A Semantic Situation Awareness Framework for Indoor Cyber-Physical Systems

Recently, the domain of cyber-physical systems (CPSs) has emerged as a successor to the traditional embedded systems and the wireless sensor networks. The relatively new cyber-physical domain offers tight integration of control, communication and computation components to develop advanced web based application in various heterogeneous domains such as health care, disaster management, automation and environment monitoring. The applications of indoor CPSs include remote patient monitoring, smart home, etc. with focus on situation awareness via event identification from context information. The principal challenges associated with the development of situation awareness applications include uncertainty in contextual data, incomplete domain knowledge, interoperability between interconnected systems and effective utilization of spatial information. This dissertation addresses these challenges by providing a comprehensive situation awareness framework for event comprehension utilizing raw sensor data and spatial information. Semantic web based annotation and mapping techniques are used to provide interoperability. The framework contains contextual situation awareness and location awareness stages towards achieving effective event assessment. The contextual situation awareness stage provides fuzzy abductive reasoning based architecture to transform raw physical sensor data to low-level fuzzy abstraction. These abstractions are used for event assessment with associated degree of certainty. The location awareness stage includes methodologies to hierarchically map indoor objects and define the object-event relationship in ontology, which is further exploited for event discrimination. This dissertation also presents a fusion based indoor positioning algorithm to provide accurate spatial information to assist location awareness. The algorithm uses extensive training of received signal strength (RSS) and time difference of arrival (TDoA) signals to estimate distance and position. The comprehensive framework is evaluated through an implementation of simulated indoor fire in a controlled environment

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

Design of cloud robotic services for senior citizens to improve independent living in multiple environments

The paper proposed a cloud robotic solution for the healthcare management of senior citizens, to demonstrate the opportunity to remotely provide continuous assistive robotic services to a number of seniors regardless to their position in the monitored environment. In particular, a medication reminding, a remote home monitoring and an user indoor localization service were outsourced in the cloud and provided to the robots, users and caregivers on request. The proposed system was composed of a number of robotic agents distributed over two smart environments: a flat at the Domocasa Lab (Peccioli, IT) and a condominium at the Angen site of the Orebro science park (Orebro, SE). The cloud acquired data from remote smart environments and enabled the local robots to provide advanced assistive services to a number of users. The proposed smart environments were able to collect raw data for the environmental monitoring and the localization of the users by means of wireless sensors, and provide such data to the cloud. On the cloud, specific algorithms improved the local robots, by providing event scheduling to accomplish assistive services and situation awareness on the users position and environments’ status. The indoor user localization service, was provided by means of commercial and ad-hoc sensors distributed over the environments and a sensor fusion algorithm on the cloud. The entire cloud solution was evaluated in terms of Quality of Service (QoS) to estimate the effectiveness of the architecture

Position paper on realizing smart products: challenges for Semantic Web technologies

In the rapidly developing space of novel technologies that combine sensing and semantic technologies, research on smart products has the potential of establishing a research field in itself. In this paper, we synthesize existing work in this area in order to define and characterize smart products. We then reflect on a set of challenges that semantic technologies are likely to face in this domain. Finally, in order to initiate discussion in the workshop, we sketch an initial comparison of smart products and semantic sensor networks from the perspective of knowledge technologies

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201