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IoT and Wireless Sensor Network in Environmental Monitoring Systems

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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: 30 June 2024 | Viewed by 5380

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Special Issue Editors

Dr. Goncalo Jesus

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Guest Editor

Hydraulics and Environment Department, University of Lisbon, 1700-066 Lisbon, Portugal
Interests: real-time prediction systems in support of emergency management; parallel computing in distributed environments; water emergency management; machine learning; reliable monitoring in environmental sensor networks; promoting the detection, categorization and correction of abnormal measurements obtained by environmental sensor networks; development of automated solutions for reliable data quality for aquatic environments
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Dr. Anabela Oliveira

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Guest Editor

Hydraulics and Environment Department, University of Lisbon, 1700-066 Lisbon, Portugal
Interests: real-time forecasting and monitoring of aquatic systems; high-performance modeling in aquatic systems; web platforms for risk and emergency analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental monitoring (EM) has various applications, with the aim to cater to multiple purposes, including weather forecasting, air pollution control, water monitoring and fire damage assessment, among many others. Nowadays, EM is possible thanks to easy-to-access heterogeneous wireless sensor networks (WSNs) that are composed of modern sensors communicating among themselves or have a gateway through specific network protocols. Internet of things (IoT) devices are employed in WSNs to perform effective tasks such as waste management, temperature control, pollution discharges, vehicle marking and others.

These technologies, such as IoT and WSNs, have made the monitoring of the environment simple for everyone, and these are now essential components of research and management initiatives. With these technologies as a backbone of environmental monitoring systems, researchers are tackling some of the particularly difficult challenges of EM, and contributing to significant advances in a plurality of areas, such as data science, networks and sensor hardware/software architecture.

This Special Issue will bring together innovative works related to “IoT and Wireless Sensor Network in Environmental Monitoring Systems”, addressing several key issues that include, but are not limited to, the following:

Intelligent environmental monitoring;
Data reliability and data quality;
Low-cost and low-maintenance monitoring networks;
Communication challenges;
Edge computation.

Dr. Goncalo Jesus
Dr. Anabela Oliveira
Guest Editors

Manuscript Submission Information

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Published Papers (5 papers)

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Research

24 pages, 2911 KiB

Open AccessArticle

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning

by Paola Pierleoni, Roberto Concetti, Alberto Belli, Lorenzo Palma, Simone Marzorati and Marco Esposito

Sensors 2023, 23(20), 8431; https://0-doi-org.brum.beds.ac.uk/10.3390/s23208431 - 13 Oct 2023

Viewed by 962

Abstract

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave “picking” directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution. Full article

(This article belongs to the Special Issue IoT and Wireless Sensor Network in Environmental Monitoring Systems)

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20 pages, 2338 KiB

Open AccessArticle

Task Assignment Optimization in Multi-UAV-Assisted WSNs Considering Energy Budget and Sensor Distribution Characteristics

by Qile Xie, Wendong Zhao, Cuntao Liu and Laixian Peng

Sensors 2023, 23(18), 7842; https://0-doi-org.brum.beds.ac.uk/10.3390/s23187842 - 12 Sep 2023

Viewed by 938

Abstract

In emergency situations, such as disaster area monitoring, deadlines for data collection are strict. The task time minimization problem concerning multi-UAV-assisted data collection in wireless sensor networks (WSNs), with different distribution characteristics, such as the geographical or importance of the information of the sensors, is studied. Our goal is to minimize the mission time for UAVs by optimizing their assignment, trajectory, and deployment locations, while the UAV energy constraint is taken into account. For the coupling relationship between the task assignment, trajectory, and hover position, it is not easy to solve the mixed integer non-convex problem directly. The problem is divided into two sub-problems: (1) UAV task assignment problem and (2) trajectory and hover position optimization problem. To solve this problem, an assignment algorithm, based on sensor distribution characteristics (AASDC), is proposed. The simulation results show that the collection time of our scheme is shorter than that of existing comparison schemes when using the same data size. Full article

(This article belongs to the Special Issue IoT and Wireless Sensor Network in Environmental Monitoring Systems)

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21 pages, 1379 KiB

Open AccessArticle

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning

by Wei Fu, Qin Peng and Canwei Hu

Sensors 2023, 23(17), 7393; https://0-doi-org.brum.beds.ac.uk/10.3390/s23177393 - 24 Aug 2023

Viewed by 831

Abstract

In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations. Full article

(This article belongs to the Special Issue IoT and Wireless Sensor Network in Environmental Monitoring Systems)

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29 pages, 4043 KiB

Open AccessArticle

A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks

by Danqi Chen, Yanxia Zhang, Guoli Pang, Fangping Gao and Li Duan

Sensors 2023, 23(11), 5068; https://0-doi-org.brum.beds.ac.uk/10.3390/s23115068 - 25 May 2023

Cited by 5 | Viewed by 1246

Abstract

Disaster monitoring is a primary task for wireless sensor networks. Systems for the rapid reporting of earthquake information are a crucial aspect of disaster monitoring. Furthermore, during emergency rescue after a large earthquake, wireless sensor networks can provide pictures and sound information to save lives. Therefore, when accompanied by multimedia data flow, the alert and seismic data sent by the seismic monitoring nodes must be sufficiently fast. We present herein the architecture of a collaborative disaster-monitoring system that can obtain seismic data in a highly energy-efficient manner. In this paper, a hybrid superior node token ring MAC scheme is proposed for disaster monitoring in wireless sensor networks. This scheme consists of set-up and steady-state stages. A clustering approach was proposed for heterogeneous networks during the set-up stage. The proposed MAC operates in the duty cycle mode at the steady-state stage and is based on the virtual token ring of ordinary nodes, the polling all the superior nodes in one period, and alert transmissions with a low-power listening and shortened preamble approach during the sleep state. The proposed scheme can simultaneously satisfy the requirements of three types of data in disaster-monitoring applications. Based on embedded Markov chains, a model of the proposed MAC was developed and the mean queue length, mean cycle time, and mean upper bound of the frame delay were obtained. Using simulations under various conditions, the clustering approach performed better than the pLEACH approach, and the theoretical results of the proposed MAC were verified. We found that alerts and superior data have outstanding delay and throughput performances even under heavy traffic intensity, and the proposed MAC can provide a data rate of several hundred kb/s for superior and ordinary data. Considering all three types of data, the frame delay performances of the proposed MAC are better than those of the WirelessHART and DRX schemes, and the alert data of the proposed MAC have a maximum frame delay of 15 ms. These satisfy the application requirements of disaster monitoring. Full article

(This article belongs to the Special Issue IoT and Wireless Sensor Network in Environmental Monitoring Systems)

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12 pages, 7081 KiB

Open AccessArticle

Automatic Monitoring Alarm Method of Dammed Lake Based on Hybrid Segmentation Algorithm

by Ziming Cai, Liang Sun, Baosheng An, Xin Zhong, Wei Yang, Zhongyan Wang, Yan Zhou, Feng Zhan and Xinwei Wang

Sensors 2023, 23(10), 4714; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104714 - 12 May 2023

Cited by 1 | Viewed by 835

Abstract

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system. Full article

(This article belongs to the Special Issue IoT and Wireless Sensor Network in Environmental Monitoring Systems)

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