Search

CENS researchers are developing flexible wireless sensing technologies that can be used in a variety of scientific and social applications. These technologies produce data that often have value to both the immediate research questions and to longer-term studies of longitudinal phenomena. CENS sensing systems are being deployed in many different real-world settings. Managing sensor deployments and the resulting data can be difficult. This poster outlines our work in developing tools to help CENS researchers conduct deployments and manage the resulting data, specifically the CENS Deployment Center, Sensorbase, and the deployment webpages created for the Seismic Deployment in Peru. The CENS Deployment Center (CENSDC) is a web-based repository for CENS deployment information. The CENSDC provides a central location for researchers to document deployment activities through the creation of pre-deployment plans and post-deployment feedback/notes. By allowing users to describe their deployment experiences, including lessons learned, troubleshooting techniques, and guidance for future deployments, the CENSDC attempts to capture the tacit knowledge about equipment setups, deployment locations, and field preparations that play a critical role in data collection techniques. Sensorbase is a database for CENS sensor collected data. Users can set up automated data uploads into Sensorbase from remote wifi enabled nodes deployed in the field, enabling researchers to monitor and manage their data remotely. Sensorbase can also generate email alerts when user-defined conditional changes in data occur, eliminating the need to search through the collected data to see that something is wrong (or right) with the deployment. Also, a programmatic approach to doing some of the features previously allowed only in the web user interface has been implemented so that sensors in the field can do more without human interaction. Finally, Sensorbase allows users to designate all or portions of their data to be shared with other researchers. The Peru deployment is a joint UCLA and Caltech project to study seismic activity along the South American subduction zone. Along with the seismic data, the seismic team is collecting various kinds of technical data to measure the health of the seismic stations, as well as of the wireless networks that connect them to each other and to the internet. These measures help the seismic team to identify problems as they arise. We created a number of interfaces that display network health metrics for the installed stations to enable members of the seismic team to view the current status of the wireless links across the transect, and helping them to be more responsive to emerging problems. These tools facilitate more efficient sensor deployments by allowing researchers to discover problems with data in real-time, identify and describe the problems, and annotate the solutions for future deployments. Through this process, the resulting data should be of higher quality in the short term, and more easily used and reused in the long term.

CENS researchers are developing flexible wireless sensing technologies that can be used in a variety of scientific and social applications. These technologies produce data that often have value to both the immediate research questions and to longer-term studies of longitudinal phenomena. CENS sensing systems are being deployed in many different real-world settings. Managing sensor deployments and the resulting data can be difficult. This poster outlines our work in developing tools to help CENS researchers conduct deployments and manage the resulting data, specifically the CENS Deployment Center, Sensorbase, and the deployment webpages created for the Seismic Deployment in Peru. The CENS Deployment Center (CENSDC) is a web-based repository for CENS deployment information. The CENSDC provides a central location for researchers to document deployment activities through the creation of pre-deployment plans and post-deployment feedback/notes. By allowing users to describe their deployment experiences, including lessons learned, troubleshooting techniques, and guidance for future deployments, the CENSDC attempts to capture the tacit knowledge about equipment setups, deployment locations, and field preparations that play a critical role in data collection techniques. Sensorbase is a database for CENS sensor collected data. Users can set up automated data uploads into Sensorbase from remote wifi enabled nodes deployed in the field, enabling researchers to monitor and manage their data remotely. Sensorbase can also generate email alerts when user-defined conditional changes in data occur, eliminating the need to search through the collected data to see that something is wrong (or right) with the deployment. Also, a programmatic approach to doing some of the features previously allowed only in the web user interface has been implemented so that sensors in the field can do more without human interaction. Finally, Sensorbase allows users to designate all or portions of their data to be shared with other researchers. The Peru deployment is a joint UCLA and Caltech project to study seismic activity along the South American subduction zone. Along with the seismic data, the seismic team is collecting various kinds of technical data to measure the health of the seismic stations, as well as of the wireless networks that connect them to each other and to the internet. These measures help the seismic team to identify problems as they arise. We created a number of interfaces that display network health metrics for the installed stations to enable members of the seismic team to view the current status of the wireless links across the transect, and helping them to be more responsive to emerging problems. These tools facilitate more efficient sensor deployments by allowing researchers to discover problems with data in real-time, identify and describe the problems, and annotate the solutions for future deployments. Through this process, the resulting data should be of higher quality in the short term, and more easily used and reused in the long term.

We have examined seismic data and wireless network characteristics from 100 broadband stations installed from Acapulco to Tampico in Mexico over a period of 1.5 years (2005-2007). The instruments were part of the MASE (Middle America Subduction Experiment) which has the objective to build a geodynamical model of the subduction process beneath the Middle America Trench. The stations had a 5-6 km spacing and were connected wirelessly with each other providing a unique data set. It allows examination of various aspects in tomography, shear wave splitting, wave travel times as well as extensive analysis of the wireless network used for data delivery. Tomographic and SKS splitting studies in this area show the presence of a 50-80 km thick flat slab under the western part of the array, and a steeply dipping slab beneath its center to a depth of ~550km and ~375 km inland.The research in wireless area displays no correlation between SNR and throughput and a rough correlation between the throughput and the distance for the majority of the links. Various network tuning techniques were applied to further investigate the properties of the communication hardware and data transmission. A new disruption tolerant shell (DTS) was tested that provides duplex communication with the network and handles breaks in the RF communication by temporarily storing data and transmitting when a conection has been restored.

