TY - CONF T1 - Design and Implementation of an Energy-Neutral Solar Energy System for Wireless Sensor-Actuator Nodes T2 - IEEE Global IoT Summit (GIoTS-2017) Y1 - 2017 A1 - Knapp,J A1 - PG Flikkema JF - IEEE Global IoT Summit (GIoTS-2017) T3 - IEEE Global IoT Summit (GIoTS-2017) CY - Geneva, Switzerland VL - 2017 N1 - [Original String]:Knapp, J. and Flikkema, P.G. “Design and Implementation of an Energy-Neutral Solar Energy System for Wireless Sensor-Actuator Nodes”, 2017 IEEE Global IoT Summit (GIoTS-2017), 6-9 June 2017, Geneva, Switzerland. ER - TY - CONF T1 - Support of distributed ecological experiments via closed-loop environmental control T2 - 2017 IEEE Conference on Technologies for Sustainability (SusTech), Y1 - 2017 A1 - J.D. Knapp A1 - M. Middleton A1 - P.L. Heinrich A1 - A.V. Whipple A1 - P.G. Flikkema KW - closed-loop KW - distributed experiments KW - Ecology KW - environmental control KW - SEGA KW - technology AB -

Improved understanding of the effects of climate and weather patterns on plant survival and growth is critical for improving management of wildland, rangeland, and crop ecosystems. The Southwest Experimental Garden Array (SEGA) is a distributed research instrument comprising of an array of 10 common gardens across an elevational gradient in Northern Arizona. SEGA's cyber infrastructure facilitates monitoring and control of soil moisture at experimental plots using drip irrigation and wireless sensor/actuator nodes. This paper describes development of software-based workflows for the sensing and control of soil moisture conditions across experimental plots and gardens with different temperature and rainfall regimes, and the necessary hardware and software infrastructure to support this capability.

JF - 2017 IEEE Conference on Technologies for Sustainability (SusTech), PB - IEEE SusTech CY - Phoenix, AZ UR - https://ieeexplore.ieee.org/document/8333478/ ER - TY - JOUR T1 - Process Modeling for Soil Moisture Using Sensor Network Data . JF - Statistical Methodology (Special issue on modern statistical methods in ecology) Y1 - 2014 A1 - Ghosh,S A1 - Bell,DM A1 - Clark,JS A1 - Gelfand,AE A1 - Flikkema,P VL - 12 N1 - [Original String]:Ghosh S, Bell DM, Clark JS, Gelfand AE, and Flikkema P. 2014. Process Modeling for Soil Moisture Using Sensor Network Data . Statistical Methodology (Special issue on modern statistical methods in ecology)12: 99-112. ER - TY - CONF T1 - Towards Intelligent Closed-Loop Workflows for Ecological Research Dynamic Data-driven Environmental Systems T2 - Dynamic Data-driven Environmental Systems Science Conference (DyDESS) Y1 - 2014 A1 - Knapp,J A1 - Elo,M A1 - Schaffer,J A1 - PG Flikkema JF - Dynamic Data-driven Environmental Systems Science Conference (DyDESS) T3 - Dynamic Data-driven Environmental Systems Science Conference (DyDESS) PB - DyDESS CY - Cambridge, MA, USA VL - 2014 N1 - [Original String]:Knapp, J.D., M. Elo, J. Shaeffer, and P.G. Flikkema. 2014. Towards Intelligent Closed-Loop Workflows for Ecological Research. Dynamic Data-driven Environmental Systems Science Conference (DyDESS), November 5-7, 2014, Cambridge, MA. ER - TY - Generic T1 - Cyber-Eco Technology: Engineering of Ecological Systems [Point of View] T2 - Proceedings of the IEEE Y1 - 2013 A1 - PG Flikkema AB -

The Earth's human population and per capita use of resources have grown dramatically in the past century. Rapid conversion of land from wilderness to agricultural and urban use, air and water pollution, drawdown of potable water resources, overfishing, and increased use of fossil-based fuels have changed the Earth's natural systems, as indicated by increasing average global temperatures, invasions of exotic species, modification of the level and chemistry of our oceans, and rapidly declining biodiversity. Examines the impact to the global ecosystems and discusses initiatives to manage and control the terrestrial, aquatic, and martime systems impacted.

