Next‑Generation IoT‑Enabled Smart Home Framework: A Secure,

Scalable and Energy‑Efficient Solution for Intelligent Living

Environments

B. Amarnath Naidu

, K. Vanisree

, Rama Prasanna Dalai

, N. Babu

Suriya R.

and M. Soma Sabitha

Department of EEE, G. Pulla Reddy Engineering College, Kurnool‑518007, Andhra Pradesh, India

Department of ECE, Samskruti College of Engineering and Technology, Telangana, India

Department of EEE, Centurion University of Technology and Management, Odisha, India

Department of Electrical and Electronics Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil

Nadu, India

Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad‑500043, Telangana, India

Keywords: IoT‑Enabled Smart Homes, Energy Efficiency, Blockchain Security, Edge Computing, Intelligent

Automation.

Abstract: The rapid pace of development of smart home technologies requires an intelligent architecture that can provide

satisfaction of user comfort other than to overcome the several challenges that are facing intelligent home

such as devices interoperability, energy wastage, data privacy, and high installation cost. To this aim, this

work introduces a next paradigm IoT-based smart home enriched by modular, cloud-optimized devices using

standardized protocols for fluid communication and real-time actions. They propose a new security

architecture that integrates blockchain and lightweight cryptographic protocols to achieve strong data security

and user identity management. The system also uses adaptive AI models with transfer learning to offer

context-aware automation and predictive control which is energy-efficient. User-focused design is also

pursued to facilitate accessibility needs and ease of control especially for elderly, disabled users. Real-world

validation shows a substantial increase in the scalability of the system and how it can be more robust in

environments of low connectivity, resulting in a reduction of the required consumed energy. This paper

provides a scalable, secure and sustainable architecture of an intelligent living space.

1 INTRODUCTION

In an age where technology is constantly redefining

communities with the next big thing, smart homes

have evolved and will continue to be a vital

component of smart infrastructure. The use of

Internet of Things (IoT) devices in a home setting

means that domestic environments are now offering

unprecedented comfort, security and ease. With

voice-activated assistants, automated lighting and

climate control, smart homes are moving past the

novel and impractical into mass adoption.

Nevertheless, the fast development of this area has

on the other hand introduced a confluence of

difficulties— such as device compatibility, energy

consumption, security concerns, and human-centric

concern of user conduct.

The vast majority of current smart home systems

are cloud-dependent and adopt proprietary protocols,

resulting in a collection of narrow-area systems which

are not scalable as well as maintainable. Furthermore,

although machine learning has been utilized for

automation, most solutions lack generalization due

to insufficient data and absence of context awareness.

Solutions to these challenges need a comprehensive

and future-oriented solution that bridges the gap

among disparate devices, utilize resources efficiently,

privacy home users’ data, and adaptively react to

users’ behaviour.

A next generation IoT-based smart home model

is also proposed in this work to address the

Naidu, B. A., Vanisree, K., Dalai, R. P., Babu, N., R., S. and Sabitha, M. S.

Next-Generation IoT-Enabled Smart Home Framework: A Secure, Scalable and Energy-Efﬁcient Solution for Intelligent Living Environments.

DOI: 10.5220/0013943400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

767-773

ISBN: 978-989-758-777-1

767

shortcomings that exist in current ones. With the use

of edge computing, transfer learning, and security

protocols based on blockchain, the system not only

keeps the energy consumption and latency under

check but also secures the decentralization of the data.

The architecture emphasizes the inclusive and user

friendly: adaptive interfaces for a scalable

deployment on different types of technology and in

different socioeconomic and geographic contexts.

By way of an on-site deployment and experimental

evaluation, this paper illustrates that the convergence

of technology may create smarter, safer and

sustainable living environments.

2 PROBLEM STATEMENT

Despite growing adoption, IoT smart home systems

face interoperability issues, latency, and privacy risks

from cloud dependence, limiting real-time decision-

making and widespread, energy-efficient

implementation.

Security is also a severe concern, as most systems

do not include strong authentication or encryption,

leaving them open to unauthorized access and data

theft. In addition, the introduction of AI into smart

environment is not always efficient, as it is based on

an extensive amount of labelled data, which is hard to

achieve in home scenarios. This leads to low context

awareness, diminished personalization, and low

adaptability of automation systems.

Furthermore, its costly deployment and non-

scalability make it a hindrance for adoption in low-

resource or rural areas where reliable connectivity is

not ensured. Therefore, there is an urgent demand for

an energy-efficient, secure, and unified smart home

platform that can provide an accessible, intelligent,

and real-time control based on the users, and scale

well with low-cost and in a variety of contexts.

This research proposes an IoT-enabled smart

home architecture integrating edge intelligence,

blockchain-based security, standardized device

communication, and user-centric automation to create

a robust, scalable, and intelligent living environment.

