Team: Gilvir Gill, Ravikiran Ramjee, Vikhyath Mondreti, Henry ZhengMentor: Ruby Roy

Sensor Network to monitor lake water pollution

Table of ContentsBackground and ProblemHypothesesSolution

● Key Components● Diagram

ScalabilityMethodologyThe Technology

● Data Node● Drone

The EconomicsResults

● Area-Cost Prediction Function

● Expert SurveyConclusionsAcknowledgementsReferences

Background and Problem

In the US, 46% of the lakes are polluted and are considered

unhealthy for swimming, fishing or aquatic life.

● 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

Background and Problem

Our Goal: Design an effective and cost-efficient way to solve this problem

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.

Null Hypothesis

Alternative HypothesisVarious 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.

Hypotheses

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 lake. They serve the simple purpose of collecting the required data and transferring the data to the “parent” drone.

● Mobile “parents”■ Drone/Quadcopter that keeps track and serves the needs of its “children".

■ It sends the data from the Data Nodes to the cloud for analysis.

■ It also has an electromagnet which enables it to relocate a Data Node to any point on the lake

■ This system requires a charging point.

■ The drone can fly to this point to charge itself and can also carry the Data Nodes there for charging.

Solution - Diagram

Cloud

Government

Scientists

Other Orgs

Data Node

Mobile Sensor Network to monitor lake water pollution

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

● 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 had laid down the basics.

Methodology

● 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

●

The Technology: Data Node

Data Node Model

● 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

The Technology: Drone

Drone Model

The Electromagnet Model

Drone -Approximately - $450

1. ArduPilot - $1502. Brushless motors - DYS BE1806-13 - $103. Chassis - Neewer X525 - $21-254. ESC - <= $105. Propellers x 4 - Any - $2-$36. Voltaic V44 USB Battery - $657. Arduino Pro Mini 328 - $3-58. LoRa Radio Shield for Arduino - 868 MHz - $809. UBLOX Neo-6M GPS module - $15

10. Waterproofing case for electronics and battery - $10-$2011. 433Mhz Wireless Serial Transceiver Module - $12 (1KM

range module)12. Strong Electromagnet - $50-$100

Data Node -Approximately - $524

1. 433Mhz Wireless Serial Transceiver Module - $12 (1KM range module)

2. Arduino Mega 2560 - $373. WaterProof Casing - $4-104. Voltaic V44 USB Battery - $655. 208DH Conductivity - $33 - Cyberplant EC mini v3.2 - $236. DFRobot pH meter kit - $56 7. DFRobot DS1B20 temperature kit - $88. Atlas Scientific Dissolved Oxygen kit - $2579. 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.

The Economics - Prototype

The following function gives us the approximate cost for a given acreage -

Off the Shelf Production Cost = (19.480136 * Acres) + 2458.659844 Cost = (12.100112 * Acres) + 1823.327734

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

Results: Area-Cost Prediction Function

X-axis - Area (Acres)Y-axis - Cost ($)

X-axis - Area (Acres)Y-axis - Cost ($)

● 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

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 - ○ Inlets, Outlets, Wetlands, Islands, Boundary,

Middle

Results: Expert Survey

● Cost -

○ Current price (industry): $10,000 per 100 acres

○ Our model not only predicts a much

cheaper cost but it also shows that

the cost per acre reduces as the coverage area increases

● The final iteration of the prototype was designed with these features

○ Validated solution with experts and received positive feedback

Drone-Node based system provides

● Mobility and Scalability at lowest cost per acre

● 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

Conclusions

● 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

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

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

Acknowledgements

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): 46-54.

"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. 2016.

Rinkesh. "40 Facts About Water Pollution - Conserve Energy Future." Conserve Energy Future. Conserve Energy Future, 05 May 2016. Web. 17 Dec. 2016.

Thompson, Andrea. "Pollution May Cause 40 Percent of Global Deaths." LiveScience. Purch, 10 Sept. 2007. Web. 7 Dec. 2016.

References