Abstract: Methods, system, and apparatuses may support end-to-end (E2E) quality of service (QoS) through the use of service layer (SL) sessions. For example, an application can communicate with a targeted device based on application specified schedule, latency, jitter, error rate, throughput, level of security, and cost requirements.

Abstract: Profile based services and content can assist with the backward and forward compatibility of conventional service layers or the like. Using a profile to manage content and access to services offered may reduce the overhead or processing associated with providing those value added services by service layers.

Abstract: Existing approaches to security within network, for instance oneM2M networks, are limited. For example, content might only be protected while the content is in transit between entities that trust each other. Here, the integrity and the confidentiality of content in an M2M network are protected. Such content may be “at rest,” such that the content is stored at a hosting node. Only authorized entities may store and retrieve the data that is stored at the hosting node, and the data may be protected from a confidentiality perspective and an integrity perspective.

Abstract: Methods, system, and apparatuses may support end-to-end (E2E) quality of service (QoS) through the use of service layer (SL) sessions. For example, an application can communicate with a targeted device based on application specified schedule, latency, jitter, error rate, throughput, level of security, and cost requirements.

Abstract: A modular and distributed architecture for data stream processing and analysis is described to incorporate data stream analytics capabilities, called Data Stream Analytics Service (DSAS) in the IoT/M2M service layer. Each service layer node hosting DSAS can be split into two independent modules, Stream Forwarder and Stream Analytics Engine. Stream Forwarder is a light weight processing modules that can be responsible for data preprocessing and routing. Stream Analytics Engine is responsible for performing actual analytics on the data stream. Separating the two functionalities enables the service layer nodes to efficiently distribute stream analytics tasks across multiple nodes.