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Sensor Networks for Environmental Observations

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 June 2018) | Viewed by 68360

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Special Issue Editor

Prof. Dr. Steven D. Glaser

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Guest Editor

Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720-1758, USA
Interests: wireless sensor networks; snow hydrology; field experiments; laboratory earthquakes; nanoseismology; enhanced geothermal reservoirs

Special Issue Information

Dear Colleagues,

Low-power wireless sensor networks (WSN) have found a myriad of applications. They have become central to the Internet of Things (IoT). Areas of application include factory automation, structural health monitoring, pipeline behavior, wind generator operation, environmental monitoring, power management, and asset monitoring. The field is beginning to mature as the hardware becomes reliable and networking theory catches up to the realities of work in the field. Several firms market radio transceivers that are extremely reliable, low power, long range, and are self-assembling mesh topologies. WSN is now a hardened tool used by industries for real-time operations. Previously WSN was basically the purview of experimenters and university researchers. The reliability of data transmission is now good enough that they are beginning to be used in control-loop operations.

Sensors is devoting a Special Issue to the new theory and application of WSNs. The journal is looking for papers that present achievements in the area of environmental monitoring that are new, and more importantly helpful to the community. Theoretical papers should tie in to actual deployments and deployment reports will provide enough fundamental information to allow other practitioners can solve similar problems. Papers can report on any aspect of hydrologic, geophysical, and environmental monitoring as long as they meet these requirements.

Prof. Dr. Steven D. Glaser
Guest Editor

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Keywords

Wireless sensor networks
field experiments
environmental monitoring and control
hydrology

Published Papers (14 papers)

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Research

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37 pages, 6451 KiB

Open AccessArticle

Evaluation of IEEE802.15.4g for Environmental Observations

by Jonathan Muñoz, Tengfei Chang, Xavier Vilajosana and Thomas Watteyne

Sensors 2018, 18(10), 3468; https://doi.org/10.3390/s18103468 - 15 Oct 2018

Cited by 29 | Viewed by 4352

Abstract

IEEE802.15.4g is a low-power wireless standard initially designed for Smart Utility Networks, i.e., for connecting smart meters. IEEE802.15.4g operates at sub-GHz frequencies to offer 2–3× longer communication range compared to its 2.4 GHz counterpart. Although the standard offers 3 PHYs (Frequncy Shift Keying, Orthogonal Frequency Division Multiplexing and Offset-Quadrature Phase Shift Keying) with numerous configurations, 2-FSK at 50 kbps is the mandatory and most prevalent radio setting used. This article looks at whether IEEE802.15.4g can be used to provide connectivity for outdoor deployments. We conduct range measurements using the totality of the standard (all modulations with all further parametrization) in the 863–870 MHz band, within four scenarios which we believe cover most low-power wireless outdoor applications: line of sight, smart agriculture, urban canyon, and smart metering. We show that there are radio settings that outperform the “2-FSK at 50 kbps” base setting in terms of range, throughput and reliability. Results show that highly reliable communications with data rates up to 800 kbps can be achieved in urban environments at 540 m between nodes, and the longest useful radio link is obtained at 779 m. We discuss how IEEE802.15.4g can be used for outdoor operation, and reduce the number of repeater nodes that need to be placed compared to a 2.4 GHz solution. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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14 pages, 2079 KiB

Open AccessArticle

Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network

by Sinan Sousan, Alyson Gray, Christopher Zuidema, Larissa Stebounova, Geb Thomas, Kirsten Koehler and Thomas Peters

Sensors 2018, 18(9), 3008; https://doi.org/10.3390/s18093008 - 08 Sep 2018

Cited by 22 | Viewed by 4064

Abstract

Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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11 pages, 8224 KiB

Open AccessArticle

Shaping Streamflow Using a Real-Time Stormwater Control Network

by Abhiram Mullapudi, Matthew Bartos, Brandon Wong and Branko Kerkez

Sensors 2018, 18(7), 2259; https://doi.org/10.3390/s18072259 - 13 Jul 2018

Cited by 30 | Viewed by 4170

Abstract

“Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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19 pages, 6441 KiB

