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Introduction

Visualization is a key component of IoT systems. Choosing the right visualization for the development and production requires assessment of available tools in relation to their flexibility, ease of use and the features they support. This page provides the comparison of  IoT visualization tools: Thingsboard and Grafana.

Objective

This page compares IoT visualization tools with emphasis on Thingsboard and Grafana. 

Available Tools for IoT Visualization

There are a number of IoT visualization tools. Examples include: DeviceHive [1], Grafana [2], Thinger [3], and Thingsboard [4]. So far, comparison is done between Thingsboard and Grafana with regard to ease of use, flexibility and avaialbility of features. 

Regarding requirement of database, we need Time-Series databases (TSDB) database (Graphite, Elasticsearch, CloudWatch, InfluxDB, OpenTSDB, KairosDB, or Prometheus) between Grafana and our IoT device. Whereas, in Thingsboard, we can utilize the built-in database (i.e., HSQLDB) for development purpose. This frees us from putting in place a separate database for the sole purpose of setting up the visualization system.

Regarding ease of use, in Grafana, i) if the device we want to monitor isn’t attached to a network already we might need a gateway device (e.g., a Raspberry Pi); ii) we need to write a software interface to take the data from our device via a Serial Port or MODBUS or CAN bus or whatever is available; iii) we can then send that data out over the network to our TSDB; iv) once we start pumping data into our database with our language and hardware of choice, we just start up Grafana, navigate to the hosted site, and connect it to our DB via the web interface. In summary, Grafana is challenging to implement first, but once it is up and running Grafana is a very powerful tool that can give some very detailed insights into the data with a very clean and easy to use front-end.

On the other hand, in Thingsboard, device managment, data collection, processing, and visualization are readily available. It has a built-in database for development purposes. It also supports external databases, with Cassandra and PostgreSQL being the recommended ones. 

While Thingsboard allows to create rich IoT Dashboards for data visualization and remote device control in real-time and has several customizable widgets to build end-user custom dashboards for most IoT use-cases, it does not beat the flexibility and extensibility of Grafana.

Demonstration of Thingsboard

Thingsboard is installed and tested with SQL (HSQLDB) and NoSQL (Cassandra) available at [5]. and can be accessed from local network with username: tenant@thingsboard.org, password: tenant  or sys_admin@thingsboard.org, password: sysadmin.

Conclusion

While this page provides the summary of comparison of Thingsboard and Grafana, it is yet to include more visualization tools in order to simplify the choice of a tools for a particular purpose. 

Sources

  1. https://www.devicehive.com/
  2. https://grafana.com/
  3. https://thinger.io/
  4. https://thingsboard.io/
  5. http://129.26.160.243:9090/home

Appendix

Here is the list of some other tools. (This is incomplete. But anyone can add)

ToolDescriptionPositive pointsWhat is missingReference
Thingsboard



Grafana



SpotFire



Zoomdata



InfozoomThis is a startup partially located in Fraunhofer premises.Can load very large data and do basic analyticsStream data visualization
Tableau



KibanaBy elastic search


ManY Eye

  Collaborarive visualization tool over the web (Asynchronous, Distributed)
Asynchronous distributed

  1. Annotation ,Feedback, Mashup
  2. Bookmark visualizations, annotate within bookmarks, Commenting
  3. Rating Data and visualization

Isenberg, P., Elmqvist, N., Scholtz, J., Cernea, D., Ma, K.-L., & Hagen, H. (n.d.). Collaborative visualization: Definition, challenges, and research agenda. https://doi.org/10.1177/1473871611412817  (2011)
Web based CollaboratoryData warehouse

Subramanian S, Malan GR, Shim HS, Lee JH, Knoop P, Weymouth dTE, Jahanian F and Prakash A. Software architec- ture for the UARC web-based collaboratory. IEEE Internet Comput 1999; 3(2): 46–54.
TeleMed


Kilman DG and Forslund DW. An international collaboratory based on virtual patient records. Commun ACM 1997; 40(8): 110–117.
Particle PhysicsData Grid Collaboratory Pilot


US Department of Energy CollaboratoriesParticle physics data grid collaboratory pilot.Available from: http://www.doecollabora- tory.org/research2/ppdg/homepage.html. (Last Accessed June 2011).
Earth System Grid


Bernholdt D, Bharathi S, Brown D, Chanchio K, Chen M, Chervenak A, Cinquini L, Drach B, Foster I, Fox P, Garcia J, Kesselman C, Markel R, MiddletonD, NefedovaV, Pouchard L, Shoshani A, Sim A, StrandGand WilliamsD. The earth system grid: Supporting the next generation of climate modeling research. Proc IEEE 2005; 93(3): 485–495.
NAtional Fusion Collaboratory


SchisselDP, Burruss JR, Finkelstein A, Flanagan SM, Foster IT, Fredian TW, Greenwald MJ, Johnson CR, Keahey K, Klasky SA, Li K, McCune DC, Papka M, Peng Q, Randerson L, Sanderson A, Stillerman J, Stevens R, Thompson MR and Wallace G. Building the US national fusion grid: Results from the national fusion collaboratory project. Fusion Eng Des 2004; 71(1-4): 245–250.

Collaboratory for Multi scale chemical Sience 




Sandia National Laboratories. Collaboratory for multi-scale chemical science. Available from: http://cmcs.org. (Last Accessed 2010)
Time Searcher 1-3Good candidate for time series searching* TimeSearcher allows users to specify different regions (Motif discovery ) of interest from a query time series, rather than feeding the entire query for matching.User selected patterns are automatically grouped together. * It provides an extended version of timeboxes, variable time timeboxes. It can be used to identify items in a data set that have a values in a given range for an interval consisting of a number of consecutive measurements. * Ability to define a query that is somehow a "reciprocal" of a previously defined query. * It not only supports conjunctive Queries (value range for all) but also Disjunctive ("anyof") Queries interpretation: timeboxes that find items that have a value in the range for at least one time point during the interval. * It supports two different strategies for normalizing data. a) Extreme Normalized b) Deviation Normalized* users still need to specify the query regions in order to find similar patterns (Motif discovery). * Users may need to have some prior knowledge about the datasets and need to have a general idea of what is interesting. * It suffers from its limited scalability, which restricts its utility to smaller datasets, and is impractical for the task at hand.
Sensorcloud


https://sensorcloud.microstrain.com/SensorCloud/data/Z3MFURATHIB8A032/
VizTree



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