Abstract
The Internet of Things (IoT) enables the communication among smart objects promoting the pervasive presence around us of a variety of things or objects that are able to interact and cooperate jointly to reach common goals. IoT objects can obtain data from their context, such as the home, office, industry or body. These data can be combined to obtain new and more complex information applying data fusion processes. However, to apply data fusion algorithms in IoT environments, the full system must deal with distributed nodes, decentralized communication and support scalability and nodes dynamicity, among others restrictions. In this paper, a novel method to manage data acquisition and fusion based on a distributed service composition model is presented, improving the data treatment in IoT pervasive environments.
Highlights
The main idea of the Internet of Things (IoT) is the pervasive presence around us of many smart things or objects
We propose a new method to manage data acquisition and fusion based on a distributed service composition model applied to an IoT case study
These values help us to analyze the data acquisition mechanism implemented by the service composition model
Summary
The main idea of the Internet of Things (IoT) is the pervasive presence around us of many smart things or objects These objects can be real-world physical devices as sensors, actuators or devices, Sensors 2014, 14 as well as data resources that are able to interact and cooperate with their neighbors to reach common goals [1]. The building of new collaborative services with complex functionality, data fusion algorithms applied to obtain context-awareness, the semantic representation of the information and the final applications can be seen as composite operations distributed between services over a well-formed middleware [13]. In this context, we analyze data acquisition and fusion methods using lightweight collaborative services
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