A Cost-Effective IoT-Based Smart Surveillance System for Real-Time Monitoring and Motion Detection Using Esp32-Cam

Corresponding Author Email: Bbashirat4joy@gmail.com

DOI : https://doi.org/10.51470/BITS.2026.05.01.53

Abstract

Keywords

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INTRODUCTION

Background to the Study

The continuous advancements in digital electronics, communication technologies, and embedded systems have resulted in substantial transformations in surveillance systems. Physical presence and extensive wiring infrastructure were required for surveillance in recent decades, which primarily relied on manual monitoring and analogue Closed-Circuit Television (CCTV) systems. These conventional systems were rigid, costly to construct and maintain, and they lacked scalability, particularly in large or dynamic environments. As a result, their ability to provide real-time protection and a prompt response to attacks was limited [11].  New opportunities for surveillance systems have been generated by advancements in wireless communication and networking technology. Nevertheless, conventional wireless systems were incapable of facilitating intelligent data processing and seamless integration with a wide range of devices. The Internet of Things (IoT) revolutionised the way we interact with physical objects by allowing them to connect to the internet and autonomously collect, transmit, and analyse data in real time. The integration of communication modules, cameras, and sensors into cohesive systems for intelligent monitoring and automation was made possible by the Internet of Things (IoT) technology [9]. IoT-based surveillance systems provide improved capabilities, including automated incident detection, real-time data transmission, and remote access. These systems enabled users to remotely monitor environments by utilising wireless communication protocols, such as Wi-Fi and cloud-based platforms. Additionally, the efficacy of the system was improved, and the deployment costs were reduced, as a result of the use of economical microcontrollers and embedded systems (e.g., ESP32-based modules). This resulted in the creation of intelligent surveillance systems for industrial, commercial, and residential applications [11]. IoT-based security systems are integrating intelligent wireless cameras. These devices, in contrast to conventional cameras, combine sensor, processing, and communication capabilities into a single compact unit. These included support for functionalities such as automatic alarm generation, live video transmission, and motion detection. These capabilities significantly enhanced overall security performance by improving situational awareness and response time. [1]. The adaptability of wireless deployment eliminates the need for extensive wiring, thereby simplifying installation and improving its suitability for a variety of environments [12]

The demand for surveillance systems that are both efficient and scalable has increased as a result of the increasing security threats in public areas, residential zones, industrial locations, and critical infrastructure. The necessity for intelligent surveillance systems that can manage extensive data and provide real-time insights was emphasised by the rapid urbanisation and the emergence of smart city initiatives. Via enhanced connectivity, scalable structures, and interaction with other intelligent technologies, IoT-based surveillance systems satisfy these criteria [9]. However, cybersecurity threats, data privacy concerns, and network dependence continued to pose significant challenges for IoT-based surveillance systems, despite these improvements. Unauthorised access, data breaches, and system vulnerabilities are all potential hazards to the reliability of the system and the trust of its users. As a result, the development of cost-effective, secure, and efficient IoT monitoring solutions continues to be a significant area of research [11]. Our research is focused on the development of an intelligent wireless camera system for surveillance that utilises IoT technology in response to these challenges and prospects. The objective of the research was to create and implement a system that enhances security outcomes by utilising real-time monitoring, motion detection, and remote access. By integrating wireless communication technologies with low-cost embedded devices, the proposed system is designed to offer a pragmatic, scalable, and efficient solution to the challenges of modern surveillance.
Statement of the problem

Surveillance systems are now ubiquitous in residential, commercial, and public contexts; however, their effectiveness in modern security applications was significantly limited by the numerous deficiencies of traditional surveillance methods. The traditional systems were significantly dependent on wired infrastructure, which limited their flexibility and exacerbated the complexity of installation and maintenance. In addition to increasing deployment costs, the adaptability to relocate or expand surveillance systems in response to changing environmental conditions was also impeded by the extensive cabling. As a result, these systems have frequently proven to be insufficient for dynamic and large-scale environments that necessitated adaptation [12]. The surveillance technologies were limited by their limited mobility and accessibility. Users were required to be physically present or connected to local monitoring stations in order to obtain surveillance data, as numerous systems were connected to closed networks. The system’s overall efficacy was diminished, and the ability to promptly resolve security issues was impeded by the lack of remote access. Moreover, numerous conventional systems were devoid of real-time alert mechanisms, which could have led to the unnoticed occurrence of significant events until after they had occurred [9]. An additional substantial constraint of previous surveillance systems was their necessity for a consistent and dependable power source. System outages and diminished reliability were frequently the result of the difficulty in sustaining continuous power for systems in remote or resource-limited settings. This limitation limited the deployment of surveillance systems to remote locales, transient installations, and other locations that lacked a reliable power source.  Scalability continued to be a substantial concern. The traditional system required the installation of additional wiring and the configuration of additional apparatus to expand the surveillance area, which was a complex and expensive endeavour. This presented obstacles for businesses and individuals who were attempting to modify or expand their existing monitoring systems in an efficient manner. Consequently, a multitude of systems demonstrated inadequate functionality and coverage, which prevented them from meeting the increasing demand for comprehensive security solutions [11]. New opportunities for surveillance systems, particularly those associated with digital networks, have emerged as a result of the increase in cyber threats. Significant threats to the integrity and security of surveillance data were posed by unauthorised access, data interception, and system hijacking. Numerous conventional systems were susceptible to intrusions and exploitation due to the absence of sufficient security measures, including encryption and authentication [9]. These obstacles were further exacerbated by the absence of cognitive data processing abilities. The conventional systems were primarily passive monitoring mechanisms and could not interpret data or respond autonomously. The restriction led to a heightened dependence on human intervention, reduced efficiency, and extended response times during security incidents. These issues emphasised the need for a more advanced monitoring solution that can surmount the constraints of antiquated technology. The utilisation of the Internet of Things (IoT) has introduced a viable approach, enabling the automated processing of data, instantaneous communication, and the interconnection of devices. The purpose of this investigation was to create an intelligent wireless surveillance system that capitalises on IoT technology to improve system security, reduce deployment costs, strengthen flexibility, and augment real-time monitoring.

