizde WASM olarak çalışır, komutları ve matematiği maskeler, böylece yalnızca düzyazı kontrol edilir.

İndirmek

Özgür. Açık kaynak. Senin.

En son sürümden önceden oluşturulmuş bir yükleyici edinin veya kaynaktan oluştur. Hesap yok, telemetri yok.

macOS Windows Linux

Derlemeler henüz kodla imzalanmadığından işletim sisteminiz ilk başlatmada uyarı verir. kurulum kılavuzu tek seferlik kilit açma özelliğine sahiptir.

Yazmak (()=>{var e=async t=>{await(await t())()};(self.Astro||(self.Astro={})).load=e;window.dispatchEvent(new Event("astro:load"));})();araştırma makaleleriözgeçmiştezlerhibe teklifleriaraştırma makalelerikod yazdığın gibi.

araştırma makaleleri

Beklediğiniz diğer her şey. Makinenizde.

Sahip olduklarını içe aktar

Gittiğiniz yeri dışa aktarın

Senin olsun

Kod veya Görsel Olarak Yazın

SyncTeX, kelime düzeyinde

Kod zekası

Eğik çizgi komutları

DOI'ye göre alıntılar

Kütüphanenizin tamamını getirin

Komut paleti

Kendi yapay zekanızı getirin

Dokunarak araştırma yapın

Çevrimdışı yazım ve dil bilgisi

Hatırlayan bir ajan

Bir sayfadan altı yüze kadar

Gerçek okuma için bir PDF görüntüleyici

Diyagram Oluşturucu

Üç motor, tek çalışma alanı

The LaTeX editor

The AI assistant

The reference manager

The resume builder

The version control

The PDF toolkit

Araştırın, yazın, doğrulayın, yayınlayın.

Araştırma

Yazmak

Doğrulamak

Yayınla

Diğerlerinin yerini almak üzere tasarlanan tek araştırma çalışma alanı.

Varsayılan olarak yerel. Seçime göre ağ.

Her zaman yerel

Ağ, yalnızca harekete geçtiğinizde

Belgeler hizmetlerden daha uzun süre dayanmalıdır.Belgeler hizmetlerden daha uzun süre dayanmalıdır.

Üretim disiplini ile bağımsız bir proje.

What to know before you switch.

Özgür. Açık kaynak. Senin.

Yazmak
araştırma makaleleri
kod yazdığın gibi.

Belgeler hizmetlerden daha uzun süre dayanmalıdır.