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ThingSpeak
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| ThingSpeak | |
|---|---|
 | |
| Repository | |
| Written in | Ruby |
| Operating system | Cross-platform |
| Available in | English, Italian, Brazilian Portuguese[1] |
| Type | API |
| License | GPL version 3 |
| Website | thingspeak.com and github |
ThingSpeak is an open-source software written in Ruby which allows users to communicate with internet enabled devices.[2] It facilitates data access, retrieval and logging of data by providing an API to both the devices and social network websites. ThingSpeak was originally launched by ioBridge in 2010 as a service in support of IoT applications.[3]
ThingSpeak has integrated support from the numerical computing software MATLAB from MathWorks,[4] allowing ThingSpeak users to analyze and visualize uploaded data using MATLAB without requiring the purchase of a MATLAB license from MathWorks.[5]
ThingSpeak has been the subject of articles in specialized "Maker" websites like Instructables,[6] Codeproject,[7] and Channel 9.[8]
See also
[edit]
References
[edit]- ^ ThingSpeak supported languages
- ^ github README file, 8 October 2021
- ^ ioBridge Blog Post, 19 December 2014
- ^ MATLAB support for ThingSpeak
- ^ "ThingSpeak".
- ^ Indoor Environmental Quality Station
- ^ An Introduction to ThingSpeak, 23 November 2014
- ^ Hello ThingSpeak
External links
[edit]
.svg){kind=logo} | This free and open-source software article is a stub. You can help Wikipedia by expanding it. |
ThingSpeak
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ThingSpeak is an IoT analytics platform service that allows users to aggregate, visualize, and analyze live data streams in the cloud without requiring server configuration.[1] Originally created by ioBridge in 2010 and acquired by MathWorks—the creators of MATLAB and Simulink—in 2014, it has been developed by MathWorks since then. It provides tools for instant data visualizations, scheduled MATLAB code execution for advanced analysis, event-based email alerts, and data storage capabilities.[1] By 2016, ThingSpeak had grown to over 60,000 users, supporting a wide range of public channels for sharing IoT data and visualizations.[2]
The platform facilitates data collection from devices using REST APIs or MQTT protocols, along with cloud-to-cloud integrations for networks like LoRaWAN and cellular connections from providers such as The Things Network and Particle.io.[1] Its free tier for non-commercial use includes limits like 3 million messages per year (approximately 8,200 messages per day), 15-second update intervals, and restrictions on analytics execution, making it accessible for prototyping and experimentation.[3] ThingSpeak's built-in MATLAB support enables users to perform complex computations, such as filtering historical data or generating custom plots like spline, bar, or step charts, directly in the cloud.[4]
Targeted at students, educators, and professionals, ThingSpeak is widely applied in fields including air quality monitoring, energy management, smart farming, and environmental sensing, where it supports rapid iteration from data acquisition to actionable insights.[1] By hosting a version of MATLAB in the cloud, it lowers barriers to IoT development, allowing seamless transitions from simple visualizations to sophisticated algorithmic processing and device control.[1]
This computes 11 peaks over 60 points and writes the count to another channel.[33] Statistical analyses, such as outlier removal with the Hampel filter from the Signal Processing Toolbox, preprocess data for reliability; in tidal forecasting applications, it cleans depth measurements before further modeling.[34] Machine learning capabilities enable predictive modeling via toolboxes like the Deep Learning Toolbox; a neural network example trains a feedforward model on temperature data from channels to forecast future values, combining historical IoT streams with external datasets like wind speeds.[35][34]
Example workflows illustrate practical applications, such as anomaly detection and predictive modeling with automation. For anomaly detection, scripts schedule Hampel filtering to identify and remove outliers in real-time sensor data, ensuring clean inputs for downstream alerts. Predictive modeling workflows, like NARX neural networks for 0-12 hour tide forecasts, read channel data into timetables, train models offline or in-cloud, and deploy predictions back to ThingSpeak for visualization. These can be automated using TimeControl to run periodically, processing incoming data and updating derived channels without manual intervention.[34] A dew point calculation workflow combines temperature and humidity fields statistically:
This reads 20 points, computes dew point using the Magnus formula, and writes results with timestamps.[36]