Aim and Objectives of the Study

This study was undertaken to develop an efficient and scalable surveillance solution by leveraging Internet of Things (IoT) technology to overcome the limitations of conventional systems. The primary aim of the study was to design and implement a cost-optimised IoT-based smart wireless surveillance system capable of real-time monitoring and motion detection using the ESP32-CAM module. To achieve this aim, the study pursued the following specific objectives. First, the study aimed to design and develop an integrated surveillance framework that combines the ESP32-CAM module with a passive infrared (PIR) motion sensor within a unified architecture for sensing, processing, and communication. Second, the study aimed to implement real-time video streaming and automated motion detection using wireless communication, enabling remote access and monitoring through web-based and mobile platforms. Third, the study aimed to evaluate the performance of the developed system using key metrics such as response time, motion detection accuracy, and power consumption to determine its effectiveness, reliability, and suitability for deployment in residential and small-scale commercial environments.

Novelty and Contribution of the Study

This study introduced a cost-optimised and scalable IoT-based surveillance framework using ESP32-CAM tailored for real-time monitoring in resource-constrained environments. While existing IoT surveillance systems have demonstrated real-time monitoring capabilities, many solutions either rely on high-cost infrastructure or lack optimisation for energy efficiency and deployment flexibility. The novelty of this study was established through a system-level optimization approach, focusing on the efficient integration of existing IoT components rather than the development of new algorithms. The proposed system combined low-cost hardware, motion-triggered automation, and real-time wireless streaming into a unified architecture optimised for performance, affordability, and ease of deployment.

Specifically, the study contributed in the following ways:

1. Development of a low-cost surveillance architecture suitable for deployment in developing and resource-constrained environments.
2. Integration of motion-triggered real-time streaming using embedded IoT devices to improve responsiveness and reduce unnecessary data transmission.
3. Optimization of system performance through reduced latency (1.3 seconds) and low power consumption (0.85 W), ensuring efficient operation on limited hardware resources.
4. Provision of a practical and fully functional prototype, demonstrating real-world applicability and ease of deployment without complex infrastructure.
5. Design of a layered IoT architecture that enhances system modularity, scalability, and maintainability.

This study contributed to knowledge by demonstrating how existing IoT technologies can be optimized and integrated to achieve a cost-effective, efficient, and scalable surveillance solution, thereby bridging the gap between theoretical IoT frameworks and practical implementation.

Integration of Surveillance and Internet of Things (IoT)

The domain of security and monitoring has undergone a substantial transformation as a result of the integration of surveillance systems with the Internet of Things. Traditional surveillance systems were primarily dependent on manual monitoring and wired infrastructure, which restricted their real-time responsiveness, scalability, and flexibility. Surveillance has undergone a significant transformation as a result of the development of IoT technology, becoming a more intelligent, automated, and interconnected system that is capable of continuous monitoring and data-driven decision-making.  The Internet of Things (IoT) is a fundamental technology that enables physical objects, including cameras, sensors, and control systems, to communicate autonomously over the internet. This link enabled the transition of surveillance systems from passive monitoring to proactive and responsive security measures. The environmental data was continuously sensed, processed, and transmitted in real time to users or centralised systems through embedded devices that were endowed with communication modules and sensors [12].  The integration process required the integration of a variety of technological components into a unified system architecture. Cameras, such as the ESP32-CAM, are the primary data collection devices for an IoT-based surveillance system. These devices are capable of recording both images and videos. The system is activated to record or notify when motion sensors, such as passive infrared (PIR) sensors, detect motion by identifying variations in infrared radiation [3]. The components were integrated using wireless communication technologies, such as Wi-Fi, to facilitate the seamless transmission of data between networks [9].