3 LITERATURE SURVEY

The ubiquitous expansion of the IoT technologies has

greatly impacted on the progress of the smart home

systems, stimulating the full-range automation and

creation of comfortable domestic environment.

Nevertheless, the efficiency and scalability of these

systems have been constrained by shortcomings in

interoperability, energy efficiency, and security

which studies continue to expose. Akçay et al. (2024)

addressed energy efficient approaches in smart home

area and they noted that smart homes need unified

communication standards to reduce resource

consumption without loss of performance.

Chen and Zhang (2022), to counter security

issues, proposed the implementation of blockchain in

IoT networks, which could be employed to

decentralize identity management and tamper-proof

data flows. Zhao and Wang (2024) explored more

privacy-preserving mechanisms, namely lightweight

cryptographic protocols applied to resource-

constrained smart things. Their results indicate that

secure-by-design approaches are the key to user

acceptance in home automation.

In terms of usability, Brown and Green (2022)

highlighted the demand for user centeredness in smart

systems, and in particular among the elderly.

According to the study, many commercial systems

lack of accessibility and simplicity in use. The same

was demanded by Lopez and Gonzalez (2024) who

examined the adaptive UI models as well and also

requested that there be included some personalization

real-time capabilities that adjust to the users’ patterns

of using their application.

Energy saving is still the main issue. Singh and

Sharma (2023) used machine learning for predicting

energy control but pointed out the computational

overhead of edge cloud-based inference. For this,

Ahmed and Khan (2021) introduced a hybrid

approach of combining local edge computation with

cloud servers to minimize latency and energy

utilization. Li and Zhou (2022) improved this method

by transfer learning so that a few pre-trained models

can well fit to individual home with little data.

Lack of device connectivity has been long

recognized in many research studies. Lee and Kim

(2021) performed an exhaustive review on protocol

fragmentation in IoT-based smart homes and

suggested the use of standard APIs such as MQTT

and CoAP. Gonzalez and Martinez (2023) shared the

same worries and proposed a middleware-based

architecture that bring together cross-platform

devices under the same control layer.

The issue of scalability was approached by

Kumar and Singh (2023) with a modular design,

which could be used in smart homes in rural settings.

They incorporated low power networking standards

(Zigbee and LoRaWAN) so they can still

communicate in under dense areas. In addition to this,

Wang and Liu (2024) proposed edge computing

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models for the offloading of processing burdens and

central server dependency mitigation.

Security improvement in the domain was also

explored by Kim and Park (2023), who utilized

multilayer authentication to secure data access. Their

model was able to prevent typical IoT attacks such as

spooking and device theft. Ezugwu and Ezugwu

(2025) advanced this by opening a debate on the

ethical implications of smart home surveillance and

consolidating policy-compliant architectures that

honour users’ consent.

Real deployments were also discussed by Chen

and Yang (2023) in which sensors were installed in a

real testbed, and performance improvements were

observed using real-time models for anomaly

detection. Garcia and Torres (2021) tested motion

and energy information to automatic control of

HVAC and lighting. In the mean-time, Patel and

Desai (2022) proposed an comparative study of

commercial smart home hubs through a trade-off

between flexibility and integration complexity.

Last but not least, Maurya and Rana [2023]

explore the environmental implications of smart

homes, suggesting that eco-friendly design should

focus on low carbon footprints through intelligent

power scheduling. Their work also promotes green

energy analytics for IoT systems which in turn

enhances environmental savings over a long run.

Together, these works provide pioneering

findings for the issues and solutions of the smart

home. Nevertheless, there still exists a lack of

integration of those solutions based on a unified

approach that scales and preserves the privacy,

energy efficiency and real-world usability and can be

properly automated-which this work is aiming at

satisfy.

4 METHODOLOGY

The architecture of the smart home was derived

through modularized and layered development; it

should be secure, scalable, adaptable, and fast

enough to support more and more applications and

services. The architecture was designed as a first step

which is built on Internet of Things (IoT) model using

open-standards communication protocols (MQTT,

CoAP) to achieve seamless interaction across

different devices. Microcontroller units such as the

ESP32 and Raspberry Pi 4 acted as the central nodes

of the decentralized system for different types of

sensor and actuator nodes to sense and actuate

locally.

The edge computing layer, that stored sensors

readings and pre-processed (real-time) the data

before transferring raw features to the cloud, was the

backbone of the system. This layer reduced

bandwidth usage, latency, quick response times such

as with the detection of motion, power control, and

security break-ins. Computational efficiency was the

key driver and lightweight deep learning models like

MobileNet and TinyML tailored for on-the-edge

processing, trained on the custom datasets from actual

home environments. Transfer learning methods were

also applied to fine-tune the models so that they can

accommodate the behavior of the household with

minimal amount of extra data. Figure 1 gives the

information about Smart Home System Workflow

From User Interaction to Continuous Learning.

