Collaborative wireless sensor networks in industrial and business processes

Wireless Sensor Networks (WSNs) create the technological basis for building pervasive, large-scale distributed systems, which can sense their environment in great detail, communicate the relevant information via the wireless medium, reason collectively upon the observed situation and react according to the application-specific goals. Embedding sensing, processing and communication in one tiny device (the sensor node or simply mote), which can subsequently collaborate with peers and build a self-organizing, self-healing network, stimulates a long list of applications from various domains, ranging from environmental monitoring to industrial processes, and even further to cognitive robotic systems or space exploration. At first glance the complexity of such applications is overwhelming, given the serious resource limitations of sensor nodes, in terms of computational power, storage space, radio performance and battery power. However, WSNs have a unique feature that balances the inherent resource limitations: the ability of in-network collaboration at scale. Through collaboration WSNs can organize efficiently, prolong system lifetime, handle dynamics, detect and correct errors, all with the final goal of eventually executing reliably the user application. Following this line, researchers devised an impressive number of collaborative WSN algorithms and protocols in recent years. Significant progress has also been made on the market side, so that nowadays we can claim that WSNs are no longer just lab prototypes. Standardization initiatives (such as IEEE 802.15.4) are being put into practice and the general industry trend strongly suggests that the epoch of pioneering research in building and experimenting with “motes” is approaching an end. It is now the logical time for system integration and for creating bridges to connected fields. This thesis focuses on WSN integration in industrial and business processes, and, more specifically, on exploring collaborative techniques to make WSNs more reliable, intelligent, effective and easy-to-use in industry-related scenarios

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed 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 leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Measurement of dynamic comfort in cycling using wireless acceleration sensors

Comfort in cycling is related to the level of vibration of the bicycle: more vibration results in less comfort for the rider. In this study, the level of vibration is measured in real time using wireless inertial acceleration sensors mounted at four places on the bike: front wheel axel, rear wheel axel, stem and seatpost. In this way, we measure both the input and output of the frame and fork, and consequently establish the transfer function of the frame and front fork. Besides the transfer of vibrations through the frame, we also investigate the input to the frame and fork. Moreover, we determine the effect of the road surface, speed, wheels and tire pressure on the vibrations induced to the frame and fork. Our analysis shows that road surface, speed and the tire pressure have a significant influence on the induced vibrations. On the contrary different wheelsets have no significant influence. Additionally, the vibrations propagate through the frame within a duration of 5 ms

Automatic Recognition of Object Use Based on Wireless Motion Sensors

In this paper, we present a method for automatic, online detection of a user’s interaction with objects. This represents an essential building block for improving the performance of distributed activity recognition systems. Our\ud 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

Wave Monitoring with Wireless Sensor Networks

Real-time collection of wave information is required for short and long term investigations of natural coastal processes. Current wave monitoring techniques use only point-measurements, which are practical where the bathymetry is relatively uniform. We propose a wave monitoring method that is suitable for places with varying bathymetry, such as coral reefs. Our solution uses a densely deployed wireless sensor network, which allows for a high spatial resolution and 3D monitoring and analysis of the waves. The wireless sensor nodes are equipped with low-cost, low-power, MEMS-based inertial sensing. We report on lab experiments with a Ferris wheel contraption, which is a technique used in practice to evaluate and calibrate the state-of-the-art wave monitoring solutions.\u

Energy-Efficient Assessment of Physical Activity Level Using Duty-Cycled Accelerometer Data

AbstractThis paper describes an energy efficiency improvement of the IMA accelerometer-based method for estimating the level of physical activity of a person. The sensor sampling and data processing requirements are significantly reduced by duty-cycling sensor sampling, thus making implementation and long-lasting operation possible on resource-constrained devices as sensor nodes. By duty-cycling, the system maintains adequate bandwidth, while still reducing the effective number of samples taken per unit of time. We analyze in detail the impact of duty-cycling on the accuracy of the method and show that we can reduce the duty-cycle to as little as 10%, incurring a mean error of only about 4%. This translates into energy saving of up to 60% on the sensor node

A study on automatic recognition of object use exploiting motion correlation of wireless sensors

An essential component in the ubiquitous computing vision is the ability of detecting with which objects the user is interacting during his or her activities. We explore in this paper a solution to this problem based on wireless motion and orientation sensors (accelerometer and compass) worn by the user and attached to objects. We evaluate the performance in realistic conditions, characterized by limited hardware resources, measurement noise due to motion artifacts and unreliable wireless communication. We describe the complete solution, from the theoretical design, going through simulation and tuning, to the full implementation and testing on wireless sensor nodes. The implementation on sensor nodes is lightweight, with low communication bandwidth and processing needs. Compared to existing work, our approach achieves better performance (higher detection accuracy and faster response times), while being much more computationally efficient. The potential of the concept is further illustrated by means of an interactive multi-user game. We also provide a thorough discussion of the advantages, limitations and trade-offs of the proposed solution