Sensor Network to monitor lake water pollution. Team: Gilvir Gill, Ravikiran Ramjee, Vikhyath Mondreti, Henry Zheng Mentor: Ruby Roy

Transcription

1 Sensor Network to monitor lake water pollution Team: Gilvir Gill, Ravikiran Ramjee, Vikhyath Mondreti, Henry Zheng Mentor: Ruby Roy

2 Table of Contents Background and Problem Hypotheses Solution Key Components Diagram Scalability Methodology The Technology Data Node Drone The Economics Results Area-Cost Prediction Function Expert Survey Conclusions Acknowledgements References

3 Background and Problem In the US, 46% of the lakes are polluted and are considered unhealthy for swimming, fishing or aquatic life.

4 Background and Problem The inland waters of the world are in a state of disaster, resulting in rapid reduction in biodiversity According to estimates, polluted water in Africa and India causes 1.4 million deaths each year With high levels of pollution pointing at severe consequences ranging from starting epidemics to eliminating entire species, never has solving this problem been more significant than the present Real-time monitoring of a water source has always been a major problem for authorities, organisations and scientists Our Goal: Design an effective and cost-efficient way to solve this problem

5 Hypotheses Null Hypothesis Various water conditions in lakes cannot be more cost efficiently monitored in a scalable manner using a H2nOde system compared to pre-existing methods to quickly trigger corrective actions. Alternative Hypothesis Various water conditions in lakes can be more cost efficiently monitored in a scalable manner using a H2nOde system compared to pre-existing methods to quickly trigger corrective actions.

6 Solution - Key Components We designed a node-hub sensor network system to solve this problem. It consists of two key components Data Nodes - Immobile, cylindrical Structures present near the surface of the la serve the simple purpose of collecting the required data and transferring the da parent drone. Mobile parents Drone/Quadcopter that keeps track and serves the needs of its children". data from the Data Nodes to the cloud for analysis. It also has an electromagnet which enables it to relocate a Data Node to a the lake This system requires a charging point. The drone can fly to this point to ch and can also carry the Data Nodes there for charging.

7 Solution - Diagram Government Cloud Scientists Data Node Other Orgs Mobile Sensor Network to monitor lake water pollution

8 Scalability Need for scalability is enormous due to large areas the system needs to monitor Technical Scalability: Our solution is scalable as it involves a rotation system. This means that, at regular intervals, the drone can pick up a data node and place it at another key point in the lake Extra Drone = Higher rotation rate Extra Data Node = More data coming in real-time Economical Scalability: This makes it possible to deploy our system even in large lakes at a reasonable price point Drones are much cheaper than the Data Nodes Adding a drone instead of adding a new data node can be a viable option in many cases

9 Methodology The team s solution was based primarily for use by government organizations in lakes. Rather than hosting an open survey, we consulted experts in the field to take into account their opinion on the epitome of a mobile sensor network. These experts were from three categories: Engineers Water/Environment specialists Economists/Financial specialists After reviewing problems that our team would try to solve, the team initiated the design process to deal with these issues. After a clear cut solution was designed, the team divided responsibilities for creating multiple aspects of the solution. Seeing as to how the nature of the solution (cost constraints) prevented a physical prototype from being feasible, the team designed 3-D models and sketches accordingly. A cost-prediction model was also made. The solution was built and the feedback was collected once again from the same professionals that

10 The Technology: Data Node The node uses radio to communicate the collected data when the drone is in range It measures 4 key variables: Dissolved Oxygen (using the Atlas Scientific kit), ph (DFRobot ph kit), temperature (DFRobot temperature kit) and electrical conductivity (208DH Conductivity + Cyberplant EC mini connector) All probes are production grade therefore permitting long-term immersion in water. Also, all the probes are connected to an Arduino Mega 2560 through BNC connectors It s body is made up of a 3D-printed plastic casing wrapped in Giron film (2-3 layers) to shield the inner electronics from the electromagnet. The film is moderately corrosion-resistant. In addition, painting it can lead to an extended lifetime. This lets it be immersed in water for long periods of time Most importantly, the node is designed like a hollow cylinder to let it float at the surface of the water while minimizing weathering. There is a slight concave on the back that allows the sensor probes to be easily deployed

11 Data Node Model

12 The Technology: Drone The drone s flight is controlled by a flight controller, which in this case is an ArduPilot. This controls the basic navigation, movements and is connected to the GPS module (Ublox Neo-6M) for autonomy. The drone communicates with the Data Node using radio (1 km range) and can also use it to determine the Data Node s location (radar) All the data collected by the Data Nodes are passed onto the drone and relayed to the cloud (using platforms such as Google Cloud). This long-range communication is done using another radio module (LoRa radio shield). LoRa can get long ranges (about 21 km) which drastically reduces the size of the backbone network (repeaters, gateways or concentrators) Most importantly, the electromagnet (controlled by the Arduino Pro Mini) is used to pick up the Data Node and drop it off in another place. This, in combination with the radar, is used to prevent the Data Node from drifting away from it s target region

