Quantitative comparison of SensibleThings and Microsoft Azure IoT Hub

With the development of the Internet-of-Things, more and more IoT platforms come up with different structures and characteristics. Making balance of their advantages and disadvantages, we should choose the suitable platform in differ- ent scenarios. For this project, I make comparison of a cloud-based centralized platform, Microsoft Azure IoT hub and a fully distributed platform, Sensi- bleThings. Quantitative comparison is made for performance by 2 scenarios, messages sending speed adds up, devices lie in different location. General com- parison is made for security, utilization and the storage. Finally I draw the con- clusion that SensibleThings performs more stable when a lot of messages push- es to the platform. Microsoft Azure has better geographic expansion. For gener- al comparison, Microsoft Azure IoT hub has better security. The requirement of local device for Microsoft Azure IoT hub is lower than SensibleThings. The SensibleThings are open source and free while Microsoft Azure follow the con- cept “pay as you go” with many throttling limitations for different editions. Microsoft is more user-friendly.

Theoretical and Quantitative Comparison of SensibleThings and GSN

This project is aimed at making comparison between current existing Internet- of-Things (IoT) platforms, SensibleThings (ST) and Global Sensors Networks (GSN). Project can be served as a further work of platforms’ investigation. Comparing and learning from each other aim to contribute to the improvement of future platforms development. Detailed comparison is mainly with the respect of platform feature, communication and data present-frequency performance under stress, and platform node scalability performance on one limited device. Study is conducted through developing applications on each platform, and making measuring performance under the same condition in household network environment. So far, all these respects have had results and been concluded. Qualitatively comparing, GSN performs better in the facets of node’s swift development and deployment, data management, node subscription and connection retry mechanism. Whereas, ST is superior in respects of network package encryption, platform reliability, session initializing latency, and degree of developing freedom. In quantitative comparison, nodes on GSN has better data push pressure resistence while ST nodes works with lower session latency. In terms of data present-frequency, ST node can reach higher updating frequency than GSN node. In the aspect of node sclability on one limited device, ST nodes take the advantage in averagely lower latency than GSN node when nodes number is less than 15 on limited device. But due to sharing mechanism of GSN, on one limited device, it's nodes shows more scalable if platform nodes have similar job.

Quantitative Comparison of SensibleThings and Dweet.io

The objective of this paper is to perform a quantitative comparison of Dweet.io and SensibleThings from different aspects. With the fast development of internet of things, the platforms for internet-of-things face bigger challenges. This paper will evaluate both systems in four parts. The first part shows the general comparison of input ways and output functions provided by the platforms. The second part shows the security comparison, which focuses on the protocol types of the packets and the stability during the communication. The third part shows the scalability comparison when the value becomes bigger. The fourth part shows the scalability comparison when speeding up the processes. After the comparisons, I concluded that Dweet.io is more easy to use on devices and supports more programming languages. Dweet.io realizes visualization and it can be shared. Dweet.io is safer and more stable than SensibleThings. SensibleThings provides more openness. SensibleThings has better scalability in handling big values and quick speed.