Internet of Things and Long-Range-Based Smart Lampposts for Illuminating Smart Cities

: Intelligent and resilient infrastructure is necessary for smart cities for contributing ﬂexible and smart amenities to the citizens. Concerning the United Nations (UN) estimation, the global population residing in urban cities will reach 68% by 2050. Additionally, the Sustainable Energy Action Plans (SEAP) report suggests implementing energy efﬁciency technologies in smart cities to meet the rising urban population requirement. Internet of Things (IoT) technology empowers to achieve the goal of energy efﬁciency by integrating sensors, wireless technology, and renewable energy sources in the lighting system. At present, the IoT-based lighting system in urban cities is implemented with streetlamps and lampposts. In this study, we are focusing on lampposts, as it has the ﬂexibility of establishing and implementing a multitude of applications on a single system. Due to technological advancement, the lamppost is embedded with multiple sensors, communication protocols, and energy distribution infrastructure for delivering smart and affordable amenities to the citizens residing in the smart cities. This motivates us to implement a smart lamppost that provides a multitude of applications such as smart light, digital signs, environmental monitoring conditions, electric vehicle (EV) charging port, wireless ﬁdelity (Wi-Fi) hotspot, etc., on a single lamppost. This study proposed the IoT-assisted fog and edge-based smart lamppost for the smart cities to realize the smart infrastructure. Further, this smart lamppost is integrated with low power and long-range communication, i.e., Long Range (LoRa), enabling the smart lamppost to communicate the sensory data to a long-range. Additionally, LoRa is integrated with a Wi-Fi module for establishing the interconnection between the smart lamppost and IoT server. Generally, the proposed architecture is broad perspective; however, we have developed and implemented the hardware models of three components including lighting system, environmental parameters and image sensing in real time. Lighting system and environmental parameter monitoring are integrated on same hardware model for sensing and logging the real-time values of temperature, humidity, CO and light intensity on the IoT server. The developed image sensing prototype based on ESP 32 controller is also evaluated in real-time scenarios, and the performance of the prototype is efﬁcient. The proposed system delivers reliable performance in terms of sensing and communicating environmental parameters and images to the IoT server. Moreover, in future, we will complete the development of other components of the smart lamppost for enhancing the smarter infrastructure in smart cities.


a.
IoT server assisted fog and edge-based architecture is proposed for smart lampposts. b.
Smart lampposts are integrated with low power and long-range communication, i.e., Long-Range, enabling the smart lamppost to communicate the sensory data to a long-range. c.
LoRa is integrated with a Wi-Fi module for establishing the interconnection between the smart lamppost and IoT server. d. The proposed architecture is broad perspective; we have designed and implemented three components, namely, lighting system, environment parameter monitoring and image sensing in real time environment. e.
A hybrid system is implemented for monitoring the lighting system, environment parameter monitoring and, moreover, the sensing values of these components are plotted in the graph. f.
Master and slave controller-based mechanism is implemented for the intercommunicating the components of smart lampposts. g.
A proteus simulation is performed for the smart lighting system. h. The working of developed image sensing prototype based on ESP 32 is also discussed in the study with real-time implementation results.
The structure of the article is organized as follows: Section 2 covers the related works and overview of IoT and nearby technologies. The proposed architecture of the smart lamppost is covered in Section 3. Section 4 covers the hardware and software description. Section 5 covers simulation. Results are covered in Section 6 and the article concludes in Section 7.

Related Works and Overview of IoT
In this section, we will discuss the existing studies on smart cities and lighting systems. Further, we will explain the overview of IoT with nearby technologies that assist in realizing the smart lamppost in the smart city scenario.