CENS research related to developing and implementing structural health monitoring (SHM) systems is advancing on two distinct but related fronts; ShakeNet, a portable wireless sensor network for instrumenting civil structures and SHMnet, for monitoring of tall buildings in Los Angeles. SHM is the process of assessing the state of health (e.g., damage) of instrumented structures from measurements. The goal of SHM is to improve safety and reliability of infrastructure systems by detecting damage before it reaches a critical state, or to allow rapid post-event assessment. The primary objective of the SHMnet research is the development of a robust SHM system along with the associated hardware and software, using tall and special buildings in Los Angeles as a testbed. To manage this large-scale, multi-disciplinary goal, the work tasks are divided into several key focus areas, each with specific objectives, including the development of; a robust wireless Data Acquisition (DAQ) toolbox suitable for rapid urban deployments, a suite of state-of-the-art sensors for monitoring key structural responses including an innovative laser/photodiode for directly measuring interstory drift, and probabilistic post-event assessment algorithms based on experimental motion-damage relationships. A confluence of events, namely the local boom in tall building design and construction, the LA-DBS instrumentation requirements for tall buildings, the technology and experience of CENS and nees@UCLA, coupled with the active participation of key industrial partners, provides the synergy required to enable the proposed research.ShakeNet is a vibration sensor network designed for use in civil structures such as buildings and bridges for system identification and for identifying potential locations of damage due to earthquake motions. ShakeNet can also be used in test structures loaded to failure to characterize damage signals in waveform data such as those produced by moment-frame weld fractures. ShakeNet is a multi-tier wireless sensing system of 75 wireless nodes that can be rapidly deployed by 2-3 people on, for example, multiple floors of a large building. It will be designed to collect structural vibration measurements for up to a week from each node within the network. This portable system can be used to instrument large structures within hours immediately after an earthquake. Significant aftershocks of even moderate-size earthquakes occur for up to a week after the earthquake. What makes ShakeNet application-realistic is careful attention to the design of its data-acquisition hardware, the ShakeBoard. The ShakeBoard is a 24-bit high resolution acceleration data acquisition system that uses commercial low-noise MEMS accelerometers and conforms to specifications currently being established by the US Geological Survey for their Advanced National Seismic System (ANSS). ShakeNet’s software subsystem will be built upon Tenet; programmable wireless sensing software designed for multi-tier sensor networks. The strength of ShakeNet lies in the planned field tests in order to make it application-realistic. Future tests of ShakeNet, pending project funding, include deployments in moment-frame steel and reinforced concrete buildings, a bridge, and a dam near the San Andreas Fault.

Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporating a mixture of elements from extremely constrained 8- or 16-bit “Motes” to less resource- constrained 32-bit embedded “Microservers”.Emstar is a software environment for developing and deploying complex applications on such heterogeneous networks. Emstar is designed to leverage the additional resources of Microservers by trading-off some performance for system robustness in sensor network applications. It enables fault isolation, fault tolerance, system visiblity, in-field debugging, and resource sharing across multiple applications.In order to accomplish these objectives, Emstar is designed to run as a multi-process system and consists of libraries that implement message-passing IPC primitives, services that support networking, sensing, and time synchronization, and tools that support simulation, emulation, and visualization of live systems, both real and simulated. We evaluate this work by discussing the Acoustic ENSBox, a platform for distributed acoustic sensing that we built using Emstar. We show that by leveraging existing Emstar services, we are able to significantly reduce development time while achieving a high degree of robustness. We also show that a sample application was developed much more quickly on this platform than it would have been otherwise.

Wireless sensor networks using 802.11b long-distance links enable domain scientists to measure physical phenomena in real time and at a scientifically meaningful scale. Although deployments in this setting are increasing, a characterization of the network- throughput is lacking which makes it difficult to predict the performance of sensing applications on these types of networks. We present a study of the MASE seismic data collection network which includes 64 nodes spread across mountains, valleys, rural plains, and urban environments. The network spans 250km with links as long as 43km and has been operating continuously for more than 2 years. The contributions of this paper are three fold: (a) we present a throughput characterization of wireless links for a network in operational use, (b) we suggest settings for the wireless parameters to improve the performance of the network, and (c) we document the uptime of the nodes and the data yield of the network. Our findings are beneficial for the design of future wireless sensor network deployments using 802.11b.In our networks widely varied settings, we find that while signal-to-noise ratio is not correlated with throughput or distance, throughput is roughly correlated with distance and does not change significantly over time. At the link-layer, enabling the RTS-CTS handshake decreases the throughput while enabling retries improves the throughput. In addition, the default threshold-based rate adaptation algorithm for the transmission bit-rate is suboptimal. We find that the majority of fault cases are due to system issues and not network issues. The data yield over the course of an eight month period was 78%.

