On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision

Sun, Yuanhao; Ding, Weimin; Shu, Lei; Li, Kailiang; Zhou, Zhangbing; Han, Guangjie

doi:10.1109/jsyst.2021.3104107

Cited by 22 publications

(6 citation statements)

References 50 publications

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“…Very recent studies have foreseen crowdsensing monitoring techniques as indispensable components in the emerging contexts of smart agriculture [49]. Thanks to the evolution in the worldwide economy, even in underdeveloped countries, an increasing proportion of farmers have at least one smart device and would be willing to use it for further increasing their income [258].…”

Section: Crowdsensing For Land Monitoringmentioning

confidence: 99%

“…The shift toward the use of mobile crowdsensing as a viable and cost-effective data collection paradigm has been thoroughly discussed in [47] and further investigated in [48], where the emerging technologies of wearable sensors have been classified and characterized in detail. A rather complete overview of the potential uses of mobile crowdsensing in smart agriculture was provided in [49], while [50] illustrated the main applications of smartphone-based environmental monitoring in the rising IoT era. In addition, a comprehensive analysis on the use of social media as a promising crowdsensing tool for natural-disaster management has been conducted in [51].…”

Section: Introductionmentioning

confidence: 99%

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Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

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Section: Crowdsensing For Land Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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“…Mobile Crowdsourcing: Mobile Crowdsourcing (MCS) utilizes the power of mobile devices to accomplish specific sensing and data collection tasks without requiring predeployed dedicated infrastructure. MCS and crowdsensing concepts are intertwined with many applications of the IoT systems, such as ride-sharing, transportation, disaster management, agriculture, and community healthcare [56]- [58]. The IoT devices that could play the role of service providers and/or service requesters in the SIoT network can be captured and utilized in MCS.…”

Section: Practical Applicationsmentioning

confidence: 99%

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

et al. 2022

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Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and contextaware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a lowcomplexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices' features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

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“…Smart agriculture is established based on the digital process, combining the data in the agricultural field and the Information and Communications Technology (ICT) (Friha et al, 2021 ; Sun et al, 2021 ). As for the intelligent plant protection, the related data have various sources, such as the sensing of soil (Yin et al, 2021 ), light intensity (Yu et al, 2021 ), water stress (Mundim and Pringle, 2018 ; Ihuoma and Madramootoo, 2019 ), mixture of water and fertilizer (Jia et al, 2019 ), temperature and humidity (Mekala and Viswanathan, 2019 ), etc.…”

Section: Introductionmentioning

confidence: 99%

Distance-Entropy: An Effective Indicator for Selecting Informative Data

Yang

Chao

2022

Front. Plant Sci.

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Smart agriculture is inseparable from data gathering, analysis, and utilization. A high-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, while ignoring the crucial information aspect. In this work, taking the crop pest recognition task as an example, we proposed an effective indicator of distance-entropy to distinguish the good and bad data from the perspective of information. Many comparative experiments, considering the mapping feature dimensions and base data sizes, were conducted to testify the validity and robustness of this indicator. Both the numerical and the visual results demonstrate the effectiveness and stability of the proposed distance-entropy method. In general, this study is a relatively cutting-edge work in smart agriculture, which calls for attention to the quality assessment of the data information and provides some inspiration for the subsequent research on data mining, as well as for the dataset optimization for practical applications.

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On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision

Cited by 22 publications

References 50 publications

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Distance-Entropy: An Effective Indicator for Selecting Informative Data

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