Today, increasing number of industrial application cases rely on the Machine to Machine (M2M) services exposed from physical devices. Such M2M services enable interaction of physical world with the core processes of company information systems. However, there are grand challenges related to complexity and "vertical silos" limiting the M2M market scale and interoperability. It is here expected that horizontal approach for the system architecture is required for solving these challenges. Therefore, a set of architectural principles and key enablers for the horizontal architecture have been specified in this work. A selected set of key enablers called as autonomic M2M manager, M2M service capabilities, M2M messaging system, M2M gateways towards energy constrained M2M asset devices and creation of trust to enable end-to-end security for M2M applications have been developed. The developed key enablers have been evaluated separately in different scenarios dealing with smart metering, car sharing and electric bike experiments. The evaluation results shows that the provided architectural principles, and developed key enablers establish a solid ground for future research and seem to enable communication between objects and applications, which are not initially been designed to OPEN ACCESSFuture Internet 2014, 6 262 communicate together. The aim as the next step in this research is to create a combined experimental system to evaluate the system interoperability and performance in a more detailed manner.
The number of industrial applications relying on the Machine to Machine (M2M) services exposed from physical world has been increasing in recent years. Such M2M services enable communication of devices with the core processes of companies. However, there is a big challenge related to complexity and to application-specific M2M systems called-vertical silos‖. This paper focuses on reviewing the technologies of M2M service networks and discussing approaches from the perspectives of M2M information
An ever increasing number of interconnected embedded devices, or Machine-to-Machine (M2M) systems, are changing the way we live, work and play. M2M systems as a whole are typically characterized by the diversity in both the type of device and type of network access technology employed, and such systems are often still today task-specific and built for just one specific application. Smart lighting, remote monitoring and control of all kinds of consumer devices and industrial equipment, safety and security monitoring devices and smart health and fitness products, exemplify this revolution of intercommunicating machines. However, the differences in communication technologies and data formats among such devices and systems are leading to a huge complexity explosion problem and a strongly fragmented market, with no true interoperability. Due to these problems, the full potential of M2M technology has yet to be fulfilled. In this paper, we examine the suitability of the Extensible Messaging and Presence Protocol (XMPP) and experiment with its potential to rise to the challenge of machine-to-machine communications and meet the needs of modern pervasive applications. Experimental implementations and some proof-of-concept solutions are also presented.
Pervasive sensor systems offer unbounded possibilities for monitoring and tracking objects, machines, and spaces. To maximize the benefit from a sensor system, sensor data requires efficient preprocessing and analysis. Big data techniques make distributed processing of huge amounts of data fast and cost-effective, making them a practical necessity for sensor data. However, the realtime requirements and the sheer velocity and volume of data from large sensor systems require a dedicated approach to designing the data processing pipeline. This paper discusses viewpoints and requirements for designing a sensor data pipeline, with specific focus on data input, live preprocessing, and storage.
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