Getting started with satellite IoT
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John Staveley presented on using satellite communications for telemetry data collection. He outlined objectives of capturing temperature and counter data at regular intervals and maximizing receipt back to the data center. The presentation covered calculating satellite pass predictions, hardware and software setup using an Arduino, transmitting payloads to the Kineis satellites, processing the incoming data with Azure functions, and analyzing reception rates with different transmission scenarios. Future developments discussed extracting more data from messages and receiving messages from satellites.
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Topics:
✅ Introduction
✅ Embedded Systems
➡️ Energy Meter
➡️ ESP32
➡️ Raspberry Pi
✅ Acquisition
➡️ Example
✅ Identification
➡️ System Identification Toolbox - Matlab
✅ Control Design
➡️ Energy consumption prediction
✅ Hardware Design
➡️ Raspberry and ESP32
✅ Related Works
✅ Future Work
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
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Selecting the right Ethernet standard and configuring all the network devices in the embedded systems accurately is an extremely hard and rigorous job. The configuration depends on the topology, workloads of the connected devices, processing overhead at the switches, and the external interfaces. Network calculus, mathematical models and analytical techniques provide worst case execution time (WCET), but their probability of activity is extremely wide. This leads to overdesign which leads to higher costs, power consumption, weight, and size. Simulating the network is the best way to measure the throughput of the entire system. Digital system simulation provides better latency and throughput accuracy, but the accuracy is still limited because it does not consider the latency associated with the network OS, cybersecurity processing and scheduling. In many cases, these factors can reduce the throughput by 20-40%.
In this paper, we will present our research on modeling the entire Ethernet network, including the workloads, network flow control, scheduling, switch hardware, and software. To substantially increase the coverage and compare topologies, we have developed a set of benchmarks that provides coverage for different combination of deterministic, rate-constrained, and best effort traffic. During the presentation, we will cover the benchmarks, the list of attributes required to accurately model the traffic, nodes, switches, and the scheduler settings. We will also look at the statistics and reports required to make the configuration decision. In addition, we will discuss how the model must be constructed to study the impact of future requirements, failures, network intrusions, and security detection schemes.
Key Takeaways:
1. Learn how to efficiently use network simulation to design Ethernet systems
2. Develop a reusable benchmark and associated statistics to test different configurations
3. The role and impact of the CDT slots, guard band, send slope, idle slope, shuffle scheduling, flow control and virtual channels
How to leverage Quantum Computing and Generative AI for Clean Energy Transiti...
Explore the cutting-edge: Quantum computing and generative AI solutions for accelerating the clean energy transition. Discover applications for optimization, simulation, and innovation in the clean energy and utility sector.
Lightning Acquisition and Processing On Sensor Node Using NI cRIO
In this paper, we propose a digital design implementation for continuous real time data acquisition in case of a lightning event. The proposed system is capable of acquiring the lightning data at the rate of 1 MS/s on the FPGA. The system is also capable of providing very accurate timestamps in microsecond’s resolution. The data logging is carried out continuously on the PC using the TDMS file format with each and every sample being timestamped. The entire acquisition design is being implemented using NI cRIO hardware. The components of this assembly consist of the cRIO-9030 chassis , which contains a Kintex-7 FPGA , NI 9223 which is a sixteen bit ADC module, along with the NI 9467 which is a GPS module with the receiver antenna, it is used for accurate timestamping. The analog front end has been already developed, it comprises of an anti aliasing filter ,Low Noise Amplifiers and level shifters which can provide the signal with necessary gain to be processed by the digital section. The output of the analog front end is then fed to the NI 9223 four channel ADC module for sampling at 1MS/s. The samples along with their respective timestamps would be processed for identifying probable lightning data chunks on a preliminary basis which would then be transmitted over an Ethernet interface to the Central Processing system using Network Streams. The proposed digital design implementation for the entire acquisition system on the FPGA is briefly discussed in this paper
Monitoring Cassandra at Scale (Jason Cacciatore, Netflix) | C* Summit 2016
Cassandra is the dominant data store used at Netflix and it's health is critical to many of its services. In this talk we will share details of the recent redesign of our health monitoring system and how we leveraged a reactive stream processing system to give us a real-time view our entire fleet while dramatically improving accuracy and reducing false alarms in our alerting.
About the Speaker
Jason Cacciatore Senior Software Engineer, Netflix
Jason Cacciatore is a Senior Software Engineer at Netflix, where he's been working for the past several years. He's interested in stateful distributed systems and has a diverse background in technology. In his spare time he enjoys spending time with his wife and two sons, reading non-fiction, and watching Netflix documentaries.