For offline work, users can export channel datasets directly to desktop MATLAB via download options in the ThingSpeak interface, supporting further refinement with full toolboxes. Integration with desktop environments also uses API calls like
History
Development and Launch
ThingSpeak was developed and launched by ioBridge, Inc., as an open-source platform aimed at simplifying the connection of devices to the web for Internet of Things (IoT) applications.[5] Founded to address the growing need for accessible tools in handling sensor data, it provided a free hosted service that allowed users to aggregate, process, and visualize live data streams without the necessity of setting up personal servers.[5] This approach was particularly motivated by the desire to democratize IoT development for hobbyists, educators, and developers, enabling them to build sensor logging applications, location tracking systems, and social networks of devices using standard web technologies.[5] The platform's creation was inspired by the surge in interest in connected devices following the Arduino's rise in popularity during the late 2000s, which lowered barriers to electronics prototyping but highlighted challenges in data management and online integration.[6] By offering an application programming interface (API) for data storage and retrieval via HTTP, ThingSpeak facilitated easy deployment of IoT projects, including status updates and plugin extensions, without requiring advanced infrastructure knowledge.[5] Its open-source nature encouraged community contributions, with developers able to host it locally or use the cloud-based version for rapid prototyping.[5] ThingSpeak entered beta in October 2010, attracting over 200 active developers who contributed to its API and features during the testing phase.[5] The full public release occurred on April 18, 2011, marking it as the first comprehensive open-source solution for IoT data handling and distribution.[7] This launch positioned ThingSpeak as a key enabler for early IoT experimentation, emphasizing visualization tools and location-based services to make device data actionable and shareable.[5]Evolution and Key Updates
In 2012, ThingSpeak introduced APIs for accessing historical data in channels, enabling users to retrieve older entries beyond recent logs by specifying start and end parameters in API requests.[8] This enhancement supported long-term data analysis for IoT applications. Concurrently, the platform emphasized community channel sharing through public channels, allowing users to publish and access shared datasets openly, fostering collaboration among developers.[9] By 2014, ThingSpeak underwent significant upgrades, including the launch of a redesigned website that improved user interface and navigation for data management and visualization tasks.[10] Enhanced integration with MATLAB visualization tools was also implemented, permitting direct data pulls from channels into MATLAB for advanced plotting and analysis without additional licensing for basic use.[11] That year marked a pivotal ownership transition, as ThingSpeak was acquired by MathWorks, which has since maintained and expanded the platform while preserving open-source elements in its API libraries for hardware integration.[2] From 2016 to 2018, ThingSpeak advanced its alerting capabilities with the introduction of React apps, which trigger actions such as HTTP requests or notifications when channel data meets predefined conditions, like event detection in sensor streams.[12] Support for MATLAB toolboxes expanded during this period, enabling machine learning applications directly on the platform; for instance, the Statistics and Machine Learning Toolbox became available in 2016 for predictive modeling on IoT data.[13] By 2018, integration with the Predictive Maintenance Toolbox allowed users to develop condition indicators and estimate remaining useful life from channel data, broadening its utility for industrial IoT.[14] In 2020, ThingSpeak reached a major milestone with over one million channels created, reflecting widespread adoption for aggregating and analyzing live IoT data streams.[15] The platform saw increased use in public health initiatives, including integrations for COVID-19 tracking projects where channels visualized global case statistics from sources like weather stations adapted for epidemiological data.[16] ThingSpeak supports environmental monitoring applications, such as air quality and pollution sensor integrations, for deployment of MATLAB models in predictive analytics.[17] It complies with GDPR through privacy policies emphasizing data protection and user consent in channel operations.[18] For example, in 2024, ThingSpeak was used in an anomaly detection system at the Gunma Industrial Technology Center to share sensor data and send notifications for production irregularities.[19] These developments underscore ThingSpeak's maturation as a robust, MathWorks-maintained IoT analytics service with open-source API support for extensible hardware connectivity.[20]Features
