Determining and analysing the emergent behaviour from context-aware devices

With the continued miniaturization of technology and the incorporation of Moore’s law, smartphones are more powerful. These mobile devices contain technologies that add other functionalities to them. Technologies such as sensors constantly provide information to the device. The constant stream of information from these sensors often leads to information overload of relevant and irrelevant information. To work towards solving this problem context-aware computing was introduced. Our major concern is that context information in context aware computing is not completely being utilized. The aggregation of context information could unlock more possibilities. This research seeks to aggregate the context of multiple devices such that, through analysis, some emergent behaviour can be observed. In this research context information from the sensors of devices is collected using an Android application and a central Server. The context information is used for pattern analysis. A pattern analysis algorithm is designed and used to observe patterns throughout the data set. It shows patterns that are similar within the dataset. In the case that the pattern observed has no similar pattern or few similar patterns this behaviour can be stated as emergent in the dataset. Further study of this emergent behaviour can be performed were a classifier can be used to give the exact activities that were being performed at that time. The research found this was possible and has many uses. One of these is in disaster prevention were the behaviour of a group of individuals may be monitored to observe any random changes such as masses running at the same time. This could be used as a first warning to natural disasters