Intelligent Living Space Control Using AI and Wireless Sensors

Priyanka R

, Logesh K

Sugumar R

and Suresh M

Department of Electrical Electronic Engineering S A Engineering College, Anna University, Tamil Nadu, India

Keywords: Intelligent Living Space, Artificial Intelligence, Wireless Sensor Networks, Smart Homes, Energy Efficiency,

Home Automation, IoT, Sustainability, Security.

Abstract: This project presents an AI-based smart home automation system and Bluetooth technology. The system

integrates sensors, devices, and an Android app for remote operation. Machine learning algorithms optimize

energy consumption. The system enhances comfort, user satisfaction, and promotes sustainable living. The

concept of an intelligent living space integrates artificial intelligence (AI) and wireless sensor networks

(WSNs) to create smart, adaptive environments that enhance comfort, energy efficiency, and security. This

system employs AI-driven algorithms to interpret data from wireless sensors, enabling dynamic control of

various household systems such as lighting, temperature, humidity, and security. The wireless sensors collect

real-time data, including motion, environmental conditions, and user preferences, which is processed by a

central AI module. The AI utilizes machine learning to analyse patterns, predict user behaviour, and optimize

system responses Energy efficiency is a significant focus, with the system minimizing wastage by deactivating

unused appliances and employing adaptive power management strategies. Key features include intelligent

appliance control, remote monitoring, energy efficiency, and personalized experience.

1 INTRODUCTION

The evolution of technology has led to significant

advancements in various sectors, including home

automation. As the demand for smart living

environments continues to grow, integrating

innovative technologies into residential spaces has

become a focal point for enhancing comfort, security,

and energy efficiency. This project introduces an AI-

based smart home automation system that utilizes

Wireless Sensor Networks (WSN) and Bluetooth

technology to provide an intelligent and user-friendly

solution for home management. The system aims to

streamline the control of household appliances,

offering enhanced convenience and optimizing

energy consumption.

The concept of smart homes is predicated on the

integration of various electronic devices that

communicate with each other, allowing homeowners

to manage their living spaces more effectively.

Traditional home automation systems often lack the

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adaptability and intelligence required to cater to the

dynamic nature of user needs. This project addresses

this gap by incorporating machine learning

algorithms that analyze sensor data, enabling the

system to learn user preferences and adjust appliance

operation accordingly.

Wireless Sensor Networks (WSN) serve as the

backbone of the proposed smart home automation

system. By deploying a network of interconnected

sensors, the system can monitor various

environmental parameters, such as temperature,

humidity, and occupancy. This real-time data

collection facilitates a deeper understanding of the

home environment, enabling the system to make

informed decisions about appliance control. For

instance, if the temperature exceeds a certain

threshold, the system can automatically adjust the fan

speed or turn on air conditioning, ensuring a

comfortable living space for the occupants.

Bluetooth technology plays a critical role in

enhancing the connectivity of the smart home system.

It enables seamless communication between the

130

R, P., K, L., R, S. and M, S.

Intelligent Living Space Control Using AI and Wireless Sensors.

DOI: 10.5220/0013636200004639

In Proceedings of the 2nd International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2024), pages 130-134

ISBN: 978-989-758-756-6

Android application and the various connected

devices, providing users with a straightforward

interface for controlling their home environment.

This wireless communication ensures that users can

operate their appliances remotely, allowing them to

monitor and manage their home even when they are

away. Such capabilities are particularly valuable for

individuals with busy lifestyles, as they can easily

adjust settings or receive notifications about their

home status through their mobile devices. The

system's design prioritizes energy efficiency, an

increasingly crucial factor in modern households. By

utilizing machine learning algorithms to analyze

usage patterns, the smart home automation system

can optimize the operation of connected devices,

minimizing energy consumption. For example, the

system may identify periods of inactivity and

automatically switch off lights or appliances that are

not in use. This proactive approach not only reduces

energy bills but also contributes to environmental

sustainability by decreasing overall power usage.

2 PROPOSED SYSTEM

The proposed system for Intelligent Living Space

Control integrates artificial intelligence (AI) with

wireless sensor networks (WSNs) to create an

adaptive and efficient home automation framework.

This system aims to enhance comfort, optimize

energy consumption, and ensure security by utilizing

real-time data and intelligent decision-making

algorithms. It is designed to be scalable, user-

friendly, and sustainable, catering to the diverse needs

of modern households.

2.1 Wireless Sensor Network (WSN):

▪ A network of wireless sensors deployed across

the living space collects data on parameters such as

temperature, humidity, light intensity, motion, and air

quality.

▪ The sensors communicate wirelessly with a

central control unit, eliminating the need for

extensive wiring and making the system suitable for

both new and retrofitted homes.

2.2 Central AI Control Unit

The AI module processes sensor data, learns user

preferences, and makes intelligent decisions. It uses

machine learning algorithms to identify patterns,

predict behavior, and provide personalized responses.

AI also ensures seamless integration with IoT devices

and external data sources, such as weather forecasts,

to improve system accuracy and adaptability.

