Colleen Josephson | UCSC OSPO

Environmental NeTworked Sensor (ENTS)

Fri, 30 Jan 2026 00:00:00 +0000

ENTS I: Usability improvements for visualization dashboard

Topics: Data Visualization, Backend, Frontend, UI/UX, Analytics
Skills:
- Required: React, Javascript, Python, SQL, Git
- Nice to have: Flask, Docker, CI/CD, AWS, Authentication
Difficulty: Medium
Size: Large (350 hours)
Mentors: Colleen Josephson, Alec Levy, John Madden

The Environmental NeTworked Sensor (ENTS) platform, formally Open Sensing Platform (OSP), implements data visualization website for monitoring microbial fuel cell sensors (see GitHub). The mission is to scale up the current platform to support other researchers or citizen scientists in integrating their novel sensing hardware or microbial fuel cell sensors for monitoring and data analysis. Examples of the types of sensors currently deployed are sensors measuring soil moisture, temperature, current, and voltage in outdoor settings. The focus of the software half of the project involves building upon our existing visualization web platform, and adding additional features to support the mission. A live version of the website is available here.

Below is a list of project ideas that would be beneficial to the ENTS project. You are not limited to the following projects, and encourage new ideas that enhance the platform:

Drag and drop charts functionality
Creation of unique charts by users (with unique equations)
Customizable options of charts (color, line width, datapoint/line style, axis labels)
Exportable charts (with customizable options)
Saving layouts via url

ENTS II: Migration to TockOS

Topics: Embedded system, operating system
Skills:
- Required: Rust, C/C++, Git, Github
- Nice to have: STM32 HAL, python
Difficulty: Hard
Size: Large (350 hours)
Mentors: Colleen Josephson, John Madden

The current version of the hardware firmware is implemented in baremetal through the use of STM hardware abstraction layer (HAL) drivers. We are interested in porting the firmware implementation to an operating system (OS) to allow for additional functionality to support environmental data logging. TockOS is an embedded operating system designed for running multiple concurrent, mutually distrustful applications on low-memory and low-power microcontrollers that will be used. TockOS allows for OTA updates, dynamic app loading, hardware multiplexing, and more. We envision multiple users utilizing shared ENTS hardware that provides communication and measurement capabilities. Thus, the initial cost of deploying wireless sensor networks is reduced.

The TockOS kernel is written in Rust to enhance security. Userspace apps can be written in either C, C++, or Rust. Development will be done through a remote development server to access the hardware. See the following repos for the current status of the project:

Userspace library: libtock-c
Kernel: tock
Baremetal: ENTS-node-firmware

Scope of work:

Writing kernel peripheral drivers.
- Done entirely in Rust.
- Low-level understanding of microcontroller
- Basic kernel functionality knowledge.
Porting baremetal components to userland apps.
- Involves porting STM HAL calls to TockOS syscalls.
- Primarily done in C.
- Understanding of syscalls.

WaDAR

Wed, 05 Feb 2025 00:00:00 +0000

WaDAR (Water Radar) is an innovative, low-cost, hybrid approach to soil moisture sensing that combines the benefits of in-ground (in situ) and remote sensing technologies. Traditional soil moisture measurement methods suffer from drawbacks: in situ sensors are expensive and difficult to maintain, while remote sensing offers lower accuracy and resolution. WaDAR bridges this gap by using inexpensive underground backscatter tags paired with above-ground radars, enabling completely wireless, high-resolution soil moisture monitoring.

Key Features of WaDAR

Uses RF backscatter tags buried underground to provide high-accuracy soil moisture readings.
Uses ultra-wideband radar for above-ground sensing.
Offers an average error of just 1.4%, comparable to state-of-the-art commercial sensors.
Reduces deployment costs significantly, making it accessible for widespread agricultural use.
Supports real-time, scalable, and maintenance-free soil moisture monitoring for farmers.

Improving and Optimizing Data Processing Pipeline for More Accurate Soil Moisture Measurements

Topics: Digital Signal Processing Machine Learning
Skills: C/embedded, signal processing, machine learning, MATLAB (optional)
Difficulty: Moderate
Size: Medium (175 hours)
Mentors: Colleen Josephson, Eric Vetha

Enhance the accuracy of soil moisture measurements by refining the data processing pipeline.

Tasks:

Develop and test algorithms for noise reduction and signal improvement.
Implement advanced filtering and statistical techniques to improve measurement precision.
Validate improvements using real-world field data.
Translate algorithms into embedded to be implemented in real-time embedded hardware.

