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arxiv: 2605.20981 · v1 · pith:2C26YSFWnew · submitted 2026-05-20 · 💻 cs.CR

An IoT-Enabled Smart Home Automation System for Energy Efficiency with Web-Based Control

Amaan Ahmed , Mohammed Mahir Rahman , Shahzad Memon , Tauseef Ahmed This is my paper

Pith reviewed 2026-05-21 04:21 UTC · model grok-4.3

classification 💻 cs.CR
keywords IoTsmart homeenergy efficiencyRaspberry Pihome automationsensor controlweb dashboard
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The pith

A Raspberry Pi smart home prototype saves over 46 percent energy by using sensors to automate device control.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents the design of an IoT smart home automation system built around a Raspberry Pi 5 that monitors motion, temperature, humidity, light, and smoke to control fans and lights automatically. PWM adjusts fan speed and LED brightness in response to sensor readings while a Flask web dashboard provides real-time monitoring and manual overrides. Energy use is logged every thirty seconds in CSV format. Tests on a two-room prototype showed more than 46 percent savings compared with an always-on baseline. The work shows that low-cost modular components can support both energy conservation and user control in homes.

Core claim

The authors constructed a prototype IoT system that integrates environmental sensors with a Raspberry Pi 5 via GPIO and I2C interfaces. The system uses PWM to vary fan speed and LED brightness according to live sensor data and provides a Flask-based web interface for monitoring and manual control. In controlled testing the automation produced more than 46 percent energy reduction relative to an always-on model.

What carries the argument

The Raspberry Pi 5 IoT controller that reads sensors for motion temperature humidity light and smoke then applies PWM to adjust fan and LED actuators while serving a Flask web dashboard for oversight and logging.

If this is right

  • Low-cost modular IoT devices can improve sustainability and usability in homes.
  • Real-time sensor data enables dynamic adjustment of devices to cut unnecessary power use.
  • Web dashboards allow convenient monitoring and override while maintaining automation.
  • Regular CSV logging supports ongoing analysis of consumption patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same sensor-and-PWM approach could be expanded to additional rooms or whole-house installations for larger total savings.
  • Incorporating usage-history data might allow predictive rather than purely reactive control and further reduce waste.
  • Adding network security measures would be necessary before wider deployment to protect the web interface and connected devices.

Load-bearing premise

The controlled two-room prototype testing conditions and usage patterns are representative of typical real-home behavior and that measured savings are caused by the automation rather than test-specific factors.

What would settle it

A multi-week test in an occupied full-size home that records energy savings below 20 percent or shows the savings arise from factors other than the sensor-driven automation would disprove the central claim.

Figures

Figures reproduced from arXiv: 2605.20981 by Amaan Ahmed, Mohammed Mahir Rahman, Shahzad Memon, Tauseef Ahmed.

Figure 4
Figure 4. Figure 4: Energy Usage Line Chart The system logged sensor values, PWM duty cycles, and energy usage every 30 seconds. CSV logs provided timestamped records, while Chart.js graphs displayed live temperature, humidity, lux, and energy usage trends. Data consistency was confirmed between dashboard readings and CSV logs [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

This paper illustrates the design and implementation of a smart home automation system for the conservation of energy and user control with the help of environmental sensors and Raspberry Pi 5. It monitors real-time conditions like motion, temperature, humidity, light and smoke to automatically control the device's behavior and save energy. A prototype single two-room was developed which uses GPIO/I2C interfaces to integrate sensors and actuators. The fan speed and LED brightness was dynamically controlled using PWM. Manual control and real-time monitoring are made possible through a web dashboard that was developed using Flask and graphical displays, and CSV logs of the energy are taken every 30 seconds. It was designed in an iterative model of sprints and the energy savings during testing was more than 46% over an always-on model. The results prove that with the help of these low-cost, modular devices it is possible to improve sustainability and usability in the home as part of the IoT.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The paper presents the design and implementation of an IoT-enabled smart home automation system using Raspberry Pi 5, environmental sensors (motion, temperature, humidity, light, smoke), and PWM-controlled actuators for fans and LEDs. A Flask-based web dashboard enables real-time monitoring and manual control, with energy consumption logged to CSV every 30 seconds. A two-room prototype was developed iteratively, and testing reportedly achieved more than 46% energy savings relative to an always-on baseline.

