Riboni and Bettini have proposed a techinique for human activity recognition using ontologies and ontological reasoning combined with statistical inferencing [1]. The technique does not relies on large amount of training data which is difficult to obtain in applications such as rehabilitation systems, chronic disease management or monitoring of the elderly. [1] Riboni, Daniele, and […]
Month: March 2014
A Distributed Reasoning Engine Ecosystem for Semantic Context-Management in Smart Environments
Context information inference consumes a lot of time when dealing with large amount of data. This situation is common in modern ubiquitous computing when there are a lot of sensors and devices available. Therefore, Almeida and López-de-Ipiña have proposed an agent-based system architecture to distribute the context reasoning problem into smaller parts in order to […]
Intelligent context-aware energy management using the incremental simultaneous method in future wireless sensor networks and computing systems
Nam et al. minimize energy wasting in wireless sensor network using simultaneous human activity pattern and Hierarchical Hidden Markov Model (HHMM) [1]. Activity context model is constructed by analyzing the simultaneous activity patterns using Incrementally Simultaneous Method (ISM) and representing the simultaneous activity patterns using Allen’s temporal relation logic. The result shows that weekly saving […]
Power Management for Context Aware System
Energy efficiency is important issue in wireless sensor network application. Sensor nodes run on batteries which needs to replaced frequently. Thus, operational states of the sensor nodes should be adapted according to the surrounding to achieve best power consumption but without compromising accurate and good sensing service. Additionally, users could provide some inputs by setting […]
Context Modeling
One of the method of context modeling is by using ontologies. Ontologies facilitate structuring data and representing knowledge. It define a given domain in a graph structure which contains concepts present in the modelled domain, the relationship between concepts and potentially classification axioms which specify generic knowledge about the domain. The ontologies is exploited by […]
Input for Context Aware System
Not every input to context aware system is information. It could be raw data. For example, location and temperature. Temperature is a raw data from temperature sensor and location is an information which generated by processing raw data from accelerometer, gyrosensor? and magnetometer?. Both values could be inputs to context aware system. Therefore there is […]
Taxonomy of Context Aware IoT middleware solution
Context acquisition -> Context Modeling -> Context Reasoning -> Context Distribution
Context Acquisition
Acquiring context is based on five factors which are responsibility, frequency, context source, sensor type and process of acquisition [1]. Based on responsibility Responsibility factor is about who is making the decision on sensing and communication, either the sensor hardware or user software. This method of context acquisition is called push and pull. It is […]
Context Aware Computing
A survey has been conducted by Perera et al. [1] on the functionalities of Middleware for IoT. Functionalities that have been considered are device management, interoperation, platform portability, context-awareness, and security and privacy. The survey shows majority of Middleware solution do not provide context-awareness functionality. According to Abowd and Dey [2], context is defined as […]
Sensors for Context Aware System
There are two categories of sensors for healthcare application. The first category is called ambient sensors. Ambient sensor is used to monitor the general activity patterns based on spatial and temporal information processing. Examples of ambient sensors are fall detectors, door monitors, bed alerts, pressure mats, smoke and heat alarms etc. More recent works on […]