Raluca Marin-Perianu

Movement-based group awareness with wireless sensor networks

We propose a method through which dynamic sensor nodes determine that they move together, by communicating and correlating their movement information. We describe two possible solutions, one using inexpensive tilt switches, and another one using low-cost MEMS accelerometers. We implement a fast, incremental correlation algorithm, with an execution time of 6ms, which can run on resource constrained devices. The tests with the implementation on real sensor nodes show that the method is reliable and distinguishes between joint and separate movements. In addition, we analyze the scalability from four different perspectives: communication, energy, memory and execution speed. The solution using tilt switches proves to be simpler, cheaper and more energy efficient, while the accelerometer-based solution is more reliable, more robust to sensor alignment problems and, potentially, more accurate by using extended features, such as speed and distance.

Energy-Efficient Cluster-Based Service Discovery in Wireless Sensor Networks

We propose an energy-efficient service discovery protocol for wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the communication costs during discovery of services and maintenance of a functional distributed service registry. We compare theoretically and by simulation the impact of the chosen clustering algorithm on the service discovery protocol

A performance analysis of a wireless body-area network monitoring system for professional cycling

It is essential for any highly trained cyclist to optimize his pedalling movement in order to maximize the performance and minimize the risk of injuries. Current techniques rely on bicycle fitting and off-line laboratory measurements. These techniques do not allow the assessment of the kinematics of the cyclist during training and competition, when fatigue may alter the ability of the cyclist to apply forces to the pedals and thus induce maladaptive joint loading. We propose a radically different approach that focuses on determining the actual status of the cyclist’s lower limb segments in real-time and real-life conditions. Our solution is based on body area wireless motion sensor nodes that can collaboratively process the sensory information and provide the cyclists with immediate feedback about their pedalling movement. In this paper, we present a thorough study of the accuracy of our system with respect to the gold standard motion capture system. We measure the knee and ankle angles, which influence the performance as well as the risk of overuse injuries during cycling. The results obtained from a series of experiments with nine subjects show that the motion sensors are within 2.2° to 6.4° from the reference given by the motion capture system, with a correlation coefficient above 0.9. The wireless characteristics of our system, the energy expenditure, possible improvements and usability aspects are further analysed and discussed.

Keep on moving! activity monitoring and stimulation using wireless sensor networks

Because health condition and quality of life are directly influenced by the amount and intensity of daily physical activity, monitoring the level of activity has gained interest in recent years for various medical and wellbeing applications. In this paper we describe our experience with implementing and evaluating physical activity monitoring and stimulation using wireless sensor networks and motion sensors. Our prototype provides feedback on the activity level of users using a simple colored light. We conduct experiments on multiple test subjects, performing multiple normal daily activities. The results from our experiments represent the motivation for and a first step towards robust complex physical activity monitoring with multiple sensors distributed over a persons body. The results show that using a single sensor on the body is inadequate in certain situations. Results also indicate that feedback provided on a persons activity level can stimulate the person to do more exercise. Using multiple sensor nodes and sensor modalities per subject would improve the activity estimation performance, provided that the sensor nodes are small and inconspicuous.

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.

Follow me! mobile team coordination in wireless sensor and actuator networks

Autonomous vehicles are used in areas hazardous to humans, with significantly greater utility than the equivalent, manned vehicles. This paper explores the idea of a coordinated team of autonomous vehicles, with applications in cooperative surveillance, mapping unknown areas, disaster management or space exploration. Each vehicle is augmented with a wireless sensor node with movement sensing capabilities. One of the vehicles is the leader and is manually controlled by a remote controller. The rest of the vehicles are autonomous followers controlled by wireless actuator nodes. Speed and orientation are computed by the sensor nodes in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leaders movement pattern. The solution is not restricted to vehicles on wheels, but supports any moving entities capable of determining their velocity and heading, thus opening promising perspectives for machine-to-machine and human-to-machine spontaneous interactions in the field. Visit [1] to see a video demonstration of the system.

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.

Organizing Context Information Processing in Dynamic Wireless Sensor Networks

This paper presents the e-SENSE middleware architecture for distributed processing of context information in dynamic wireless sensor networks. At the lower layer, sensor nodes organize into clusters spontaneously based on shared context. These clusters form the basis for the service-oriented processing layer, where the functionality of the sensor network is expressed using service task graphs supporting distributed execution of applications. The higher layer is responsible for complex context inference and recognition. As a concrete example we evaluate the distributed recognition of human activities in a car assembly process.

Automatic recognition of object use based on wireless motion sensors

In this paper, we present a method for automatic, online detection of a users interaction with objects. This represents an essential building block for improving the performance of distributed activity recognition systems. Our method is based on correlating features extracted from motion sensors worn by the user and attached to objects. We present a complete implementation of the idea, using miniaturized wireless sensor nodes equipped with motion sensors. We achieve a recognition accuracy of 97% for a target response time of 2 seconds. The implementation is lightweight, with low communication bandwidth and processing needs. We illustrate the potential of the concept by means of an interactive multi-user game.

CODE: A Description Language for Wireless Collaborating Objects

This paper introduces CODE, a Description Language for Wireless Collaborating Objects (WCOs), with the specific aim of enabling service management in smart environments. WCOs extend the traditional model of wireless sensor networks by transferring additional intelligence and responsibility from the gateway level to the network. WCO are able to offer complex services based on cooperation among sensor nodes. CODE provides the vocabulary for describing the complex services offered by WCO. It enables description of services offered by groups, on-demand services, service interface and sub-services. The proposed methodology is based on XML, which is widely used for structured information exchange and collaboration. CODE can be directly implemented on the network gateway, while a lightweight binary version is stored and exchanged among sensor nodes. Experimental results show the feasibility and flexibility of using CODE as a basis for service management in WCO.