Design and validation of a light inference system to support embedded context reasoning

Abstract

Embedded context management in resourceconstrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications-it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Javaenabled handheld devices. Data management and reasoning This paper is an extension of the work entitled 'A light reasoning infrastructure to enable context-aw are mobile applications' presented in the

An Ontology-Based Context Model for Wireless Sensor Network (WSN) Management in the Internet of Things

“…However, XML is seen as a 'heavy' syntax for resource-constrained devices. Thus, for implementing the proposed ontology on sensor nodes, more compact XML representations such as binary XML formats should be used [27]. Another promising approach uses streaming HDT as lightweight serialization format for RDF and Wiselib Tuplestore for storing RDF data locally on embedded IoT devices such as sensor nodes is proposed in [28].…”

A novel framework of context-aware system based on space mapping

“…In [4], a formal ontology context model combined by FOPL is proposed. Besides, in [5], [6], [7] and [8], ontologies are all used to model contexts. Though 978-1-4673-5000-6/13/$31.00 ©2013 IEEE 475 ontology models are suitable for context-aware computing, it may not be the best choice for some portable devices, which offer limited computing resources.…”

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

“…The application relies on a set of classifiers based on Decision Trees and Tables, previously analyzed in [11], which have proven to be light enough to be integrated in mobile devices, to recognize the following activities: slow, normal and rush walking, running, standing and sitting. These activities have a direct translation into energy expenditure [12] and are common in standard daily settings (office, home, commuting, etc. ).…”