Open AccessArticle

Spatio-Temporal Optimization of Perishable Goods’ Shelf Life by a Pro-Active WSN-Based Architecture

by Daniela De Venuto and Giovanni Mezzina

Sensors 2018, 18(7), 2126; https://doi.org/10.3390/s18072126 - 02 Jul 2018

Cited by 28 | Viewed by 4869

Abstract

The waste in the perishable goods supply-chain has prompted many global organizations (e.g., FAO and WHO), to develop the Hazard Analysis and Critical Control Points (HACCP) protocol that ensures a high degree of food quality, minimizing the losses in all the stages of the farm-to-fork chain. It has been proven that good warehouse management practices improve the average life of perishable goods. The advances in wireless sensors network (WSN) technology offers the possibility of a “smart” storage organization. In this paper, a low cost reprogrammable WSN-based architecture for functional warehouse management is proposed. The management is based on the continuous monitoring of environmental parameters (i.e., temperature, light exposure and relative humidity), and on their combination to extract a spatial real-time prediction of the product shelf life. For each product, the quality decay is computed by using a 1st order kinetic Arrhenius model to the whole storage site area. It strives to identify, in a way compatible with the other products’ shelf lives, the position within the warehouse that maximizes the food expiration date. The shelf life computing and the “first-expired first-out” logistic problem are entrusted to a Raspberry Pi-based central unit, which manages a set of automated pallet transporters for the displacement of products, according to the computed shelf lives. The management unit supports several commercial light/temperature/humidity sensor solutions, implementing ZigBee, Bluetooth and HTTP-request interfaces. A proof of concept of the presented pro-active WSN-based architecture is also shown. Comparing the proposed monitoring system for the storage of e.g., agricultural products, with a typical one, the experimental results show an improvement of the expected expiration date of about 1.2 ± 0.5 days, for each pallet, when placed in a non-refrigerated environment. In order to stress the versatility of the WSN solution, a section is dedicated to the implemented system user interfaces that highlight detecting critical situations and allow timely automatic or human interventions, minimizing the latter. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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31 pages, 14229 KiB

Open AccessArticle

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks

by Amalia Luque, Jesús Gómez-Bellido, Alejandro Carrasco and Julio Barbancho

Sensors 2018, 18(6), 1803; https://doi.org/10.3390/s18061803 - 03 Jun 2018

Cited by 17 | Viewed by 3604

Abstract

The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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20 pages, 658 KiB

Open AccessArticle

Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement

by Venkat Roy, Andrea Simonetto and Geert Leus

Sensors 2018, 18(6), 1778; https://doi.org/10.3390/s18061778 - 01 Jun 2018

Cited by 12 | Viewed by 3349

Abstract

We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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41 pages, 6654 KiB

Open AccessArticle

A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs

by Xin Xu, Minjiao Yuan, Xiao Liu, Anfeng Liu, Neal N. Xiong, Zhiping Cai and Tian Wang