Related Works
A smart city is a novel concept that is explored globally for establishing and enhancing the affordability and suitability of the lives of the inhabitants [21]. The concept is to utilize the emerging and advanced technologies for transforming each element in the city into an autonomous element. The autonomous elements will provide an opportunity Sustainability 2021, 13, 6398 4 of 20 to reduce the impact of environmental hazards by implementing eco-friendly and costeffective technologies. The significance of information and communication technology (ICT) empowers the concept of smart cities for delivering services and amenities to the masses [22]. The leveraging of ICT also enables us to achieve the target of energy efficiency that is relevant throughout the world. As discussed earlier, energy management is possible by managing the streetlights efficiently, as streetlamps are consuming a major proportion of the energy consumption globally. The streetlamp and lamppost are the sources of light in the streets and roads during night. However, in many places, such as Florida (United states), the streetlamps continues to glow even in daylight and due to this, the amount of energy consumption increases and sometimes it leads to defects in the lamps and extreme conditions it is challenging for the respective authorities to switch off the streetlamps for avoiding the necessary damage to the streetlamps [23]. An innovative and smart light energy management system is an optimal solution for reducing the energy consumption and monitoring of the streetlamps [24]. "Energy on demand" is the concept under which the energy is provided when it is required [25]. The technology facilitates the implementation of intelligent street lighting that works on the sensing of individual vehicles and the dimming of the streetlamps accordingly. The energy savings rise more than 50% even in low-traffic areas.
However, this kind of intelligent lighting system is not widely implemented practically because this kind of system is not cost-efficient [26]. The key aspect of the smart street lighting system is: (i) to light the streetlamps with the well-developed and integrated lighting system; (ii) to provide eco-friendly lighting which is also easy to maintain; (iii) to lessen power consumption and wastage of energy [27]. Basically, the energy savings of the lighting system are evaluated on the basis of standard formulated by European standards [28]. At present, EN 13201-1-5 explains the calculation of energy performance for road lighting installations. Moreover, these indicators are helpful for analyzing the energy performance of distinct road light solutions. Green Public Procurement organization is suggesting dimming the light by 50% for reducing the pollution and CO2 emissions [29].
With fast pace depletion of non-renewable resources and a negative impact on the environment, the need for smart streetlights is greater than ever. The model proposed here can be made more eco-friendly by adding a solar panel. Solar energy is eco-friendly because it does not generate CO2 and other gases during the generation of electricity. Moreover, solar energy is obtained from natural sources [30]. Furthermore, data from all the sensor nodes can be saved onto a central monitoring system for further use. Streetlights are an integral part of road safety and security on streets but we often find them lit up throughout the night and, due to lack of attention, they stay lit up during the day as well [31]. The target of the project is to come up with an intelligent system, which can reduce this waste of energy by making decisions for light control, either to switch lights on, off, or dim the light depending on the strength of ambient light [32]. The system allows the person who is monitoring the system to control in relation to the current intensity of light as well as predict monthly power consumption. The load sensing functionality of each unit allows us to detect whether the light is at fault [33]. The system detects the movement of the pedestrian and illuminates his path till the next streetlight and shuts off when a pedestrian walks out of the lamp range [34]. The most recent evolution in solid-state lighting based on leading technology accelerated the phase-out of inefficient lighting systems [35]. Adding IoT connectivity to streetlights adds the benefits of sustainable development [36]. Smart lighting helps save energy, reduces the cost of power, reduce maintenance, and saves tax payer's money; along with automation and network control, the system becomes even more efficient. A cloud-based system is proposed by integrating ZigBee and NodeMCU for monitoring the environmental parameter from any remote location [37]. An IoT-based pollution monitoring system is implemented with the assistance global system for mobile communication (GSM) module and distinct gas sensors are utilized for sensing the gases [38]. Real-time air quality monitoring is designed and developed for detecting pollutant levels by employing distinct gas sensors [39]. From the above literature, it is realized they are need of implementing fog-and edge-assisted IoT server architecture for a smart lamppost to support the smart infrastructure that is required in smart cities. From the above review, it is concluded that the distinct wireless communication and sensor technologies are implemented in the lighting system for enhancing energy efficiency. However, with respect to smart cities, the resource management and establishment of hybrid framework-based system are lacking as it delivers the multitude applications with single system. Multitude applications are possible with smart lamppost as it provides the smart infrastructure and amenities to the citizens. Before addressing the proposed architecture, we will discuss the overview of IoT and enabling technologies that supports the implementation of multitude applications with smart lamppost.

Overview of IoT
According to GSM association (GSMA) intelligence forecasting, the number of IoT connections will reach 25 billion globally by 2025 [40]. The growth of IoT devices from 2015 to 2025 is presented in Figure 1. detecting pollutant levels by employing distinct gas sensors [39]. From the above literature, it is realized they are need of implementing fog-and edge-assisted IoT server architecture for a smart lamppost to support the smart infrastructure that is required in smart cities. From the above review, it is concluded that the distinct wireless communication and sensor technologies are implemented in the lighting system for enhancing energy efficiency. However, with respect to smart cities, the resource management and establishment of hybrid framework-based system are lacking as it delivers the multitude applications with single system. Multitude applications are possible with smart lamppost as it provides the smart infrastructure and amenities to the citizens. Before addressing the proposed architecture, we will discuss the overview of IoT and enabling technologies that supports the implementation of multitude applications with smart lamppost.