Dual-radio, dual-processor nodes are an emerging class of Wireless Sensor Network devices that provide both low- energy operation as well as substantially increased computational performance and communication bandwidth for applications. In such systems, the secondary radio and processor operates with sufficiently low power that it may remain always vigilant, while the the main processor and primary, high-bandwidth radio remain off until triggered by the application. By exploiting the high energy efficiency of the main processor and primary radio along with proper usage, net operating energy benefits are enabled for applications. The secondary radio provides a constantly available multi-hop network, while paths in the primary network exist only when required. This paper describes a topology control mechanism for establishing an end-to-end path in a network of dual-radio nodes using the secondary radios as a control channel to selectively wake up nodes along the required end-to-end path. Using numerical models as well as testbed experimentation, we show that our proposed mechanism provides significant energy savings of more than 60% compared to alternative approaches, and that it incurs only moderately greater application latency.

We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox’s integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20–30 m distances between neighboring nodes.

Wireless network technology is being applied to a wide range of scientific and engineering problems and across a wide dynamic range of spatial scales. When node placement is constrained by the application (e.g, coupled to sensor placement needs), and can not rely on pre-existing infrastructure (e.g., cellular infrastructure or power-lines), such systems may experience erratic link qualities and intermittent node disconnection. These characteristics, combined with unpredictable environmental conditions, make it difficult to rely upon traditional end to end connections for regular high bandwidth data acquisition and for system management and configuration. We have implemented and deployed such a "challenged network" system of 50 nodes for use by seismologists along a part of the Mesoamerican Subduction Experiment (MASE) broadband seismic array, stretching 500 KM from Acapulco to Tampico through Mexico city. In addition to supporting Delay Tolerant data transfer of relatively high bandwidth seismic data, our system includes a reliable asynchronous remote shell interface (referred to as Disruption Tolerant Shell, DTS) to accomplish the management on these types of system. We present the implementation of this solution and its evaluation on a 13 node portion of the MASE network.

Biodiversity not only responds to environmental change, but has been shown to be one of the key drivers of ecosystem function and service delivery. Forest soil biodiversity is also governed by these principles, the structure of soil biological communities is clearly determined by spatial, temporal and hierarchical factors. Global environmental change, together with land-use change and forest ecosystem management, impacts the aboveground structure and composition of European forests. Due to the close link between the above- and belowground parts of forest ecosystems, we know that soil biodiversity is also impacted. However, very little is known about the nature of these impacts; effects they have on the overall level of biodiversity, the functions it fulfills, and on the future stability of forests and forest soils. Even though much remains to be learned about the relationships between soil biodiversity and forest ecosystem functionality, it is clear that better effort needs to be made to preserve existing soil biodiversity and forest conservation strategies taking soils into account must be considered.

This document is an editorial note for a special issue on Multiple Valued Logic in the Journal of Applied Logics. The note highlights the importance of Multiple Valued Logic in the context of the imminent change in technology towards quantum computing. The special issue includes papers in three different areas: circuits, computing and algorithms, and theory. The papers cover topics such as data transmission quality evaluation, quantum algorithms for covering problems, embedding first-order logic into higher order intuitionistic logic, weak bases for computational problems, representation of maximally asymmetric functions, and p-valued Maiorana-McFarland functions. The note concludes by expressing the belief that this special issue will contribute to the study of the latest trends in Multiple Valued Logic. [Extracted from the article]

We study the approximation of function classification by machine learning. We first determine all the NPN and NPNP classes of three-bit reversible and one to five-bits irreversible functions. Then we apply machine learning to learn the labels for each set of functions. We then search the parameter space for several optimization criteria, such as the size of the neural network, the architecture, and learning parameters that would allow the network under training to learn the NPN classification problem with up to 100% accuracy. We also determine if, for n-bit functions, a classification can be performed using two smaller networks, each classifying n - 1-bit functions. Additionally, we discuss the logic solvability of all four-bit functions using the game of Nonograms. We determine a relation between the learnability of the classification and logic solvability. We determined that while there is a set of functions that can be classified by representing a function as a product of two subfunctions, a more complex model is needed to classify a n-bit NPN functions as a function of n - 1-bit NPN functions. In addition, we determine that approximate classification is always possible, with certain NPN classes being perfectly classified while others are not. Moreover, we can observe a direct relationship between the learnability of certain classes and the speed of finding a solution for these classes when used in a logic game. Finally, we show that the approximation of the classification using machine learning can replace standard databasebased classification. The advantages of machine learning methods are end-to-end learning, the lack of circuit design requirements for classification, and the ability to easily learn the class distinction problem. [ABSTRACT FROM AUTHOR]