Sensors 2018, 18(5), 1422; https://doi.org/10.3390/s18051422 - 03 May 2018

Cited by 27 | Viewed by 3385

Abstract

In wireless sensor networks (WSNs), communication links are typically error-prone and unreliable, so providing reliable and timely data routing for loss- and delay-sensitive applications in WSNs it is a challenge issue. Additionally, with specific thresholds in practical applications, the loss and delay sensitivity implies requirements for high reliability and low delay. Opportunistic Routing (OR) has been well studied in WSNs to improve reliability for error-prone and unreliable wireless communication links where the transmission power is assumed to be identical in the whole network. In this paper, a Cross-layer Optimized Opportunistic Routing (COOR) scheme is proposed to improve the communication link reliability and reduce delay for loss-and-delay sensitive WSNs. The main contribution of the COOR scheme is making full use of the remaining energy in networks to increase the transmission power of most nodes, which will provide a higher communication reliability or further transmission distance. Two optimization strategies referred to as COOR(R) and COOR(P) of the COOR scheme are proposed to improve network performance. In the case of increasing the transmission power, the COOR(R) strategy chooses a node that has a higher communication reliability with same distance in comparison to the traditional opportunistic routing when selecting the next hop candidate node. Since the reliability of data transmission is improved, the delay of the data reaching the sink is reduced by shortening the time of communication between candidate nodes. On the other hand, the COOR(P) strategy prefers a node that has the same communication reliability with longer distance. As a result, network performance can be improved for the following reasons: (a) the delay is reduced as fewer hops are needed while the packet reaches the sink in longer transmission distance circumstances; (b) the reliability can be improved since it is the product of the reliability of every hop of the routing path, and the count is reduced while the reliability of each hop is the same as the traditional method. After analyzing the energy consumption of the network in detail, the value of optimized transmission power in different areas is given. On the basis of a large number of experimental and theoretical analyses, the results show that the COOR scheme will increase communication reliability by 36.62–87.77%, decrease delay by 21.09–52.48%, and balance the energy consumption of 86.97% of the nodes in the WSNs. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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17 pages, 10027 KiB

Open AccessArticle

Comparing Building and Neighborhood-Scale Variability of CO₂ and O₃ to Inform Deployment Considerations for Low-Cost Sensor System Use

by Ashley Collier-Oxandale, Evan Coffey, Jacob Thorson, Jill Johnston and Michael Hannigan

Sensors 2018, 18(5), 1349; https://doi.org/10.3390/s18051349 - 26 Apr 2018

Cited by 11 | Viewed by 3733

Abstract

The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select deployment sites and how to strategically place sensors at these sites. Given that sensors are often placed at homes and businesses, ideal placement is not always possible. Considerations such as convenience, access, aesthetics, and safety are also important. To explore this issue, we placed multiple sensor systems at an existing field site allowing us to examine both neighborhood-level and building-level variability during a concurrent period for CO₂ (a primary pollutant) and O₃ (a secondary pollutant). In line with previous studies, we found that local and transported emissions as well as thermal differences in sensor systems drive variability, particularly for high-time resolution data. While this level of variability is unlikely to affect data on larger averaging scales, this variability could impact analysis if the user is interested in high-time resolution or examining local sources. However, with thoughtful placement and thorough documentation, high-time resolution data at the neighborhood level has the potential to provide us with entirely new information on local air quality trends and emissions. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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21 pages, 24967 KiB

Open AccessArticle

A Wireless Sensor Network for the Real-Time Remote Measurement of Aeolian Sand Transport on Sandy Beaches and Dunes

by Alessandro Pozzebon, Irene Cappelli, Alessandro Mecocci, Duccio Bertoni, Giovanni Sarti and Fernanda Alquini

Sensors 2018, 18(3), 820; https://doi.org/10.3390/s18030820 - 08 Mar 2018

Cited by 24 | Viewed by 4669

Abstract

Direct measurements of aeolian sand transport on coastal dunes and beaches is of paramount importance to make correct decisions about coast management. As most of the existing studies are mainly based on a statistical approach, the solution presented in this paper proposes a sensing structure able to orient itself according to wind direction and directly calculate the amount of wind-transported sand by collecting it and by measuring its weight. Measurements are performed remotely without requiring human action because the structure is equipped with a ZigBee radio module, which periodically sends readings to a local gateway. Here data are processed by a microcontroller and then transferred to a remote data collection centre, through GSM technology. The ease of installation, the reduced power consumption and the low maintenance required, make the proposed solution able to work independently, limiting human intervention, for all the duration of the expected experimental campaign. In order to analyze the cause-effect relationship between the transported sand and the wind, the sensing structure is integrated with a multi-layer anemoscope-anemometer structure. The overall sensor network has been developed and tested in the laboratory, and its operation has been validated in field through a 48 h measurement campaign. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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27 pages, 11406 KiB