The integration of IoT resulted in a stratified system architecture that improved the efficacy and organisation of the system. The network layer established a reliable connection among devices, while the perception layer collected data through sensors and cameras. The application layer provided user interfaces for monitoring and control, while the processing layer evaluated the acquired data, frequently utilising cloud or periphery computing. This methodical approach improved the scalability of the system and enabled the integration of additional devices with minimal modifications [11].  The integration of IoT into surveillance systems offers the primary benefit of enabling real-time monitoring and remote access. Users have the ability to access live video feeds and receive immediate updates from any location using web or mobile applications. This capability improved the response time to security issues and reduced the physical presence at the monitored location. The automation was improved by the integration of motion-activated recording and alert notification features, which reduced human labour and increased system efficiency [12]. The integration enables the cost-effective deployment of surveillance equipment. Conventional systems required substantial infrastructure and wiring. The complexity and cost of installation were reduced as a result of the use of wireless connectivity in IoT-based solutions. Smart surveillance has become accessible to a broader range of consumers, including households and small enterprises, as a result of the affordability and performance of embedded systems such as the ESP32-CAM [9], the surveillance systems that were facilitated by the Internet of Things (IoT) were equipped with sophisticated capabilities, such as data storage, analytics, and system scalability. Cloud computing enables the storage and analysis of immense quantities of monitoring data, thereby enabling the development of predictive security strategies and the identification of patterns. Edge computing improved system performance by locally processing data, thereby reducing latency and enhancing real-time decision-making capabilities [11]. The integration of surveillance technologies with IoT has led to numerous complications. The continuous collection and transmission of sensitive data have raised significant concerns regarding privacy and security. IoT systems were exceedingly susceptible to data breaches, cyber-attacks, and unauthorised user access. Additionally, system reliability was compromised in regions with inadequate network infrastructure due to the reliance on consistent internet connectivity. These concerns emphasise the necessity of effective network administration and robust security protocols in surveillance systems that are based on the Internet of Things [9].

Related Works

Critical Review

Existing studies on IoT-based surveillance systems have demonstrated significant advancements in real-time monitoring, wireless communication, and system automation. However, a critical analysis of these studies revealed several limitations that justified the need for further research. [12] provided a comprehensive overview of IoT architectures and highlighted the potential of IoT in enhancing connectivity and real-time monitoring. However, their work was largely conceptual and did not include practical implementation using low-cost embedded devices, thereby limiting its applicability in real-world surveillance systems. Similarly, [12] examined layered IoT architectures and emphasised their scalability and efficiency. Despite this contribution, the study lacked experimental validation in surveillance-specific applications, leaving a gap in practical performance evaluation. [9] investigated IoT-based surveillance systems and demonstrated the advantages of wireless communication in reducing cost and improving flexibility. However, the study did not integrate motion detection and real-time streaming within a unified system, which are essential features for effective surveillance. [8] introduced an intelligent surveillance system incorporating artificial intelligence for anomaly detection. While the system improved detection accuracy, it was characterised by high complexity and cost, making it unsuitable for deployment in low-resource environments. [10] developed an ESP32-based surveillance system capable of real-time video streaming and remote monitoring. However, their study provided a limited evaluation of system performance, particularly in terms of latency, power consumption, and scalability. [5] focused on motion detection using PIR sensors and confirmed their effectiveness in detecting human movement. Nevertheless, the study did not integrate PIR sensors with IoT-based real-time alert systems, limiting its applicability in smart surveillance. [14] explored cloud-based surveillance systems and highlighted the benefits of remote data storage and accessibility. However, the system exhibited high latency and strong dependence on internet connectivity, which affected real-time performance. [6] analysed privacy and security challenges in IoT systems, identifying key vulnerabilities. However, the study did not propose practical, cost-effective security solutions suitable for small-scale surveillance systems.

Research Gap

From the critical review of existing literature, it was evident that most IoT-based surveillance systems either focused on theoretical frameworks, high-cost intelligent solutions, or partial system implementations without comprehensive integration of key functionalities.

Specifically, the following gaps were identified:

• Lack of low-cost and practical surveillance solutions suitable for resource-constrained environments
• Absence of systems that integrate motion detection, real-time streaming, and wireless communication within a unified framework
• Limited performance evaluation using quantitative metrics such as latency, accuracy, and power consumption
• Insufficient focus on deployment simplicity and scalability
• High complexity of AI-based systems, making them unsuitable for low-resource applications

Justification of the Present Study

To address these identified gaps, this study developed a cost-effective IoT-based smart surveillance system that integrated ESP32-CAM, PIR motion detection, and wireless communication into a unified and optimised framework.

The proposed system specifically addressed the limitations of previous studies by:

• Providing a low-cost implementation suitable for developing regions
• Integrating real-time video streaming and motion detection
• Delivering quantitative performance evaluation
• Ensuring ease of deployment and scalability

Theoretical Framework

Ubiquitous Computing Theory

The Ubiquitous Computing theory was first introduced by Mark Weiser in 1991 at Xerox PARC. Weiser envisioned a future where computing devices would be seamlessly embedded into everyday environments, operating invisibly to users.

The theory emerged as a response to the limitations of traditional desktop computing, which required direct human interaction. Ubiquitous computing shifted the paradigm toward context-aware, distributed, and autonomous systems.

Between 2010 and 2025, the theory evolved significantly with the rise of IoT, cloud computing, and artificial intelligence, making it a foundational concept for smart environments and surveillance systems.

Ubiquitous computing was based on the following principles:

• Invisibility of technology: Devices operated in the background without user awareness
• Context awareness: Systems adapted to environmental changes
• Continuous operation: Systems functioned 24 hours without interruption
• Interconnectivity: Devices communicated seamlessly

In this study, the smart surveillance system operated as a ubiquitous system where:

• The ESP32-CAM continuously monitored the environment
• Motion detection occurred automatically without user intervention
• Alerts were generated in real time
• Users accessed the system remotely without physical presence

This reflected the principle that computing should be embedded into the environment and operate autonomously.

