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The Sensor Location-Allocation Problem for Environmental Sensing
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The Sensor Location-Allocation Problem for Environmental Sensing

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 15191

Special Issue Editor

Dr. Shai Kendler

E-Mail Website
Guest Editor
Technion Enviromatics Lab (TechEL) Department of Environmental, Water and Agricultural Engineering Faculty of Civil & Environmental Engineering, Technion Israel Institute of Technology, Haifa 3200003, Israel
Interests: sensing, sensor design and operation; spectroscopy; hyperspectral imaging; data analysis; network deployment; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring air, water, and soil quality and seismic, smoke, and fire activity requires the use of sensing technologies. Recently, mobile autonomous sensing systems for crop disease detection, irrigation, and fertilization monitoring have been developed. The Wireless Distributed Sensor Network (WDSN) offers new opportunities thanks to its excellent communication protocols, low cost, and miniature size. However, even with the WDSN, detailed monitoring of the environment is still challenging. Hence, sensor location allocation has to consider the application requirements and resources.

We ask researchers to share papers describing sensor innovative miniature sensors, highly mobile sensing platforms (such as light aircraft and drones), methodologies for solving the location allocation problem using theoretical tools, experimental studies, and others. These include, but are not limited to:

  • Air pollution WDSN deployment;
  • Water quality monitoring;
  • Sensors placement for precise agriculture;
  • Search path planning for mobile autonomous sensing systems;
  • Industrial hygiene sensing network deployment;
  • Seismic activity sensors;
  • Ad hoc sensor network deployment;
  • Highly mobile sensing systems;
  • Miniature sensors.

Dr. Shai Kendler
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. Sensors 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 2600 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

  • sensor systems
  • safety management
  • air pollution
  • water quality
  • soil contamination
  • precise agriculture
  • optimization

Published Papers (7 papers)