Improving Backscatter Tag PCB

Topics: Hardware Design Signal Processing
Skills: PCB design, RF knowledge
Difficulty: Moderate
Size: Medium (175 hours)
Mentors: Colleen Josephson, Eric Vetha

Enhance the performance of WaDAR’s backscatter tags by optimizing PCB design for improved signal-to-noise ratio (SNR) and implementing a communication protocol for tag identification.

Tasks:

Redesign PCB for improved readings.
Implement and test a communication protocol to distinguish between multiple tags.
Evaluate hardware changes in real-world field conditions.
Optimize power consumption and scalability for practical deployment.

Environmental NeTworked Sensor (ENTS)

Fri, 31 Jan 2025 00:00:00 +0000

ENTS I: Web portal for large-scale sensor networks

Topics: Data Visualization, Backend, Frontend, UI/UX, Analytics
Skills:
- Required: React, Javascript, Python, SQL, Git
- Nice to have: Flask, Docker, CI/CD, AWS, Authentication
Difficulty: Medium
Size: Large (350 hours)
Mentors: Colleen Josephson, John Madden, Alec Levy

Below is a list of project ideas that would be beneficial to the ENTS project. You are not limited to the following projects, and encourage new ideas that enhance the platform:

Improve streaming functionality
Generic interface for sensor measurements
Logger registration
Over the air (OTA) configuration updates
Implement unit tests and API documentation

ENTS II: Hardware to for large-scale field sensor networks

Topics: Embedded system, wireless communication, low-power remote sensing
Skills:
- Required: C/C++, Git, Github, PlatformIO
- Nice to have: STM32 HAL, ESP32 Arduino, protobuf, python, knowledge of standard communication protocols (I2C, SPI, and UART)
Difficulty: Hard
Size: Large (350 hours)
Mentors: Colleen Josephson, John Madden, Jack Lin

The Environmental NeTworked Sensor (ENTS) node aims to be a general purpose hardware platform for outdoor sensing (e.g. agriculture, ecological monitoring, etc.). The typical use case involves a sensor deployment in an agricultural field, remotely uploading measurements without interfering with farming operations. The current hardware revision (Soil Power Sensor was originally designed for monitoring power output of microbial fuel cells using high fidelity voltage and current measurement channels, as well as auxiliary sensors such as the SDI-12 TEROS-21 soil moisture sensor. The primary activities of this project will involve low-level firmware design and implementation, but may also incorporate hardware design revisions if necessary. We are looking to expand functionality to other external sensors, as well as optimize for power consumption, via significant firmware design activities.

Long-range, low-power wireless communication is achieved through a LoRa capable STM32 microcontroller with in-lab experiments using an ESP32 microcontroller to enable the simpler WiFi interface. Both wireless interfaces communicate upload measurements to our data visualization dashboard, ENTS I. The combined goal across both of these projects is to create a system that enables researchers to test and evaluate novel sensing solutions. We are looking to make the device usable to a wide range of researchers which may not have a background in electronics, so are interested in design activities that enhance user friendliness.

In total there will be 2-4 people working on the hardware with progress being tracked on GitHub. Broader project planning is tracked through a Jira board. We intend to have weekly meetings to provide updates on current issue progress along with assigning tasks. Please reach out to John Madden if there are any questions or specific ideas for the project.

Below is a list of project ideas that would be beneficial to the ENTS project. You are not limited to the following projects, and encourage new ideas that enhance the platform:

Backup logging via SD card
I2C multiplexing for multiple of the same sensors
Batch sensor measurement uploading

Open Sensing Platform (OSP)

Mon, 05 Feb 2024 00:00:00 +0000

Open Sensing Platform I: Software to enable large scale outdoor sensor networks

Topics: Data Visualization, Backend, Web Development, UI/UX, Analytics
Skills:
- Required: React, Javascript, Python, SQL, Git
- Nice to have: Flask, Docker, CI/CD, AWS, Authentication
Difficulty: Medium
Size: Large (350 hours)
Mentors: Colleen Josephson, John Madden, Aaron Wu

Open Sensing Platform (OSP) is a new initiative expanding from our prior project DirtViz, a data visualization web platform for monitoring microbial fuel cell sensors (see GitHub). The mission is to scale up the current platform to support other researchers or citizen scientists in integrating their novel sensing hardware or microbial fuel cell sensors for monitoring and data analysis. Examples of the types of sensors currently deployed are sensors measuring soil moisture, temperature, current, and voltage in outdoor settings. The focus of the software half of the project involves building upon our existing visualization web platform, and adding additional features to support the mission. A live version of the website is available here.