JF - Proceedings of the IEEE T3 - Proceedings of the IEEE PB - IEEE VL - 101(5) UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6504492 ER - TY - CONF T1 - Towards Cyber-Eco Systems: Networked Sensing, Inference and Control for Distributed Ecological Experiments T2 - IEEE International Conference on Cyber, Physical and Social Computing Y1 - 2012 A1 - PG Flikkema A1 - Yamamoto,KR A1 - Boegli,S A1 - Porter,C A1 - PL Heinrich JF - IEEE International Conference on Cyber, Physical and Social Computing T3 - IEEE International Conference on Cyber, Physical and Social Computing ER - TY - JOUR T1 - Inferential ecosystem models, from network data to prediction. JF - Ecological applications : a publication of the Ecological Society of America Y1 - 2011 A1 - James S Clark A1 - Agarwal,Pankaj A1 - Bell,David M A1 - Flikkema,Paul G A1 - Gelfand,Alan A1 - Nguyen,Xuanlong A1 - Ward,Eric A1 - Yang,Jun KW - Bayes Theorem KW - Data Interpretation, Statistical KW - Ecology KW - Ecosystem KW - Forecasting KW - Models, Biological KW - Models, Statistical KW - Plant Transpiration KW - Plants KW - Time Factors AB -

Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations "expensive"; networks will typically be deployed in remote locations without access to infrastructure (e.g., power). The capacity to sample intensively makes sensor networks valuable, but high-frequency data are informative only at specific times and locations. Sampling intervals will range from meters and seconds to landscapes and years, depending on the process, the current states of the system, the uncertainty about those states, and the perceived potential for rapid change. Given that intensive sampling is sometimes critical, but more often wasteful, how do we develop tools to control the measurement and transmission processes? We address the potential of data collection controlled and/or supplemented by inferential ecosystem models. In a given model, the value of an observation can be evaluated in terms of its contribution to estimates of state variables and important parameters. There will be more than one model applied to network data that will include as state variables water, carbon, energy balance, biogeochemistry, tree ecophysiology, and forest demographic processes. The value of an observation will depend on the application. Inference is needed to weigh the contributions against transmission cost. Network control must be dynamic and driven by models capable of learning about both the environment and the network. We discuss application of Bayesian inference to model data from a developing sensor network as a basis for controlling the measurement and transmission processes. Our examples involve soil moisture and sap flux, but we discuss broader application of the approach, including its implications for network design.

VL - 21 SN - 1051-0761 UR - http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related%20Articles&IdsFromResult=21830699&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 5 ER - TY - CONF T1 - Progressive coding and iterative source-channel decoding in wireless data gathering networks . T2 - Proceedings of 2011 IEEE Global Telecommunications Conference Y1 - 2011 A1 - Li,C A1 - PG Flikkema A1 - Howard,SL JF - Proceedings of 2011 IEEE Global Telecommunications Conference T3 - Proceedings of 2011 IEEE Global Telecommunications Conference PB - IEE GLOBECOM CY - Houston, TX, USA N1 - [Original String]:Li C, Flikkema PG, Howard SL. 2011. Progressive coding and iterative source-channel decoding in wireless data gathering networks . Proceedings of 2011 IEEE Global Telecommunications Conference, GLOBECOM 2011; Dec 5-9; Houston. ER - TY - Generic T1 - Energy-efficient model inference in wireless sensing: asymmetric data processing . T2 - Proceedings of the Ninth IEEE Sensors Conference Y1 - 2010 A1 - PG Flikkema JF - Proceedings of the Ninth IEEE Sensors Conference T3 - Proceedings of the Ninth IEEE Sensors Conference PB - IEEE CY - Kona, HI N1 - [Original String]:Flikkema PG. 2010. Energy-efficient model inference in wireless sensing: asymmetric data processing . Proceedings of the Ninth IEEE Sensors Conference, 2010; Nov 1-4; Kona, HI; p 1843-1847. ER - TY - CHAP T1 - WiSARDNet field-to-desktop: building a wireless cyberinfrastructure for environmental monitoring. T2 - The Colorado Plateau IV: Shaping Conservation Through Science and Management Y1 - 2010 A1 - Yamamoto,K A1 - He,Y A1 - PL Heinrich A1 - Orange,A A1 - Ruggeri,B A1 - Wilberger,H A1 - PG Flikkema ED - van Riper III,C ED - Wakeling,BF ED - Sisk, TD JF - The Colorado Plateau IV: Shaping Conservation Through Science and Management T3 - The Colorado Plateau PB - The University of Arizona Press CY - Tucson, AZ, USA VL - IV ER - TY - Generic T1 - Prospector: Multiscale Energy Measurement of Networked Embedded Systems with Wideband Power Signals T2 - Proceedings of 12th IEEE International Conference on Computational Science and Engineering Y1 - 2009 A1 - Yamamoto,KR A1 - PG Flikkema AB -

