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Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 38375

Special Issue Editor

Dr. Evan Thomas

E-Mail Website
Guest Editor
Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
Interests: global engineering; global health; water; sanitation; agriculture; energy; ICT4D; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring and managing distributed water, sanitation, agricultural, and energy resources and services in remote and/or low-income regions are increasingly important as population pressures and climate change impact the reliability of these resources. The aim and scope of this Special Issue of Sustainability is to present and review emerging methods and technologies including “internet of things” sensor systems, cellular-based data collection, remote sensing, machine learning, and other analytical tools designed to support the remote monitoring and management of water, sanitation, agricultural, and energy resources in remote and/or low-income regions. Examples may include remotely reporting sensor technologies for monitoring water service infrastructure; satellite-based remote sensing of agricultural yields; localized air quality monitoring; cellular-based survey and decision support tools; and machine learning-enabled analytics. Papers selected for this Special Issue will be subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

Assoc. Prof. Dr. Evan Thomas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IOT
  • Sensors
  • Remote sensing
  • Global health
  • ICT4D
  • Water
  • Sanitation
  • Agriculture
  • Energy

Published Papers (7 papers)

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Research

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11 pages, 2051 KiB  
Article
Demonstration of Tryptophan-Like Fluorescence Sensor Concepts for Fecal Exposure Detection in Drinking Water in Remote and Resource Constrained Settings
by Emily Bedell, Taylor Sharpe, Timothy Purvis, Joe Brown and Evan Thomas
Sustainability 2020, 12(9), 3768; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093768 - 06 May 2020
Cited by 12 | Viewed by 6411
Abstract
Low-cost, field-deployable, near-time methods for assessing water quality are not available when and where waterborne infection risks are greatest. We describe the development and testing of a novel device for the measurement of tryptophan-like fluorescence (TLF), making use of recent advances in deep-ultraviolet [...] Read more.
Low-cost, field-deployable, near-time methods for assessing water quality are not available when and where waterborne infection risks are greatest. We describe the development and testing of a novel device for the measurement of tryptophan-like fluorescence (TLF), making use of recent advances in deep-ultraviolet light emitting diodes (UV-LEDs) and sensitive semiconductor photodiodes and photomultipliers. TLF is an emerging indicator of water quality that is associated with members of the coliform group of bacteria and therefore potential fecal contamination. Following the demonstration of close correlation between TLF and E. coli in model waters and proof of principle with sensitivity of 4 CFU/mL for E. coli, we further developed a two-LED flow-through configuration capable of detecting TLF levels corresponding to “high risk” fecal contamination levels (>10 CFU/100 mL). Findings to date suggest that this device represents a scalable solution for remote monitoring of drinking water supplies to identify high-risk drinking water in near-time. Such information can be immediately actionable to reduce risks. Full article
(This article belongs to the Special Issue Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions)
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16 pages, 2069 KiB  
Article
Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference
by Zaid Tashman, Christoph Gorder, Sonali Parthasarathy, Mohamad M. Nasr-Azadani and Rachel Webre
Sustainability 2020, 12(7), 2897; https://0-doi-org.brum.beds.ac.uk/10.3390/su12072897 - 05 Apr 2020
Cited by 10 | Viewed by 5316
Abstract
For billions of people living in remote and rural communities in the developing countries, small water systems are the only source of clean drinking water. Due to the rural nature of such water systems, site visits may occur infrequently. This means broken water [...] Read more.
For billions of people living in remote and rural communities in the developing countries, small water systems are the only source of clean drinking water. Due to the rural nature of such water systems, site visits may occur infrequently. This means broken water systems can remain in a malfunctioning state for months, forcing communities to return to drinking unsafe water. In this work, we present a novel two-level anomaly detection system aimed to detect malfunctioning remote sensored water hand-pumps, allowing for a proactive approach to pump maintenance. To detect anomalies, we need a model of normal water usage behavior first. We train a multilevel probabilistic model of normal usage using approximate variational Bayesian inference to obtain a conditional probability distribution over the hourly water usage data. We then use this conditional distribution to construct a level-1 scoring function for each hourly water observation and a level-2 scoring function for each pump. Probabilistic models and Bayesian inference collectively were chosen for their ability to capture the high temporal variability in the water usage data at the individual pump level as well as their ability to estimate interpretable model parameters. Experimental results in this work have demonstrated that the pump scoring function is able to detect malfunctioning sensors as well as a change in water usage behavior allowing for a more responsive and proactive pump system maintenance. Full article
(This article belongs to the Special Issue Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions)
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15 pages, 7664 KiB  
Article
Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning
by Pulkit Sharma, Achut Manandhar, Patrick Thomson, Jacob Katuva, Robert Hope and David A. Clifton
Sustainability 2019, 11(22), 6312; https://0-doi-org.brum.beds.ac.uk/10.3390/su11226312 - 11 Nov 2019
Cited by 2 | Viewed by 2238
Abstract
In the context of developing countries, effective groundwater resource management is often hindered by a lack of data integration between resource availability, water demand, and the welfare of water users. As a consequence, drinking water-related policies and investments, while broadly beneficial, are unlikely [...] Read more.