2.3 Actuators and Smart Devices

The system controls actuators and smart devices,

including lighting, HVAC systems, appliances, and

security cameras. Commands are executed based on

AI-driven decisions, ensuring optimal performance

and energy efficiency.

2.4 User Interface

A mobile application and voice-activated assistant

provide a user-friendly interface for monitoring and

controlling the system. Users can remotely adjust

settings, view real-time data, and receive alerts for

security or maintenance issues.

3 MERITS

Merits of the AI-Based Smart Home Automation

System the AI-based smart home automation system

offers numerous advantages that enhance the overall

living experience for homeowners.

3.1 Convenience and Comfort:

One of the primary merits of this system is its ability

to provide users with seamless control over their

household appliances. The intuitive Android

application allows for remote operation and

monitoring, enabling users to manage their home

environment from anywhere at any time. This

flexibility enhances user comfort, as individuals can

adjust settings without being physically present.

3.2 Energy Efficiency

The integration of machine learning algorithms

enables the system to analyze usage patterns and

optimize energy consumption effectively. By

automatically adjusting appliance operation based on

real-time data, such as occupancy and environmental

conditions, the system reduces unnecessary energy

expenditure. This proactive energy management

contributes to lower utility bills and promotes

sustainable living practices.

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131

3.3 Personalization

The system's ability to learn user preferences and

adapt to individual habits further enhances the smart

home experience. By understanding the unique

patterns of its users, the system can automate routine

tasks, such as adjusting lighting or temperature,

ensuring a comfortable living environment tailored to

specific needs.

3.4 Enhanced Security

The smart home automation system can integrate

security features, such as surveillance cameras and

motion sensors, providing real-time monitoring and

alerts. This added layer of security ensures

homeowners feel safe and secure in their living

spaces.

3.5 Scalability

The modular design of the system allows for easy

expansion and integration of additional devices and

features. As technology evolves, users can upgrade

their systems without significant changes to the

existing infrastructure, ensuring long-term viability

and adaptability.

4 LITERATURE REVIEW

1. Smart Home Devices for Supporting Adults:

A Systematic Review-Authors:

M. Ghafurian, K. Wang, I. Dhode, M. Kapoor, P.

P. Morita, K. Dautenhahn-Published In: IEEE

Access, 2023.

2. Smart Home Security: An Efficient Multi-

Factor Authentication Protocol Authors:

G. Sarbishaei, A. Masoud Aminian Modarres, F.

Jowshan, F. Zahra Khakzad, H. Mokhtari

Published In: IEEE Access, 2024.

3. An Automation Script Generation

Technique for the Smart Home Authors:

J. Kuang, G. Xue, Z. Yan, J. Liu

Published In: Journal of Web Engineering, 2023.

4. EmoSecure: Enhancing Smart Home

Security with FisherFace Emotion Recognition

and Biometric Access Control Authors:

P. Ghadekar, M. Ranjan Pradhan, D. Swain, B.

Acharya Published In: IEEE Access, 2024.

5. Ontology-Based Classification and

Detection of the Smart Home Automation Rules

Conflicts Authors:

A. M. Ansari, M. Nazir, K. Mustafa

Published In: IEEE Access, 2024.

5 BLOCK DIAGRAM

5.1 NodeMCU

Definition: Open-source IoT platform based on

ESP8266 Wi-Fi Soc.

Firmware

: Uses Lua scripting, built on eLua

project and Es press if non-OS SDK.

Figure 1: Block Diagram.

Features:

- ESP-12E Wi-Fi module with additional

GPIOs.

-Micro USB for power, programming, and

debugging.

- Includes GPIO, SPI, UART, ADC, and power

pins.

- Low-cost (< $2) for prototyping IoT

applications.

- Power Requirements: Operates at 3.3V; not 5V

tolerant.

5.2 Bluetooth Technology

Definition: Short-range wireless technology for

device interconnection.

Key Characteristics:

- Operates in 2.45 GHz frequency band.

- Max range: 10 meters; data rates up to 2 Mbps.

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- Uses Frequency-Hopping Spread Spectrum

(FHSS) for interference avoidance.

Piconet: Network of Bluetooth devices, typically

with one master and up to seven slaves.

Security Tips:

- Activate only when needed.

- Verify sending devices.

- Use antivirus software.

5.3 L293D Motor Driver

Function: Controls two DC motors in both directions

using an H-bridge configuration.

Pin Configuratio:

- 16 pins; requires high signals on enable pins (1

and 9) to operate.

- Four input pins control motor direction

(clockwise/anticlockwise).

Applications: Common in robotics for

controlling DC motors.

5.4 Power Supply Components

Transformer: Converts AC mains to a suitable

amplitude; ensures sufficient current capacity.

Rectifier: Converts AC to DC, typically using a

bridge rectifier for full-wave rectification.

Filter:

- Reservoir Capacitor: Smooths DC output by

storing charge.

- Low Pass Filter: Further removes AC ripples.

