Background:
Access to clean water and a healthy environment is central to multiple UN Sustainable Development Goals (SDGs)—including SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 15 (Life on Land).
However, in many parts of India and the Global South:
Water quality is not continuously monitored,
Environmental parameters (air, noise, soil, biodiversity) lack affordable sensing,
Data collected is siloed and inaccessible to communities.
Open hardware + IoT + DSP (digital signal processing) + SocialCalc + py-libp2p can play a critical role in democratizing access to environmental intelligence.
Open hardware sensors (low-cost pH, turbidity, TDS, dissolved oxygen, particulate matter, acoustic sensors, etc.) can gather ground-level data.
DSP techniques can help in filtering, denoising, and interpreting raw sensor signals for reliability.
IoT integrations (LoRa, Wi-Fi, MQTT, edge compute) can connect communities and governments.
SocialCalc (collaborative spreadsheet) can act as a real-time data dashboard for analysis, visualization, and decision-making—accessible on low-power devices and even offline-first setups.
The Challenge:
Design an open hardware + IoT prototype that captures water or environmental parameters, applies signal processing for data reliability, and streams data into a SocialCalc-powered collaborative dashboard with py-libp2p as the networking layer for community-driven monitoring and decision-making.
Participants should aim to:
Build/repurpose open hardware sensors (for water quality, air quality, or environmental monitoring).
Apply DSP techniques to improve signal quality and extract meaningful insights.
Integrate IoT communication protocols for real-time or batch data transfer.
Visualize data in SocialCalc spreadsheets with graphs, alerts, and collaborative features.
Showcase an SDG-relevant use case, such as:
Water quality monitoring in rural communities.
River/lake pollution tracking.
Air quality mapping in cities.
Soil moisture & nutrient monitoring for sustainable agriculture.
Noise/light pollution assessment in urban spaces.
Deliverables:
Open Hardware Prototype (documented design + working demo).
Signal Processing Pipeline (DSP algorithms used for calibration, noise reduction, or anomaly detection).
SocialCalc Integration (live data feed into a collaborative spreadsheet dashboard).
Demo Video (5–7 mins) showing the hardware setup, data processing, and dashboard.
Documentation & GitHub Repo with circuit design, code, and deployment instructions.
Evaluation Criteria:
Innovation & Impact – Relevance to SDG 6/13/14/15.
Technical Depth – Hardware design + DSP implementation + IoT data pipeline.
Accessibility – Cost-effectiveness, open-source replicability.
Data Usability – How well SocialCalc helps communities/authorities interpret and act on the data.
Scalability – Potential to scale beyond prototype into community/city/state-level deployment.
References to Explore:
SocialCalc for DPG: https://github.com/AspiringDevelopers/c4gt-website-nodejs
Open Hardware Sensor Projects: Public Lab, Arduino-based pH & TDS sensors
IoT Protocols: MQTT Basics, LoRaWAN
DSP Tutorials: Scipy Signal Processing, MIT DSP OpenCourseWare
SDG Goals Reference: United Nations SDGs
Background:
Access to clean water and a healthy environment is central to multiple UN Sustainable Development Goals (SDGs)—including SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 15 (Life on Land).
However, in many parts of India and the Global South:
Water quality is not continuously monitored,
Environmental parameters (air, noise, soil, biodiversity) lack affordable sensing,
Data collected is siloed and inaccessible to communities.
Open hardware + IoT + DSP (digital signal processing) + SocialCalc + py-libp2p can play a critical role in democratizing access to environmental intelligence.
Open hardware sensors (low-cost pH, turbidity, TDS, dissolved oxygen, particulate matter, acoustic sensors, etc.) can gather ground-level data.
DSP techniques can help in filtering, denoising, and interpreting raw sensor signals for reliability.
IoT integrations (LoRa, Wi-Fi, MQTT, edge compute) can connect communities and governments.
SocialCalc (collaborative spreadsheet) can act as a real-time data dashboard for analysis, visualization, and decision-making—accessible on low-power devices and even offline-first setups.
The Challenge:
Design an open hardware + IoT prototype that captures water or environmental parameters, applies signal processing for data reliability, and streams data into a SocialCalc-powered collaborative dashboard with py-libp2p as the networking layer for community-driven monitoring and decision-making.
Participants should aim to:
Build/repurpose open hardware sensors (for water quality, air quality, or environmental monitoring).
Apply DSP techniques to improve signal quality and extract meaningful insights.
Integrate IoT communication protocols for real-time or batch data transfer.
Visualize data in SocialCalc spreadsheets with graphs, alerts, and collaborative features.
Showcase an SDG-relevant use case, such as:
Water quality monitoring in rural communities.
River/lake pollution tracking.
Air quality mapping in cities.
Soil moisture & nutrient monitoring for sustainable agriculture.
Noise/light pollution assessment in urban spaces.
Deliverables:
Open Hardware Prototype (documented design + working demo).
Signal Processing Pipeline (DSP algorithms used for calibration, noise reduction, or anomaly detection).
SocialCalc Integration (live data feed into a collaborative spreadsheet dashboard).
Demo Video (5–7 mins) showing the hardware setup, data processing, and dashboard.
Documentation & GitHub Repo with circuit design, code, and deployment instructions.
Evaluation Criteria:
Innovation & Impact – Relevance to SDG 6/13/14/15.
Technical Depth – Hardware design + DSP implementation + IoT data pipeline.
Accessibility – Cost-effectiveness, open-source replicability.
Data Usability – How well SocialCalc helps communities/authorities interpret and act on the data.
Scalability – Potential to scale beyond prototype into community/city/state-level deployment.
References to Explore:
SocialCalc for DPG: https://github.com/AspiringDevelopers/c4gt-website-nodejs
Open Hardware Sensor Projects: Public Lab, Arduino-based pH & TDS sensors
IoT Protocols: MQTT Basics, LoRaWAN
DSP Tutorials: Scipy Signal Processing, MIT DSP OpenCourseWare
SDG Goals Reference: United Nations SDGs