Today鈥檚 wirelessly networked embedded systems underlie a vast array of electronic devices, performing computation,communication, and input/output. A major design goal of these systems is energy efficiency. To achieve this goal, these systems are based on processors with numerous power and clock domains, variable clock rates, voltage scaling, and multiple hibernation states. These processors are designed into systems with sophisticated wireless transceivers and a diverse array of off-chip peripherals, and are linked through regulators to increasingly complexenergy supplies. As a result, modern networked embeddedsystems are characterized by myriad power consumption statesand significant power signal transients. Moreover, their power demands are multiscale in both magnitude and time, combining short bursts of high demand with long intervals of power-sipping sleep states. Thus the power supply signals have wideband spectra. In addition, due to noise, uniform relative precision across magnitude scales requires that measurement time increases with decreasing power. Tools are needed that support modeling, hardware/software optimization, and debugging for energy-centric embedded systems. This paper describes Prospector, an energy data acquisition system architecture for embedded systems thatallows rapid, accurate, and precise assessment of system-level power usage. Prospector uses a distributed control architecture; each component contributes efficiently to control, precision and accuracy, analysis, and visualization. It is based on computer-based control of multimeters to maximize accuracy, precision,铿俥xibility, and minimize target system overhead. Experimental results for a prototype Prospector system with a contemporary 16-bit ultra-low power microcontroller show that it can effectivelymeasure power over the extreme time and magnitude scales found in today鈥檚 embedded systems.

JF - Proceedings of 12th IEEE International Conference on Computational Science and Engineering T3 - Proceedings of 12th IEEE International Conference on Computational Science and Engineering; PB - IEEE CY - Vancouver, Canada VL - 2 UR - http://dl.acm.org/citation.cfm?id=1633490 ER - TY - CONF T1 - System-level characterization of single-chip radios for wireless sensor network applications . T2 - Proceedings of the IEEE 10th Annual Wireless and Microwave Technology Conference Y1 - 2009 A1 - He,Y A1 - PG Flikkema JF - Proceedings of the IEEE 10th Annual Wireless and Microwave Technology Conference T3 - Proceedings of the IEEE 10th Annual Wireless and Microwave Technology Conference PB - IEEE CY - Clearwater, Florida, USA N1 - [Original String]:He Y, Flikkema PG. 2009. System-level characterization of single-chip radios for wireless sensor network applications . Proceedings of the IEEE 10th Annual Wireless and Microwave Technology Conference, 2009; Apr 20-21; Clearwater, FL. ER - TY - CONF T1 - From Data Reverence to Data Relevance: Model-Mediated Wireless Sensing of the Physical Environment T2 - ICCS 2007, 7th International Conference Y1 - 2007 A1 - PG Flikkema A1 - Agarwal,PK A1 - Clark,JS A1 - Ellis,C A1 - Gelfand,A ED - Albada,G ED - Dongarra,J ED - Sloot,P AB -