Data Collection and Channels
ThingSpeak channels serve as the fundamental units for data storage and organization within the platform, enabling users to collect and manage time-series data from IoT devices, sensors, and applications. Each channel supports up to eight numeric fields for storing quantitative data such as temperature, humidity, or voltage readings, along with dedicated location fields for latitude and longitude coordinates to track geospatial information. Additionally, channels accommodate metadata including descriptions, tags, and custom properties to provide context and facilitate organization.[21] Data ingestion into channels occurs primarily through the RESTful API using HTTP GET or POST requests, allowing devices to send individual or bulk data entries in formats like JSON or CSV, or via the MQTT protocol for publish-subscribe messaging suitable for real-time IoT streams. The platform automatically assigns timestamps to incoming data entries based on the server receipt time, ensuring chronological ordering, while bulk write operations support buffering multiple messages in a single API call to optimize transmission efficiency. In the free tier, updates are limited to a minimum interval of 15 seconds per channel to manage resource usage, with free accounts restricted to four channels total.[22][3] Storage in ThingSpeak channels is subject to capacity limits designed for scalability; free accounts are capped at 3 million messages per year across all channels, equivalent to approximately 8,200 messages per day, with a total lifetime storage of up to 10 million messages. Each message can include data across the eight fields, location, and metadata, up to a maximum size of 3,000 bytes per write. Channels can be configured as private, requiring an API read key for access, or public, allowing open viewing without credentials to promote data sharing and collaboration among users. Private channels also support sharing with up to three additional users, granting view-only access without exposing API keys or sensitive configurations.[3][21]Visualization and Alerting
ThingSpeak provides a suite of built-in visualization tools that enable users to render data graphically without requiring custom coding. These include time-series plots for tracking trends over time, gauges for displaying real-time values, and maps for visualizing location-based data from channels.[23] Users can customize these charts through the web interface, adjusting parameters such as colors, scales, and data ranges to suit specific monitoring needs.[23] In addition to core charts, ThingSpeak offers pre-built apps and widgets that extend visualization options, such as histograms for analyzing data distributions and status indicators for quick overviews of device or system states. These widgets are embeddable in external websites via the Channels and Charts API, allowing seamless integration into dashboards or reports.[24][25] For alerting, ThingSpeak's React feature automates responses to data conditions by setting thresholds on channel values, triggering notifications when limits are exceeded. Alerts can be delivered via email or webhooks to external services, with options for scheduling periodic checks to monitor ongoing conditions.[26] This event-based system supports the Alerts API for sending HTTP POST requests to initiate notifications and retrieving history via GET requests.[27][28] ThingSpeak's web interface supports mobile access, enabling users to view and interact with visualizations and dashboards from smartphones or tablets through any standard browser.[29]Analytics with MATLAB
ThingSpeak integrates MATLAB for advanced analytics directly within its cloud environment, enabling users to process and derive insights from IoT data streams without requiring a local MATLAB installation. This integration leverages a hosted version of MATLAB Online, allowing execution of custom scripts on channel data for tasks such as data transformation, statistical computation, and model deployment.[30][31] Access to MATLAB analytics occurs through the MATLAB Analysis app in ThingSpeak, where users can write and run code on live or historical channel data. No separate MATLAB license is needed for basic operations, as MathWorks hosts the environment in the cloud; however, access to specialized toolboxes like the Signal Processing Toolbox or Statistics and Machine Learning Toolbox requires a licensed MathWorks account.[32][31] Scripts can read data using functions likethingSpeakRead, process it, and optionally write results back to channels via thingSpeakWrite. Execution is limited to 60 seconds for standard or academic accounts and 20 seconds for free users, with a minimum scheduling interval of 5 minutes.[30][31]
