Mingsen Xu

Air-dropped sensor network for real-time high-fidelity volcano monitoring

This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multi-hop wireless network. The distance between stations is up to 2 km. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design and evaluation of a robust sensor network to replace data loggers and provide real-time long-term volcano monitoring. The system supports UTC-time synchronized data acquisition with 1ms accuracy, and is online configurable. It has been tested in the lab environment, the outdoor campus and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 120 miles per hour, the sensor network has achieved a remarkable packet delivery ratio above 99% with an overall system uptime of about 93.8% over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system have alleviated the doubts of domain scientists and prove to them that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

Design and Deployment of Sensor Network for Real-Time High-Fidelity Volcano Monitoring

This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five self-contained stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multihop wireless network. The transmit distance between stations was up to 8 km with favorable topography. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design of a robust sensor network optimized for rapid deployment during periods of volcanic unrest and provide real-time long-term volcano monitoring. The system supports UTC-time-synchronized data acquisition with 1 ms accuracy, and is remotely configurable. It has been tested in the lab environment, the outdoor campus, and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 160 km per hour, the sensor network has achieved a remarkable packet delivery ratio above 99 percent with an overall system uptime of about 93.8 percent over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system demonstrated to discipline scientists that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

Real-World Sensor Network for Long-Term Volcano Monitoring: Design and Findings

This paper presents the design, deployment, and evaluation of a real-world sensor network system in an active volcano - Mount St. Helens. In volcano monitoring, the maintenance is extremely hard and system robustness is one of the biggest concerns. However, most system research to date has focused more on performance improvement and less on system robustness. In our system design, to address this challenge, automatic fault detection and recovery mechanisms were designed to autonomously roll the system back to the initial state if exceptions occur. To enable remote management, we designed a configurable sensing and flexible remote command and control mechanism with the support of a reliable dissemination protocol. To maximize data quality, we designed event detection algorithms to identify volcanic events and prioritize the data, and then deliver higher priority data with higher delivery ratio with an adaptive data transmission protocol. Also, a light-weight adaptive linear predictive compression algorithm and localized TDMA MAC protocol were designed to improve network throughput. With these techniques and other improvements on intelligence and robustness based on a previous trial deployment, we air-dropped 13 stations into the crater and around the flanks of Mount St. Helens in July 2009. During the deployment, the nodes autonomously discovered each other even in-the-sky and formed a smart mesh network for data delivery immediately. We conducted rigorous system evaluations and discovered many interesting findings on data quality, radio connectivity, network performance, as well as the influence of environmental factors.

TelosW: Enabling ultra-low power wake-on sensor network

Sensor networks are typically sensor or radio event driven. Exploiting this property we propose a novel wakeon sensor network design. In this context we have designed a new sensor platform called TelosW. The wake-on sensing capability of TelosW lets designated sensors wake up the microcontroller(MCU) only on occurrence of some event with preconfigurable threshold. TelosW also includes the CC1101 [3] Wake-On Radio (WOR) hardware that performs low power listening without intervention of MCU. These all lead to a completely event driven wake-on sensor network that reduces energy consumption considerably. TelosW is also equipped with an on-board energy meter that can precisely measure in-situ energy consumption. Using the energy meter it is possible to get the insight of energy states of nodes in a network at any time. This makes it possible to practically analyze energy-efficient protocols. The experiments show that the energy consumption has been significantly reduced comparing to same application without wake-on design.

FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments

In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion data stream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity, (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories, FindingHumo applies the following techniques on the collected motion data stream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.

Optimized Autonomous Space In-Situ Sensor Web for Volcano Monitoring

In response to NASAs announced requirement for Earth hazard monitoring sensor-web technology, a multidisciplinary team involving sensor-network experts (Washington State University), space scientists (JPL), and Earth scientists (USGS Cascade Volcano Observatory (CVO)), have developed a prototype of dynamic and scalable hazard monitoring sensor-web and applied it to volcano monitoring. The combined Optimized Autonomous Space - In-situ Sensor-web (OASIS) has two-way communication capability between ground and space assets, uses both space and ground data for optimal allocation of limited bandwidth resources on the ground, and uses smart management of competing demands for limited space assets. It also enables scalability and seamless infusion of future space and in-situ assets into the sensor-web. The space and in-situ control components of the system are integrated such that each element is capable of autonomously tasking the other. The ground in-situ was deployed into the craters and around the flanks of Mount St. Helens in July 2009, and linked to the command and control of the Earth Observing One (EO-1) satellite.

Cacades: A reliable dissemination protocol for data collection sensor network

In this paper, we propose a fast and reliable data dissemination protocol Cascades to disseminate data from the sink(base station) to all or a subset of nodes in a data collection sensor network. Cascades makes use of the parent-monitor-children analogy to ensure reliable dissemination. Each node monitors whether or not its children have received the broadcast messages through snooping childrens rebroadcasts or waiting for explicit ACKs. If a node detects a gap in its message sequences, it can fetch the missing messages from its neighbours reactively. Cascades also considers many practical issues for field deployment, such as dynamic topology, link/node failure, etc.. It therefore guarantees that a disseminated message from the sink will reach all intended receivers and the dissemination is terminated in a short time period. Notice that, all existing dissemination protocols either do not guarantee reliability or do not terminate [1, 2], which does not meet the requirement of real-time command control. We conducted experiment evaluations in both TOSSIM simulator and a sensor network testbed to compare Cascades with those existing dissemination protocols in TinyOS sensor networks, which show that Cascades achieves a higher degree of reliability, lower communication cost, and less delivery delay.

Adaptive Linear Filtering Compression on realtime sensor networks

We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed Adaptive Linear Filtering Compression (ALFC) algorithm performs predictive compression, using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow samples in each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than some other lossless compression approaches such as S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency.

Imaging Seismic Tomography in Sensor Network

Tomography imaging, applied to seismology, requires a new, decentralized approach if high resolution calculations are to be performed in a sensor network configuration. The real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. In this paper, we present a distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network, while avoiding costly data collections and centralized computations. The new algorithm distributes the computational burden to sensor nodes and performs real-time tomography inversion under the constraints of network resources. We implemented and evaluated the system design in the CORE emulator. The experiment results validate that our proposed algorithm not only balances the computation load, but also achieves low communication cost and high data loss-tolerance.

A wake-on sensor network

This paper present a wake-on sensor network formed with the wake-on motes, TelosW. Our wake-on hardware and software design enable lower power operations and longer network lifetime.