Deliverables:
- Create a system for remote collaborators/citizen scientists to set up their sensors and upload securely, eg. designing user flow to create sensors
- Craft an intuitive navigation system so that data from deployment sites around the world can be easily viewed, eg. designing experience/system to locate deployment sites.
- Refine our web-based visualization tools to add additional features for users to analyze collected data, eg. lazy loading out-of-range data or caching queried data.
- Document the tool thoroughly for future maintenance

Open Sensing Platform II: Hardware to enable large scale outdoor sensor networks

Topics: Embedded system, wireless communication, low-power remote sensing
Skills:
- Required: C/C++, Git, Github, Platformio
- Nice to have: PCB design and debugging experience, STM32 HAL, ESP32 Arduino, protobuf, python, knowledge of standard communication protocols (I2C, SPI, and UART)
Difficulty: Hard
Size: Large (350 hours)
Mentors: Colleen Josephson, John Madden, Stephen Taylor

The Open Sensing Platform hardware aims to be a general purpose hardware platform for outdoor sensing (e.g. agriculture, ecological monitoring, etc.). The typical use case involves a sensor deployment in an agricultural field, remotely uploading measurements without interfering with farming operations. The current hardware revision (Soil Power Sensor) was originally designed for monitoring power output of microbial fuel cells using high fidelity voltage and current measurement channels, as well as auxiliary sensors such as the SDI-12 TEROS-12 soil moisture sensor. The primary activities of this project will involve low-level firmware design and implementation, but may also incorporate hardware design revisions if necessary. We are looking to expand functionality to other external sensors, as well as optimize for power consumption, via significant firmware design activities.

Long-range, low-power wireless communication is achieved through a LoRa capable STM32 microcontroller with in-lab experiments using an ESP32 microcontroller to enable the simpler WiFi interface. Both wireless interfaces communicate upload measurements to our data visualization dashboard, Open Sensing Platform I. The combined goal across both of these projects is to create a system that enables researchers to test and evaluate novel sensing solutions. We are looking to make the device usable to a wide range of researchers which may not have a background in electronics, so are interested in design activities that enhance user friendliness.

Deliverables: Contribution via commits to the GitHub repository with documentation on completed work. A changelog of contributions to the firmware.

DirtViz (2022)

Mon, 07 Nov 2022 10:15:56 -0700

DirtViz is a project to visualize data collected from sensors deployed in sensor networks. We have deployed a number of sensors measuring qualities like soil moisture, temperature, current and voltage in outdoor settings. This project involves extending (or replacing) our existing plotting scripts to create a fully-feledged dataviz tool tailored to the types of data collected from embedded systems sensor networks.

Visualize Sensor Data

Topics: Data Visualization, Analytics
Skills: javascript, python, bash, webservers, git, embedded systems
Difficulty: Easy/Moderate
Size 175 hours
Mentor: Colleen Josephson

Develop set of visualization tools (ideally web based) that easily allows users to zoom in on date ranges, change axes, etc.
Document the tool thoroughly for future maintenance
If interested, we are also interested in investigating correlations between different data streams

DirtViz 2.0 (2023)

Mon, 07 Feb 2022 00:00:00 +0000

DirtViz is a project to visualize data collected from sensors deployed in sensor networks. We have deployed a number of sensors measuring qualities like soil moisture, temperature, current and voltage in outdoor settings. This project involves extending our existing visualization stack, DirtViz 1.0 (see github), and expanding it to version 2.0. The project goal is to create a fully-fledged dataviz tool tailored to the types of data collected from embedded systems sensor networks.

Visualize Sensor Data

Topics: Data Visualization, Analytics
Skills: javascript, python, bash, webservers, git, embedded systems
Difficulty: Easy/Moderate
Size: Large, 350 hours
Mentors: Colleen Josephson, Sonia Naderi, Stephen Taylor, John Madden

Specific tasks:

Refine our web-based visualization tools to easily allow users to zoom in on date ranges, change axes, etc.
Create a system for remote collaborators/citizen scientists to upload their own data in a secure manner
Craft an intuitive navigation system so that data from deployment sites around the world can be easily viewed
Document the tool thoroughly for future maintenance
If interested, we are also open to you investigating correlations between different data streams and doing self-directed data analysis