Various analysis types are supported, including signal processing, statistics, and machine learning. For signal processing, users can apply filters and peak detection; for instance, the findpeaks function identifies local maxima in time-series data, such as detecting high-density traffic periods exceeding 10 cars per second in a channel monitoring vehicle counts. The following code snippet demonstrates this workflow on channel data:
matlab
data = thingSpeakRead(38629, 'NumPoints', 60, 'Fields', 1, 'outputFormat', 'table');
[peakValues, peakLocations] = findpeaks(data.DensityOfWestboundCars, data.Timestamps, 'MinPeakHeight', 10);
numOccurences = length(peakValues);
thingSpeakWrite(17504, numOccurences, 'WriteKey', '23ZLGOBBU9TWHG2H');
data = thingSpeakRead(38629, 'NumPoints', 60, 'Fields', 1, 'outputFormat', 'table');
[peakValues, peakLocations] = findpeaks(data.DensityOfWestboundCars, data.Timestamps, 'MinPeakHeight', 10);
numOccurences = length(peakValues);
thingSpeakWrite(17504, numOccurences, 'WriteKey', '23ZLGOBBU9TWHG2H');
matlab
readChId = 12397;
[temp, time] = thingSpeakRead(readChId, 'Fields', 4, 'NumPoints', 20);
humidity = thingSpeakRead(readChId, 'Fields', 3, 'NumPoints', 20);
tempC = (5/9)*(temp-32);
b = 17.62; c = 243.5;
gamma = log(humidity/100) + b*tempC./(c+tempC);
dewPoint = c*gamma./(b-gamma);
dewPointF = (dewPoint*1.8) + 32;
thingSpeakWrite(writeChId, [temp, humidity, dewPointF], 'Fields', [1, 2, 3], 'TimeStamps', time, 'WriteKey', writeKey);
readChId = 12397;
[temp, time] = thingSpeakRead(readChId, 'Fields', 4, 'NumPoints', 20);
humidity = thingSpeakRead(readChId, 'Fields', 3, 'NumPoints', 20);
tempC = (5/9)*(temp-32);
b = 17.62; c = 243.5;
gamma = log(humidity/100) + b*tempC./(c+tempC);
dewPoint = c*gamma./(b-gamma);
dewPointF = (dewPoint*1.8) + 32;
thingSpeakWrite(writeChId, [temp, humidity, dewPointF], 'Fields', [1, 2, 3], 'TimeStamps', time, 'WriteKey', writeKey);
thingSpeakRead from the ThingSpeak Support Toolbox, allowing seamless data import for local scripting and analysis.[30][37]
Architecture
Core Components and Data Model
ThingSpeak's data model is centered around channels, which serve as the primary containers for storing and organizing IoT data streams in the cloud. Each channel can hold up to eight fields, allowing users to capture multiple data types such as numeric values, alphanumeric text, location information, or images, with each field limited to 255 characters.[38] Feeds represent the individual timestamped entries posted to these channels, forming time-series datasets that enable historical querying by entry ID or time range.[38] The backend infrastructure relies on cloud-based storage managed entirely by MathWorks, eliminating the need for users to configure or maintain servers.[4] This setup supports seamless data aggregation and retention, with data retention periods depending on the license tier, such as three years for the free tier.[1] User accounts provide secure management of channels through API keys, including write keys for posting data and read keys for retrieving it, ensuring controlled access to private channels.[38] For collaboration, channels can be shared with other MathWorks account holders via the platform's sharing features, facilitating team-based projects without public exposure.[39] To maintain scalability and prevent abuse, ThingSpeak supports over one million active channels as of 2020, with rate limits enforcing updates every 15 seconds for free accounts and every 1 second for paid ones.[15][40] Launched as a fully hosted Software as a Service (SaaS) platform in 2010, it continues to operate without on-premises deployment options as of 2025, focusing on cloud-native IoT analytics with features such as image uploads.[2][41]Data Flow and Security
Data from sensors and devices flows into ThingSpeak through internet-connected hardware or software applications that utilize the platform's APIs to transmit information to designated channels. Typically, a sensor captures environmental or operational data, which is then packaged into an HTTP request or MQTT message by the device. This request is routed to ThingSpeak's API endpoints, where it undergoes authentication before being stored in the channel's fields. For instance, using the REST API, data is sent via HTTP GET or POST methods to the update endpoint, enabling single or bulk uploads in formats like JSON or CSV. Once ingested, the data becomes available for immediate retrieval, supporting real-time visualization and MATLAB-based analytics.[22][4] The primary protocols facilitating this data movement are REST over HTTPS for request-response interactions and MQTT for publish-subscribe messaging, both optimized for IoT scenarios. REST requests are directed to api.thingspeak.com, allowing devices to push data efficiently with timestamps for precise logging, while MQTT connections to mqtt.thingspeak.com enable low-latency updates suitable for resource-constrained devices with intermittent connectivity. Third-party bridges, such as those from The Things Network or Particle.io, extend support for protocols like LoRaWAN by converting messages into compatible API calls, ensuring seamless integration without direct hardware modifications. Channel storage serves as the central endpoint, where incoming data is organized into up to eight fields per channel for structured aggregation.