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Research

39 pages, 2953 KiB  
Article
Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
by Fatma H. El-Fouly, Ahmed B. Altamimi and Rabie A. Ramadan
Sensors 2022, 22(24), 9789; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249789 - 13 Dec 2022
Cited by 3 | Viewed by 1240
Abstract
With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications. [...] Read more.
With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications. In addition, sensors might be mobile or stationary. Stationary sensors, once deployed, will not move; however, mobile nodes can move from one place to another. In most current applications, mobile sensors are used to collect data from stationary sensors. This raises many energy consumption challenges, including sensor networks’ energy consumption, urgent messages transfer for real-time analysis, and path planning. Moreover, sensors in sensor networks are usually exposed to environmental parameters and left unattended. These issues, up to our knowledge, are not deeply covered in the current research. This paper develops a complete framework to solve these challenges. It introduces novel path planning techniques considering areas’ priority, environmental parameters, and urgent messages. Consequently, a novel energy-efficient and reliable clustering algorithm is proposed considering the residual energy of the sensor nodes, the quality of wireless links, and the distance parameter representing the average intra-cluster distance. Moreover, it proposes a real-time, energy-efficient, reliable and environment-aware routing, taking into account the environmental data, link quality, delay, hop count, nodes’ residual energy, and load balancing. Furthermore, for the benefit of the sensor networks research community, all proposed algorithms are formed in integer linear programming (ILP) for optimal solutions. All proposed techniques are evaluated and compared to six recent algorithms. The results showed that the proposed framework outperforms the recent algorithms. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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9 pages, 1050 KiB  
Article
Air Pollution Detection Using a Novel Snap-Shot Hyperspectral Imaging Technique
by Arvind Mukundan, Chia-Cheng Huang, Ting-Chun Men, Fen-Chi Lin and Hsiang-Chen Wang
Sensors 2022, 22(16), 6231; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166231 - 19 Aug 2022
Cited by 33 | Viewed by 2890
Abstract
Air pollution has emerged as a global problem in recent years. Particularly, particulate matter (PM2.5) with a diameter of less than 2.5 μm can move through the air and transfer dangerous compounds to the lungs through human breathing, thereby creating major health issues. [...] Read more.
Air pollution has emerged as a global problem in recent years. Particularly, particulate matter (PM2.5) with a diameter of less than 2.5 μm can move through the air and transfer dangerous compounds to the lungs through human breathing, thereby creating major health issues. This research proposes a large-scale, low-cost solution for detecting air pollution by combining hyperspectral imaging (HSI) technology and deep learning techniques. By modeling the visible-light HSI technology of the aerial camera, the image acquired by the drone camera is endowed with hyperspectral information. Two methods are used for the classification of the images. That is, 3D Convolutional Neural Network Auto Encoder and principal components analysis (PCA) are paired with VGG-16 (Visual Geometry Group) to find the optical properties of air pollution. The images are classified into good, moderate, and severe based on the concentration of PM2.5 particles in the images. The results suggest that the PCA + VGG-16 has the highest average classification accuracy of 85.93%. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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14 pages, 3576 KiB  
Article
BREEZE—Boundary Red Emission Zone Estimation Using Unmanned Aerial Vehicles
by Oren Elmakis, Tom Shaked, Barak Fishbain and Amir Degani
Sensors 2022, 22(14), 5460; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145460 - 21 Jul 2022
Cited by 3 | Viewed by 1675
Abstract
Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These [...] Read more.
Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These teams risk their lives by manually mapping the gas dispersion. This process is currently performed using hand-held gas detectors and requires dense and exhaustive monitoring to achieve reliable maps. However, the conventional mapping process is impaired due to limited human mobility and monitoring capacities. In this context, this paper presents a method for gas sensing using unmanned aerial vehicles. The research focuses on developing a custom path planner—Boundary Red Emission Zone Estimation (BREEZE). BREEZE is an estimation approach that allows efficient red zone delineation by following its boundary. The presented approach improves the gas dispersion mapping process by performing adaptive path planning, monitoring gas dispersion in real time, and analyzing the measurements online. This approach was examined by simulating a cluttered urban site in different environmental conditions. The simulation results show the ability to autonomously perform red zone estimation faster than methods that rely on predetermined paths and with a precision higher than ninety percent. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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14 pages, 8514 KiB  
Article
Information Theory Solution Approach to the Air Pollution Sensor Location–Allocation Problem
by Ziv Mano, Shai Kendler and Barak Fishbain
Sensors 2022, 22(10), 3808; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103808 - 17 May 2022
Cited by 6 | Viewed by 1827
Abstract
Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance [...] Read more.
Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location–allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations’ informativity. This entropy method is compared to two commonly used heuristics for solving the location–allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors’ network measurements. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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17 pages, 2200 KiB  
Article
How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science
by Stefan Poslad, Tayyaba Irum, Patricia Charlton, Rafia Mumtaz, Muhammad Azam, Hassan Zaidi, Christothea Herodotou, Guangxia Yu and Fesal Toosy
Sensors 2022, 22(9), 3196; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093196 - 21 Apr 2022
Viewed by 2253
Abstract
To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse [...] Read more.
To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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15 pages, 9295 KiB  
Article
Optimal Wireless Distributed Sensor Network Design and Ad-Hoc Deployment in a Chemical Emergency Situation
by Shai Kendler and Barak Fishbain
Sensors 2022, 22(7), 2563; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072563 - 27 Mar 2022
Cited by 3 | Viewed by 1455
Abstract
Industrial activities involve the manipulation of harmful chemicals. As there is no way to guarantee fail-safe operation, the means and response methods must be planned in advance to cope with a chemical disaster. In these situations, first responders assess the situation from the [...] Read more.
Industrial activities involve the manipulation of harmful chemicals. As there is no way to guarantee fail-safe operation, the means and response methods must be planned in advance to cope with a chemical disaster. In these situations, first responders assess the situation from the atmospheric conditions, but they have scant data on the source of the contamination, which curtails their response toolbox. Hence, a sensor deployment strategy needs to be formulated in real-time based on the meteorological conditions, sensor attributes, and resources. This work examined the tradeoff between sensor locations and their attributes. The findings show that if the sensor locations are optimal, the number is more important than quality, in that the sensors’ dynamic range is a significant factor when quantifying leaks but is less important if the goal is solely to locate the leak source/s. This methodology can be used for sensor location-allocation under real-life conditions and technological constraints. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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20 pages, 1893 KiB  
Article
Area Coverage Maximization under Connectivity Constraint in Wireless Sensor Networks
by Frantz Tossa, Wahabou Abdou, Keivan Ansari, Eugène C. Ezin and Pierre Gouton
Sensors 2022, 22(5), 1712; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051712 - 22 Feb 2022
Cited by 13 | Viewed by 2428
Abstract
Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to [...] Read more.
Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to ensure maximum coverage of the chosen area and communication between the nodes forming the network. A major problem in network design is the deployment of sensors with the aim to ensure both maximum coverage and connectivity between sensor node. The maximum coverage problem addressed here focuses on calculating the area covered by the deployed sensor nodes. Thus, we seek to cover any type of area (regular or irregular shape) with a predefined number of homogeneous sensors using a genetic algorithm to find the best placement to ensure maximum network coverage under the constraint of connectivity between the sensors. Therefore, this paper tackles the dual problem of maximum coverage and connectivity between sensor nodes. We define the maximum coverage and connectivity problems and then propose a mathematical model and a complex objective function. The results show that the algorithm, called GAFACM (Genetic Algorithm For Area Coverage Maximization), covers all forms of the area for a given number of sensors and finds the best positions to maximize coverage within the area of interest while guaranteeing the connectivity between the sensors. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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