Computer-aided design of redundant sensor networks

10 - Design of Sensor Networks

Aircraft structural damage detection is becoming of increased importance. Technologies such as acousto-ultrasonic have been suggested for this application; however, an optimization strategy for sensor network design is required to ensure a high detection probability while minimizing sensor network mass. A methodology for optimizing acousto-ultrasonic transducer placement for adhesive disbond detection on metallic aerospace structures is presented. Experimental data sets were acquired using three-dimensional scanning laser vibrometry enabling in-plane and out-of-plane Lamb wave components to be considered. This approach employs a novel multi-sensor site strategy which is difficult to achieve with physical transducers. Different excitation frequencies and source–damage–sensor paths were considered. A fitness assessment criterion which compared baseline and damaged data sets using cross-correlation coefficients was developed empirically. Efficient sensor network optimization was achieved using a bespoke genetic algorithm for different network sizes with the effectiveness assessed and discussed. A comparable numerical data set was also produced using the local interaction simulation approach and optimized using the same methodology. Comparable results with those of the experimental data set indicated a good agreement. As such, the numerical approach demonstrates that acousto-ultrasonic sensor networks can be optimized using simulation (with some further refinement) during an aircraft design phase, being a useful tool to sensor network designers.

A duality based framework for integrating reliability and precision for sensor network design

In one model of a distributed large scale sensor network a roving "base-station" polls whatever sensors are within its field of view as to their (local) decisions on a common hypothesis. A simple variation on the ALOHA protocol is assumed, and the collisions between sensor observations are a serious concern. In this paper we examine a scheme in which sensors' quantization is, in a sense, ternary: the transmitted decision can be "no" or "yes", but the local sensor can choose not to respond. Thus, sensors whose observations are uninformative reduce the communication load, and thereby improve the ability of those sensors with important data to be heard there is consequently a cross-linking between detection and communication strategy. The results appear to be impressive.

In this paper, we present the design of a practical underwater sensor network for offshore fish farm cages. An overview of the current structure of an offshore fish farm, applied sensor network solutions, and their weaknesses are given. A mixed wireless–wired approach is proposed to mitigate the problem of wire breakage in underwater wired sensor networks. The approach is based on the serial arrangement of identical sections with wired and wireless interconnections areas. Wireless section alleviates underwater maintenance operations when cages are damaged. The analytical model of the proposed solution is studied in terms of maximum power transfer efficiency and the general formulas of the current in their transmitting antennas and sensor nodes are provided. Subsequently, based on simulations, the effects of parasitic resistance across the network are evaluated. A practical underwater sensor network to reach the 30 m depth with sensor nodes distanced 6 m is used to determine the proposal compliance with the ISO 11784/11785 HDX standard in its normal operation. Taking into account the cable breakage scenario, the results from experiments demonstrate the robustness of the proposed approach to keep running the sensor nodes that are located before the short circuit. Sensor node run time is reduced only 4.07% at most using standard values when a cable breakage occurs at the second deepest section.

Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.

Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. The widespread use of Structural Health Monitoring (SHM) systems is limited due to unavailability of specialized data acquisition equipment, high cost of generic equipment, and absence of fully automatic decision support systems. The goals of the presented project include: first, design of a Wireless Intelligent Sensor and Actuator Network (WISAN) and creation of an inexpensive set of instrumentation for the tasks of structural health monitoring; second, development of a SHM method, which is suitable for autonomous structural health monitoring. The design of the wireless sensor network is aimed at applications of structural health monitoring, addressing the issues of achieving a low cost per sensor, higher reliability, sources of energy for the network nodes, energy-efficient distribution of the computational load, security and coexistence in the ISM radio bands. The practical applicability of the sensor network is increased through utilization of computational intelligence and support of signal generation capabilities. The automated SHM method is based on the method of modal strain energy, though other SHM methods will be supported as well. The automation tasks include automation of the modal identification through ambient vibrations, classification of the acquired mode shapes, and automatic evaluation of the structural health.

This chapter presents some considerations related to the applications in environment monitoring of some concepts as: estimation, fault detection and diagnosis, theory of distributed parameter systems and artificial intelligence based on the modern technology of wireless sensor networks. All these concepts allow treatment of large, complex, non-linear and multivariable system of the environment by learning and extrapolation. The environment may be seen as a complex ensemble of different distributed parameter systems, described with partial differential equations. Sensor networks (Akyildiz & all, 2002) have large and successful applications in monitoring the environment, they been capable to measure, as a distributed sensor, the physical variables, on a large area, which are characterizing the environment, and also to communicate at long distance the measured values, from the distributed parameter environmental processes. A lot of papers and books have been published in the fields of using sensor networks in environment monitoring in the last years. Some related work is surveyed as follows. The paper (Cuiyun & all., 2006) presents some research consideration related the changes of urban spatial thermal environment, for sustainable urban development, to improve the quality of human habitation environment. The urban thermal phenomenon is revealed using thermal remote sensing imagery, based on the instantaneous radiant temperature of the land surfaces. An architecture of sensor network for environment is presented in (Lan & all, 2008). Environmental pollution and meteorological processes may be studied using various kinds of environmental sensor networks. The modern intelligent sensor networks comprise automatic sensor nodes and communication systems which communicate their data to a sensor network server, where these data are integrated with other environmental information. The paper (Giannopoulos & all, 2009) presents the design and implementaion of a wireless sensor network for monitoring environmental variables and evaluates its effectiveness. It has application in environment variable monitoring such as: temperature, humidity, barometric pressure, soil moisture and ambient light, for research in agriculture, habitat monitoring, weather monitoring and so on. In order to improve the capacity of the environmental sensor networks different techniques may be used. The paper (Talukder & all, 2008) is using a model predictive control for optimal resource management in environment sensor networks, for with application at spatiotemporal events of a coastal monitoring and forecast system. The paper (Dardari & all, 2007)

