Streetlight that emits too much light or shines when and where it‟s not needed is wasteful. Wasting energy has huge economic and environmental consequences. As well as due to the manual nature of operation, streetlights are most often seen turned „on‟ in the daytime and turned „off‟ during evening time in Bangladesh that is pretty unfortunate to observe even in this age of twenty first century. Automated streetlight management system is needed to resolve these problems. This study aims to develop a novel streetlight management system powered by computer vision technology mounted with the CCTV camera that allows the LED streetlight to automatically light up with proper brightness by recognizing the presence of pedestrians or vehicles and reversely dimming the streetlight in their absence by semantic image segmentation from video. Subsequently, our model can recognize the daytime and nighttime by itself that made the automation process of turning „on‟ and „off‟ the streetlight possible to minimize the cost of energy consumption. We trained the U-net model based with ResNet-34 as backbone to accomplish the tasks. The validity of the models is ensured through corresponding evaluation matrices. The proposed model is much simpler, cost effective, durable and robust than the conventional, physical sensor-based existing intelligent solutions.

Energy Efficient Automatic Streetlight Controlling System using Semantic Segmentation

One of the most neglected sources of energy loss is streetlights which generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of the operation, streetlights are frequently seen being turned ON during the day and OFF in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight 'ON' and 'OFF' to save energy consumption costs. According to the aforementioned approach, geolocation sensor data could be utilized to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. The validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting, and more resilient than conventional alternatives.

CNN based Intelligent Streetlight Management Using Smart CCTV Camera and Semantic Segmentation

Conventional public streetlight controlling and monitoring systems are carried out locally without being able to be carried out remotely. Hence, it is slow to handle if there is damage. Regarding security, it is not uncommon for the public streetlight to become the object of vandalism and theft. The existence of smart public streetlights with IoT technology that can display the current status of public streetlights is urgent. In this study, our main intention is to develop a smart streetlight monitoring system that has exclusive features by integrating various devices. All data generated from integrated devices will be displayed in real-time via the IoT-Blynk platform and recorded on a Google Spreadsheet per minute. A smart streetlight system monitoring is implemented on the main road of the Jakarta State Polytechnic to determine the system’s performance. This system has been functioning correctly, as evidenced by the reading and sending of all sensor data appropriately to smartphones and Google Spreadsheets via a Wi-Fi network connection. Based on data analysis, the total energy consumption required of smart streetlights for 24 hours is 0.117 kWh. 

A Real-time Performance Monitoring of IoT based on Integrated Smart Streetlight

Many individuals lose their lives in traffic accidents. Sleepy driving is one of the leading causes to road accidents and mortality. Frequently, fatigue and micro nap at the wheel are the underlying causes of major accidents. Before a dangerous scenario arises, early symptoms of drowsiness can be identified. The majority of conventional approaches for detecting sleepiness are based on behavioural factors. So, a light weight real time drowsiness detection model employing Deep Learning algorithms are used to detect the drowsiness of the drivers. Based on adaptive threshold technique, the system detects the sleepiness of the driver by identifying facial landmarks. This model can be mounted in Raspberry Pi for the real time monitoring. Based on the threshold value of the output, the buzzer warning will be given to the drivers to detect the drowsy state of the driver.

Driver Drowsiness Detection using Deep Learning

Abstract A street lighting system is a very essential part of the highways and streets of a smart city. Managing power consumption and maintenance of a street light system will be a challenging task in huge countries. The proposed work is mainly focused on the minimization of power consumption in the implementation of a smart street lighting system. Also, use a mobile application for setting up the brightness levels of the lamps in an encrypted form so that an unauthorized person will not be able to modify the settings. In the existing streetlight system, wireless sensors are installed to control and monitor the streetlamps. In the proposed system, using an nRF24L01 radio transceiver module, a secured communication link is established to operate the streetlights depending on the ambient weather conditions, movement of humans, vehicles and any other objects. A failsafe mechanism is implemented in the modules for conventional lamp operation in the case of module failures. Light-dependent resistor (LDR) is used to determine the ambient brightness levels to automatically turn on/off the streetlights based on weather conditions and lighting on roads. Using smartphones, we access and control the brightness information from the master node at which the nRF24L01 radio transceiver module is installed, and the same information is relayed to all the slave nodes. The results show that we could effectively monitor and control the brightness of streetlights in a secure way and there is a significant amount of power savings. The proposed system saves the average powers of 53.45%, 44.76%, 39.39%, and 32.25% respectively for 10%, 20%, 30%, and 50% idle mode brightness compared to the state-of-the-art techniques present in the literature. 

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Smart streetlight system using mobile applications: secured fault detection and diagnosis with optimal powers

IoT-Based Smart Street Light for Improved Road Safety

Technology’s extensive use in the twenty-first century has a negative impact on the environment. Researchers are making more progress on renewable technologies to mitigate global warming, while demand for advanced technology is also gaining greater interest for safety purposes. Traditionally, most high-energy consumption street lights have been operated manually. Also, road accidents cannot be easily identified in real-time due to a lack of immediate data to the authorities. Numerous solutions have been taken to solve these problems but none of them ensures an optimum solution. Internet of Things and AI-based integrated control system can be implemented to solve this problem that can monitor street light intensity, identify car accidents with name-plate detection, and identify the face in unusual situations when someone presses an emergency push button. This automated system will ensure protection and power consumption in this way. This paper will illustrate the existing automated system and the suggested system.

IoT Based Smart Self Power Generating Street Light and Road Safety System Design: A Review

AbstractThis paper presents a millimeter-wave dual-band slotted antenna (26.28/37.72 GHz) for fifth-generation (5G) network. The dielectric layer of the antenna is made of Rogers RT 5880 material and has a thickness of 0.254 mm.The resonant frequencies of 26.28 GHz and 37.72 GHz, among the most desirable frequency bands for 5G communication in the mobile network, are used in this study. The designed dual-band slotted antenna has an architectural structure of 8 mm × 6mm × 0.254 mm. It achieves a gain of 5.464 dB and 6.705 dB and directivity of 6.18 dBi and 7.538 dBi with the assistance of the Reflection Co-efficient at resonant frequencies of –21.79 dB and –34.55 dB. With bandwidths of 0.867 GHz and 1.054 GHz, the proposed antenna’s efficiency was successfully attained 84.81% and 82.55%, respectively, which is suitable and enough efficient for 5G communication. The Microwave CST Studio Suite tool is used to design and simulate this proposed antenna. According to the intended results, the suggested antenna works excellently, and due to its fundamental architecture, the proposed antenna will be suitable for mobile networks. The designed antenna can be appropriate for faster mobile networks and 5G communication technologies by estimating all the parameters.KeywordsMillimeter-waveDual-band slotted antenna5G communicationVSWRGain enhancementCSTNetworking 

Khadija Yeasmin Fariya

Papers

IoT-Based Smart Street Light for Improved Road Safety

IoT Based Smart Self Power Generating Street Light and Road Safety System Design: A Review

Millimeter-Wave Dual-Band Slotted Antenna for 5G Applications