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This talk will be about difficulties they have met on the road, and how they overcame some of them. It will reveal both - technical and management side.
Alberts Bušinskis (Garuda Dev Giri) - Yoga & Meditation teacher, IT enthusiast, software developer. Many years ago he had left a very good job in Switzerland and went to Indian Himalayas to practice Yoga. After he realized that Software Development is not different from Yoga, he came back to homeland trying to be helpful and inspiring.
Edge optimized architecture for fabric defect detection in real-time
In textile industry, fabric defect relies on human inspection traditionally, which is inaccurate, inconsistent, inefficient and expensive. There were automatic systems developed on the defect detection by identifying the faults in fabric surface using the image and video processing techniques. However, the existing solution has insufficiencies in defect data sharing, backhaul interconnect, maintenance and etc. By evolving to an edge-optimized architecture, we can help textile industry improve fabric quality, reduce operation cost and increase production efficiency. In this session, I’ll share:
What’s edge computing and why it’s important to intelligence manufacturing
What’s the characteristics, strengths and weaknesses of traditional fabric defect detection method
Why textile industry can benefit from edge computing infrastructure
How to design and implement an edge-enabled application for fabric defect detection in real-time
Insights, synergy and future research directions
Internet of Things - Technicals
I used this slide to deliver a talk in "Face the Future through IoT" seminar, where I talked technicality behind IoT and delivered a comprehensive demo from the sensor, connectivity, and process with Machine Learning, all on top of Azure.
Programmable Exascale Supercomputer
We leave in the era where the atomic building elements of silicon computers, e.g., transistors and wires, are no longer visible using traditional optical microscopes and their sizes are measured in just tens of Angstroms. In addition, power dissipation per unit volume is bounded by the laws of Physics that all resulted among others in stagnating processor clock frequencies. Adding more and more processor cores that perform simpler and simpler tasks in an attempt to efficiently fill the available on-chip area seems to be the current trend taken by the Industry.
RV silpa Resume
This document contains the resume of Silpa Pushpan, seeking a job in physical design in ASIC field. It lists her educational qualifications including a postgraduate diploma in ASIC physical design and BTech in electronics and communication. It details her core competencies in areas like ASIC flow, logic design, floorplanning, timing closure etc. It also provides details of her project experience in areas like block implementation, static timing analysis, automation using TCL during her internship.
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Presentation_Final
Seagate relies heavily on big data analytics to ensure high quality in data storage. As data storage needs grow exponentially, predictive analytics are crucial to avoid costly failures. Seagate collects terabytes of manufacturing, testing, component, and field data daily. This data is analyzed using machine learning algorithms to predict and prevent drive failures, helping ensure the reliability of over 1 billion drives expected in cloud datacenters by 2020. Seagate's big data analytics infrastructure combines comprehensive data collection, large-scale analytics capabilities, and data-driven decision making to advance quality control in high-volume data storage manufacturing.
PANSAT COM AB05-CD06 Final Report
This document summarizes a project to evaluate the packet loss and data throughput of a software TNC (Terminal Node Controller) for communicating with a low Earth orbit amateur satellite. Terrestrial tests of the existing ground station equipment were conducted at 1200 and 9600 baud. 1200 baud performed well with 0.25% packet loss and average throughput near the theoretical maximum. 9600 baud had very high (~67%) packet loss, preventing connectivity tests. No satellite passes provided an opportunity for throughput testing. Models were developed to predict satellite pass characteristics and their impact on communication link quality and bit error rate.
OPAL-RT RT13 Conference: New communication protocols
This document discusses various communication protocols used in power systems and smart grids. It provides overviews of DNP3, C37.118, OPC, IEC 61850 and IRIG-B protocols. DNP3 allows communication between intelligent electronic devices and SCADA/EMS systems to monitor and control electrical networks. C37.118 defines synchronized phasor measurements for wide area monitoring. OPC provides a common interface for process control devices. IEC 61850 specifies communication for substation automation. IRIG-B transmits a timecode signal for synchronization. The protocols support bidirectional communication of measurements and enable interoperability between different devices.
Finding New Sub-Atomic Particles on the AWS Cloud (BDT402) | AWS re:Invent 2013
This session will describe how members of the US Large Hadron Collider (LHC) community have benchmarked the usage of Amazon Elastic Compute Cloud (Amazon EC2) resource to simulate events observed by experiments at the European Organization for Nuclear Research (CERN). Miron Livny from the University of Wisconsin-Madison who has been collaborating with the US-LHC community for more than a decade will detail the process for benchmarking high-throughput computing (HTC) applications running across multiple AWS regions using the open source HTCondor distributed computing software. The presentation will also outline the different ways that AWS and HTCondor can help meet the needs of compute intensive applications from other scientific disciplines.
