SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks

Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using 6 datasets shows that our proposed algorithm can achieve sub-metre accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm show that our algorithm can reduce the error by 10-60 cm for the same compression ratio

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis propos- es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW- FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition opera- tion and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisa- tion; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the lo- calisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presente

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Cluster-based Vibration Analysis of Structures with GSP

This article describes a divide-and-conquer strategy suited for vibration monitoring applications. Based on a low-cost embedded network of microelectromechanical accelerometers, the proposed architecture strives to reduce both power consumption and computational resources. Moreover, it eases the sensor deployment on large structures by exploiting a novel clustering scheme, which consists of unconventional and nonoverlapped sensing configurations. Signal processing techniques for inter- and intracluster data assembly are introduced to allow for a fullscale assessment of the structural integrity. More specifically, the capability of graph signal processing is adopted for the first time in vibration-based monitoring scenarios to capture the spatial relationship between acceleration data. The experimental validation, conducted on a steel beam perturbed with additive mass, reveals high accuracy in damage detection tasks. Deviations in spectral content and mode shape envelopes are correctly revealed regardless of environmental factors and operational uncertainties. Furthermore, an additional key advantage of the implemented architecture relies on its compliance with blind modal investigations, an approach that favors the implementation of autonomous smart monitoring systems