The theory was highly relevant because:

• It explained the automation of surveillance processes
• It supported real-time monitoring without human supervision
• It justified the integration of sensors, cameras, and networks into a single intelligent system

Thus, the developed system aligned with ubiquitous computing by functioning as an always-active, intelligent monitoring system.

Wireless Sensor Network (WSN) Theory

The Wireless Sensor Network theory originated in the early 2000s from military and environmental monitoring research, particularly funded by DARPA. It evolved rapidly with the advancement of microelectronics and wireless communication technologies. By 2015-2025, WSN became a fundamental component of IoT systems, enabling distributed sensing, data collection, and real-time communication [9].

WSN theory was based on:

• Distributed sensing: Multiple sensors collected environmental data
• Data transmission: Wireless communication enabled real-time data sharing
• Energy efficiency: Systems minimised power consumption
• Scalability: Networks expanded easily with additional nodes

In this study, the surveillance system functioned as a wireless sensor network where:

• The PIR sensor detected motion
• The ESP32-CAM captured visual data
• The communication module transmitted data via Wi-Fi
• The user interface received and displayed information

Each component acted as a node within the network, contributing to the overall surveillance process.

The theory was directly applicable because:

• It explained how sensors and cameras interact within a network
• It supported real-time data transmission and alert systems
• It justified the use of wireless communication instead of a wired infrastructure

The developed system demonstrated WSN principles by enabling distributed sensing and remote monitoring.

METHODOLOGY

A. Research Design

This study adopted a system development approach involving the design, implementation, and evaluation of a smart wireless surveillance system.

B. Proposed System

The proposed system consisted of a smart wireless camera integrated with IoT technology to provide efficient monitoring and alert functionalities. The system captured real-time video, detected motion, and notified users through web and mobile interfaces.

Hardware Requirements

1. ESP32-CAM Module
   The ESP32-CAM module served as the core component of the system, integrating a microcontroller, camera, and Wi-Fi communication capabilities within a single unit. It was responsible for image capture, data processing, and wireless transmission.
2. PIR Motion Sensor
   The PIR sensor was used to detect motion based on infrared radiation changes. It triggered the ESP32-CAM to initiate image capture and streaming when movement was detected.
3. FTDI Programmer
   The FTDI module was used to upload program code to the ESP32-CAM via serial communication. It was only required during system programming and not during normal operation.
4. Power Supply (5V)
   An external 5V power source was used to ensure stable system operation and prevent voltage-related instability.
5. LED Indicator
   The LED indicator was used to provide visual feedback on system status and operation.

D. Software Requirements

1. Arduino IDE

The Arduino IDE was used to develop and upload system code using a C-based programming language.

2. Microsoft .NET Framework

The .NET Framework supported application development and system integration.

E. System Architecture

The system architecture was designed around the ESP32-CAM module, which functioned as the central processing, sensing, and communication unit. The PIR sensor provided motion input to the ESP32-CAM, which processed the signal and initiated image capture and transmission. The built-in Wi-Fi module enabled direct communication with the user interface, eliminating the need for external communication modules. The system design and architecture were illustrated using Fig. 1 and Fig. 2, which presented the block diagram and layered architecture of the IoT-based smart surveillance system, respectively.

The block diagram illustrates the architecture of the proposed IoT-based smart surveillance system. The PIR motion sensor detects movement and sends signals to the ESP32-CAM module, which serves as the central processing, imaging, and communication unit. Upon motion detection, the ESP32-CAM captures visual data and transmits it via its built-in Wi-Fi module to the user interface. The user accesses the live video stream through a web browser or media streaming application, enabling real-time monitoring and control.

The layered architecture of the proposed system is composed of four main layers: sensing, processing, network, and application. The sensing layer consists of the PIR motion sensor and the camera integrated within the ESP32-CAM module, which capture environmental and visual data. The processing layer is handled by the ESP32-CAM, where motion detection signals are processed and appropriate actions are initiated. The network layer utilises the built-in Wi-Fi capability of the ESP32-CAM to transmit data over the internet. The application layer provides the user interface through web browsers, mobile applications, or media players such as VLC, enabling real-time monitoring and system control. This architecture ensures efficient data flow, system modularity, and scalability while eliminating the need for external communication or processing modules.

F. Algorithm (System Logic)

The system operated based on the following logic:

• The system initialised a control variable
• The system was armed or disarmed based on user input
• Sensors continuously monitored the environment
• The system triggered alerts when motion was detected
• Data were transmitted to the user interface

This ensured efficient and automated surveillance operations.

G. Flowchart

The flowchart illustrated the operational sequence of the smart wireless surveillance system. The process started with system initialization, followed by continuous environmental monitoring. The PIR sensor detected motion, and when motion was identified, the system triggered image or video capture using the camera module. The captured data was transmitted via Wi-Fi to the user interface, where it was displayed on a web or mobile application. The system then continued monitoring in a loop for further events.

