Introduction: Smart Street Lights
Smart lights aims to minimize the energy/electricity wastage caused by impractical use of lights on the streets (street lights) along with automated fault tolerance and mobile control. The product uses a presence detection technology to decide the on/off states of lights or to control the brightness levels of these lights (LED’s).
It’s a common sight these days that the lights in the corridors of buildings or streets are kept glowing even while there is absolutely no one in the surrounding to make use of them. On a sharp contrast to the above condition, we also see cases where in there is no sufficient lighting facilities at places where it is required the most. Proper handling of the above two conditions can lead to an efficient energy usage and effective distribution of light resource. The product developed by us aims at achieving this very bridging process by implementing sensor based lighting systems.
Uniqueness and Innovative attributes
· Optimal Sensor placement to judiciously use the resources.
· Leveraging an uniformly distributed and highly available resource (Street lights) for low cost setup of system
· Real time local weather and other important sensor data collection.
· Automated fault detection for easy centralized control through a mobile application.
· Gathering the movement of vehicles across blocks that can be effectively leveraged by the advertising agencies.
Arduino, C++, Python, Intel Edison Board
Cloud Connectivity Utilization
Cloud connectivity has been simulated with the use of a local server, which receives the sensor data from different Edison boards in real time. The data being collected at the server can be easily used to analyze and visualize the different outputs of the sensors present in the system. The Intel IoT cloud system could not been used since it is just an analytical platform and we require computation to be performed on the cloud and also send certain necessary control signals to the Smart Lights system.
Sensor Utilization (From the Dev Kit)
· Grove-Light Sensor: To determine the intensity of daylight in the surrounding, and hence turn On/Off the lights
· Grove -Temperature Sensor_V1.1: To determine the effective temperature level in the city
· Wifi+Bluetooth: To connect the system to the cloud and server to effectively communicate different kinds of sensor data.
· Grove -Air quality sensor: To determine the effective pollution level in the city. It is also instrumental in determining the smog levels to turn on the lights
· Grove- PIR Sensor: To Detect the arrival of a vehicle or human and effectively trigger the increase in intensity of lights. Used as an alternative to IR sensors for detection of vehicles.
· Grove- InfraRed Sensor / Distance Sensor: To Detect the arrival of a vehicle or human and effectively trigger the increase in intensity of lights.
An Android based mobile application is developed for the administrator of the system, using which he can control the intensity of the street lights and also to enable or disable the smart feature as per the requirements.
Data is collected continuously from the temperature, air pollution, IR and other sensors that can be used to derive meaningful insights.
Analytics can be performed on the huge amount of data collected to determine various factors like effective noise and air pollution in the city and also locally, effective temperature in the city. Also, analytics can be used to price the advertisement boards based on vehicular population.
Components Usage- Hardware
· Intel Edison: 3 sets of Intel Edison boards are used to simulate 3 master nodes (Street lights with sensors) along with 2 slave nodes each to every master node.
· Sensors: Various sensors as mentioned in 4.0.4 are used to make the system more robust and provide various kinds of information.
· Miscellaneous: Other Hardware tools like LED’s are used to simulate street lights.
· Android Device: To test the android application developed for centralized control.
Components Usage - Software
· Arduino IDE: Used to program the Intel Edison
· Python: Used to set up the server
· Android SDK: Used to develop an android application for centralized control of the entire system.
· The Government, can use this technology to maintain the lighting system efficiently.
· The advertisers can get real time statistics of end user reachability of their ads.
· The universities and corporates can use the system to effectively manage their parking lots and corridors.
We have successfully managed to inter connect 3 Edison boards to achieve a prototype of our system using the power of IoT for a social and economic cause.
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Step 1: Connect the LDR Sensors
Connect LDR sensors to the input pins of Edison board.
These sensors will help in distinguishing between day\night conditions and switch off\on lights accordingly.
Step 2: Connect Motion Detection Sensors
Connect motion detection sensors to the input pins of the edison board
These sensors can be either UV sensors or IR sensors.
Whenever there is motion detected, there is a change in state of the signal sent by these sensors.
Since we are trying to simulate three blocks of lighting, one sensor is used for each of the three edison boards
Step 3: Connect LED's to Output Pins
Connect the LED's to the analog output pins of the edison board.
Required number of led's are connected to each of the blocks of sensors.(to each edison boards)
Depending on the configuration settings and the requirements, the brightness and states of these led's are controlled.
Step 4: SET Up Server
A server is set up locally for sending the settings configurations like
- Minimum or maximum brightness
- Turn on or off the smart feature
Step 5: Load the Code on to Edison
Download the code from https://github.com/build3r/Smart-Street-Lights and load it on to your edison board
Step 6: Send Configuration Settings From the Server\app
Configuration settings can be sent either directly from the server or through a mobile application to the server.
Step 7: Test the Project
Now, the led's should automatically turn on or off depending on the readings from the LDR and vary its brightness based on the readings from the sensors.