[42][43][4] Security measures in ThingSpeak prioritize authentication, access control, and encrypted transmission to safeguard data integrity and confidentiality. Each channel is assigned unique 16-digit API keys for write and read operations, with private channels requiring these keys to prevent unauthorized access—public channels allow open reads but still enforce write protections. All REST API communications use HTTPS with TLS 1.2 or higher for end-to-end encryption in transit, while MQTT supports SSL over WebSockets to secure publish and subscribe actions against interception. These features ensure that sensitive IoT data remains protected during transfer from devices to the cloud.[40][43][44] Error handling and reliability are addressed through standardized response mechanisms and diagnostic tools. The APIs return HTTP status codes (e.g., 200 for success, 429 for rate limits) alongside ThingSpeak-specific error codes, such as -301 for data interval violations, enabling devices to log issues and implement retries. For failed uploads due to connectivity disruptions, applications can buffer data locally and perform bulk resubmissions once reconnected, minimizing data loss in unreliable networks. Comprehensive logging of API interactions further aids in troubleshooting, with diagnostics available via response payloads.[45][22]Applications and Use Cases
IoT Prototyping and Monitoring
ThingSpeak facilitates IoT prototyping by enabling developers to quickly integrate sensors with cloud storage and visualization without building custom backend infrastructure. A common prototyping example involves weather stations using Arduino microcontrollers to collect environmental data such as temperature, humidity, and rainfall, which is then transmitted to ThingSpeak channels for real-time logging and analysis.[46][47] For smart home applications, sensors monitoring temperature and humidity via Arduino setups send data to ThingSpeak, allowing hobbyists to prototype automated responses like alerts for unusual conditions.[48][49] In monitoring scenarios, ThingSpeak supports ongoing surveillance of industrial equipment and environmental conditions, particularly through air quality sensors that track pollutants like CO2 and particulate matter. Systems using sensors such as the MQ135 or Sharp GP2Y1010AU0F transmit data to ThingSpeak for visualization and predictive analysis, enabling early warnings in production environments or warehouses.[17][50] For public health applications, ThingSpeak has been deployed in dashboards monitoring health-related metrics, such as air pollution impacts during events like the 2020 pandemic, where IoT devices visualized pollutant levels to inform community responses.[51] Industrial tracking examples include IoT systems for trucks and warehouses that monitor gas levels and air quality to ensure compliance and safety.[52] The platform's benefits include rapid deployment, as users can connect devices via REST API or MQTT without managing servers, supporting prototypes from a single sensor to large fleets of connected devices.[1] This scalability allows aggregation of live data streams for analysis, with MATLAB integration enabling advanced processing directly in the cloud.[53] However, limitations in the free tier, such as a 15-second minimum update interval and a cap of 8,200 messages per day, make it suitable for low-frequency prototyping but inadequate for high-speed industrial monitoring requiring sub-second updates.[54] Paid tiers address this by permitting updates as frequent as once per second.[29]Educational and Research Applications
ThingSpeak has been integrated into various STEM curricula to facilitate hands-on learning in IoT and data analytics. MathWorks provides free curriculum modules, such as the 2024 "Hardware Connectivity in Action" module, which introduces IoT basics through interactive examples covering signals, control systems, and physical modeling with ThingSpeak.[55] These resources enable educators to teach project-based learning, allowing students to collect and visualize sensor data without requiring advanced programming knowledge.[56] In higher education, ThingSpeak supports courses on IoT and AI; for instance, professors at Arizona State University incorporate it to introduce students to sensor integration and machine learning applications.[57] At the Slovak University of Technology, it is used in industrial IoT programs to foster practical skills in robotics and cybernetics through real-time data monitoring projects.