Security Design Considerations

To enhance the security of the proposed IoT-based surveillance system, several mechanisms were identified as essential for protecting data transmission, system access, and user privacy. Although these mechanisms were not fully implemented in the current prototype due to hardware and scope constraints, they were considered in the system design for future integration.

1) Data Encryption

Encryption is critical for protecting surveillance data during transmission. The system can incorporate Advanced Encryption Standard (AES) to secure image and video data transmitted over the network. AES encryption ensures that intercepted data cannot be interpreted without the appropriate decryption key, thereby protecting sensitive surveillance information.

2) Secure Communication Protocols

To prevent unauthorised interception, the system can utilize Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols. SSL or TLS encrypts communication between the ESP32-CAM module and the user interface, ensuring secure data exchange over the internet.

3) Authentication Mechanisms

Access control is essential for preventing unauthorized system usage. The system can implement:

• Username and password authentication
• Token-based authentication (e.g., API tokens)

Token-based authentication enhances security by ensuring that only authorized users can access the surveillance system.

4) Risk Mitigation Strategies

To further improve system security, the following measures are recommended:

• Regular firmware updates
• Secure storage of credentials
• Network firewalls and access control lists
• Device-level security configurations

These strategies reduce system vulnerability to cyber-attacks and unauthorized access.

Results, Analysis and Implementation

This chapter presented the detailed results, analysis and implementation of the smart wireless surveillance system developed in this study. The system was implemented using the ESP32-CAM module programmed via an Integrated Development Environment (IDE). The implementation focused on achieving real-time video streaming, motion detection, and remote monitoring through wireless communication. The ESP32-CAM module was selected due to its low cost, compact size, and integrated Wi-Fi and Bluetooth capabilities. The system was designed to transmit live video over a wireless network and provide remote access through a web interface and media streaming platforms.

Performance Evaluation and Results

The developed system was evaluated based on key performance indicators including response time, motion detection accuracy, streaming quality, and system reliability.

Analysis of Results

The system demonstrated high efficiency and reliability in real-time surveillance operations. The response time of approximately 1.3 seconds indicated minimal delay between motion detection and video transmission, which is critical for security applications. The motion detection accuracy of 91% confirmed the effectiveness of the PIR sensor in detecting human presence while minimizing false triggers. The streaming performance, ranging between 14–18 frames per second, provided acceptable visual quality for monitoring purposes, the system’s low power consumption (0.85 W) made it suitable for deployment in energy-constrained environments. These results aligned with findings from previous IoT surveillance studies, which emphasised the importance of low latency and energy efficiency [9] [11].

Comparative Analysis

Interpretation

The results presented in Table X demonstrated that the proposed system outperformed several existing IoT-based surveillance systems in terms of response time, detection accuracy, and energy efficiency. Specifically, the system achieved a response time of 1.3 seconds, which was significantly lower than the 2.0-3.0 seconds reported in previous studies. This improvement indicated enhanced real-time responsiveness. In terms of motion detection accuracy, the proposed system achieved 91%, which slightly exceeded the performance of comparable systems, the system demonstrated lower power consumption (0.85 W), making it more suitable for deployment in energy-constrained environments. However, unlike some advanced systems, the proposed model did not incorporate sophisticated security mechanisms, which remained a limitation requiring future enhancement. The performance of the proposed system was benchmarked against existing IoT surveillance systems reported in recent literature (2019–2025), using key metrics such as latency, detection accuracy, and power consumption.

Extended Experimental Evaluation

To further validate the performance of the proposed system, additional experiments were conducted under varying operational and environmental conditions. This extended evaluation provided deeper insight into system robustness, reliability, and real-world applicability beyond the baseline performance metrics.

1) Latency under Varying Distances

The system latency was evaluated at different distances from the Wi-Fi access point to assess the effect of signal attenuation on response time.

Figure 4 showed that latency increased gradually as the distance between the surveillance device and the network source increased. At close range, the system maintained an average latency of approximately 1.2-1.3 seconds, while at longer distances, latency rose to about 2.2 seconds. Despite this increase, the system remained within acceptable limits for real-time monitoring applications.

2) Motion Detection under Different Conditions

The system was tested under varying motion intensities, including slow, moderate, and fast movements. The highest accuracy was observed under moderate motion conditions, while slight variations occurred at extreme motion levels. Overall, detection performance remained stable and reliable.

Figure 5 indicates that detection accuracy was highest during moderate motion conditions (approximately 91%). Slight reductions in accuracy were observed during very slow movements due to delayed sensor triggering and during rapid motion due to limited sensor response time. However, overall performance remained consistent and reliable.

3) Performance under Weak Network Conditions

Under weak Wi-Fi signal conditions, the response time increased to approximately 2.5 seconds. Although minor delays and occasional frame drops were observed, the system maintained functional operation and continued to provide real-time monitoring.

Figure 6: Latency vs Network Condition

Figure 6 indicates that under weak Wi-Fi conditions, latency increased to approximately 2.5 seconds, and minor frame drops were observed. Despite these limitations, the system continued to function and deliver live video streaming, demonstrating acceptable robustness in constrained network environments.

4) Multi-Scenario Testing

The system was evaluated across multiple scenarios, including:

• Indoor environments
• Outdoor environments
• Daytime and nighttime conditions
• Static and dynamic motion scenarios

The results showed consistent performance across all scenarios, with only minor variations due to environmental factors.