Figure 1: Smart Home System Workflow from User

Interaction to Continuous Learning.

Security was addressed by leveraging blockchain

based decentralized identity (DID) protocols. Each

device, with a unique identity authenticated in the

blockchain, and all transactions between the device

and the server are verified using smart contracts and

elliptic-curve cryptography. Such architecture

avoided unauthorized device access and ensured data

integrity, which further hardened the system from

man-in-the-middle and spoofing attacks.

The automation control was controlled by a cloud-

based AI control module that took decisions through

context awareness. The history data (e.g.,

Next-Generation IoT-Enabled Smart Home Framework: A Secure, Scalable and Energy-Efﬁcient Solution for Intelligent Living

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temperature, motion, illumination, and power

consumption) from sensors were collected and

recorded, and were analyzed by a fusion method of

semi-supervised learning and reinforced learning.

The system was thus able to learn optimal energy

usage patterns and forecast occupancy, in order to

proactively control environmental parameters to

improve the comfort of users. For instance, the

HVAC system was adaptively controlled towards

room occupancy forecasts, meteorological data, and

user preferences. Table 1 gives the Hardware and

Sensor Modules in Experimental Setup.

Table 1: Hardware and Sensor Modules in Experimental

Setup.

Component

Model/Typ

Function

Conne

ctivity

Power

Rating

Microcontrol

ler

ESP32

Data

processing

& control

Wi-Fi/

BLE

3.3V

Edge

Processor

Raspberry

Pi 4

Local AI

inference

Wi-Fi/

Ethern

Temperature

Sensor

DHT22

Ambient

temperatur

e reading

GPIO Low

Motion

Detecto

HC-SR501

Occupancy

detection

GPIO Low

Smart Plug

Sonoff

Basic

Appliance

control

Wi-Fi 230V

In terms of user interaction, to make the app

accessible for multiple platforms, we developed a

cross-platform interface by using React Native for

smartphones, tablets and voice control smart

speakers. It was an adaptive interface, elderly-

friendly controls were created with larger fonts,

simplified menus, and voice feedback. Furthermore,

user contributions and preferences were at all times

utilized to personalize automation logic such as

lighting presets, wake-up routines, etc., thus further

reinforcing engagement and convenience.

For the sake of comparison and robustness under

different network conditions (Wi-Fi, Zigbee, and

LoRaWAN), the system is experimentally evaluated

under multiple scenarios. Failover logic was built in

to scale over to offline fallback routines whenever

connectivity was lost, keeping the work flowing.

Additionally, a monitoring and diagnostics module

was developed to log system health, identify outliers,

and alert the user with notifications on their

smartphone or in-system dashboards.

The framework was tested in a simulated real-

time smart home environment that simulates a three-

bedroom layout and contains 50+s IoT sensors and

actuators. Performance Aarch Center A recall Mem

Modeling Energy Usability performance center the

software suite communicated with the Power ISA

model through a custom interface. The effectiveness

of the proposed framework was validated by its

performance measures for energy savings, latency,

device response time, security breach, and user

satisfaction scores.

5 RESULTS AND DISCUSSION

The experimentation, deployment, and evaluation of

the proposed IoT-based smart home framework in a

real-world setting, showed significant benefits in

terms of communication latency, energy efficiency,

and system and user responsiveness. The

performance advantages of the newly proposed edge-

optimized system compared with the conventional

cloud-centric smart home models were evident from

comparative analysis. As illustrated in Table 2, we

observed that timing for delay measurements for the

different automated tasks, motion detection,

temperature control and lighting automation,

confirms that edge-based processing reduced

response time to 69% on average, as compared to the

conventional cloud-based delay execution. In

particular, the latency regarding motion detection

decreased from 1020 ms to 320 ms, thus, greatly

improving the real-time performance for the security

and the convenience purposes.

Table 2: Latency Comparison Between Edge and Cloud-

Based Execution.

Operation

Type

Average

Latency

(Edge)

Average

Latency

(Cloud)

Improvement

(%)

Motion

Detection

320 ms 1020 ms 68.6%

Temperature

Control

280 ms 930 ms 69.9%

Lighting

Automation

300 ms 1000 ms 70.0%

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Figure 2: Edge vs Cloud Latency.

Figure 2 gives the edges vs cloud latency. Energy

savings results were similarly impressive. The

benefits of adopting intelligent automation was

reflected in significant monthly energy

(energy*power) savings from different devices types

as summarized in Table 3. The lighting systems

achieved 30% energy savings, HVAC systems

30.9%, and an average of 23.2% reduction in

appliance use. These savings not only prove the

efficacy of predictive automation and context-

dependent device management, but also demonstrate

the viability of energy-aware smart home systems,

which contribute towards the sustainability targets for

living environments. Figure 3 gives the information

of Energy Consumption Before vs After Automation.