Summary: Wireless sensor networks can be viewed as the integration of three subsystems: a low-impact in situ data acquisition and collection system, a system for inference of process models from observed data and a priori information, and a system that controls the observation and collection. Each of these systems is connected by feedforward and feedback signals from the others; moreover, each subsystem is formed from behavioral components that are distributed among the sensors and out-of-network computational resources. Crucially, the overall performance of the system is constrained by the costs of energy, time, and computational complexity. We are addressing these design issues in the context of monitoring forest environments with the objective of inferring ecosystem process models. We describe here our framework of treating data and models jointly, and its application to soil moisture processes.

JF - ICCS 2007, 7th International Conference T3 - ICCS 2007, 7th International Conference PB - Springer Berlin/Heidelberg CY - Beijing, China VL - 4487 UR - http://link.springer.com/10.1007/978-3-540-72584-8_130 ER - TY - CONF T1 - Model-Driven Dynamic Control of Embedded Wireless Sensor Networks T2 - Computational Science - ICCS 2006, Lecture Notes in Computer Science, 6th International Conference Y1 - 2006 A1 - PG Flikkema A1 - Agarwal,PK A1 - Clark,JS A1 - Ellis,C A1 - Gelfand,A ED - Alexandrov,V ED - van Albada,G ED - Sloot,P ED - Dongarra,J AB -

Next-generation wireless sensor networks may revolutionize understanding of environmental change by assimilating heterogeneous data, assessing the relative value and costs of data collection, and sche

JF - Computational Science - ICCS 2006, Lecture Notes in Computer Science, 6th International Conference T3 - Computational Science - ICCS 2006, Lecture Notes in Computer Science, 6th International Conference, PB - Springer Berlin/Heidelberg CY - Reading, UK VL - 3993 UR - http://www.springerlink.com/content/5603gh1252528020 ER - TY - CONF T1 - The precision and energetic cost of snapshot estimates in wireless sensor networks . T2 - Proceedings of 11th Annual IEEE Symposium on Computing and Communications 2006 Y1 - 2006 A1 - PG Flikkema JF - Proceedings of 11th Annual IEEE Symposium on Computing and Communications 2006 T3 - Proceedings of 11th Annual IEEE Symposium on Computing and Communications 2006 PB - IEEE ISCC ’06 CY - Cagliari Italy N1 - [Original String]:Flikkema PG. 2006.The precision and energetic cost of snapshot estimates in wireless sensor networks . Proceedings of 11th Annual IEEE Symposium on Computing and Communications 2006 (IEEE ISCC ’06); 2006 June 26-29; Cagliari, Italy; p 603-608. ER - TY - CONF T1 - WiSARDNET: a system solution for high performance in situ environmental monitoring. T2 - Proceedings of the 2nd International Workshop on Networked Sensing Systems Y1 - 2005 A1 - Yang,Z A1 - Ruggeri,B A1 - PG Flikkema A1 - Johnson,D A1 - Wright,M A1 - Xia,K JF - Proceedings of the 2nd International Workshop on Networked Sensing Systems T3 - Proceedings of the 2nd International Workshop on Networked Sensing Systems PB - IEEE CY - San Diego, CA, USA ER - TY - CONF T1 - Clique-based randomized multiple access for energy-efficient ad hoc wireless networks. T2 - Proceedings of the Communication and Networking Conference IEEE WCNC 2003 Y1 - 2003 A1 - PG Flikkema A1 - West,B JF - Proceedings of the Communication and Networking Conference IEEE WCNC 2003 T3 - Proceedings of the Communication and Networking Conference IEEE WCNC 2003 PB - IEEE WCNC CY - New Orleans, LA, USA N1 - [Original String]:Flikkema PG, West B. 2003. Clique-based randomized multiple access for energy-efficient ad hoc wireless networks. Proceedings of the IEEE 2003 Wireless Communication and Networking Conference (IEEE WCNC 2003); 2003 Mar 16-20; New Orleans, LA; p 977-981. ER -