AN INTELLIGENT SOLAR LED STREET LIGHTING SYSTEM
S. Rajarajan, R.Abinaya, P.Thenmozhi, A.vidhya Assistant Professor,UG Students,Department of Computer Science Engineering Kings College of Engineering, Thanjavur.
IJTCSE Research /ISSN 2349–1582 conference publication
Abstract -The street lighting system is safety and energy conservation are very important advantages of smart cities. Namely, the city street lamp is correlated with both safety and energy conservation. In this project to address the existing problems, a smart street lamp (SSL) based on decentralized computing for smart cities are proposed in this paper. The proposed SSL is dynamic brightness adjustment, all street lamps can be adjusted dynamically; autonomous alarm on abnormal states, each street lamp can report the abnormal status independently, such as broken, fault, and so on. The experimental results showed that proposed SSL can improve energy efficiency and reduce danger.
Keywords — Smart LED street light; Solar; Energy efficient; Street Lighting, Illumination.
no activity is detected, but brightens when movement is detected. This type of lighting is different from traditional, stationary illumination, or dimmable street lighting that dims at pre-determined times.
Introduction:
The main aim of the smart city relates to safer, more convenient, and more comfortable operation, and better energy conservation. Therefore, make an urban infrastructure be smarter is necessary for promoting the smart cities. The street lamp as an essential part of urban infrastructure in the city, closely relates to the safety and energy conservation. Nowadays, it is impossible to imagine how the city would look like without street lamps. However, it is easy to predict that in that case the danger from traffic, robbery, and stealing would increase seriously. Moreover, it is necessary to optimize the current street lamp management because of its high energy consumption on daily basis.
Recently, the use of light-emitting diode (LED) lamp for
streetlight has grown significantly. LED based streetlight technology has considerable advantages in terms of both energy efficiency and optical luminescence as compared to conventional streetlight technologies such as the high-pressure sodium (HPS) and low- pressure sodium (LPS) lamps. In addition to being eco-friendly due to its low electrical energy consumption, it also affords plenty of benefits, namely, uniformity of illumination levels via arrays of many LED chips, visibility of the streetlight through correlated colour temperature (CCT), and visual performance improvement by virtue of high colour rendering index. Even though LED streetlight has higher initial cost, it has a longer lifespan which makes the maintenance cost cheaper over the time compared to HPS streetlight. LED streetlight also produces less heat, which makes the physical design simpler, whereas HPS streetlight needs a proper cooling mechanism to keep its temperature in normal range. Thence, due to incredible promise of LED technology and also as part of smart city applications, many countries nowadays have started replacing the HPS/LPS lamp system by LED for both indoor and outdoor lighting systems.
Intelligent Street Lighting
Features
Sreet lights can be made intelligent by placing cameras or other sensors on them, which enables them to detect movement (e.g. Sensitivity’s Light Sensory Network, GE’s “Currents”). Additional technology enables the street lights to communicate with one another. Different companies have different variations to this technology. When a passer-by is detected by a camera or sensor, it will communicate this to neighbouring street lights, which will brighten so that people are always surrounded by a safe circle of light.
Some companies also offer software with which the street lights can be monitored and managed wirelessly. Clients, or other companies, can access the software from a computer, or even a tablet. From this software, they can gather data, pre-set levels of brightness and dimming time; receive warning signals when a light defects.
Manual switching: This is the classic and omnipresent technique. The light is switched ON and OFF by a human attendant Light dependent resistance (LDR): LDR-based lights can switch themselves ON and OFF according to the ambient light conditions.
Tolerance variation in LDRs requires manual tuning of threshold levels in individual lights, typically using potentiometers. Dust deposits can also affect the sensitivity. Such factors reduce the reliability of the system.
Astronomical Timers: These devices choose the switch ON or switch OFF time depending on the date on the calendar. The devices are pre- programmed according to the location of the installation. This scheme is inflexible, does not take care of variable light situations such as overcast, dust storm etc.
The above systems are simple, economical and easy to install. However, these are not flexible and do not lend themselves to modern power-saving strategies. They provide limited monitoring capabilities.
Street lights are doing more than ever in today’s smart cities. With digital networks and embedded sensors, they collect and transmit information that helps cities monitor and respond to any circumstance, from traffic and air quality to crowds and noise. They can detect traffic congestion and track available parking spaces. Those very same networks can remotely control LED lights to turn on and off, flash, dim and more, offering cities a chance to maximize low-energy lighting benefits while also improving pedestrian and bicyclist safety. With street lights creating a network canopy, those networks of data can be used by more than just lighting departments, empowering even schools and businesses via a lighting infrastructure that brightens the future of the digital city.