Overview of IoT
According to GSM association (GSMA) intelligence forecasting, the number of IoT connections will reach 25 billion globally by 2025 [40]. The growth of IoT devices from 2015 to 2025 is presented in Figure 1. IoT is an interconnection of physical things with a virtual environment through internet protocol (IP) connectivity. In IoT, the physical things are embedded with sensors and actuators for sensing and controlling. A communication protocol is integrating with the sensors and actuators for updating and transmitting the sensory data to the server. Integration of sensor/actuators, connectivity, data processing, and user interface (UI) forms an IoT network. IoT comprises the four components that define the functionality and it is shown in Figure 2.
A sensor is a device that senses and responds to the input from the physical environment. The output signal of the sensor is converted into a digital signal. Connectivity is necessary for the transmission of sensory data and they are different wireless protocols for establishing connectivity. The distinct kinds of wireless communication technologies are existing in the Internet of Things. GSM/global packet for radio service (GPRS), Zigbee, BLE (Bluetooth Low Energy), IPv6 over low -power wireless personal area networks (6LoWPAN), radio frequency identification (RFID), LoRa, Sigfox, narrow band-IoT (NB-IoT), long term evolution (LTE), IEEE 802.11g Wi-Fi, near field communication (NFC), and Z-wave are the communication technologies. Zigbee, Z-wave, BLE, 6LoWPAN, and IEEE IoT is an interconnection of physical things with a virtual environment through internet protocol (IP) connectivity. In IoT, the physical things are embedded with sensors and actuators for sensing and controlling. A communication protocol is integrating with the sensors and actuators for updating and transmitting the sensory data to the server. Integration of sensor/actuators, connectivity, data processing, and user interface (UI) forms an IoT network. IoT comprises the four components that define the functionality and it is shown in Figure 2.
A sensor is a device that senses and responds to the input from the physical environment. The output signal of the sensor is converted into a digital signal. Connectivity is necessary for the transmission of sensory data and they are different wireless protocols for establishing connectivity. The distinct kinds of wireless communication technologies are existing in the Internet of Things. GSM/global packet for radio service (GPRS), Zigbee, BLE (Bluetooth Low Energy), IPv6 over low -power wireless personal area networks (6LoW-PAN), radio frequency identification (RFID), LoRa, Sigfox, narrow band-IoT (NB-IoT), long term evolution ( Wi-Fi, the remaining two wireless communication technologies consume low power for the data transmission. LoRa, NB-IoT, and Sigfox are emerging low power and long-range communication technologies that meet the goal of the IoT. Among the three low-power wide-area network (LPWAN) technologies, LoRa is the optimal communication system with secure bi-directional communication and free licensed band capabilities. RFID and NFC are identification technologies that work on radio waves. Both technologies communicate up to a range of 4 cm-10 cm. The data processing is one more component of the IoT, where the sensory data is converted to machine-readable form. Classification, sorting, and calculations are performed on the data for getting meaningful information. Different tools are existing for data processing in the IoT. The technical specifications of the communication technologies are briefly mentioned in Table 1.