In the context of developing countries, effective groundwater resource management is often hindered by a lack of data integration between resource availability, water demand, and the welfare of water users. As a consequence, drinking water-related policies and investments, while broadly beneficial, are unlikely to be able to target the most in need. To find the households in need, we need to estimate their welfare status first. However, the current practices for estimating welfare need a detailed questionnaire in the form of a survey which is time-consuming and resource-intensive. In this work, we propose an alternate solution to this problem by performing a small set of cost-effective household surveys, which can be collected over a short amount of time. We try to compensate for the loss of information by using other modalities of data. By combining different modalities of data, this work aims to characterize the welfare status of people with respect to their local drinking water resource. This work employs deep learning-based methods to model welfare using multi-modal data from household surveys, community handpump abstraction, and groundwater levels. We employ a multi-input multi-output deep learning framework, where different types of deep learning models are used for different modalities of data. Experimental results in this work have demonstrated that the multi-modal data in the form of a small set of survey questions, handpump abstraction data, and groundwater level can be used to estimate the welfare status of households. In addition, the results show that different modalities of data have complementary information, which, when combined, improves the overall performance of our ability to predict welfare. Full article
(This article belongs to the Special Issue Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions)
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14 pages, 4957 KiB  
Article
Changes in Land Cover in Cacheu River Mangroves Natural Park, Guinea-Bissau: The Need for a More Sustainable Management
by Eva M. García del Toro and María Isabel Más-López
Sustainability 2019, 11(22), 6247; https://0-doi-org.brum.beds.ac.uk/10.3390/su11226247 - 07 Nov 2019
Cited by 6 | Viewed by 2970
Abstract
The aim of this paper is to study the evolution of vegetation and potential changes in land use in the Cacheu River Mangroves Natural Park in the Republic of Guinea-Bissau. To do this, we will study variations in the Normalized Difference Vegetation Index [...] Read more.
The aim of this paper is to study the evolution of vegetation and potential changes in land use in the Cacheu River Mangroves Natural Park in the Republic of Guinea-Bissau. To do this, we will study variations in the Normalized Difference Vegetation Index (NDVI). In order to perform the calculations and subsequent analysis, images of the park from the years 2010 and 2017, corresponding to the same period of the year, so that the phenological stage is the same, were used. To perform a more reliable analysis, the park was divided into five different areas based upon the vegetation type or main use of the land in each of them; i.e.: mangals, palm forest, paddies, savannahs and others. Using a statistical sample, the NDVIs were calculated for each of these areas. The study made it possible to conclude that the changes in land cover observed represent a decrease in mangrove swamps, which are probably being replaced by other land uses, despite the fact that these forests constitute the most important ecological area of all those that make up the park. The park will therefore benefit from a more sustainable management. Full article
(This article belongs to the Special Issue Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions)
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16 pages, 484 KiB  
Article
Improved Drought Resilience Through Continuous Water Service Monitoring and Specialized Institutions—A Longitudinal Analysis of Water Service Delivery Across Motorized Boreholes in Northern Kenya
by Nick Turman-Bryant, Corey Nagel, Lauren Stover, Christian Muragijimana and Evan A. Thomas
Sustainability 2019, 11(11), 3046; https://0-doi-org.brum.beds.ac.uk/10.3390/su11113046 - 29 May 2019
Cited by 15 | Viewed by 6007
Abstract
Increasing frequency and severity of drought is driving increased use of groundwater resources in arid regions of Northern Kenya, where approximately 2.5 million people depend on groundwater for personal use, livestock, and limited irrigation. As part of a broader effort to provide more [...] Read more.
Increasing frequency and severity of drought is driving increased use of groundwater resources in arid regions of Northern Kenya, where approximately 2.5 million people depend on groundwater for personal use, livestock, and limited irrigation. As part of a broader effort to provide more sustainable water, sanitation, and hygiene services in the region, we have collected data related to site functionality and use for approximately 120 motorized boreholes across five counties. Using a multilevel model to account for geospatial and temporal clustering, we found that borehole sites, which counties had identified as strategic assets during drought, ran on average about 1.31 h less per day compared to non-strategic borehole sites. As this finding was contrary to our hypothesis that strategic boreholes would exhibit greater use on average compared to non-strategic boreholes, we consider possible explanations for this discrepancy. We also use a coupled human and natural systems framework to explore how policies and program activities in a complex system depend on consistent and reliable feedback mechanisms. Funding was provided by the United States Agency for International Development. The views expressed in this article do not necessarily reflect the views of the United States Agency for International Development or the United States Government. Full article
(This article belongs to the Special Issue Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions)
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17 pages, 2892 KiB  
Article
An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques
by KiJeon Nam, Pouya Ifaei, Sungku Heo, Gahee Rhee, Seungchul Lee and ChangKyoo Yoo
Sustainability 2019, 11(10), 2970; https://0-doi-org.brum.beds.ac.uk/10.3390/su11102970 - 24 May 2019
Cited by 12 | Viewed by 3344
Abstract
Detection and isolation of burst locations in water distribution networks (WDN) are challenging problems in urban management because burst events cause considerable economic, social, and environmental losses. In the present study, a novel monitoring and sensor placement approach is proposed for rapid and [...] Read more.
Detection and isolation of burst locations in water distribution networks (WDN) are challenging problems in urban management because burst events cause considerable economic, social, and environmental losses. In the present study, a novel monitoring and sensor placement approach is proposed for rapid and robust burst detection. Accordingly, a hybrid principal component analysis (PCA) and standardized exponential weighted moving average (EWMA) system is proposed for WDN monitoring and management. In addition, the optimal sensor configuration is obtained using PCA, k-means clustering, and a sensitivity analysis considering the diurnal patterns and the noises of pressure and flowrate data in the WDN. The proposed system is applied to a branched WDN, and the results are compared to those obtained with conventional monitoring systems. The results show that the proposed system detected the burst occurrence regardless of noise size with a detection rate of 93%. Compared to conventional systems, the isolation ratio improved by 10%, indicating that the bursts were isolated more accurately. In addition, the corresponding sensor configuration was 40% less expensive than the conventional systems. Full article
(This article belongs to the Special Issue Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions)
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Review