Real-time, Sensor-based Monitoring of Shipping Containers
This presentation describes a sensor-based solution for real-time monitoring of high-value assets in-transit so shippers can react quickly to unplanned events such as delays, cargo damage, and even thefts.
Selected as one of the best presentations at the 2012 Vail Computer Elements Workshop. For 42 years, this 4-day workshop has served leading architects of the computer industry. The agenda is 100% invited technical talks and the audience is mostly previous speakers.
Enabling SDN for Service Providers by Khay Kid Chow
1. The document discusses how programmable networks and network functions virtualization (NFV) enable new use cases and business models for service providers by making networks software-defined and services elastic.
2. Key aspects covered include centralizing network control, virtualizing network functions, and using orchestration to dynamically provision and monitor virtualized services across compute and network infrastructure on demand.
3. The benefits highlighted are automating network operations, enabling new self-service capabilities, and decreasing time to revenue through agile service creation.
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
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Getting started with satellite IoT
- 1. Starting with Satellite Communications John Staveley MVP 01/03/2022 https://uk.linkedin.com/in/johnstaveley/ @johnstaveley
- 2. About Me John Staveley MVP IT Contractor in C#, Mvc, Sql Server, Angular and Azure Organise LeedsSharp and IoTNorth etc
- 3. What I will cover Objectives Transmission strategy Calculating pass predictions Hardware setup Software setup Kineis Web portal Processing data Testing scenarios Results
- 4. Objectives Capture useful telemetry at regular intervals Counter Temperature Maximise receipt back to data centre
- 5. Kineis Constellation 8+ satellites Polar orbit (100min) 5000km satellite footprint => Calculate when to transmission
- 6. When is a satellite able to receive? Predictions valid for 30 days DEMO: Kineis website satellite predictions
- 7. When can I transmit my data?
- 8. How does coverage vary over 20 days?
- 9. Calculate pass predictions Why we have to calculate passes overhead Gaps in coverage Reduce energy consumption Website / Windows tool / C Code
- 12. Hardware setup Power. 12V 2.1A battery for current consumption, USB2 does not supply enough power. Needs to match transmission power and more than 500mW Arduino Wifi for memory size Not Uno, Not Mega 400Mhz Aerial with output impedance: 50 Ω
- 13. Test setup 20m
- 14. Software Requirements Get Temperature data every 10-20 minutes and store in stack Satellite pass data Real Time Clock for accurate transmission times Check every minute, if satellite passing, then transmit as many readings as possible See Arduino sketch (Transmit) https://github.com/johnstaveley/Satellite Power set to 1000mW
- 15. Kineis payload 64 bytes total GPS Reading date User data: Counter and temperature (22 bytes) Error correction bytes: CRC – Checksum BCH – Bit correction
- 16. Receiving data DEMO: ArgosWeb Display of incoming data MQTT, SMTP, HTTP
- 17. Cloud setup
- 18. Processing incoming data Azure function and how it unpacks Kineis data Show data in blob and table storage See Azure function (Receive) https://github.com/johnstaveley/Satellite Compare number sent to number received
- 19. Analysis Time to send each message (8s) Reception rate % per # of transmissions (No error correction) 1 transmission: 10% 2 transmissions: 24% 3 transmissions: 45% CRC_OK=Y, 3 transmissions: 40%
- 20. Doppler effect Location On average 600-800m off Satellites A1, NK
- 21. Use cases Occasionally connected, external aerial only Not replacement for mobile (LTE-M or NB-IoT) Small data size (22 bytes plus date, plus GPS) Tolerant of faults (CRC and BCH) Relatively slow send (30-60 sec gap) One way messaging (2 way coming) Can't use solution on aeroplanes Connected to PC/Arduino/STM32
- 22. Future Developments Extract date and time from message Put satellite pass calculations on the device Kineis to launch 25 satellites in 2023 KIM2 - receive messages from satellites
- 23. Any Questions? @johnstaveley Demo code: https://github.com/johnstaveley/Satellite Slides: https://www.slideshare.net/johnstaveley/ Connect: https://uk.linkedin.com/in/johnstaveley/
Editor's Notes
- https://argos-system.cls.fr/argos-cwi2/main.html
- https://argos-system.cls.fr/argos-cwi2/main.html
- https://argos-system.cls.fr/argos-cwi2/main.html