Table 3: Energy Consumption Before and After

Automation Integration.

Device

Category

Traditional

Usage

(kWh/Month

)

Proposed

System

(kWh/Mont

Energy

Savings

(%)

Lighting 60 42 30%

HVAC 210 145 30.9%

Appliance

s (avg.)

95 73 23.2%

Figure 3: Energy Consumption Before vs After

Automation.

Security analysis highlighted the robustness of the

system towards standard IoT related vulnerabilities.

Through integrating blockchain-enabled

decentralized identity management and lightweight

cryptographic protocols, vulnerabilities to

unauthorized access, replay attacks, and device

masquerading were successfully addressed. The

voice popularity implementation showed quick form

detection and response times and secure data and

system availability in presence of the simulated

attacks. This secure-by-design strategy builds users

trust and reinforces the sustainability of smart home

systems for critical applications such as elder

monitoring and remote healthcare.

In terms of usability, the system received good

ratings for all main usability aspects. Responses from

structured questionnaires revealed a mean ease of use

score of 4.6 out of 5 (with a standard deviation of 0.3)

focusing on systemic positive experience. The

satisfaction with system responsiveness score was 4.5

while the comfort and convenience index scored just

below perfect (4.7), indicative of the effectiveness of

adaptive automation and the ease of use of the

interface. Voice assistant connectivity was likewise

strong, with 85% reporting good or great command

recognition and response. The findings of this work,

detailed in Table 4, highlight the significance of user

centred design for greater receptivity towards smart

home technology, especially among the elderly and

disabled groups.

Table 4: User Experience Evaluation Results.

Metric

Avg. Score

(out of 5)

Std.

Deviation

Positive

Feedback (%)

Ease of Use 4.6 0.3 92%

Response

Time

Satisfaction

4.5 0.4 88%

Comfort &

Convenience

4.7 0.2 95%

Voice

Assistant

Accuracy

4.4 0.5 85%

Next-Generation IoT-Enabled Smart Home Framework: A Secure, Scalable and Energy-Efﬁcient Solution for Intelligent Living

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Figure 4: User Satisfaction Ratings.

Figure 4 gives the information about User

Satisfaction Ratings. The robustness of the system

under various network environments was also a key

validation result. Failover testing determined that the

framework smoothly fall into offline modes when

Wi-Fi failed, to maintain local automation without

losing functionality. Connectivity through low-power

wide-area network type systems such as LoRaWAN

has kept the system going with essential services such

as security warning lights and environmental control,

even in low-bandwidth areas, an important portrayal

for rural or underprivileged areas.

The daily usage patterns over a monitoring period

of 30 days indicate that the system managed to

flatten peak loads by predicting and scheduling,

leading to more predictable grid connections and

reduced cost. Smart scheduling algorithms learned

user habits in real time, adapting HVAC, lighting and

appliance operation schedules to occupancy patterns,

optimizing energy savings. Such self-optimization is

a key design factor in enabling to operate the system

at high efficiency and with long life times without the

requirement of constant manual re-tuning its

parameters.

In conclusion, the performance results

demonstrate that our proposed smart home system

overcomes the major limitations of the existing

systems by providing a secure, scalable, energy-

efficient, and user-adaptive solution. It provides next

gen intelligent living environment based on edge

intelligence, blockchain security and real time

context aware automatization. The modular design

also enables the future expansion, such as adding the

renewable energy peripherals, the smart grid

peripherals, and AI-based health monitoring

application, makes TStation the complete platform

for sustainable, smart living in today's age.

6 CONCLUSIONS

This study has introduced a powerful, intelligent, and

future proof smart home architecture, which provides

practical solutions to the perennial problems of home

security, energy consumption, integration, and user

adjustability in the IoT-based smart home systems.

Edge computing combined with adaptive AI models

and blockchain-authenticated identity verification in

the proposed infrastructure would make it a low-

latency, secure and scalable space for real-time

decisions, and smooth device interactions.

The results of the experiments confirm that the

system can effectively reduce a large amount of

energy consumption, improve the response time, be

able to resist possible cyber-attacks, and still have a

great level of usability on different kinds of users. In

addition, its modular structure and open-protocol

compatibility facilitates device interoperability, thus

reducing vendor lock-in and promoting wider

adoption.

This framework can serve as an alternative

pathway to realizing smart homes with feeling, and

complies with global sustainable development and

accessibility, providing protection for personal and

resident data. Further work will be devoted to the

enhancement of these features by adding support for

renewable energy sources, context-aware ambient

intelligence and real-time anomaly diagnostics, with

the aim to increase the potential of the smart home

experience.

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