In order for smart cities to harness the full potential use of LED streetlight system, there still is an open research area we need to dig out. First of all, making the streetlight LED lamps smart and use of a web-based management system can further bring an enormous energy savings. Secondly, incorporating energy-efficient electronic sensors and integration of wireless networked modules can furnish an optimal platform for an innovative LED streetlight application. Finally, the use of weather data aware CCT based smart LEDs in streetlights will be an incredible success towards building a User- friendly platform for smart cities, which is our subject in this
article.
In this project, we investigate the use of an integrated IoT- based management system for the wirelessly networked sensor- equipped LED streetlight system. Speaking of the wireless network infrastructure, we use a low power, low cost and low data rate ZigBee based wireless sensor network (WSN). We chose ZigBee over other WSN protocols because it is more suitable to public streetlight system in terms of data rate, distance of communication coverage, as well as price. It offers a self-healing, self-forming, tree, star, or mesh topology network structure that facilitates significant secure communication among the different streetlight elements. Our proposed streetlight system also employs sensors to detect and measure several environmental and electrical parameters such as relative humidity, temperature, particle concentration, voltage, and current acting as an input for the web based server.
A smart street lamp (SSL) based on fog computing for smarter cities to meet the above four abilities. The proposed SSL consists of three main parts: an intelligent sensing street lamp, which can adjust lamp brightness, an autonomous alarm which reports about abnormal behaviour; an efficient network, which is used for real-time communication between managers and massive street lamps; and lastly, a flexible management platform, which is easy and highly automated.
- PROPOSED SYSTEM
Automate street lights are necessary while we are trying to survive in the era of smart world. As automation provides perfection and efficiency. In this paper we are focusing on automated street lighting, as current system is facing many problems. Here we are considering the problems which are done manually. A user has to deal with numerous problems like maintenance problem, timer problem, connectivity problem, display problem. Streetlights are among a city’s strategic assets, providing safe roads, inviting public areas, and enhanced security in homes, businesses, and city centres. However they’re usually very costly to operate, and they use in average 40% of a city’s electricity spending. As the cost of electricity continues to rise and as wasting energy is a growing concern for public and authorities, it’s becoming crucial that municipalities, highway companies and other streetlight owners deploy control systems to dim the lights at the right light level at the right time, to automatically identify lamp and electrical failures and enable real time control. Street Light Monitoring & control is an automated system designed to increase the efficiency and accuracy of an industry by automatically timed controlled switching of street lights. This project describes a new economical solution of street light control systems. A method for modifying street light illumination by using sensor at minimum electrical energy consumption ,when object presence is detected, street lights glow at their brightest mode, else they stay in the dim mode during night time Internet of things (IOT) is used to visualize the real time updates of street processing and notifying the changes occur. This shall reduce heat emissions, power consumption, maintenance and replacement costs and carbon dioxide emissions.
REFERENCES
[1] Vladimir Bychkovsky, Bret Hull, Allen Miu, Hari Balakrishnan, Samuel Madden. A measurement study of vehicular internet access using in situ Wi-Fi networks. In MobiCom’06, pp. 50–61, 2006.
[2] B Chatschik. An overview of the Bluetooth wireless technology. IEEE Communications Magazine, 39(12): 86–94, 2001.
[3] H Farahani. ZigBee and IEEE 802.15.4 Protocol Layers C ZigBee Wireless Networks and Transceivers. Zigbee Wireless Networks & Transceivers, pp. 33–135, 2008.
[4] S Schwarz, C Mehlfuhrer, M Rupp. Calculation of the spatial preprocessing and link adaption feedback for 3GPP UMTS/LTE. Wireless Advanced, pp. 1–6, 2010.
[5] R. He, B. Ai, G. Wang, K. Guan, Z. Zhong. High-Speed Railway Communications: From GSM-R to LTE-R. IEEE Vehicular Technology Magazine, 11(3): 49–58, 2016.
[6] RS Sinha, Y. Wei, SH Hwang. A survey on LPWA technology: LoRa and NB-IoT. lct Express, 3(1), 2017.
[7] Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, Lanyu Xu. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3(5), PP. 637–646, 2016.
[8] Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, Khaled B. Letaief. A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys & Tutorials, DOI: 10.1109/COMST.2017.2745201, 2017.
[9] G. Han, L. Liu, S. Chan, R. Yu, Y. Yang. HySense: A Hybrid Mobile CrowdSensing Framework for Sensing Opportunities Compensation under Dynamic Coverage Constraint. IEEE COMMUNICATIONS MAGAZINE, 55(3): 93–99, 2017.
[10] Tarik Taleb, Sunny Dutta, Adlen Ksentini, Muddesar Iqbal, Hannu Flinck. Mobile Edge Computing Potential in Making Cities Smarter. IEEE Communications Magazine, 55(3), pp. 38- 43, 2017.