Proposed Smart Lamppost Architecture
The advancements in sensor and wireless communication technology have encouraged us to build a hybrid system that is able to provide the multiple features with less space. Here, the smart lamppost is a hybrid system, where a single lamppost is integrating with multiple features that are required for implementing a smarter infrastructure in smart cities. Smart lampposts are the integration of distinct unique features that are embedded in it for providing smart and efficient features to the citizen of urban cities. In this study, we are proposing and implementing a smart lamppost that provides features such as smart lighting, digital signage, environmental sensing, push to talk system, charging port infrastructure, Wi-Fi mobile and mesh, image sensing pedestrian counting, parking monitoring and public security, and digital street signs. The proposed architecture is illustrated in Figure 3 where it is integrating with fog computing and edge computing for reducing the latency in between the smart lamppost and IoT server. The architecture is the amalgamation of multiple wireless communication protocols for establishing reliable and secure connectivity between a smart lamppost and IoT servers. As mentioned before regarding the applications that exist in the smart lamppost, it can monitor environmental parameters such as air quality and noise. Smart lighting feature in the lamppost enables us to illuminate the light according to the light intensity that is around the lamppost. Digital signage provides details such as weather data, digital images, information, video, and streaming media. Additionally, it is also utilized for outdoor adverting, marketing, and wayfinding. Image sensing is for sensing the visuals of surroundings to estimate possible security concerns, and to count the number of vehicles and passengers passing across the smart lamppost through a camera. Push to talk systems provide an opportunity for asking queries related to the route of a restaurant, petrol station, hospital, etc.
Here, we need to push the button available on the smart lamppost. Charging port infrastructure is a feature that enables citizens to power the electric charging to their vehicles. Solar panel-based power infrastructure is also available in the smart lamppost, so it powers the controller board of the lamppost. The Wi-Fi hotspot feature is present in the smart lamppost, where it delivers free Internet access to the citizens passing across the lamppost. A digital street sign is embedded in the lamppost as a traffic signal. Bio-diversity is included in the smart lamppost for encouraging the building of artificial nests for the sustainability of birds during extreme conditions.
The architecture of the smart lamppost also illustrates the mechanism of integration of the smart lamppost with IoT servers via edge nodes and fog nodes. Here, sensor input to the multiplexer is embedded with a computing unit, where it enables us to transmit data related to lamppost to the IoT server via LoRa based gateway. LoRa communication acts as a transceiver in the LoRa-based gateway and the Wi-Fi module available in the gateway logs the data related to a lamppost on the IoT server.
Edge nodes in the architecture assist the smart lamppost in detecting the security issues nearby the lamppost in a short interval of time. Edge/fog nodes are generally installed near the lamppost because they can perform analytics on the images receiving from the image sensing. As the mobile application is integrating with the IoT server, the smart lamppost is monitored in real-time. Preprocessing, cleaning, and analytics are done at the IoT server. Figure 4 illustrates the technological architecture that provides the hardware working of distinct features availing in the smart lamppost. In the feature of charging port infrastructure, the electricity is providing in two distinct forms namely alternating current (AC) and direct current (DC). The power generating from the windmill, generator, and grid back up is rectified according to requirable voltage to the lamppost. Further, the battery is loaded and charged with electricity coming from the windmill and photovoltaic (PV) solar array. As the smart lamppost has two different powering sources (AC, DC) and it is controlled with AC load and DC load. Two slave controllers are embedded in the smart lamppost for sensing the environmental parameters, adjusting light, and image capturing. A single master controller is used for delivering the features of the speaker and display screen. Here, the slave controller initiates working after receiving the instructions from the master controller. Slave controller '1' performs sensing the parameters such as air quality and noise pollution. Additionally, this controller works during adjustments in the intensity of light. Slave controller '2' is used for supporting the feature of image capturing, counting the number of passengers and vehicles, and communicating to the IoT server through an inbuilt Wi-Fi modem.
The architecture illustrates the interfacing of sensors and actuators with master and slave controllers and it is shown in Figure 5. The master controller is interfaced with slave controller '1' and slave controller '2' and it is highlighted in yellow. As discussed earlier, the slave controller '1' is embedded for monitoring the environmental parameters. To realize this, the slave controller '1' is embedded with a light-emitting resistor (LDR) sensor, noise sensor, DHT sensor, CO sensor, and NO sensor. Slave controller '1' is based on Atmega 2560 as it interfaces with the relay for adjusting the intensity of LED light. Digital signage is also an interface to the slave controller '1' through the signage driver. Slave controller '2' is embedded with an ESP 32 hybrid module that is inbuilt with BLE, Wi-Fi module, and camera. This controller assists in the feature of image sensing. The master controller delivers the features of the mic for listening to the queries during the push to talk feature and, simultaneously, the speaker delivers the queries invoice format.  The architecture illustrates the interfacing of sensors and actuators with master and slave controllers and it is shown in Figure 5. The master controller is interfaced with slave controller '1′ and slave controller '2′ and it is highlighted in yellow. As discussed earlier, mega 2560 as it interfaces with the relay for adjusting the intensity of LED light. Digital signage is also an interface to the slave controller '1′ through the signage driver. Slave controller '2′ is embedded with an ESP 32 hybrid module that is inbuilt with BLE, Wi-Fi module, and camera. This controller assists in the feature of image sensing. The master controller delivers the features of the mic for listening to the queries during the push to talk feature and, simultaneously, the speaker delivers the queries invoice format.

