1.       Introduction

With the rapid growth of urbanization and the increasing complexity of city life, ensuring public safety has become a critical concern for governments and urban planners. Traditional security measures, such as manual patrolling and basic CCTV systems, often fall short in detecting and responding to real-time threats efficiently. To address these challenges, Intelligent Urban Security Systems (IUSS) have emerged as a promising solution, leveraging modern technologies like the Internet of Things (IoT), artificial intelligence (AI), machine learning, and advanced sensors.

These systems enable continuous monitoring of streets, public spaces, and critical infrastructure, providing real-time alerts for unusual activities, traffic violations, or potential criminal incidents. By integrating predictive analytics and automated decision-making, IUSS not only improves response times but also enhances resource allocation for law enforcement and emergency services. Moreover, these systems contribute to the development of smart cities, where technology plays a central role in maintaining safety, reducing crime, and ensuring a higher quality of urban life. In essence, Intelligent Urban Security Systems represent a shift from reactive to proactive urban safety, offering scalable, efficient, and data-driven solutions for modern cities.

Implementing Intelligent Urban Security Systems faces several challenges despite their potential benefits. One of the major issues is the high cost associated with installing advanced sensors, IoT devices, AI-based analytics, and integrated surveillance infrastructure, which can be a significant barrier for many cities. Data privacy and security also pose serious concerns, as continuous monitoring involves collecting sensitive information that must be stored and transmitted securely to prevent misuse. Integrating multiple technologies into a seamless system is technically complex, and AI-based detection systems may produce false alarms or fail to recognize certain threats, affecting reliability. Additionally, maintaining these systems requires skilled personnel and regular updates, which may not always be available. Scalability is another challenge, as urban environments continuously grow and evolve, requiring adaptable systems that can handle increasing demands. Finally, legal and ethical considerations must be addressed to ensure that public safety measures do not infringe on citizens’ rights.

The motivation behind developing Intelligent Urban Security Systems stems from the growing need for safer and smarter cities. With urban populations rising rapidly, traditional security measures such as manual patrolling and basic CCTV surveillance are often insufficient to handle real-time threats, traffic management, and crime prevention effectively. By leveraging advanced technologies like IoT, artificial intelligence, and data analytics, these systems can monitor public spaces continuously, detect suspicious activities, and provide timely alerts to authorities. The adoption of such intelligent solutions not only enhances public safety but also optimizes the allocation of resources, reduces response times during emergencies, and contributes to the creation of sustainable, technologically advanced urban environments. Ultimately, the goal is to shift from reactive security measures to proactive, data-driven strategies that make cities safer and more resilient.

2.       Circuit description and Mathematical Modelling

A basic automatic night light circuit using an LDR and an NPN transistor (BC547), as shown in Figure 1(a). The circuit operates from a 9 V DC supply. The LDR and resistor (100 kΩ) form a voltage divider network that provides a control voltage to the base of the transistor. The LED, along with the current-limiting resistor (470 Ω), is connected in the collector circuit of the transistor. The resistance of the LDR decreases significantly, pulling the base voltage of the transistor below the base–emitter threshold voltage during bright light conditions. As a result, the transistor remains in the cut-off region, and the LED remains OFF. In dark conditions, the LDR resistance increases, raising the base voltage above the threshold level, thereby driving the transistor into saturation. This allows current to flow through the LED, turning it ON automatically.

Fig. 1 Basic LDR–transistor night light circuit

An enhanced version of the automatic night light circuit using an operational amplifier (LM358) configured as a comparator, as shown in Figure 1(b). The LDR and resistor network generate a voltage proportional to ambient light intensity, which is compared with a reference voltage set by a potentiometer (RP1). The comparator output drives a BC547 transistor through a base resistor, which in turn energizes a relay to control a high-power AC bulb. A diode (1N4148) is connected across the relay coil for protection against back electromotive force (EMF).

Fig. 2 Op-amp-assisted night light with relay output

The mathematical model of the system is based on the behavior of the LDR, voltage divider action, and transistor switching characteristics. The resistance of the LDR varies inversely with the ambient light intensity and can be approximated by equation (1).

                                                   (1)

Where R_LDR is the LDR resistance (Ω). is the light intensity (lux), and are sensor-dependent constants. As light intensity decreases, increases sharply. The base voltage of the transistor is determined by the voltage divider formed by and  by equation (2).

                                        (2)

Where is the base voltage of the transistor, is the supply voltage. In darkness, , resulting in a higher base voltage. The transistor turns ON when the base voltage satisfies equation (3).

                                      (3)                                                            

The base current is illustrated by equation (4).

                                                 (4)

The collector current is given by equation (5).

                                                     (5)                                                                                                                                             

Where is the current gain of the transistor.  When the transistor is in saturation, the LED or relay current is given by equation (6).

                              (6)                                                                                                 

Where is the LED forward voltage or the relay coil voltage. In bright light conditions, decreases, causing , which forces the transistor into cut-off and switches the load OFF.

3.       Methodology

The methodology followed in this work focuses on the systematic design, implementation, and validation of an automatic night light using a transistor-based switching circuit. The approach integrates theoretical analysis with practical experimentation to ensure reliable and energy-efficient operation, as shown in Figure 3.

Fig. 3 Experimental setup for an automatic night light using a transistor

The proposed methodology begins with defining system requirements for automatic ambient light detection and autonomous light switching. An LDR was selected as the sensing element and combined with a fixed resistor to form a voltage divider that produces a control signal proportional to illumination. This signal drives an NPN transistor (BC547) configured as a switch, operating in cut-off during daylight and saturation under low-light conditions to control an LED load. To enhance threshold accuracy and load capability, an advanced design employing an LM358 comparator with a potentiometer-defined reference was implemented, enabling relay-based control of high-power loads. Circuit behavior was verified through simulation before hardware implementation, followed by experimental validation, which confirmed stable and reliable automatic operation.

Fig. 3 Flow chart

The flowchart describes the operation of an automatic night light system that functions based on ambient light conditions. Once the power supply is switched ON, the circuit becomes active and the Light Dependent Resistor (LDR) continuously senses the surrounding light intensity. The sensed light level is compared with a predefined threshold value. When the ambient light intensity falls below or equals the threshold, indicating darkness, the resistance of the LDR increases, providing sufficient base current to the transistor, which then operates in the saturation region and turns the light ON. Conversely, when the ambient light intensity exceeds the threshold under bright conditions, the LDR resistance decreases, the transistor remains in the cut-off region, and the light remains OFF. This process ensures automatic and energy-efficient lighting without human intervention.

4.       Conclusion

This work successfully demonstrates the design, implementation, and validation of a low-cost automatic night light system using simple analog components. By employing an LDR for ambient light sensing and a transistor-based switching mechanism, the proposed circuit achieves reliable automatic ON–OFF operation without the need for microcontrollers or complex digital control. The system exhibits stable performance under varying light conditions and effectively reinforces fundamental concepts such as sensor interfacing, voltage divider operation, and transistor biasing. The enhanced configuration using an operational amplifier and relay further extends the applicability of the design to higher-power loads. Overall, the proposed approach offers an energy-efficient, economical, and educational solution for intelligent lighting applications, with potential for future improvements through sensitivity tuning and integration with advanced control techniques.