[58] High school initiatives, like the MATLAB STEM Challenge, leverage ThingSpeak for student-led projects such as building fitness trackers or smart ping pong paddles to analyze motion and environmental data.[59][60] ThingSpeak aids scientific research by enabling the storage and analysis of IoT datasets from sensor networks. A 2022 study analyzed 38,000 public ThingSpeak visualizations to explore patterns in IoT data deployment, revealing common practices in real-world sensor applications and informing future platform enhancements.[61] In environmental research, it has been employed for monitoring systems; an IEEE study developed a wireless sensor network using ThingSpeak to track air quality parameters like temperature and pollution levels in real time.[62] Researchers can leverage its MATLAB integration for processing large datasets, such as aggregating sensor readings for trend analysis in ecological studies.[63] The platform's community features promote collaborative learning and data sharing through public channels, where users publish datasets for educational purposes, such as student projects on air quality or weather monitoring.[64] Early contests, like MathWorks' 2011 Internet of Things Contest, encouraged over 300 channel creations and engaged thousands of participants in building and sharing IoT prototypes by 2014.[65] ThingSpeak's free tier, supporting up to 3 million messages annually, removes cost barriers for student and academic projects, allowing non-commercial use for data collection and visualization without licensing fees.[54] This accessibility has enabled widespread adoption in resource-limited educational settings, where learners can prototype IoT solutions using affordable hardware like Arduino.[56]API and Integrations
RESTful API Usage
ThingSpeak provides a RESTful API that enables programmatic interaction with channels for data ingestion and retrieval, supporting HTTP methods such as GET and POST over HTTPS.[41] The API is designed for simplicity, allowing devices and applications to send and fetch data without complex configurations.[41] The primary endpoint for writing data to a channel ishttps://api.thingspeak.com/update, which accepts both GET and POST requests.[66] Required parameters include api_key, a unique write key generated per channel, while optional parameters encompass field1 through field8 for numeric data values, lat and long for location coordinates, elevation for altitude in meters, status for a text message, and created_at for a custom ISO 8601 timestamp.[66] For example, a GET request to write a value might use: https://api.thingspeak.com/update?api_key=YOUR_WRITE_API_KEY&field1=99, returning the entry ID on success or an error code otherwise.[66] POST requests support JSON payloads for batch updates, such as sending multiple field values in a single call to improve efficiency for high-frequency data sources.[66]
Since August 2022, the API also supports uploading images to image-enabled channels via POST requests to https://api.thingspeak.com/channels/{channel_id}/images, using multipart/form-data with the image file and required api_key. This allows devices to send visual data, such as from cameras, for storage and analysis.[67]
Reading data occurs via endpoints like https://api.thingspeak.com/channels/{channel_id}/feeds, where {channel_id} is the numeric identifier of the channel.[68] This GET request requires a api_key read key for private channels and supports parameters like results to limit the number of returned entries (defaulting to 8000 maximum) and date filters such as start and end in ISO 8601 format.[68] Specific fields can be queried using /fields/{field_id}/feeds, and output formats include JSON (default), XML (via .xml extension), or CSV (via .csv).[68] For instance: https://api.thingspeak.com/channels/123456/feeds.json?api_key=YOUR_READ_API_KEY&results=10 retrieves the last 10 entries in JSON.[68]
Authentication relies on per-channel API keys: a write key for updates and a separate read key for queries, both accessible via the channel settings in the ThingSpeak interface.[25] These keys ensure secure access without user credentials, with read keys optional for public channels.[25]
Best practices include using batch updates via JSON POST to minimize requests and respect rate limits, which for free tiers restrict writes to one per 15 seconds per channel.[66] Error handling is crucial; common HTTP status codes include 429 ("Too Many Requests") for exceeding rate limits, 401 ("Authorization Required") for invalid API keys, 400 ("Bad Request") for syntax issues, and 404 ("Not Found") for invalid resources.[45] Responses include a JSON or XML body with status details, such as {"status":"Too Many Requests","http_code":429}.[45] Always enforce HTTPS for all API calls, as supported since the platform's inception and required for security.[41] The API has remained stable since its launch in 2010, with enhancements like improved bulk operations over time.[7]