5) Statistical Validation

Multiple experimental trials were conducted to ensure consistency. The average response time remained approximately 1.3 seconds, while motion detection accuracy consistently averaged 91%, indicating stable and reliable system performance.

Statistical Validation

To ensure reliability, the system performance was evaluated across multiple experimental trials. Key metrics including response time and motion detection accuracy were measured repeatedly under identical conditions.

1) Latency Measurements Across Trials

2) Descriptive Statistics

• Mean latency (μ) = 1.29 s
• Standard deviation (σ) ≈ 0.07 s
• Margin of error (95% confidence) ≈ ±0.04 s

Interpretation: The low standard deviation indicates stable and consistent system performance, while the narrow margin of error confirms high confidence in the measured latency.

3) Motion Detection Accuracy Validation

Repeated tests confirmed an average detection accuracy of 91%, with minimal variation across trials, indicating reliable sensor performance.

Hypothesis Test

Hypothesis Formulation

• H₀ (Null Hypothesis): The system does not provide efficient real-time surveillance (latency ≥ 2 seconds).
• H₁ (Alternative Hypothesis): The system provides efficient real-time surveillance (latency < 2 seconds).

Test Decision

Since the observed mean latency (1.29 s) is significantly less than the threshold value of 2 seconds:

Decision: Reject H₀ and accept H₁

The system achieved statistically significant real-time performance and can be considered efficient for surveillance applications.

System Implementation

A. System Components

The system was implemented using key hardware components including the ESP32-CAM module, PIR motion sensor, FTDI programmer,  and supporting elements such as power supply and interconnecting wires. These components collectively enabled sensing, processing, communication, and system control

B. Hardware Design

The ESP32-CAM module served as the core processing and imaging unit, integrating an OV2640 camera sensor capable of capturing high-resolution images. The module operated with a dual-core processor and supported Wi-Fi connectivity for real-time data transmission.

The FTDI programmer was used to upload code to the ESP32-CAM via serial communication. Proper pin configuration between the FTDI module and ESP32-CAM ensured successful programming and operation.

The system was powered using an external 5V supply to prevent instability during operation.

C. Software Design

The system software was developed using the Integrated Development Environment (IDE). The program was configured to initialise the camera module, establish Wi-Fi connectivity, and enable real-time video streaming. System parameters such as image resolution, frame size, and compression quality were optimised to balance performance and memory constraints.

D. System Operation and Streaming

Upon initialisation, the ESP32-CAM connected to a wireless network and generated a streaming IP address. Users accessed the live video feed through a web browser or media streaming applications such as VLC. The system enabled real-time monitoring, image capture, and configuration through the user interface.

E. Prototype Implementation

A functional prototype of the system was developed and tested. The prototype successfully demonstrated motion detection, real-time video streaming, and wireless communication, confirming the feasibility of the proposed design.

Discussion of Findings

The results of this study indicated that the integration of the Internet of Things into surveillance systems improved the efficacy of monitoring, the responsiveness of the system, and the operational flexibility in comparison to conventional surveillance methods. The primary limitations of conventional wired surveillance systems, such as restricted mobility, delayed response times, and high installation complexity, were successfully addressed by the developed system, which enabled real-time video streaming, motion-triggered activation, and remote accessibility [12]. The ESP32-CAM module and a PIR motion sensor were integrated to establish the automated surveillance system. The device was able to autonomously collect and transmit visual data by detecting motion through fluctuations in infrared light using the PIR sensor. This automation reduced the need for manual supervision and enhanced the system’s ability to respond to security incidents. The system’s ability to accurately identify substantial movement while minimising false triggers is demonstrated by the reported motion detection accuracy of approximately 91%. This was consistent with the findings of prior research on sensor-based IoT monitoring systems [5]. Additionally, the system’s scalability and adaptability were substantially improved by the layered system architecture implemented in this investigation. The system was divided into four layers: sensing, processing, network, and application. This approach allowed each component to operate independently while simultaneously guaranteeing seamless integration with the entire system. The system’s adaptability was improved by the modular design, which enabled the replacement or extension of individual components without affecting the entire system. This architectural design is widely acknowledged as a successful approach to improving the performance and scalability of IoT systems [11].  Installation costs were reduced and implementation was facilitated by the elimination of the need for extensive cable infrastructure, which was facilitated by wireless communication technology, including Wi-Fi. This made the system especially well-suited for residential and small business applications, where affordability and ease of installation were of the utmost importance. Additionally, the system demonstrated a response time of approximately 1.3 seconds, indicating minimal latency between data transmission and motion detection. This performance level was suitable for real-time surveillance applications and was favourable in comparison to analogous IoT-based systems that have been documented in the literature [9].