Open AccessArticle

LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

by Xinyan Zhou, Xiaoyu Ji, Yi-chao Chen, Xiaopeng Li and Wenyuan Xu

Sensors 2018, 18(3), 747; https://doi.org/10.3390/s18030747 - 01 Mar 2018

Cited by 9 | Viewed by 3643

Abstract

Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol,

L E S S

, which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate

L E S S

in both real WSN systems and a large-scale simulation, and the results show that

L E S S

can reduce energy and bandwidth consumption by up to

50 %

while still achieving more than

90 %

link quality estimation accuracy. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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16 pages, 1903 KiB

Open AccessArticle

Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications

by Jaume Segura-Garcia, Juan Miguel Navarro-Ruiz, Juan J. Perez-Solano, Jose Montoya-Belmonte, Santiago Felici-Castell, Maximo Cobos and Ana M. Torres-Aranda

Sensors 2018, 18(3), 690; https://doi.org/10.3390/s18030690 - 26 Feb 2018

Cited by 14 | Viewed by 5080

Abstract

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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2817 KiB

Open AccessArticle

Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks

by Siddhartha Bhandari, Neil Bergmann, Raja Jurdak and Branislav Kusy

Sensors 2018, 18(1), 11; https://doi.org/10.3390/s18010011 - 22 Dec 2017

Cited by 9 | Viewed by 4491

Abstract

Wireless sensor networks are widely used in environmental monitoring. The number of sensor nodes to be deployed will vary depending on the desired spatio-temporal resolution. Selecting an optimal number, position and sampling rate for an array of sensor nodes in environmental monitoring is a challenging question. Most of the current solutions are either theoretical or simulation-based where the problems are tackled using random field theory, computational geometry or computer simulations, limiting their specificity to a given sensor deployment. Using an empirical dataset from a mine rehabilitation monitoring sensor network, this work proposes a data-driven approach where co-integrated time series analysis is used to select the number of sensors from a short-term deployment of a larger set of potential node positions. Analyses conducted on temperature time series show 75% of sensors are co-integrated. Using only 25% of the original nodes can generate a complete dataset within a 0.5 °C average error bound. Our data-driven approach to sensor position selection is applicable for spatiotemporal monitoring of spatially correlated environmental parameters to minimize deployment cost without compromising data resolution. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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6698 KiB

Open AccessArticle

Real-Time Alpine Measurement System Using Wireless Sensor Networks

by Sami A. Malek, Francesco Avanzi, Keoma Brun-Laguna, Tessa Maurer, Carlos A. Oroza, Peter C. Hartsough, Thomas Watteyne and Steven D. Glaser

Sensors 2017, 17(11), 2583; https://doi.org/10.3390/s17112583 - 09 Nov 2017

Cited by 23 | Viewed by 7566

Abstract

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km

^{2}

network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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Review

Jump to: Research

22 pages, 1184 KiB

Open AccessReview

Energy Harvesting Sources, Storage Devices and System Topologies for Environmental Wireless Sensor Networks: A Review

by Michal Prauzek, Jaromir Konecny, Monika Borova, Karolina Janosova, Jakub Hlavica and Petr Musilek

Sensors 2018, 18(8), 2446; https://doi.org/10.3390/s18082446 - 27 Jul 2018

Cited by 167 | Viewed by 10789

Abstract

The operational efficiency of remote environmental wireless sensor networks (EWSNs) has improved tremendously with the advent of Internet of Things (IoT) technologies over the past few years. EWSNs require elaborate device composition and advanced control to attain long-term operation with minimal maintenance. This article is focused on power supplies that provide energy to run the wireless sensor nodes in environmental applications. In this context, EWSNs have two distinct features that set them apart from monitoring systems in other application domains. They are often deployed in remote areas, preventing the use of mains power and precluding regular visits to exchange batteries. At the same time, their surroundings usually provide opportunities to harvest ambient energy and use it to (partially) power the sensor nodes. This review provides a comprehensive account of energy harvesting sources, energy storage devices, and corresponding topologies of energy harvesting systems, focusing on studies published within the last 10 years. Current trends and future directions in these areas are also covered. Full article

(This article belongs to the Special Issue Sensor Networks for Environmental Observations)

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