Hardware and Software Description
The section provides information related to hardware development and software development. The smart lamppost is proposed as broad perspective concept, where we have implemented three features proposed in the smart lamppost. Environmental parameter monitoring, lighting systems and image sensing systems are designed and implemented. alize this, the slave controller '1′ is embedded with a light-emitting resistor (LDR) sensor, noise sensor, DHT sensor, CO sensor, and NO sensor. Slave controller '1′ is based on Atmega 2560 as it interfaces with the relay for adjusting the intensity of LED light. Digital signage is also an interface to the slave controller '1′ through the signage driver. Slave controller '2′ is embedded with an ESP 32 hybrid module that is inbuilt with BLE, Wi-Fi module, and camera. This controller assists in the feature of image sensing. The master controller delivers the features of the mic for listening to the queries during the push to talk feature and, simultaneously, the speaker delivers the queries invoice format.

Hardware and Software Description
The section provides information related to hardware development and software development. The smart lamppost is proposed as broad perspective concept, where we have implemented three features proposed in the smart lamppost. Environmental parameter monitoring, lighting systems and image sensing systems are designed and implemented.

Hardware and Software Description
The section provides information related to hardware development and software development. The smart lamppost is proposed as broad perspective concept, where we have implemented three features proposed in the smart lamppost. Environmental parameter monitoring, lighting systems and image sensing systems are designed and implemented. Generally, for lightning systems, we have integrated the LDR sensor and PIR sensor to the slave controller 1. LDR sensor is utilized for sensing the change in light intensity of outdoor environment. The AC 256 level dimmer integrating to the slave controller 1 enables dims the light according to light intensity value provided by LDR sensor. AC 256 level AC dimmer is utilized in the applications where the AC power is between 110 and 220 V AC, and it is interfaced to the pin of PWM signal in a microcontroller. The LDR sensor is an analog sensor, and it senses the intensity of the light as voltage.
For the environmental parameter monitoring, the two gas sensors are integrated to the slave controller 1 to sense the CO and NO level in the air. MQ 7 gas sensor is CO sensor that is embedded for sensing the CO level in the air around the lamppost. The CO level is generally represented in particles per million (ppm) and its range is between 10 and 10,000 ppm. Image sensing is a system that is specifically for security purposes on the roads, for sensing unexpected events that occur near the smart lamppost. A hardware description of the three components is presented along with the schematic view of the proposed system. A software description provides the level of IoT that supports the proposed architecture.

Hardware Description:
To check the performance of the proposed system, we have designed hardware so that it can sense the environmental parameters and is also able to accurately sense people through a camera. Figure 6 illustrates the connection between the distinct components of the smart lamppost system. Arduino UNO is the master controller that is addressed in the previous section. Slave controller '2' is the raspberry pi controller that is interfaced with pi camera for image sensing. The sensors including DHT11, LDR, and gas sensors are interfaced to the slave controller '2'. A +12 V power supply is powered to the complete circuit for performing the essential action. The technical specifications of the sensors and other components are illustrated in Table 2.

Software Development
In any system, the hardware node hardly stores the encryption keys and, therefore, it is challenging for the hardware node to handle and manage the encryption simultaneously. However, in parallel with its neighbors, network designers can provide the encryption facility in the node hardware. To overcome this challenge, we have a Request-Response communication model, and it is shown in Figure 7. In this communication model, the client generates the request to the server and the server acknowledges the request. Concerning the request from the client, the server retrieves the data, formulates the re-

Software Development
In any system, the hardware node hardly stores the encryption keys and, therefore, it is challenging for the hardware node to handle and manage the encryption simultaneously. However, in parallel with its neighbors, network designers can provide the encryption facility in the node hardware. To overcome this challenge, we have a Request-Response communication model, and it is shown in Figure 7. In this communication model, the client generates the request to the server and the server acknowledges the request. Concerning the request from the client, the server retrieves the data, formulates the response, and sends the response to the client.

Software Development
In any system, the hardware node hardly stores the encryption it is challenging for the hardware node to handle and manage the en ously. However, in parallel with its neighbors, network designers can tion facility in the node hardware. To overcome this challenge, we sponse communication model, and it is shown in Figure 7. In this com the client generates the request to the server and the server acknow Concerning the request from the client, the server retrieves the dat sponse, and sends the response to the client.

