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Smart devices (or things) in the realm of IoT (Internet of Things) talk to each other and transfer data over the Internet.IoT vendors provide APIs for their clients to send data to the gateways and application servers. However, there is a lack of guidelines on how a vendor would design its API and resource URIs (Uniform Resource Identifiers). A generic design solution –design patterns– would make the API design and development easier for the vendors. Design patterns are reusable solutions to recurring problems and provide improved reusability and understandability. Currently, there are no design patterns for URIs of IoT APIs that IoT vendors can use. In this paper, we analyzed more than 1,300 URIs from 13 IoT APIs including IBM Watson and Microsoft Azure, and proposed eight novel design patterns for URIs of IoT APIs. We analyzed one dataset divided into two subsets: (1) analysis set with 70% of all our URIs to define design patterns for URIs of IoT APIs and (2) validation set with the remaining 30% of the URIs to verify the prevalence of the defined design patterns. We could map 84% of our validation set to the defined design patterns, i.e., design patterns are prevalent in the IoT domain.

REST (Representational state transfer) is an architectural style for distributed hypermedia systems. The simplicity of REST allows straightforward communication between HTTP clients and servers using URIs (Uniform Resource Identifiers) and HTTP methods, e.g., GET, POST, PUT, and DELETE. To do the communication effectively between clients and servers, there is a set of best design practices (design and linguistic patterns) shall be followed, and a set of poor design practices (design and linguistic antipatterns) shall be avoided. This study aims to determine whether there is a relationship between design and linguistic quality in Google RESTful APIs. To find this relation, a tool is developed to detect patterns and antipatterns in REST APIs both in terms of design and linguistic quality. The input of this tool is qualitative data (Google APIs) and its output is quantitative data. Using this quantitative data, a statistical study is then performed to detect the relation. The tests that are conducted to obtain the final results are Chi-squared and Phi Coefficient tests. The result of Chi-squared that considered all the groups of patterns and antipatterns shows that there is a statistically significant relation between design and linguistic quality. However, when we assess the individual pair of patterns and antipatterns, our Phi Coefficient tests show that for most of the cases, there is no or negligible relationship between linguistic and design patterns and antipatterns.