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

Online Movement Correlation of Wireless Sensor Nodes

Sensor nodes can autonomously form ad-hoc groups based on their common context. We propose a solution for grouping sensor nodes attached on the same vehicles on wheels. The nodes periodically receive the movement data from their neighbours and calculate the correlation coefficients over a time history. A high correlation coefficient implies that the nodes are moving together. We demonstrate the algorithm using two types of movement sensors: tilt switches and MEMS accelerometers. We place the nodes on two wirelessly controlled toy cars, and we observe in real-time the group membership via the LED colours of the nodes. In addition, a graphical user interface running on the base station shows the movement signals over a recent time history, the latest sampled data, the correlation between each two nodes and the group membership

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

Wireless sensor networks in motion : clustering algorithms for service discovery and provisioning

The evolution of computer technology follows a trajectory of miniaturization\ud and diversification. The technology has developed from mainframes (large computers used by many people) to personal computers (one computer per person)\ud and recently, embedded computers (many computers per person). One of the\ud smallest embedded computers is a wireless sensor node, which is a batterypowered\ud miniaturized device equipped with processing capabilities, memory,\ud wireless communication and sensors that can sense the physical parameters of\ud the environment. A collection of sensor nodes that communicate through the\ud wireless interface form a Wireless Sensor Network (WSN), which is an ad-hoc,\ud self organizing network that can function unattended for long periods of time.\ud Although traditionally WSNs have been regarded as static sensor arrays\ud used mainly for environmental monitoring, recently, WSN applications have\ud undergone a paradigm shift from static to more dynamic environments, where\ud nodes are attached to moving objects, people or animals. Applications that\ud use WSNs in motion are broad, ranging from transport and logistics to animal\ud monitoring, health care and military, just to mention a few.\ud These application domains have a number of characteristics that challenge\ud the algorithmic design of WSNs. Firstly, mobility has a negative effect on\ud the quality of the wireless communication and the performance of networking\ud protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis focuses on the problems and enhancements brought in by networ mobility, while also accounting for heterogeneity, transparency, energy efficiency and scalability. We propose a set of algorithms that enable WSNs to self-organize efficiently in the presence of mobility, adapt to and even exploit dynamics to increase the functionality of the network. Our contributions include an algorithm for motion detection, a set of clustering algorithms that can be used to handle mobility efficiently, and a service discovery protocol that enables dynamic user access to the WSN functionality

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

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

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

CODE: description language for wireless collaborating objects

This paper introduces CODE, a Description Language for Wireless Collaborating Objects (WCO), with the specific aim of enabling service management in smart environments. WCO 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, 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

Distributed Service Discovery for Heterogeneous Wireless Sensor Networks

Service discovery in heterogeneous Wireless Sensor Networks is a challenging research objective, due to the inherent limitations of sensor nodes and their extensive and dense deployment. The protocols proposed for ad hoc networks are too heavy for sensor environments. This paper presents a resourceaware solution for the service discovery problem, which exploits the heterogeneous nature of the sensor network and alleviates the high-density problem from the flood-based approaches. The idea is to organize nodes into clusters, based on the available resources and the dynamics of nodes. The clusterhead nodes act as a distributed directory of service registrations. Service discovery messages are exchanged among the nodes in the distributed directory. The simulation results show the performance of the service discovery protocol in heterogeneous dense environments