Client Server Resources
Request Response *Receive request from client *processes requests *looks up/fetches resources *prepare responses and sends response to client Send request to server IoT comprises six different levels of the architecture in the aspec plexity, several nodes, observer nodes, and cloud servers. Our ar IoT comprises six different levels of the architecture in the aspects of hardware complexity, several nodes, observer nodes, and cloud servers. Our architecture is framed based on level 6 IoT and it is shown in Figure 8. The level 6 architecture consists of independent end nodes, observer nodes, devices, controller service, local server, and cloud server. The independent end nodes sense the data of the physical elements, and communicates with the cloud server. Generally, this architecture is applicable where a large amount of data is generated from the final nodes. As the number of data points is huge, analytic tools are required for validating the data. The analytic tools will be applied to the data, which are present in the cloud server. A cloud-based application is available in this architecture for monitoring the end nodes in a graphical user interface. based on level 6 IoT and it is shown in Figure 8. The level 6 architecture consists of independent end nodes, observer nodes, devices, controller service, local server, and cloud server. The independent end nodes sense the data of the physical elements, and communicates with the cloud server. Generally, this architecture is applicable where a large amount of data is generated from the final nodes. As the number of data points is huge, analytic tools are required for validating the data. The analytic tools will be applied to the data, which are present in the cloud server. A cloud-based application is available in this architecture for monitoring the end nodes in a graphical user interface.

Simulation of Smart Lighting System
Smart lighting systems are for controlling the light intensity with respect to the movement and intensity of outer light. However, in some cases, the system fails to the light the streetlight and, in other cases, a voltage surge occurs. Here, the dimmer control using LDR, voltage and current sensors can be implemented for monitoring voltage and current supply to the light. We have performed a simulation on proteus for identifying the change in the voltage and current and it is shown in the Figure 9. In the simulation, we have designed a circuit with the AC voltage and AC current sensor for sensing the malfunction in the light.

Simulation of Smart Lighting System
Smart lighting systems are for controlling the light intensity with respect to the movement and intensity of outer light. However, in some cases, the system fails to the light the streetlight and, in other cases, a voltage surge occurs. Here, the dimmer control using LDR, voltage and current sensors can be implemented for monitoring voltage and current supply to the light. We have performed a simulation on proteus for identifying the change in the voltage and current and it is shown in the Figure 9. In the simulation, we have designed a circuit with the AC voltage and AC current sensor for sensing the malfunction in the light.

Results and Discussion
In this study, we have proposed a smart lamppost that delivers a multitude of applications that are addressed in the above sections. However, we have developed and implemented a few components of the smart lamppost, namely, smart lighting, environmental