Thanks to key advances in wireless communication and electronics, sensors have emerged as an appealing technology for several interesting applications, such as civilian (health and environment monitoring), natural (disaster detection), military (battlefield surveillance), and agricultural (precision agriculture) applications, to name a few. When grouped together, these sensors form a network to measure and gather data of the surrounding environment with respect to a specific phenomenon. The sensors are battery-powered, tiny devices that possess all the characteristics of a traditional computer, including storage, processing, and communication capabilities. In addition, these sensors are capable of sensing the environment and collecting data regarding several parameters, such as temperature, light, sound, vibration, etc. Unfortunately, all the sensors' capabilities are limited due to their physical size. In particular, the sensors have limited battery power as usually they are equipped with AA/AAA batteries whose lifetime is short. Therefore, the main challenge in the design of this type of network is the sensors' battery power (or energy), which is a critical component for the operation of the whole network. Moreover, these sensors communicate (possibly) wirelessly with each other to collect sensed data and accomplish the goals of their missions. To this end, the sensors are required to know their locations and those of their neighbors. Therefore, sensor localization is a crucial aspect for the design and development of wireless sensor networks. Various algorithms and protocols have been developed for sensor localization in both two-dimensional and three- dimensional wireless sensor networks. However, the problem of sensor localization in a three-dimensional space has not been investigated in the literature as extensively as its counterpart in a two-dimensional space. In this book chapter, we propose to study the sensor localization problem in three-dimensional wireless sensor networks. More precisely, this book chapter's sole focus will be on three-dimensional sensor deployment, and it aims to provide an overview of the existing solutions to the localization problem in a three-dimensional space. Basically, it proposes a classification of localization algorithms, and discusses different three-dimensional sensor localization approaches along with their motivation and evaluation.

Thanks to key advances in wireless communication and electronics, sensors have emerged as an appealing technology for several interesting applications, such as civilian (health and environment monitoring), natural (disaster detection), military (battlefield surveillance), and agricultural (precision agriculture) applications, to name a few. When grouped together, these sensors form a network to measure and gather data of the surrounding environment with respect to a specific phenomenon. The sensors are battery-powered, tiny devices that possess all the characteristics of a traditional computer, including storage, processing, and communication capabilities. In addition, these sensors are capable of sensing the environment and collecting data regarding several parameters, such as temperature, light, sound, vibration, etc. Unfortunately, all the sensors' capabilities are limited due to their physical size. In particular, the sensors have limited battery power as usually they are equipped with AA/AAA batteries whose lifetime is short. Therefore, the main challenge in the design of this type of network is the sensors' battery power (or energy), which is a critical component for the operation of the whole network. Moreover, these sensors communicate (possibly) wirelessly with each other to collect sensed data and accomplish the goals of their missions. To this end, the sensors are required to know their locations and those of their neighbors. Therefore, sensor localization is a crucial aspect for the design and development of wireless sensor networks. Various algorithms and protocols have been developed for sensor localization in both two-dimensional and three- dimensional wireless sensor networks. However, the problem of sensor localization in a three-dimensional space has not been investigated in the literature as extensively as its counterpart in a two-dimensional space. In this book chapter, we propose to study the sensor localization problem in three-dimensional wireless sensor networks. More precisely, this book chapter's sole focus will be on three-dimensional sensor deployment, and it aims to provide an overview of the existing solutions to the localization problem in a three-dimensional space. Basically, it proposes a classification of localization algorithms, and discusses different three-dimensional sensor localization approaches along with their motivation and evaluation.

Safety and optimality are crucial requirements in every industrial process. The success of any fault diagnosis technique depends critically on the sensors measuring the important process variables. Choosing an appropriate sensor network is a combinatorially difficult problem, especially when the number of potential measurements is large. There has been considerable amount of work that has been done on developing algorithms for sensor network design for fault diagnosis based on quantitative and qualitative models. Various objectives, such as cost, reliability and fault resolution have been used in the sensor network design. While these design algorithms can provide the best design locations for a given cost, the value of the sensor network for fault diagnosis or benefit accrued is usually not quantified in a manner that is transparent to the user. This is an important aspect that needs to be addressed if these algorithms have to be assimilated into industrial practice. An approach for characterizing the value of a sensor network from a fault diagnosis perspective is proposed. This notion of value can be used directly in sensor network design algorithms. The proposed concepts are explained through a simple example and numerical simulations of a CSTR. © 2007 American Institute of Chemical Engineers AIChE J, 2007

Cost-optimal design of reliable sensor networks

Design and Upgrade of Nonredundant and Redundant Sensor Networks

The major concern in design and operation of wireless sensor networks is the issue of prolonging network lifetime which is basically rely on the network connectivity. And connectivity of the networks are directly depends on battery life of sensor nodes. However, the nodes have inadequate energy supplies and making battery change/substitution is very hard or even impractical. As a result, energy should be efficiently conserved to elongate sensor networks lifetime by having an energy-efficient routing algorithms. Hierarchical cluster routing algorithms can reduce energy consumption requirement; but the main problem and challenge of cluster formation is to select proper and suitable cluster heads by taking into consideration various parameters like remaining energy of the nodes, number of neighbours, position of node, and distance from base station. Cluster formation is a Non Polynomial hard problem, and evolutionary based technique is proposed to design an energy-efficient cluster to prolong lifetime of sensor networks, while ensuring energy efficient routing path construction for sensor nodes. Moreover, the proposed protocol maintains centrality of the cluster heads and let balanced energy dissipation among clusters. The protocol is implemented using OMNeT++ and the result analysis shows the protocol has good network stability period and the energy depletion of every node is smooth.