scope:

Scope of the present document

The present document identifies additional requirements to be potentially submitted to oneM2M in the areas of discovery mechanism and query languages (syntax and semantic), by means of relevant use cases. It includes discovery of specific information and of aggregated information, and interaction with external sources of data and queries. The oneM2M architecture [i.5], [i.6], the oneM2M semantic approach [i.7], the current oneM2M capabilities and SAREF [i.8], [i.9], [i.10], [i.11], [i.12], [i.13], and [i.14] are at the basis of these use cases and requirements.

The present document is structured as follows:

• Clauses 1 to 3 set the scene and provide references as well as definition of terms, symbols and abbreviations, which are used in the present document.

• Clause 4 describes the method used for collecting Use Cases.

• Clause 5 presents a kind of generic Use Case, which could be considered as either the "use-case zero", or a "parametric use-case" for Advanced Semantic Discovery (ASD) because it is suitable to be instantiated in many concrete cases. It shows the importance of fixing, formalizing and extending Formal Graph Topologies (FGT), Formal Semantic Discovery Routing Mechanism (SDRM), Formal Semantic Discovery Query Language (SDQL) and Formal Semantic Resolution Query Mechanism (SRQM).

• Clause 6 presents a Use Case, which looks at the semantic discovery requirements illustrating a Hospital that has a large number of IoT devices from different domains, which have different goals in the infrastructure. In this scenario, it is necessary to perform a discovery task of suitable devices relying on fine-grained discovery criteria. In addition, the discovery should cope devices belonging to different administrative domains.

• Clause 7 presents a Use Case, which looks at the semantic discovery requirements through a facility manager working for a supermarket chain and being responsible of dozens of buildings, who has to deal with energy efficiency strategies to all buildings and to compare buildings to detect leaks, adjust the heat and the lighting according to forecast or predictive models, assessing the warehouses stocks to refill in time, centralized fault detection to take countermeasures. Supermarkets data of their parking lot sensors and energy consumption are made available to other Service providers.

• Clause 8 presents a Use Case, which looks at the semantic discovery requirements through a networking environment between people with disease (patients), the elderly, who want to live an independent life while remaining in their homes, special invalid people with a high risk of falling in their homes, doctors/care taking people, people practicing fitness exercises to improve their health, and institutions/organizations, who manage a clinical knowledge & information data basis or analyses of patient data.

• Clause 9 provides some lessons learned and conclusions.