Results and Discussion
In this study, we have proposed a smart lamppost that delivers a multitude of applications that are addressed in the above sections. However, we have developed and implemented a few components of the smart lamppost, namely, smart lighting, environmental sensing, and image sensing. Smart lighting in the lamppost assists in maintaining the light in relation to the light intensity in an outdoor environment. Generally, the smart lighting system is for controlling the intensity of the streetlight with respect to the outer environment light intensity and movement of the objects crossing the smart lamp. The intensity of streetlight is measured with the assistance of LDR sensors, and movement of the objects is sensed through the PIR motion sensor. LDR sensors are very effective, particularly in streetlight and dark sensor circuits. The working principle of LDR sensors is that when the light falls on the LDR sensor, the resistance value of the LDR decreases, thus reducing the intensity of the streetlight accordingly. The following Equation (1) explains the relationship of resistor-inductor (RL) and lux: Therefore, the light intensity value sensed by the LDR sensor are represented in terms of analog value, i.e., voltage (V). The output of the LDR sensor that is receiving the voltage can be converted into lux with following Equations (2) and (3): where V o indicates the output voltage where V o indicates the output voltage In this study, the movement of the person or vehicle is detected through PIR sensor. At the same time, streetlights switch on at a particular dimming level as per the time scenario. Here, the LDR sensor is placed at a location on the lamppost which is not affected by the car light. The LDR sensor adjusts the intensity of the streetlight in relation to environmental sunlight and, moreover, the streetlight glowing according to the environmental intensity reduces energy consumption. The energy saving is also achieved in our system, because of 256 level AC dimmer. Briefly, the AC dimmer diverts the flow of electricity through the streetlight. As the electricity flow is diverted, the amount of electricity supplied to the streetlight also reduces and this reduces the light intensity of the streetlight. The working principle of the dimmer can be explained with firing angle equation (Equation (4)).
where V o indicates the RMS value of output voltage, Vs indicates the applied voltage, α indicates the firing angle.
In the dimmer, the alternating current (AC) phase method is utilized by for controlling intensity of streetlight. The RMS value of output voltage is controlled by controlling the firing angle. For example, if the firing angle is 180 • , then the RMS output voltage that is supplied to the streetlight is low. If the firing angle is less, e.g., 0 • , then the RMS output voltage that is supplied to the streetlight is high. Therefore, 256 level AC dimmer in the proposed system minimizes the energy consumption.
Environmental sensing is for sensing the quality of the air, temperature and humidity. Here, we have integrated the MQ 7 gas sensor for sensing the CO level in the air and the DHT sensor is utilized for sensing the temperature and humidity. The DHT sensor provides the temperature value in terms of • C and humidity value in terms of %. The MQ 7 gas sensor provides CO level in terms of parts per million (ppm). All these sensors are connected to the slave controller 1 as shown in Figure 5. Image sensing is for counting the number of pedestrians, proximity, and parking management.
The realization of smart lighting and environmental sensing smart lampposts is achieved by developing the hardware prototype, which is based on Arduino Uno. Arduino Uno is a microcontroller that is built on the ATmega328P, it comprises 14 digital input/output pins, 6 analog inputs, and an operating voltage of 5 V [42]. For environmental sensing, the DHT11 sensor is interfaced to the digital pin in Arduino Uno, and MQ 7 sensor is interfaced to the ADC pins of the Arduino Uno. To realize smart lighting in smart lampposts, a LDR sensor and passive infrared (PIR) motion sensor are interfaced to the analog pins of Arduino Uno. The image sensing is achieved by integrating the pi camera into the ESP 32 module.
The hardware prototype that is developed for environmental sensing and smart lighting is implemented in a real-time environment and it is presented in Figure 10. MQ 7 gas sensor, DHT 11 sensor, PIR motion sensor, and LDR sensor are present in the hardware prototype for sensing the CO level, temperature, humidity, motion sensing, and light intensity. The hardware prototype communicates the sensory data of distinct sensors to the IoT server via internet connectivity through a Wi-Fi modem. The graph in Figure 11c,d reveals the Cox level and light intensity values at a different time intervals. In this study, we have set the threshold for collecting the sample of the LDR sensor value for a time interval of 5 s. An average is calculated for the total number of LDR sensor values that are recorded for a time interval between 11:00 pm and 11:15 pm. Therefore, the values that are utilized for the plotting the graph are the average taken from the total number of LDR sensor values. In January 2021, at 11:00 pm, the Cox level in the air is above 120 ppm and light intensity is at 1 V (volts). Between 11:00 pm and 11:15 pm, the Cox level suddenly declines to 110 ppm and light intensity also declines below 1 V (volts). Between 11:15 pm and 11:30 pm, the Cox level is between 115 and 120 ppm and, after 11:30 pm, there is declining in the Cox level; regarding light intensity for the same time interval, there is a sudden rise in intensity of light. At 11:45 pm, the Cox level is in between 110 and 115 ppm and light intensity is in between 1 and 1.3 V (volts).
From the above discussion regarding the two graphs, it can be concluded that the cloud server is useful for continuous, real-time monitoring of temperature, humidity, Cox level, and light intensity.
The image sensing prototype is developed by integrating an ESP 32 cam-based module. The ESP 32 module is the hybrid module that is inbuilt with a camera, Bluetooth, and IEEE 802.11 b/g/n based Wi-Fi module [43]. The working architecture of the image sensing prototype is illustrated in Figure 12.
The ESP 32 controller takes the input frames from the integrated cam module and The graph in Figure 11a,b reveals the temperature and humidity values at different intervals. In January 2021, at 11:00 p.m., the temperature is recorded as 11 • C and the humidity is 65%. Between 11:00 p.m. and 11:15 p.m., the temperature increases to 12 • C and the humidity increases to 75%. Between 11:15 p.m. and 11:30 p.m., the temperature remained constant and, after 11:30 p.m., it rose in temperature (14 • C); regarding humidity for the same time interval, it is between 70 and 80%. At 11:45 p.m., the temperature declines to 12 • C, and humidity reaches nearly 70%.