Voltage Regulator: Maintains a stable DC

output, available in positive and negative

configurations (e.g., LM78XX series).

6 SOFTWARE USED

6.1 ESP8266 Software (IDE)

The ESP8266 Integrated Development Environment

or ESP8266 Software (IDE) contains a text editor for

writing code, a message area, a text console, a toolbar

with buttons for common functions and a series of

menus. It connects to the ESP8266 and Genuino

hardware to upload programs and communicate with

them.

Sketches:

1. Programs are called sketches, saved with the. ino

extension.

2. Written in a text editor with features like

cut/paste and search/replace.

3. Feedback and error messages are displayed in

the message area and console.

● Basic Functions:

1. Verify: Checks code for compilation errors.

2. Upload: Compiles and uploads code to the

configured board.

3. New/Open/Save: Manage sketches (create,

open, save).

● Menus:

1. File Menu: Access sketches, examples, and

preferences.

2. Edit Menu: Basic editing functions (cut, copy,

paste, find).

3. Sketch Menu: Includes verification, upload

options, and library management.

4. Tools Menu: Formatting, archiving, and

bootloader options.

5. Help Menu: Access to documentation and

references.

● Sketchbook:

1. A standard location for storing sketches,

automatically created on first run.

2. Allows easy access to all stored programs.

● Compilation and Uploading:

1. Select appropriate board and port from Tools

menu.

2. Use the bootloader for uploading sketches

without additional hardware.

● Libraries:

1. Libraries enhance functionality (hardware

access, data manipulation).

2. Imported via Sketch > Import Library.

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133

● Serial Monitor:

1. Displays data exchange with connected boards.

2. Allows sending commands and receiving data in

real time.

● Language Support:

1. Available in 30+ languages; set according to the

operating system or manually via preferences.

Board Selection:

Essential for compiling and uploading; varies

by board type (e.g., ESP8266 Uno, Mega, etc.).

● Additional Features:

Support for third-party hardware.

Multiple tabs for managing sketches with various

file types.

Figure 2: Simulation Output.

7 CONCLUSIONS

In conclusion, the AI-based smart home automation

system utilizing Wireless Sensor Networks and

Bluetooth technology represents a significant

advancement in home management. By integrating

various components such as sensors, micro

controllers, and machine learning algorithms, the

system enhances user convenience, energy

efficiency, and overall comfort. The intuitive Android

application allows for seamless remote control of

appliances, while the use of machine learning enables

the system to adapt to individual preferences and

optimize energy consumption.

The implementation of this smart home solution

not only simplifies daily tasks but also promotes

sustainable living practices by reducing unnecessary

energy use. With features like real-time monitoring

and automated control, homeowners can enjoy a

personalized living experience tailored to their needs.

Furthermore, the scalability of the system ensures that

it can evolve with technological advancements and

user demands. As the world increasingly shifts

towards smart technologies, this project serves as a

foundation for future developments in intelligent

home management, ultimately contributing to a more

efficient, comfortable, and sustainable lifestyle. By

embracing such innovations, homeowners can

enhance their quality of life while playing a part in

promoting environmental responsibility.

REFERENCES

A. M. Ansari, M. Nazir, and K. Mustafa, "Ontology-Based

Classification and Detection of the Smart Home

Automation Rules Conflicts," IEEE Access, (2023) vol.

12, pp. 6558-6571, doi:

10.1109/ACCESS.2024.3415632.

A. Arora, A. Jain, D. Yadav, V. Hassija, V. Chamola, and

B. Sikdar, "Next Generation of Multi-Agent Driven

Smart City Applications and Research Paradigms,"

IEEE Open Journal of the Communications Society,

(2023) vol. 4, no. 1, pp. 423-440, doi:

10.1109/OJCOMS.2023.3310528.

J. Kuang, G. Xue, Z. Yan, and J. Liu, "An Automation

Script Generation Technique for the Smart Home,"

Journal of Web Engineering, (2023) vol. 23, no. 1, pp.

102-118, doi: 10.13052/jwe1540-9589.2222.

P. Ghadekar, M. R. Pradhan, D. Swain, and B. Acharya,

"EmoSecure: Enhancing Smart Home Security With

FisherFace Emotion Recognition and Biometric Access

Control,"(2024) IEEE Access, vol. 12, pp. 4589-4605

doi: 10.1109/ACCESS.2024.3423783.

G. Sarbishaei, A. M. Aminian Modarres, F. Jowshan, F. Z.

Khakzad, and H. Mokhtari, "Smart Home Security: An

Efficient Multi-Factor Authentication Protocol," (2023)

IEEE Access, vol. 12, pp. 4995-5010, doi:

10.1109/ACCESS.2024.3437294.

M. Ghafurian, K. Wang, I. Dhode, M. Kapoor, P. P. Morita,

and K. Dautenhahn, "Smart Home Devices for

Supporting Older Adults: A Systematic

Review,"(2023) IEEE Access, vol. 11, pp. 37285-

37298, doi: 10.1109/ACCESS.2023.3266647.

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