aWESoME: A web service middleware for ambient intelligence

  • Authors:

  • Thanos G. Stavropoulos;Konstantinos Gottis;Dimitris Vrakas;Ioannis Vlahavas

  • Affiliations:

  • Department of Informatics, Aristotle University of Thessaloniki, AUTH campus, 541 24 Thessaloniki, Greece and School of Science and Technology, International Hellenic University, 14th km Thessalon ...;Department of Informatics, Aristotle University of Thessaloniki, AUTH campus, 541 24 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, AUTH campus, 541 24 Thessaloniki, Greece and School of Science and Technology, International Hellenic University, 14th km Thessalon ...;Department of Informatics, Aristotle University of Thessaloniki, AUTH campus, 541 24 Thessaloniki, Greece and School of Science and Technology, International Hellenic University, 14th km Thessalon ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

This work presents a Web Service Middleware infrastructure for Ambient Intelligence environments, named aWESoME. aWESoME is a vital part of the Smart IHU project, a large-scale Smart University deployment. The purpose of the proposed middleware within the project is twofold: for one, to ensure universal, homogeneous access to the system's functions and secondly, to fulfill functional and non-functional requirements of the system. Namely, the infrastructure itself should consume significantly low power (as it is meant for energy savings in addition to automations), without compromising reliability and fast response time. The infrastructure should enable fast and direct discovery, invocation and execution of services. Finally, on hardware level, the wireless sensor and actuator network should be optimally configured for speed and reliability as well. The proposed solution employs widely used web open standards for description and discovery to expose hardware and software functions and ensure interoperability, even outside the borders of this university deployment. It proposes a straightforward method to integrate low-cost and resource-constrained heterogeneous devices found in the market and a large-scale placement of servers and wireless sensor networks. Different server hardware installations have been evaluated to find the optimum trade-off between response time and power consumption. Finally, a range of client applications that exploit the middleware on different platforms are demonstrated, to prove its usability and effectiveness in enabling, in this scenario, energy monitoring and savings.