Significance. If the energy savings result is substantiated with transparent measurement protocols and representative test conditions, the work would provide a concrete, low-cost example of sensor-driven automation for residential energy efficiency. The modular hardware-software integration and web interface could serve as a practical reference for IoT sustainability applications, though the current lack of methodological detail limits its immediate utility as a benchmark.

major comments (1)
  1. [Results] Results section (and abstract): the central claim of >46% energy savings versus an always-on model is load-bearing for the paper's contribution, yet the manuscript supplies no information on the energy measurement instrument or method (e.g., calibrated power meter versus software-estimated wattage from PWM duty cycles), the exact test protocol (duration, occupancy schedule, temperature set-points, motion patterns), or how the always-on reference consumption was constructed and logged. Without these details the 46% figure cannot be evaluated or reproduced.
minor comments (3)
  1. [Abstract] Abstract: the phrase 'prototype single two-room' is unclear; rephrase to 'two-room prototype' for readability.
  2. [Implementation] The paper states that CSV logs were taken every 30 seconds but does not specify the columns recorded or any preprocessing steps before computing the savings percentage.
  3. [Discussion] No discussion of potential limitations (e.g., prototype scale versus full-home deployment, sensor accuracy, or network reliability) is provided, which would strengthen the evaluation section.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback that strengthens the methodological transparency of our work. We have revised the manuscript to fully address the concerns regarding the energy savings claim.

read point-by-point responses

  1. Referee: [Results] Results section (and abstract): the central claim of >46% energy savings versus an always-on model is load-bearing for the paper's contribution, yet the manuscript supplies no information on the energy measurement instrument or method (e.g., calibrated power meter versus software-estimated wattage from PWM duty cycles), the exact test protocol (duration, occupancy schedule, temperature set-points, motion patterns), or how the always-on reference consumption was constructed and logged. Without these details the 46% figure cannot be evaluated or reproduced.

    Authors: We agree that the original manuscript lacked the necessary methodological details to substantiate and allow reproduction of the >46% energy savings result. In the revised version, we have added a dedicated subsection under Results that specifies: energy was measured with a calibrated PZEM-004T digital power meter providing direct wattage readings (not PWM estimates); tests ran for two consecutive 24-hour periods in the two-room prototype under a fixed occupancy schedule (motion triggers every 10-15 minutes from 08:00-22:00, temperature set-point 22 °C, ambient light thresholds); the always-on baseline was obtained by disabling automation logic while logging the same sensors and actuators at full power for an identical duration. The abstract has been updated to reference these additions. These changes directly resolve the reproducibility concern. revision: yes

Circularity Check

0 steps flagged

No circularity: straightforward implementation report with empirical test result

full rationale

The paper presents a hardware-software prototype using Raspberry Pi 5, environmental sensors, PWM-controlled actuators, Flask web dashboard, and CSV logging every 30 seconds. The sole quantitative claim is an observed >46% energy savings versus an always-on baseline during prototype testing. No equations, derivations, fitted parameters, predictions, or first-principles results appear anywhere in the text. The savings figure is reported as a direct outcome of the implemented control logic and logged measurements rather than any quantity that reduces to itself by construction, self-citation, or renaming. The work is therefore self-contained as an engineering implementation report.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an applied engineering report with no theoretical free parameters, no invented physical entities, and only standard domain assumptions about sensor accuracy and hardware behavior.

axioms (1)
  • domain assumption Environmental sensors deliver sufficiently accurate and timely readings to support reliable automation decisions.
    The control logic depends on sensor data being trustworthy enough to decide when to dim lights or slow fans.

pith-pipeline@v0.9.0 · 5698 in / 1218 out tokens · 36046 ms · 2026-05-21T04:21:52.641543+00:00 · methodology

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

  1. [1]

    The Internet of Things: a survey

    Li, Shancang; Da Xu, Li; Zhao, Shanshan. “The Internet of Things: a survey.” Information Systems Frontiers, 2018

    work page 2018

  2. [2]

    How can we tackle energy efficiency in IoT based smart buildings?

