https://www.science.smith.edu/~jcardell/Courses/EGR328/Readings/WSNSurvey2.pdf

Wireless sensor network survey

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

/pdf/energy-conservation-in-wireless-sensor-networks-a-survey-1bl7d2obnm.pdf

Energy conservation in wireless sensor networks: A survey

Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Energy Harvesting Sensor Nodes: Survey and Implications

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future.

Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.

Wireless sensor networks: a survey on recent developments and potential synergies

We present a science-centric evaluation of a 19-day sensor network deployment at Reventador, an active volcano in Ecuador. Each of the 16 sensors continuously sampled seismic and acoustic data at 100 Hz. Nodes used an event-detection algorithm to trigger on interesting volcanic activity and initiate reliable data transfer to the base station. During the deployment, the network recorded 229 earthquakes, eruptions, and other seismoacoustic events.The science requirements of reliable data collection, accurate event detection, and high timing precision drive sensor networks in new directions for geophysical monitoring. The main contribution of this paper is an evaluation of the sensor network as a scientific instrument, holding it to the standards of existing instrumentation in terms of data fidelity (the quality and accuracy of the recorded signals) and yield (the quantity of the captured data). We describe an approach to time rectification of the acquired signals that can recover accurate timing despite failures of the underlying time synchronization protocol. In addition, we perform a detailed study of the sensor network's data using a direct comparison to a standalone data logger, as well as an investigation of seismic and acoustic wave arrival times across the network.

/pdf/fidelity-and-yield-in-a-volcano-monitoring-sensor-network-28r68uycla.pdf

Fidelity and yield in a volcano monitoring sensor network

Augmenting heavy and power-hungry data collection equipment with lighten smaller wireless sensor network nodes leads to faster, larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible, allowing scientific studies that aren't feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.

Deploying a wireless sensor network on an active volcano

The implementation, characterization, and evaluation of a low-cost infrasound sensor developed at the Infrasound Laboratory at the New Mexico Institute of Mining and Technology (Infra-NMT) are described. This sensor is based on a commercial micromachined piezoresistive differential pressure transducer that uses a mechanical high-pass filter to reject low-frequency outband energy. The sensor features a low-noise, 2.02-mPa rms (0.5–2 Hz), 5.47-mPa rms (0.1–20 Hz), or 5.62-mPa rms (0.05–20 Hz), flat response between 0.01 and at least 40 Hz; inband sensitivity of 45.13 ± 0.23 μV Pa−1; and a nominal linear range from −124.5 to +124.5 Pa. Intended for outdoor applications, the influence of thermal changes in the sensor’s response has been minimized by using a thermal compensated pressure transducer powered by an ultralow drift (<5 ppm °C−1) and noise (<4μV from peak to peak) voltage reference. The sensor consumes a minimum of 24 mW and operates with voltages above 8 V while drawing 3 mA of current. The ...

Implementation, Characterization, and Evaluation of an Inexpensive Low-Power Low-Noise Infrasound Sensor Based on a Micromachined Differential Pressure Transducer and a Mechanical Filter

Continuous volcanic infrasound signal was recorded on a three-microphone network at Kilauea in July 2008 and inverted for near-surface horizontal winds. Inter-station phase delays, determined by signal cross-correlation, vary by up to 4% and are attributable to variable atmospheric conditions. The results suggest two predominant weather regimes during the study period: (1) 6–9 m/s easterly trade winds and (2) lower-intensity 2–5 m/s mountain breezes from Mauna Loa. The results demonstrate the potential of using infrasound for tracking local averaged meteorological conditions, which has implications for modeling plume dispersal and quantifying gas flux.

Tracking near-surface atmospheric conditions using an infrasound network

We developed and deployed a wireless sensor network for monitoring seismoacoustic activity at Volcan Reventador, Ecuador. Wireless sensor networks are a new technology and our group is among the first to apply them to monitoring volcanoes. The small size, low power, and wireless communication capabilities can greatly simplify deployments of large sensor arrays and are very attractive for this application domain. This project is a follow-on to our previous infrasonic sensor network deployed at Volcan Tungurahua, also in Ecuador, in July 2004 [1].

Sensor networks for high-resolution monitoring of volcanic activity


 Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. The results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

O. Marcillo

Papers

Deploying a wireless sensor network on an active volcano

Implementation, Characterization, and Evaluation of an Inexpensive Low-Power Low-Noise Infrasound Sensor Based on a Micromachined Differential Pressure Transducer and a Mechanical Filter

Tracking near-surface atmospheric conditions using an infrasound network

Sensor networks for high-resolution monitoring of volcanic activity

Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning