Sudden Event Monitoring of Civil Infrastructure Using Demand-Based Wireless Smart Sensors

Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption; hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer; the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner

Dynamic Voltage Scaling Techniques for Energy Efficient Synchronized Sensor Network Design

Building energy-efficient systems is one of the principal challenges in wireless sensor networks. Dynamic voltage scaling (DVS), a technique to reduce energy consumption by varying the CPU frequency on the fly, has been widely used in other settings to accomplish this goal. In this paper, we show that changing the CPU frequency can affect timekeeping functionality of some sensor platforms. This phenomenon can cause an unacceptable loss of time synchronization in networks that require tight synchrony over extended periods, thus preventing all existing DVS techniques from being applied. We present a method for reducing energy consumption in sensor networks via DVS, while minimizing the impact of CPU frequency switching on time synchronization. The system is implemented and evaluated on a network of 11 Imote2 sensors mounted on a truss bridge and running a high-fidelity continuous structural health monitoring application. Experimental measurements confirm that the algorithm significantly reduces network energy consumption over the same network that does not use DVS, while requiring significantly fewer re-synchronization actions than a classic DVS algorithm.unpublishedis peer reviewe

Probabilistic assessment of high-throughput wireless sensor networks

Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved.ope

A service-oriented architecture for dynamic macroprogramming of sensor networks

In the late 1990s, advances in sensing and computer technology have enabled the development of tiny, inexpensive, low-power wireless sensor platforms. By integrating sensing, communication, and computational capabilities, these smart sensors were poised to revolutionize our view of the environment we inhabit by linking the physical world with the digital realm of traditional computing. Smart sensors have been available to researchers for more than a decade; however, few large-scale applications have emerged outside the laboratory setting, and the commercial potential of this technology has been limited. The principal reason for this outcome is the difficulty inherent in programming wireless sensor networks (WSNs) consisting of more than a handful of sensors: built from inexpensive components, individual nodes in this distributed system are prone to failures; interaction with the physical world imposes real-time constraints on computation and communication; and the limited energy of battery-powered sensor nodes leads to stringent energy efficiency requirements. Combined, these challenges have caused WSN software development to lag behind the capabilities offered by the hardware. The goal of this research is to enable robust, large-scale application development for wireless sensor networks, allowing the full potential of WSN technology to be realized. To this end, we leverage two powerful techniques, service-oriented architecture (SOA) and macroprogramming. Adapting SOA, which is typically seen in Internet-scale web applications, to WSNs enables application components to cooperate and share limited resources in an intelligent manner, while providing useful high-level programming abstractions to the application developer. Macroprogramming -- specifying the aggregate behavior of a distributed system rather than each node individually -- builds on SOA to create lightweight, mobile applications that can combine and control the services resident in the network to take advantage the capabilities of the network as a whole. This approach has proven successful, enabling a long-term deployment of a dense array of structural health monitoring (SHM) sensors on a cable-stayed bridge in Jindo, South Korea. The software resulting from this work, which integrates the service-oriented application development framework with a suite of domain services and comprehensive applications for SHM, has been released as the open-source Illinois SHM Services Toolsuite. It is currently in use by over 70 research groups worldwide

Link Quality Estimation for Data-Intensive Sensor Network Applications

The efficiency of multi-hop communication is a function of the time required for data transfer, or throughput. A key determinant of throughput is the reliability of packet transmission, as measured by the packet reception rate. We follow a data-driven statistical approach to dynamically determine a link quality estimate (LQE), which provides a good predictor of packet reception rates. Our goal is to enable efficient multi-hop communication for applications characterized by data-intensive, bursty communication in large sensor networks. Statistical analysis and experiments carried out on a network of 20 Imote2 sensors under a variety of environmental conditions show that the metric is a superior predictor of throughput for bursty data transfer workloads.unpublishedis peer reviewe