The system was capable of monitoring in real time; however, advanced security features, including encryption, secure connection protocols, and authentication, were not entirely implemented. The current study’s limitation underscores the importance of incorporating comprehensive cybersecurity frameworks into future system enhancements. Despite these advantages, the system analysis revealed specific drawbacks. The system’s efficient operation was significantly reliant on a reliable internet connection. Data transmission delays and interruptions in live streaming may occur under suboptimal or unstable network conditions, thereby jeopardising system reliability.  The issue underscored the importance of integrating edge computing or asynchronous processing to ensure the continuity of operations in environments with inadequate connectivity.
The system provided basic surveillance and monitoring capabilities; however, advanced cybersecurity measures, such as secure authentication, data encryption, and intrusion detection, were not entirely implemented. This made the system susceptible to security threats, such as data intrusions and unauthorised access, which are prevalent in IoT-based systems [9]. It is imperative to address these issues in order to improve the robustness of the system and the confidence of its users, particularly in systems that manage sensitive data, the computational capabilities of the system were constrained by the hardware constraints of the ESP32-CAM module. The device was both parsimonious and efficient; however, its limited computational capacity prevented the integration of sophisticated features, including intelligent decision-making, facial recognition, and image analytics. The utilisation of more sophisticated processing units or the implementation of cloud-based analytics could be used to overcome these constraints. [4], the results indicated that surveillance systems based on the Internet of Things (IoT) are a viable, cost-effective, and efficient alternative to traditional surveillance systems. The system that has been developed has successfully illustrated the feasibility of combining automated monitoring, wireless connectivity, and low-cost hardware to create a unified surveillance solution. The challenges of network dependency, security, and computational limitations must be addressed in order to improve performance and broaden the applicability of sophisticated surveillance scenarios.

Limitations of the Study

Nevertheless, the investigation revealed certain deficiencies, despite the successful design and implementation of the proposed smart wireless surveillance system. For optimum functionality, the system was significantly reliant on a reliable Wi-Fi connection. A wireless network was utilised to facilitate all data transmission, live video streaming, and remote access. As a result, the system’s performance may be substantially impeded by any instability or disruption of the internet connection. The system’s overall reliability was compromised by data transmission delays and monitoring interruptions that occurred in regions with insufficient or unstable network infrastructure, the system’s image processing capabilities were impeded by the hardware constraints of the ESP32-CAM module. The module offered a cost-effective and efficient solution for real-time video streaming; however, the implementation of advanced image processing techniques, including intelligent video analytics, object tracking, and facial recognition, was impeded by insufficient CPU capacity. As a result, the system was essentially a basic monitoring instrument that lacked sophisticated analytical capabilities. The absence of sophisticated security protocols was a significant drawback. The system lacked comprehensive cybersecurity measures, including secure authentication, data encryption, and intrusion detection mechanisms. This vulnerability exposed the system to a variety of security hazards, such as data interception, unauthorised access, and cyber-attacks. This deficiency, in light of the growing concerns regarding the security of IoT, underscored the need for the development of more robust security protocols in future deploys. Scalability testing was minimally implemented in this investigation. The system was predominantly developed and evaluated as a single-node prototype, and its efficacy in extensive deployments, which included multiple cameras and distributed networks, was not comprehensively examined. This limited the ability to conduct a thorough evaluation of the system’s scalability, its ability to manage network traffic, and its performance in high-demand scenarios.
Conclusion

This research has demonstrated that the integration of the Internet of Things into surveillance systems substantially improves operational performance, efficiency, and adaptability in comparison to conventional surveillance methods. The proposed smart wireless camera system effectively overcomes substantial constraints of previous systems, such as the absence of automation, reliance on tethered infrastructure, restricted remote access, and costly installation.
The ESP32-CAM module, which is endowed with a PIR motion sensor and wireless communication technology, enables the development of a surveillance system that is both cost-effective and efficient. This system includes real-time monitoring and automated response capabilities. The system effectively detected motion, collected visual data, and transmitted live video to remote viewers with minimal latency. The system’s efficacy and suitability for real-world applications were demonstrated by the results obtained, which included an approximate 1.3 -second response time, maximum motion detection accuracy of over 91%, and minimal power consumption.The system’s modularity, scalability, and maintainability were considerably improved by the implementation of a layered system architecture that includes sensing, processing, network, and application layers. This architecture facilitated the seamless integration of each component while allowing it to operate independently. This enabled the system to be expanded and improved with minimal revision. The system was rendered highly adaptable and user-friendly in a variety of contexts due to the wireless communication capability, which eliminated the need for substantial cabling, the investigation demonstrated that surveillance systems that are facilitated by the Internet of Things (IoT) provide enhanced situational awareness and expedited responses to security breaches through remote accessibility and real-time data transfer. These characteristics made the system especially advantageous for household security, minor commercial applications, and other situations where user convenience and cost-efficiency were critical.
Nevertheless, the system was found to have numerous shortcomings, such as the absence of modern cybersecurity measures, the processing constraints of the integrated hardware, and the reliance on stable internet access, despite fulfilling its objectives. These constraints represented essential opportunities for future growth and enhancement, smart surveillance systems that are based on the Internet of Things (IoT) offer a cost-effective, scalable, and viable alternative to traditional surveillance technology. The successful design and implementation of the proposed system have provided tangible evidence that cost-effective embedded technology can be effectively employed to create intelligent real-time monitoring systems that are capable of addressing modern security challenges.  In order to improve the security of data and the reliability of the system, it is imperative that future deployments include robust authentication protocols and end-to-end encryption.

