aWESoME: A web service middleware for ambient intelligence

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.

► We have developed a middleware to integrate heterogeneous sensors and devices found in the market, and provide an abstraction layer for Ambient Intelligence applications.
► By using W3C standards for description and discovery (for the Broker subsystem), the middleware provides interoperability even outside the borders of the system.
► A large-scale deployment of wireless sensors, devices and servers running the middleware has been set up at a Smart University setting.
► Web, Desktop and Mobile clients have been developed on the application layer, and enable energy savings through monitoring.
► Server tests have been carried out on PCs, netbooks and board computers to ensure minimum energy consumption and compactness of the platform, without compromising performance.