Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes

Wireless sensor nodes attached to everyday objects and worn by people are able to collaborate and actively assist users in their activities. We propose a method through which wireless sensor nodes organize spontaneously into clusters based on a common context. Provided that the confidence of sharing a common context varies in time, the algorithm takes into account a window-based history of believes. We approximate the behaviour of the algorithm using a Markov chain model and we analyse theoretically the cluster stability. We compare the theoretical approximation with simulations, by making use of experimental results reported from field tests. We show the tradeoff between the time history necessary to achieve a certain stability and the responsiveness of the clustering algorithm

Modeling Service-Oriented Context Processing in Dynamic Body Area Networks

Context processing in Body Area Networks (BANs) faces unique challenges due to the user and node mobility, the need of real-time adaptation to the dynamic topological and contextual changes, and heterogeneous processing capabilities and energy constraints present on the available devices. This paper proposes a service-oriented framework for the execution of context recognition algorithms. We describe and theoretically analyze the performance of the main framework components, including the sensor network organization, service discovery, service graph construction, service distribution and mapping. The theoretical results are followed by the simulation of the proposed framework as a whole, showing the overall cost of dynamically distributing applications on the network

On-Body Activity Recognition in a Dynamic Sensor Network

Recognizing user activities using body-worn, miniaturized sensor nodes enables wearable computers to act contextaware. This paper describes how online activity recognition algorithms can be run on the SensorButton, our miniaturized wireless sensor platform. We present how the activity recognition algorithms have been optimized to be run online on our sensor platform, and how the execution can be distributed to the wireless sensor network. The resulting algorithm has been implemented as a custom, platform-specific executable as well as integrated into TinyOS. A comparison shows that the TinyOS executable is using about 7kB more code memory, while both implementations classify the activity in up to 18 classifications per second

Embedded Task Machine with BTnode and FPGA

The aim of this semester project was to create an Embedded Task Ma-chine. The system would provide a means of executing applications that are too large to fit on the available hardware. Such applications would be broken down into smaller tasks and a description of how they relate to each other. Synchronous Data Flow is used as the modelling language for the ap-plications. The Embedded Task Machine then executes the tasks one after another on reconfigurable hardware. The system at hand consists of a task repository, a memory management module for the interexchanged data, an execution unit, and a scheduler. This report describes the architecture and the the detailed design of the complete project. Acknowledgment We would like to thank Prof. Lothar Thiele for making it possible for us to work on this interesting project. We also would like to thank our tutor, Matthias Dyer, who supported us on any problems we had and Jan Beutel who helped out when Matthias was not present. We would not have come this far if Roman Plessl would not have taken time for us in the last few weeks of his Master’s Thesis. The discussions we had with him about the concepts of his work were very helpful and helped us a lot to start off our project. Last but not least we also would like to thank the rest of the TIK-team for the various small things they did to support us

Experiences with experiments in ambient intelligence environments

The development of activity recognition techniques relies on the availability of datasets of gestures to train and validate the proposed methods. In this work we introduce and describe a new dataset for activity recognition. The dataset is made up of 8 scenarios from everyday life and includes 17 activities composed of a total of 64 gestures. Each scenario has been repeated 10 times by 2 users. All activities and gestures are labeled. 5 different sensing modalities are implemented by using body worn and environmental sensors and smart objects. The paper describes our considerations in setting up the testbed and performing the experiments to record the dataset, our experiences with recording the data and discusses possible research questions to be tackled with the dataset. 1

On-body Activity Recognition in a Dynamic Sensor Network

Recognizing user activities using body-worn, miniaturized sensor nodes enables wearable computers to act contextaware. This paper describes how online activity recognition algorithms can be run on the SensorButton, our miniaturized wireless sensor platform. We present how the activity recognition algorithms have been optimized to be run online on our sensor platform, and how the execution can be distributed to the wireless sensor network. The resulting algorithm has been implemented as a custom, platform-specific executable as well as integrated into TinyOS. A comparison shows that the TinyOS executable is using about 7kB more code memory, while both implementations classify the activity in up to 18 classifications per second

Service discovery and composition in body area networks

In pervasive environments, Body Area Networks (BANs) are characterized by the mobility of their users. BANs can continuously interact with each other, thus enabling the provision of new applications and services at runtime. New complex services can be provided by composing simpler services available on neighbouring network nodes. However, since the topology of BANs is continuously changing due to users' movements, it is unfeasible to specify a-priori all possible configurations under which a given complex service can be composed. In order to address this issue, we introduce a two-layered service discovery and composition architecture, that proactively notifies a distributed service directory with changes in service availability. In order to cope with the network mobility and intermittent connectivity, our approach is to cluster nodes in the sensor network based on their connectivity patterns. We use a multi-agent state machine to recognize the availability of complex services and to provide them. Our solution is validated by a prototype implementation of our architecture, by the study of the statistical model of complex services, and by experimental evaluations

Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection

Activity recognition from an on-body sensor network enables context-aware applications in wearable computing. A guaranteed classification accuracy is desirable while optimizing power consumption to ensure the system's wearability. In this paper, we investigate the benefits of dynamic sensor selection in order to use efficiently available energy while achieving a desired activity recognition accuracy. For this purpose we introduce and characterize an activity recognition method with an underlying run-time sensor selection scheme. The system relies on a meta-classifier that fuses the information of classifiers operating on individual sensors. Sensors are selected according to their contribution to classification accuracy as assessed during system training. We test this system by recognizing manipulative activities of assembly-line workers in a car production environment. Results show that the system's lifetime can be significantly extended while keeping high recognition accuracies. We discuss how this approach can be implemented in a dynamic sensor network by using the context-recognition framework Titan that we are developing for dynamic and heterogeneous sensor networks