13 Drone Model

14 The Electromagnet Model

15 The Economics - Prototype Drone Approximately - $ ArduPilot - $150 Brushless motors - DYS BE $10 Chassis - Neewer X525 - $21-25 ESC - <= $10 Propellers x 4 - Any - $2-$3 Voltaic V44 USB Battery - $65 Arduino Pro Mini $3-5 LoRa Radio Shield for Arduino MHz - $80 UBLOX Neo-6M GPS module - $15 Waterproofing case for electronics and battery - $10-$20 433Mhz Wireless Serial Transceiver Module - $12 (1KM range module) Strong Electromagnet - $50-$100 Data Node Approximately - $ Mhz Wireless Serial Transceiver Module - $12 (1KM range module) Arduino Mega $37 WaterProof Casing - $4-10 Voltaic V44 USB Battery - $65 208DH Conductivity - $33 - Cyberplant EC mini v3.2 - $23 DFRobot ph meter kit - $56 DFRobot DS1B20 temperature kit - $8 Atlas Scientific Dissolved Oxygen kit - $257 Magnetic Shielding Flexible Film - Giron - $50 *These represent off the shelf costs. However, the final product would have custom parts and boards, thereby drastically bringing down the overall cost to - Drone: ~$150-$200; Static Node: ~$430 *This is based on an analysis conducted by the team with industry experts.

16 Results: Area-Cost Prediction Function The following function gives us the approximate cost for a given acreage Off the Shelf Cost = ( * Acres) X-axis - Area (Acres) Y-axis - Cost ($) Production Cost = ( * Acres) X-axis - Area (Acres) Y-axis - Cost ($) Data: Link to further information: Link to further information: Google Sheets Google Sheets Takeaway: The cost would be significantly cheaper when the off the shelf costs are removed

17 Results: Expert Survey Variables to monitor Dissolved oxygen - Directly affects the biodiversity of the lake Temperature - A default variable that affects EC ph - Another default variable. Acts as an Cost Current price (industry): $10,000 per 100 acres Our model not only predicts a much cheaper cost but it also shows that indicator of diseases the organisms in the lake are vulnerable to EC - To calculate concentrations of the different amounts of ions present. This affects the carrying capacity of the lake Locations to monitor the cost per acre reduces as the coverage area increases Inlets, Outlets, Wetlands, Islands, Boundary, Middle The final iteration of the prototype was designed with these features Validated the solution with the concerned experts and received positive feedback

18 Conclusions Drone-Node based system provides Mobility and Scalability at lowest cost/area Removes the need for underwater communication and introduces an easy servicing system The rotation system makes the system scalable therefore allowing deployments in large lakes Least interference with the ecosystem achieved The Data Nodes are similar to already existing buoys making them safe and less intrusive When designed properly, Drones are proven to be fail-safe technology Industry experts expressed great interest in the concept and feedback from them on many aspects is incorporated in our solution

19 Acknowledgements 3D Warehouse & SketchUp for playing a crucial role in designing our drone prototype The New York Academy of Sciences for providing crucial aid and instruction to complete this challenge

20 Acknowledgements Dr. Abhishek Tripathi (Dragon Mission Director at SpaceX) affordability and scalability Donna Lange (FLL Coach at Thomas Cario Middle School) budget and economics Greg Bronevetsky (Software Engineer at Google) communication and economics

21 Acknowledgements John Selwyn Shimron Jebakumar (Design Engineer at ARM) general design / communications Shubha Ramachandran (Water Specialist at Biome Environmental Solutions) costs, benefits, and effects. Srinagesh Mondreti (Director at Intel, IOT group) off the shelf vs production costs

22 References Rhoades, J. D., et al. "Determining soil salinity from soil electrical conductivity using different models and estimates." Soil Science Society of America Journal 54.1 (1990): "Extreme Range Links: LoRa 868 / 900MHz SX1272 Module for Arduino, Waspmote and Raspberry Pi." Extreme Range Links: LoRa 868 / 900MHz SX1272 Module for Arduino, Waspmote and Raspberry Pi. Cooking Hacks, n.d. Web. 7 Dec Rinkesh. "40 Facts About Water Pollution - Conserve Energy Future." Conserve Energy Future. Conserve Energy Future, 05 May Web. 17 Dec Thompson, Andrea. "Pollution May Cause 40 Percent of Global Deaths." LiveScience. Purch, 10 Sept Web. 7 Dec