Our vision is to create a versatile space simulation platform that caters to individuals with diverse research interests, including networking, AI, computing, and more. Unlike traditional simulators tied to specific research applications, our design allows for seamless integration of various space-related research verticals. CosmicBeats originated from the paper: Shenoy and Chabra et al., CosMAC: Constellation-Aware Medium Access and Scheduling for IoT Satellites, ACM MobiCom, 2024.
The current version of simulator offers the capability to simulate various facets of satellite operation and communication, encompassing orbital dynamics, wireless communication, IoT networks, computation, imaging, and more. We have included numerous example scenarios that can be simulated using these functionalities, such as direct-to-satellite IoT communication networks, distributed ground station setups, imaging satellite operations, and others. However, users are not limited to these predefined scenarios. By making simple modifications in the config file, users can simulate many additional scenarios. Moreover, the platform's codebase allows for easy integration of new capabilities, enabling users to build upon the existing features smoothly. Additionally, the simulator provides runtime APIs that allow seamless interaction with the simulator for real-time control of operations. This feature facilitates easy integration of the simulator with external programs, requiring minimal or no modifications to the simulator's codebase. For instance, by utilizing the runtime APIs, users can effortlessly interface a scheduler for the ground station network.
We are thrilled to have you as a contributor to our project as our goal is to make this project community driven. Your valuable expertise and passion will play a vital role in shaping this space simulation platform for diverse research interests. Whether you are a developer, researcher, or enthusiast, there are plenty of opportunities to make a significant impact. Let's work together to create an exceptional and collaborative space simulation platform.
At the core of our simulator are "nodes" and "models." A node represents a physical endpoint entity, such as satellites, ground stations, user terminals, IoT devices, among others. Each node consists of one or multiple models, each of which replicates the functionality or behavior of the corresponding node component. These models can represent software or hardware elements, such as satellite batteries or even the orbital movements of satellites. Importantly, the user can make zero-code configuration of the models within each node based on their specific simulation requirements. This customization ensures that the simulation setup remains tailored to the intended research purpose.
Interactions between models are critical for simulating real-world scenarios accurately. For instance, a computation model might need to assess the power availability from the battery model before performing its operations. To facilitate such interactions, a model exposes public APIs accessible by other models.
Our simulator employs a discrete-time approach, executing simulated operations at regular intervals known as "epochs". This ensures consistent and predictable execution. During each epoch, the simulator invokes the Execute() method of each model within the nodes, effectively simulating the desired operations associated with those models.
With our space simulation platform, researchers can explore and contribute to a wide array of space-related research domains, thanks to its adaptable design and the ability to model diverse scenarios. You have the opportunity to contribute in various ways, such as introducing new nodes, models, SMAs, summarizers, APIs, and configs. Additionally, you can participate in fixing issues, enhancing computations, and much more. Your contributions are not limited, and we welcome your innovative ideas and improvements.
The simulator is developed using Python. To get started, ensure you have the most recent version of Python installed. Use pip to install the required package listed in requirements.txt, and you'll be ready to go.
pip install -r requirements.txtAnaconda can be used as well to setup the platform. Use environment.yml to create the environment with required packages.
Running the initial simulation scenario is swift and simple; just execute the main.py
python main.pyUpon execution, the simulator will output a sequence of logs in your terminal.
If you wish to experiment with an alternative simulation configuration, you can provide the path to any configuration file from the configs directory as an argument to the main.py script.
python main.py configs/config.jsonMost of the simulator's usage involves customizing the nodes and models within the config file. Therefore, it is essential to comprehend the concepts of nodes, models, and configuration to effectively utilize the simulator.
To learn about node, please refer to this.
To learn about model, please refer to this.
The configuration of the simulation setup is maintained in a JSON file. To know more on how to work with the configuration file, please refer to this.
The sole interface to the simulator is the Simulator class, meaning that users can run the simulator by simply creating an instance of this class.
from src.sim.simulator import Simulator
_configFilePath = "configs/config.json"
_sim = Simulator(_configFilePath)
_sim.execute()The runtime APIs can also be accessed through the Simulator class by invoking call_RuntimeAPIs() method.
To learn more about the Simulator class and architecture of the simulator, please refer to this.
The primary objective of a simulation is to generate insights, achieved by analyzing the logs produced by the simulator. As these insights are tailored to each specific use case, reusing the code written for analytics can be challenging. However, in our design, we prioritize creating an analytics pipeline that is highly adaptable and can be easily repurposed for various scenarios. To learn more about the analytics pipeline, please refer to this.
We will attempt to execute a series of end-to-end simulation examples. ** It is essential to note that the data utilized for these simulation examples may not accurately represent real-world values.** We have used public TLE files from https://celestrak.org/.
In this simulation, there are 1000 IoT devices on Earth that engage in direct communication with over 150 LEO (Low Earth Orbit) satellites. The satellites collect data from the IoT devices, store it onboard, and transmit the data to ground stations whenever an opportunity arises. Find the corresponding config file here.
python examples/iotnetwork.pyIt might take a while since we are dealing with a large number of nodes.
Once the simulation is complete, we are good to go for analyzing the logs and generating results. We have multiple sample scripts for log analysis available here that use our SMAs and Summarizers to analyze the logs and generate insights. For this example, we will use analyze_datalayer.py to find end-to-end delay of our simulated IoT network. This script analyzes the logs stored in the exampleLogs/ temporary directory and returns the results in several metrics.
python -Wignore examples/analytics_samples/analyze_datalayer.py exampleLogs/In this example, we simulate a constellation of satellites capturing earth image, called Earth Observation satellites. Find the corresponding config file here.
python examples/imagesatellite.pyIt dumps the log in the imagingLogs/ directory. Once the simulation is complete, we run analysis on the logs by running an example analyzer script that returns the power profile of satellites in the constellation.
python -Wignore examples/analytics_samples/analyze_power.py imagingLogs/We have provided several test cases here. You can use pytest package to evaluate the tests.
pytest -Wignore src/test/Please refer to LICENSE.txt
Please refer to SECURITY.md
Please refer to SUPPORT.md
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
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