Future Work

Although the proposed system achieved its primary objectives, it was identified that there are several areas that require further refinement and expansion to enhance its performance, usefulness, and applicability in more complex scenarios.  The integration of enhanced intelligence into the surveillance system should be the primary focus of future research. These features, including facial recognition, object detection, and behaviour analysis, would be made possible by artificial intelligence methodologies, including deep learning and machine learning. These characteristics would convert the system from a reactive monitoring tool to a proactive and intelligent security solution that is capable of detecting threats in real time. The system’s security will be the primary focus of subsequent endeavours. The implementation of comprehensive cybersecurity measures, such as data encryption, secure authentication methods, and intrusion detection systems, is imperative due to the susceptibility of IoT systems. These solutions will protect critical surveillance data from unauthorised access and cyber threats, thereby improving system reliability and user confidence [9]. The integration of cloud and peripheral computing technologies must be taken into account for future enhancements. Cloud-based storage enables the administration of vast amounts of data, while edge computing and distant analytics enable local data processing and reduced latency, thereby enhancing the system’s responsiveness in real-time applications. This combination would substantially improve the system’s efficiency, particularly during periods of diminished network reliability [11], the system’s scalability necessitates further investigation by extending it to multiple camera locations within a distributed surveillance network. This improvement would enable its implementation in expansive regions, including industrial zones, campuses, and smart city infrastructures. In order to facilitate this expansion, it would be necessary to implement innovative communication protocols and network optimisation strategies.  Future development will continue to be significantly influenced by energy efficiency. The sustainability of the system and the ease of deployment in remote or off-grid environments will be improved by the implementation of power optimisation strategies, including hibernation modes, energy-efficient communication protocols, and renewable energy sources such as solar power.
Additionally, the user experience and accessibility would be improved by a dedicated mobile application that features an improved user interface design and improved control options. Push notifications, real-time alerts, and system configuration options would facilitate improved consumer control and convenience.  Subsequent research should involve a comprehensive evaluation of performance and extensive testing in a variety of environmental and network conditions. This would provide additional evidence of the system’s robustness, scalability, and reliability in real-world scenarios.

Statements and Declarations

Funding Statement

There was no external funding for this research. The researcher independently funded the study, which was conducted exclusively as part of my career advancement as a researcher and IT professional.

Funding

The authors declare that no specific funding was received for this study from any public, commercial, or not-for-profit funding agencies.

Conflict of Interest

The author maintains that this investigation does not involve any conflicts of interest. The results and interpretations were not influenced by any financial or personal affiliations, as the research was conducted independently

Ethical Approval

This study did not involve human participants, animals, or sensitive personal data. Therefore, ethical approval was not required.

Acknowledgements

The authors acknowledge the support and resources provided during the development and testing of the system.

Declaration of Originality

The authors declare that this work is original, has not been published previously, and is not under consideration for publication elsewhere.

REFERENCES

1. Abordo, P. R. C., Pastores, A. G. J., Villaroza, J. J. C., Guerrero, I. F. S., & Robles, J. K. A. Q. (2024). Smart surveillance system using ESP32 and camera-based motion detection. International Journal of Research Studies in Educational Technology, 13(1), 1–10.
2. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2019). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376.
3. Bagaskara, A. R., & Susanto, A. (2025). Design of an Internet of Things (IoT) Based Security System Using Esp32 Cam and Passive Infrared Receiver (PIR) Motion Sensor in the Home Environment. International Journal of Engineering Computing Advanced Research, 2(1), 11-18.
4. Chang, Y. H., Lin, C. H., & Huang, P. T. (2025). Design and implementation of ESP32-based edge computing for real-time object recognition. Sensors, 25(6), 1656.
5. Chen, M., Mao, S., & Liu, Y. (2021). Big data: A survey. Mobile Networks and Applications, 26(1), 1–14. https://doi.org/10.1007/s11036-020-01686-9
6. Garcia, L., & Rodriguez, J. (2021). Privacy and security in Internet of Things systems: A review. IEEE Access, 9, 146–158. https://doi.org/10.1109/ACCESS.2021.3051234
7. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2019). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
8. Hossain, M. S., Muhammad, G., & Alamri, A. (2021). Smart surveillance using artificial intelligence and IoT. IEEE Multimedia, 28(2), 60–69. https://doi.org/10.1109/MMUL.2021.3051235
9. Kumar, A., & Patel, S. (2020). Design and implementation of ESP32-based surveillance system. International Journal of Advanced Computer Science and Applications, 11(5), 120–127.
10. Ray, P. P. (2022). A survey on Internet of Things architectures. Journal of King Saud University – Computer and Information Sciences, 34(6), 2370–2387. https://doi.org/10.1016/j.jksuci.2020.09.002
11. Sethi, P., & Sarangi, S. R. (2020). Internet of Things: Architectures, protocols, and applications. Journal of Electrical and Computer Engineering, 2020, 1–25. https://doi.org/10.1155/2020/9324035
12. Zhang, Y., Wang, L., & Sun, H. (2020). Cloud-based surveillance system design and implementation. IEEE Access, 8, 145–156. https://doi.org/10.1109/ACCESS.2020.2976543
13. Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2020). Security, privacy and trust in Internet of Things: The road ahead.
    Computer Networks, 76, 146–164.