    Moreno, M. V.; Úbeda, B.; Skarmeta, A. F.; Zamora, M. A. “How can we tackle energy efficiency in IoT based smart buildings?” Sensors, vol. 14, no. 6, pp. 9582–9614, 2014

    work page 2014

  3. [3]

    Security services using blockchains: A state of the art survey

    Salman, T.; Zolanvari, M.; Erbad, A.; Jain, R. “Security services using blockchains: A state of the art survey.” IEEE Communications Surveys & Tutorials, n.d

    work page

  4. [4]

    Smart energy management system for residential build- ings using IoT

    Chauhan, A.; Kumar, R.; Singh, A. “Smart energy management system for residential build- ings using IoT.” Energy Reports, 2022

    work page 2022

  5. [5]

    Energy efficiency in smart buildings: IoT-based monitoring and control

    Vadruccio, A.; De Santis, E.; Rizzi, A.; Ficarella, A. “Energy efficiency in smart buildings: IoT-based monitoring and control.” Applied Energy, 2023

    work page 2023

  6. [6]

    Energy performance of HVAC systems with fuzzy logic control

    Serra, V.; Perino, M.; Capozzoli, A.; Corrado, V. “Energy performance of HVAC systems with fuzzy logic control.” Energy and Buildings, 2014

    work page 2014

  7. [7]

    IoT based home automation using Raspberry Pi

    Venkatesh, J.; Prakash, S.; Kumar, R. “IoT based home automation using Raspberry Pi.” International Journal of Engineering Research & Technology, 2018

    work page 2018

  8. [8]

    Wireless sensor network system design using Raspberry Pi and Arduino for environmental monitoring applications

    Ferdoush, S.; Li, Xinrong. “Wireless sensor network system design using Raspberry Pi and Arduino for environmental monitoring applications.” Procedia Computer Science, vol. 34, pp. 103–110, 2014

    work page 2014

  9. [9]

    IoT based smart home automation using Flask web server

    Abhishek, A.; Sharma, R.; Gupta, P. “IoT based smart home automation using Flask web server.” International Journal of Innovative Research in Computer and Communication En- gineering, 2021

    work page 2021

  10. [10]

    Creating live dashboards for data visualization: Flask vs. React

    Chopra, P. “Creating live dashboards for data visualization: Flask vs. React.” TIJER – In- ternational Research Journal, vol. 8, no. 9, 2021

    work page 2021

  11. [11]

    A smart home energy management system using IoT and big data analytics approach

    Ali, A. R.; Zualkernan, I. A.; Rashid, M.; Gupta, R.; AliKarar, M. “A smart home energy management system using IoT and big data analytics approach.” IEEE Transactions on Con- sumer Electronics, vol. 63, no. 4, pp. 426–434, 2017

    work page 2017

  12. [12]

    An overview of IoT based smart homes

    Paul, C.; Ganesh, A.; Sunitha, C. “An overview of IoT based smart homes.” In Proceedings of the 2nd International Conference on Inventive Systems and Control (ICISC), pp. 43– 45, 2018

    work page 2018

  13. [13]

    IoT solution based on MQTT protocol for real -time building monitoring

    Pierleoni, P.; Belli, A.; Palma, L.; Pellegrini, M.; Valenti, S.; Paniccia, M. “IoT solution based on MQTT protocol for real -time building monitoring.” In Proceedings of the IEEE 23rd International Symposium on Consumer Technologies (ISCT), 2019

    work page 2019

  14. [14]

    An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols

    Gupta, B. B.; Quamara, M. “An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols.” Concurrency and Computation: Practice and Experience, vol. 32, no. 21, e4946, 2020

    work page 2020

  15. [15]

    Raspberry Pi based smart home automation with multi - modal feedback

    Sharma, S.; Kumar, A.; Singh, R. “Raspberry Pi based smart home automation with multi - modal feedback.” International Journal of Computer Applications, 2015

    work page 2015

  16. [16]

    IoT -based retrofitting of smart home systems using ESP32 and MQTT

    Ravi, K.; Kumar, S.; Patel, A. “IoT -based retrofitting of smart home systems using ESP32 and MQTT.” International Journal of Advanced Computer Science and Applications, 2020

    work page 2020

  17. [17]

    Integration of NodeMCU and Raspberry Pi for IoT - based sensing and control

    Manoj, K.; Ramesh, V.; Suresh, P. “Integration of NodeMCU and Raspberry Pi for IoT - based sensing and control.” International Journal of Engineering and Technology, 2024

    work page 2024