Dioptase: a distributed data streaming middleware for the future web of things

Abstract

The Internet of Things (IoT) is a promising concept toward pervasive computing as it may radically change the way people interact with the physical world, by connecting sensors to the Internet and, at a higher level, to the Web, thereby enacting a Web of Things (WoT). One of the challenges raised by the WoT is the in-network continuous processing of data streams presented by Things, which must be investigated urgently because it affects the future data models of the IoT, and is critical regarding the scalability and the sustainability required by the IoT. This cross-cutting concern has been previously studied in the context of Wireless Sensor Networks (WSN) given the focus on the acquisition and in-network processing of sensed data. However, proposed solutions feature various proprietary and highly specialized technologies that are difficult to integrate and complex to use, which represents a hurdle to their wide deployment. At the other end of the spectrum, cloud-based solutions introduce a too high energy cost for the envisioned IoT scale, considering the energy cost of communication over computation. There is thus a need for a distributed middleware solution for data stream management that leverages existing WSN work, while integrating it with today's Web technologies in order to support the required flexibility and the interoperability of the IoT. Toward that goal, this paper introduces Dioptase, a lightweight Data Stream Management System for the WoT, which aims to integrate the Things and their streams into today's Web by presenting sensors and actuators as Web services. The middleware specifically provides a way to describe complex fully-distributed stream-based mashups and to deploy them dynamically, at any time, as task graphs, over available Things of the network, including resource-constrained ones.

Automated synthesis of mediators for middleware-layer protocol interoperability in the IoT

“…The Data Streaming (DS) interaction paradigm is commonly used for continuous interactions. Middleware protocols such as WebSocket [3] and Dioptase [38] are based on the DS style. IoT applications (e.g., traffic management, warehouse logistics etc.)…”

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

“…As we sketch in Section I, leveraging the ontology-based formalization of the physical and cyber entities composing an IoT system is a classical approach to overcome the heterogeneity of the Things. As such, and although this is beyond the scope of this paper, the LATTICE framework may exploit existing ontologies [18], while the characterization of data flows follows from state of the art programming models associated with edge-based IoT systems [29].…”

“…Research on easing the exploitation of edge computing within IoT applications then ranges from the study of the supporting programming models and framework, to the study of the edge placement. Regarding the definition of programming models for IoT applications that get deployed over edge nodes, most solutions adopt a distributed data flow approach where the development of the application subdivides into the implementation of the node functions, and the composition of those functions [29], [30]. This further leads some work to focus on the development of libraries for the implementation of advanced features at the edge nodes like online learning [31].…”

Performance modeling of the middleware overlay infrastructure of mobile things

“…Sensors and actuators usually produce streams of data which require continuous processing. Middleware Streaming protocols, such as Websockets [15], Dioptase [16], XMPP [17], etc, support the transmission and processing of data streams through the peers involved in the interaction. Each peer can play either the producer role, which exposes data sources (e.g., sensor, database) as streams; or the consumer role, that acquires these streams.…”