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19 pages, 6057 KiB  
Review
A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
by Anish Paul Antony, Kendra Leith, Craig Jolley, Jennifer Lu and Daniel J. Sweeney
Sustainability 2020, 12(9), 3750; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093750 - 06 May 2020
Cited by 59 | Viewed by 10086
Abstract
In order to feed a growing global population projected to increase to 9 billion by 2050, food production will need to increase from its current level. The bulk of this growth will need to come from smallholder farmers who rely on generational knowledge [...] Read more.
In order to feed a growing global population projected to increase to 9 billion by 2050, food production will need to increase from its current level. The bulk of this growth will need to come from smallholder farmers who rely on generational knowledge in their farming practices and who live in locations where weather patterns and seasons are becoming less predictable due to climate change. The expansion of internet-connected devices is increasing opportunities to apply digital tools and services on smallholder farms, including monitoring soil and plants in horticulture, water quality in aquaculture, and ambient environments in greenhouses. In combination with other food security efforts, internet of things (IoT)-enabled precision smallholder farming has the potential to improve livelihoods and accelerate low- and middle-income countries’ journey to self-reliance. Using a combination of interviews, surveys and site visits to gather information, this research presents a review of the current state of the IoT for on-farm measurement, cases of successful IoT implementation in low- and middle-income countries, challenges associated with implementing the IoT on smallholder farms, and recommendations for practitioners. Full article
(This article belongs to the Special Issue Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions)
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