Recently scientists and professionals around the world have been ringing alarm bells about the water crisis. One of the symptoms of river pollution is the quality of water getting worse day by day. To solve this problem, we were inspired to create a device that would help to monitor and report on the major waterways.

Our system consists of 4 major components which are monitoring, reporting, analysis and sustainability.


Acidity: Gravity- Analog-pH-Sensor(SKU:SEN0161).

Temperature: Waterproof DS18B20 Digital temperature sensor.

TDS: Gravity-Analog-TDS-Sensor(SKU:SEN0244).

Turbidity: Gravity-Analog-Turbidity-Sensor(SKU:SEN0189).

Conductivity: Gravity-Analog-Electrical-Conductivity-Sensor (SKU:DFR0300).

Microcontroller: Arduino Uno will be used because it is affordable and compatible with ESP32 TTGO T-beam which include LoRa transceiver and GPS receiver.

All the component will be powered with 5V. The range of temperature of water that the device can work is between 0~40°C.


Geolocation (GPS): GPS data is used to locate the position of the sensors and with all the data collected, we can visualize them in the form of Choropleth Map or Heat Map and others.

Low Power Wide Area (LoRa): We use LoRa as our wireless communication network protocols. The reason is that it allows long-distance transmissions (more than 10km in rural areas) and low power consumption.

Peer-to-peer (P2P): LoRa based on P2P system which enables long distance direct communication between 2 devices using the concept of nodes. Each node will receive and transmit signal at the same time. By doing this we are able to form a network chain. The data will be able to transfer out even from the area which doesn’t have internet network and signal. Ultimately the data will be transmitted to a station. The station will decrypt the data and upload the data onto the Internet. The information of the data includes coordinate of nodes and water quality. Theoretically, the distance between node and node should be less then 10km. Besides, p2p communication had made the device easy to deploy and add into the network chain.


All the data collected from the sensors will be sent to IBM Cloud for analysis. It will be processed by a machine learning(ML) algorithm as well. ML will predict the future condition of the waterway base on large scale monitoring.

App or Website: With all the analysis done, we will convert all the result into visual that is user-friendly. For example, a Choropleth Map will release into public. We will develop a feature for the people to report any pollution that they found on the waterway. Hopefully, with the help of the citizen, we are able to improve the quality

waterway. Hopefully, with the help of the citizen, we are able to improve the quality

of our water source.


Power Consumption: To reduce the power consumption, we design our system to monitor and report once every hour. When they are not working it will remain in sleep mode. When it is active it will consume roughly 400mAh operating once each hour. When it is in sleep mode it will consume 20 uAh.

Energy Harvesting: To make it sustainable as long as possible we have implemented an energy harvesting system which include solar, water and wind energy. They will harvest energy and charge the battery. The solar panels, wind turbine and water turbine are detachable. Only necessary parts will be installed on the device depending on the environment. For example, in the forest, solar and wind energy are not reliable. Thus, only a water turbine is attached to the device.

Self-Monitoring: If some of the part is spoiled, the device will send an emergency signal out. The signal will include its current position and what the problem is.

[What makes it innovative?]

Node to node communication. Each device will transfer and received signal at the same time. The data will be able to transfer out even from the area which doesn’t have internet network and signal.

Community-driven. Where normally IoT solution rely fully on sensors but we should not forget that human is helpful as well when we want to monitor the condition of certain things.

Machine learning is used to analyze and predict the future condition for waterway for preventive purposes.

Self-sustainable where it is able to sustain itself with the help of renewable energy integrated into the system.

The monitoring device is detachable. We can modify our design based on the environment.

[How would it be produced?]

The main frame of the device will be 3D-printed. Sensors and electronics part will be embedding into the 3D-printed frame. Wind turbine, water turbine, solar panels, anchor and floater will be install into the frame. All of them except the frame will be purchased from the manufacturer.

[What its potential impact would be around the globe?]

By creating a massive monitoring network. our system will be able to monitor every single point from all around the world and we can take immediate response whenever the water quality reaches a critical stage. Thus, it can prevent a tragedy from happening.


On 13th, 14th, 15th March 2019, our country, Malaysia happened a serious incident. The incident happened in Johor, Malaysia. Over 2500 people been affected by gas poisoning. 111 schools around that area had been closed. Around fifty-five thousand students unable to attend school. The source of the toxic gas is illegally-dumped chemical waste in the river. Even the Malaysian Armed Forces had dispatched medics and two teams with expertise in chemical and radioactive waste to help with clean-up efforts. If we have a something to prevent this incident happen, thousands of people may not suffer from this tragedy and students are able attend to school. After that, I’m keep thinking about a way to prevent this incident happen. The answer that I get is a waterway monitoring system. If and only if, our country has a waterway monitoring system, we are able to prevent or discover this incident before the tragedy happen. Hope we can win this competition. So that we can enhance and employ this monitoring system around the globe.


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