Mihai Marin-Perianu

D-FLER: a distributed fuzzy logic engine for rule-based wireless sensor networks

We propose D-FLER, a distributed, general-purpose reasoning engine for WSN. D-FLER uses fuzzy logic for fusing individual and neighborhood observations, in order to produce a more accurate and reliable result. Thorough simulation, we evaluate D-FLER in a fire-detection scenario, using both fire and non-fire input data. D-FLER achieves better detection times, while reducing the false alarm rate. In addition, we implement D-FLER on real sensor nodes and analyze the memory overhead, the numerical accuracy and the execution time.

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.

Distributed Activity Recognition with Fuzzy-Enabled Wireless Sensor Networks

Wireless sensor nodes can act as distributed detectors for recognizing activities online, with the final goal of assisting the users in their working environment. We propose an activity recognition architecture based on fuzzy logic, through which multiple nodes collaborate to produce a reliable recognition result from unreliable sensor data. As an extension to the regular fuzzy inference, we incorporate temporal order knowledge of the sequences of operations involved in the activities. The performance evaluation is based on experimental data from a car assembly trial. The system achieves an overall recognition performance of 0.81 recall and 0.79 precision with regular fuzzy inference, and 0.85 recall and 0.85 precision when considering temporal order knowledge. We also present early experiences with implementing the recognition system on sensor nodes. The results show that the algorithms can run online, with execution times in the order of 40ms, for the whole recognition chain, and memory overhead in the order of 1.5kB RAM.

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.

Implementing Business Rules on Sensor Nodes

Wireless sensor networks (WSNs) will be able to assist industrial and business processes and to render rich functionality in a dependable way. Two key elements that can make this real are: a simple and efficient way of expressing the business logic, and a reliable mechanism for selectively reconfiguring sensor nodes. We present a solution that combines both elements. The main objective is to guarantee the dissemination of business rules to multicast groups of sensor nodes, while striving for energy efficiency and low overhead. Simple cross-layer optimizations are used to achieve this. For scalability reasons, our solution demands only local knowledge, performs local retransmission of lost packets and uses aggregation of acknowledgements. The results of our evaluation indicate a good ability of recovering from serious errors, even under high error rates.

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.

Experiments with Reliable Data Delivery in Wireless Sensor Networks

The increasing complexity of Wireless Sensor Networks (WSN) applications require simple, yet reliable, underlying networking mechanisms. In this paper, we describe the experiments performed to establish the real challenges and sources of errors for the reliable data delivery problem. We also discuss several implementation solutions and try to establish the issues that should be taken into account in the design phase. The results are obtained by field measurements, therefore we consider them relevant and useful. Our work relies on tight interaction among transport, routing and medium access layers, with the overall goal of achieving energy efficiency through cross-layer optimizations.

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.

RMD: Reliable Multicast Data Dissemination within Groups of Collaborating Objects

Factory and industrial automation systems gradually start to incorporate wireless networks of smart objects and sensor nodes. In this context, one fundamental problem is the reliability of data dissemination, particularly in the case of total or partial network reconfiguration. We propose RMD, a reliable data dissemination solution targeting multicast groups of collaborating objects. Compared to previous work, our approach focuses on guaranteeing the data transmission rather than improving the delivery ratio. In addition, RMD scales better by utilizing the multicast support and proves more energy efficient due to cross-layer optimizations. To achieve these, we combine end-to-end aggregated acknowledgements with local error detection and recovery, and apply a selective listening scheme for reducing unnecessary radio operation