A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring

Alippi, Cesare; Camplani, Romolo; Galperti, C.; Roveri, Manuel

doi:10.1109/jsen.2010.2051539

Cited by 240 publications

(105 citation statements)

References 17 publications

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“…The supply block consists of a battery with a capacity of 6.1 Ah and a nominal voltage of 3.7 V. The battery provides with solar panel harvesting, in optimal lighting conditions, up to 700 mW. A fully autonomous station that measures the parameters of water in the area of the Australian coral reef is presented in [53]. This station is designed to measure the water temperature and insolation conditions prevailing under the water surface.…”

Section: Monitoring Water Parametersmentioning

confidence: 99%

Survey of Energy Harvesting Systems for Wireless Sensor Networks in Environmental Monitoring

Dziadak

Makowski

Michalski

2016

Metrology and Measurement Systems

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Wireless Sensor Networks (WSNs) have existed for many years and had assimilated many interesting innovations. Advances in electronics, radio transceivers, processes of IC manufacturing and development of algorithms for operation of such networks now enable creating energy-efficient devices that provide practical levels of performance and a sufficient number of features. Environmental monitoring is one of the areas in which WSNs can be successfully used. At the same time this is a field where devices must either bring their own power reservoir, such as a battery, or scavenge energy locally from some natural phenomena. Improving the efficiency of energy harvesting methods reduces complexity of WSN structures. This survey is based on practical examples from the real world and provides an overview of state-of-the-art methods and techniques that are used to create energyefficient WSNs with energy harvesting.

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Section: Monitoring Water Parametersmentioning

confidence: 99%

Survey of Energy Harvesting Systems for Wireless Sensor Networks in Environmental Monitoring

Dziadak

Makowski

Michalski

2016

Metrology and Measurement Systems

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“…Energy harvesters, specifically tunable energy harvesters, which can adjust their own resonant frequency to match the input frequency, are deemed to be a suitable power source for wireless sensor nodes [1]. Energy supply issues in wireless sensor networks (WSNs) are becoming more and more important to both industry and academia, due to many emerging applications such as environmental sensing [2], structural monitoring [3] and pervasive healthcare [4]. Optimal design of such systems is a difficult problem due to the need to optimize the entire sensor node holistically [5] and prohibitive simulation times resulting from the complexity.…”

Section: Introductionmentioning

confidence: 99%

Fast Design Space Exploration of Vibration-Based Energy Harvesting Wireless Sensors

et al. 2013

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Abstract-An energy-harvester-powered wireless sensor node is a complicated system with many design parameters. To investigate the various trade-offs among these parameters, it is desirable to explore the multi-dimensional design space quickly. However, due to the large number of parameters and costly simulation CPU times, it is often difficult or even impossible to explore the design space via simulation. This paper presents a response surface model (RSM) based technique for fast design space exploration of a complete wireless sensor node powered by a tunable energy harvester. As a proof of concept, a software toolkit has been developed which implements the proposed design flow and incorporates either real data or parametrized models of the vibration source, the energy harvester, tuning controller and wireless sensor node. Several test scenarios are considered, which illustrate how the proposed approach permits the designer to adjust a wide range of system parameters and evaluate the effect almost instantly but still with high accuracy. In the developed toolkit, the estimated CPU time of one RSM estimation is 25µs and the average RSM estimation error is less than 16.5Index Terms-Tunable energy harvester, wireless sensor node, hardware description language, response surface model, simulation.

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“…Since wireless sensor nodes can provide information from previously inaccessible locations and from previously unachievable number of locations, many new application areas are emerging, such as environmental sensing [1], structural monitoring [2] and human body monitoring [3]. Although…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Sensor Nodes Powered by Kinetic Energy Harvesters – Design for Optimum Performance

Kázmierski¹,

Wang²,

Aloufi³

2012

ELS

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Abstract-In an energy harvester powered wireless sensor node system, as the energy harvester is the only energy source, it is crucial to configure the microcontroller and the sensor node so that the harvested energy is used efficiently. This paper outlines modelling, performance optimisation and design exploration of the complete, complex system which includes the analogue mechanical model of a tunable kinetic microgenerator, its magnetic coupling with the electrical blocks, electrical power storage and processing parts, the digital control of the microgenerator tuning system, as well as the power consumption models of sensor node. Therefore not only the energy harvester design parameters but also the sensor node operation parameters can be optimised in order to achieve the best system performance. The power consumption models of the microcontroller and the sensor node are built based on their operation scenarios so that the parameters of the digital algorithms can be optimised to achieve the best energy efficiency. In the proposed approach, two Hardware Description Languages, VHDL-AMS and SystemC-A is used to model the system's analogue components as well as the digital control algorithms which are implemented in the microcontroller and the sensor node. Simulation and performance optimisation results are verified experimentally. In the development of the fast design exploration tool based on the response surface technique, the response surface model (RSM) is constructed by carrying out a series of simulations. The RSM is then optimised using MATLAB's optimisation toolbox and the optimisation results are presented.

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A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring

Cited by 240 publications

References 17 publications

Survey of Energy Harvesting Systems for Wireless Sensor Networks in Environmental Monitoring

Survey of Energy Harvesting Systems for Wireless Sensor Networks in Environmental Monitoring

Fast Design Space Exploration of Vibration-Based Energy Harvesting Wireless Sensors

Energy Efficient Sensor Nodes Powered by Kinetic Energy Harvesters – Design for Optimum Performance

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