Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

A self-organising fuzzy-neural network (SOFNN) adapts its structure based on variations of the input data. Conventionally in such self-organising networks, the number of inputs providing the data is fixed. In this paper, we consider the situation where the number of inputs to a network changes dynamically during its online operation. We extend our existing work on a SOFNN such that the SOFNN can self-organise its structure based not only on its input data, but also according to the changes in the number of its inputs. We apply the approach to a smart home application, where there are certain situations when some of the existing events may be removed or new events emerge, and illustrate that our approach enhances cognitive reasoning in a dynamic smart home environment. In this case, the network identifies the removed and/or added events from the received information over time, and reconfigures its structure dynamically. We present results for different combinations of training and testing phases of the dynamic reconfigurable SOFNN using a set of realistic synthesized data. The results show the potential of the proposed method

Pervasive and mobile computing

The Pervasive and Mobile Computing Journal (PMC) is a professional, peer-reviewed journal that publishes high-quality scientific articles (both theory and practice) covering all aspects of pervasive computing and communications

Engineering intelligent environments: preliminary findings of a systematic review

Intelligent environments are complex systems that may require a diverse set of hardware devices, software libraries, networking and human computer interactions. New tools and techniques that can facilitate the engineering of such systems are thus critical. However, given the size and heterogeneity of the literature and in the light of, to our knowledge, there being only informal surveys restricted to specific issues have been conducted, we have seen the need to organise and synthesise the existent research corpus to obtain a clear idea on the main approaches that have been utilised for the development of IEs. To address this research gap, this systematic literature review was carried out. This paper presents the review’s preliminary findings that are expected to provide avenues for further research in this area. We find that there are different approaches for developing IEs and the development cycle consists of several phases. However, not all phases have received equal consideration. An evaluation framework which could offer guidance on the choice of the most suitable techniques per phase should also be the target of research efforts

A context-aware framework for CSCW applications in enterprise environments

Future pervasive environments will take into consideration physical and digital social relationships. Nowadays it is important to use collective intelligence, where the interpretation of context information can be harnessed as input for context-aware applications, especially for group collaboration. For collaborative applications this represents opportunities, but also new challenges in terms of using collective information for adaptability and personalization in pervasive environments. This paper presents the challenges in design and development of a context-aware framework CSCW supporting pro-behaviour capabilities in pervasive communities

Information fusion for context awareness in intelligent environments

The development of intelligent environments requires handling of data perceived from users, received from environments and gathered from objects. Such data is often used to implement machine learning tasks in order to predict actions or to anticipate needs and wills, as well as to provide additional context in applications. Thus, it is often needed to perform operations upon collected data, such as pre-processing, information fusion of sensor data, and manage models from machine learning. These machine learning models may have impact on the performance of platforms and systems used to obtain intelligent environments. In this paper, it is addressed the issue of the development of middleware for intelligent systems, using techniques from information fusion and machine learning that provide context awareness and reduce the impact of information acquisition on both storage and energy efficiency. This discussion is presented in the context of PHESS, a project to ensure energetic sustainability, based on intelligent agents and multi-agent systems, where these techniques are applied

Automation Techniques for Intelligent Environments - Prediction of Building Activity Patterns Using a Cyclic Genetic Algorithm

This work involves learning the use schedule of an academic building in order to intelligently control various aspects of the environment. Motion sensors are used to monitor and record the activity of each of the rooms in the building. After a basic preprocessing of the data, a Cyclic Genetic Algorithm (CGA) is used to pick out the patterns of use of the rooms. The CGA is seen as ideal for such a problem because of its ability to find repetitive cyclic patterns in the data. Our results show that a CGA has the ability to pick out such patterns and construct a schedule of use for a room

Pervasive CSCW for smart spaces communities

Future pervasive environments will take into consideration not only individual users' interest, but also social relationships. In today's scenarios, the trend is to make use of collective intelligence, where the interpretation of context information can be harnessed as input for pervasive systems. Therefore, social CSCW applications represent new challenges and possibilities in terms of use of group context information for adaptability and personalization in pervasive computing. The objective of this paper is to present two enterprise scenarios that support collaboration and adaption capabilities through pervasive communities combined with social computing. Collaborative applications integrated with pervasive communities can increase the activity's quality of the end user in a wide variety of tasks