The graph in Figure 11c,d reveals the Cox level and light intensity values at a different time intervals. In this study, we have set the threshold for collecting the sample of the LDR sensor value for a time interval of 5 s. An average is calculated for the total number of LDR sensor values that are recorded for a time interval between 11:00 p.m. and 11:15 p.m. Therefore, the values that are utilized for the plotting the graph are the average taken from the total number of LDR sensor values. In January 2021, at 11:00 p.m., the Cox level in the air is above 120 ppm and light intensity is at 1 V (volts). Between 11:00 p.m. and 11:15 pm, the Cox level suddenly declines to 110 ppm and light intensity also declines below 1 V (volts). Between 11:15 p.m. and 11:30 p.m., the Cox level is between 115 and 120 ppm and, after 11:30 pm, there is declining in the Cox level; regarding light intensity for the same time interval, there is a sudden rise in intensity of light. At 11:45 p.m., the Cox level is in between 110 and 115 ppm and light intensity is in between 1 and 1.3 V (volts).  The image sensing prototype is deployed in a real-time environment for analyzing the performance. Figure 14 illustrates that the developed prototype is accurately detecting the person and also labeling the individual name of the person. With the assistance of an inbuilt Wi-Fi module, the prototype logs the visuals to the IoT server via internet connectivity. Table 3 presents the comparison of the previous studies with proposed study. We have categorized the certain parameter's likely objective, lighting system, environmental From the above discussion regarding the two graphs, it can be concluded that the cloud server is useful for continuous, real-time monitoring of temperature, humidity, Cox level, and light intensity.
The image sensing prototype is developed by integrating an ESP 32 cam-based module. The ESP 32 module is the hybrid module that is inbuilt with a camera, Bluetooth, and IEEE 802.11 b/g/n based Wi-Fi module [43]. The working architecture of the image sensing prototype is illustrated in Figure 12. The image sensing prototype is deployed in a real-time environment for analyzing the performance. Figure 14 illustrates that the developed prototype is accurately detecting the person and also labeling the individual name of the person. With the assistance of an inbuilt Wi-Fi module, the prototype logs the visuals to the IoT server via internet connec- The ESP 32 controller takes the input frames from the integrated cam module and then processes them by resizing, formatting, etc. It then sends the pre-processed frames to the connected mobile phone or computer through the local network. The mobile or computer, on the other hand, in the first-time usage, downloads a tensor flow lite object classifier model specifically made for javascript from a cloud server, whose address is already present in the ESP 32's source file, which is shared to the mobile or computer once it is connected with the ESP locally.
After downloading, the model runs on the browser of the host device (i.e., the connected mobile or computer); it then takes the incoming camera feed from the local network process it to classify the content of the frame into various cases, e.g., human, dog, unknown, etc., and then returns the output result as the identified class serial number to the ESP32 controller through the local network. The serial number is then compared and analyzed at the ESP32 end to actuate or produce a reaction.
We have customized this prototype and built a 3D model prototype. In 3D printing, the 3D design file of the prototype is inserted, and it produces a 3D model that can be seen in Figure 13. parameter monitoring, image sensing, communication, architecture, and limitations for comparing with the previous studies. From the table, it is been concluded that the previous studies utilized the lamppost only for controlling the light intensity with respect to the PIR sensor and LDR sensor. However, the proposed system is capable of controlling lighting intensity as well as monitoring environmental parameters such as CO level, temperature and humidity. Additionally, our proposed system is connected to the cloud server through LoRa and a LoRa-based gateway. The sensor values of the CO level, temperature, humidity and light intensity are recorded in the cloud server and also presented in graphical form.    The image sensing prototype is deployed in a real-time environment for analyzing the performance. Figure 14 illustrates that the developed prototype is accurately detecting the person and also labeling the individual name of the person. With the assistance of an inbuilt Wi-Fi module, the prototype logs the visuals to the IoT server via internet connectivity. parameter monitoring, image sensing, communication, architecture, and limitations for comparing with the previous studies. From the table, it is been concluded that the previous studies utilized the lamppost only for controlling the light intensity with respect to the PIR sensor and LDR sensor. However, the proposed system is capable of controlling lighting intensity as well as monitoring environmental parameters such as CO level, temperature and humidity. Additionally, our proposed system is connected to the cloud server through LoRa and a LoRa-based gateway. The sensor values of the CO level, temperature, humidity and light intensity are recorded in the cloud server and also presented in graphical form.     Table 3 presents the comparison of the previous studies with proposed study. We have categorized the certain parameter's likely objective, lighting system, environmental parameter monitoring, image sensing, communication, architecture, and limitations for comparing with the previous studies. From the table, it is been concluded that the previous studies utilized the lamppost only for controlling the light intensity with respect to the PIR sensor and LDR sensor. However, the proposed system is capable of controlling lighting intensity as well as monitoring environmental parameters such as CO level, temperature and humidity. Additionally, our proposed system is connected to the cloud server through LoRa and a LoRa-based gateway. The sensor values of the CO level, temperature, humidity and light intensity are recorded in the cloud server and also presented in graphical form.