Real-Time Wireless Data Acquisition for Structural Health Monitoring and Control

Wireless smart sensor networks have become an attractive alternative to traditional wired sensor systems in order to reduce implementation costs of structural health monitoring systems. The onboard sensing, computation, and communication capabilities of smart wireless sensors have been successfully leveraged in numerous monitoring applications. However, the current data acquisition schemes, which completely acquire data remotely prior to processing, limit the applications of wireless smart sensors (e.g., for real-time visualization of the structural response). While real-time data acquisition strategies have been explored, challenges of implementing highthroughput real-time data acquisition over larger network sizes still remain due to operating system limitations, tight timing requirements, sharing of transmission bandwidth and unreliable wireless radio communication. This report presents the implementation of real-time wireless data acquisition on the Imote2 platform. The challenges presented by hardware and software limitations are addressed in the application design. The framework is then expanded for highthroughput applications that necessitate larger networks sizes with higher sampling rates. Two approaches are implemented and evaluated based on network size, associated sampling rate, and data delivery reliability. Ultimately, the communication and processing protocol allows for nearreal- time sensing of 108 channels across 27 nodes with minimal data loss.published or submitted for publicationnot peer reviewe

ActorNet: An Actor Platform for Wireless Sensor Networks

We present an actor platform for wireless sensor networks (WSNs). A typical WSN may consist of hundreds to tens of thousands of tiny nodes embdedded in an environment. Hence, manual reprogramming of nodes for development, fixing bugs and updating features is an arduous process; moreover, in some cases physical access to nodes is simply out of the question. In an attempt to address this problem, network reprogramming tools such as Deluge and MNP have been developed. Unfortunately, these bulk reprogramming services incur significant costs in terms of energy usage, latency, and loss of sensing coverage when nodes are rebooted into a new program image. ActorNet, in contrast, provides an environment for lightweight concurrent object-oriented mobile code on WSNs. As such, actorNet enables a wide range of new dynamic applications on WSNs, including support for fully customizable queries and aggregation functions, in-network interactive debugging facilities, and high-level concurrent programming on the inherently parallel sensor network platform. Moreover, actorNet cleanly integrates all of these features into a fine-tuned, multi-threaded embedded Scheme interpreter which supports compact, maintainable programs -- a significant advantage over primitive stack-based virtual machines

Architecture Design Principles to Support Adaptive Service Orchestration in WSN Applications Abstract

Our goal is to facilitate the development of sensor network applications in an open system, where applications arrive and leave dynamically and execute concurrently. We identify design principles that govern the creation of these systems, such as having a network-wide programming model, late binding and global resource management. In accordance with these principles, we assume that an application is modeled as a composite service, and propose an architecture for its adaptive orchestration on a WSN. Adaptivity here refers to automatic runtime selection of service implementations and network resources to execute the application specification in a resource-efficient and context-aware manner.

Localization of Sparse Sensor Networks Using Layout Information

Localization is the process by which sensor networks associate spatial position information with individual sensors' measurements. While manual surveying is sufficient for small-scale prototypes, it is too time-consuming and costly for the large-scale deployments anticipated in the near future. Our experiments with medium-scale outdoor sensor network deployments show that sparsity of ranging measurements is a key factor limiting the accuracy of localization; often, several solutions are equally consistent with the data. Fortunately, layout information can usually be obtained at little extra cost; for example, if it is used to guide the deployment process, or by analyzing a photograph of the network. We have developed an algorithm based on subgraph isomorphism which uses the known layout information in conjunction with ranging measurements to find a family of localization solutions for a sensor network deployment. Although subgraph isomorphism is in general NP-complete, the more specific cases that occur in real-world scenarios are usually tractable. Experiments with a 50-node network show that our algorithm is very efficient in practice

Resilient Localization for Sensor Networks in Outdoor Environments

A process which computes the physical locations of nodes in a wireless sensor network is called localization. Self-localization is critical for large-scale sensor networks because manual or assisted localization is often impractical due to time requirements, economic constraints or inherent limitations of deployment scenarios. We have developed a service for reliably localizing wireless sensor networks in environments conducive to ranging errors by using a custom hardware-software solution for acoustic ranging and a family of self-localization algorithms. The ranging solution improves on previous work, extending the practical measurement range threefold (20-30m) while maintaining a distance-invariant median measurement error of about 1% of maximum range (33cm). The localization scheme is based on least squares scaling with soft constraints. Evaluation using ranging results obtained from sensor network field experiments shows that the localization scheme is resilient against large-magnitude ranging errors and sparse range measurements, both of which are common in large-scale outdoor sensor network deployments