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

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

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

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

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

Flexible smart sensor framework for autonomous structural health monitoring

Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of full-scale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.close677

Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.close889