Internet of Things in Marine Environment Monitoring: A Review

Guobao, Xu; Shi, Yufeng; Sun, Xueyan; Shen, Weiming

doi:10.3390/s19071711

Cited by 154 publications

(99 citation statements)

References 78 publications

(143 reference statements)

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“…83 Key to operationalizing hybrid systems will be smart infrastructure capabilities-incorporating the Internet of Things-that provide accurate, current, and system-wide information for managing hybrid systems in real-time and monitor their benefits for people and nature. 84,85 Research is needed for the development of low-cost sensors that produce reliable data from harsh environments, coupled with advanced analytics to handle the massive flow of data, including artificial intelligence and machine learning algorithms to enhance prediction and real-time operation. New standards, protocols, and benchmarks are needed to support interoperability and certification of these technologies and tools, ensuring reliable, consistent, and quality data.…”

Section: Ll Open Accessmentioning

confidence: 99%

Transforming Cities through Water-Sensitive Principles and Practices

2020

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Many global cities and towns are facing complex and interrelated challenges associated with population growth, resource constraints, aging infrastructure, and degraded environments, exacerbated by increasing climate uncertainty. The United Nation's Agenda 2030 is a global call to action with 17 Sustainable Development Goals (SDGs). Water is the common currency linking nearly every SDG. SDG 11 aspires to ''Make cities and human settlements inclusive, safe, resilient, and sustainable'' and the water-sensitive city represents an aspirational future state for water management where servicing strategies deliver long-term sustainability, liveability, resilience, and prosperity. This social-technical endeavor is based on three principles of practice proposed in 2009. They have since been operationalized and adapted in many projects globally, and across a range of social, institutional, and biophysical contexts. In this Perspective, we reflect on lessons learned, required actions for mainstreaming water-sensitive practices, the next-wave research agenda, and opportunities to catalyze actions in sectors beyond water. ll

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Section: Ll Open Accessmentioning

confidence: 99%

Transforming Cities through Water-Sensitive Principles and Practices

2020

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“…The Internet of Things (IoT) systems are playing a role in advancing the development of different coastal and marine environments by accessing, securing, managing, and controlling data infrastructures [308,309]. This allows the monitoring of environmental conditions by communicating data from different networks into a single infrastructure.…”

Section: Opportunities For Improving the Integrated Coastal And Marinmentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

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“…A remote monitoring system is an emerging technique. In remote monitoring scheme, Internet of Things (IoT) is widely used method for various inspection applications, including health care [ 4 ], modern farming [ 5 , 6 ], human surveillance, environmental monitoring [ 7 , 8 ], object tracking in smart city [ 9 , 10 ] etc. Through IoT, the insect control team can monitor the insects trap anywhere on the globe.Potamitis et al [ 11 ] proposed an IoT based smart trap monitoring system for controlling the crop insects.…”

Section: Introductionmentioning

confidence: 99%

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Ramalingam

Mohan

Pookkuttath

et al. 2020

Sensors

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Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

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Internet of Things in Marine Environment Monitoring: A Review

Cited by 154 publications

References 78 publications

Transforming Cities through Water-Sensitive Principles and Practices

Transforming Cities through Water-Sensitive Principles and Practices

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

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