Comprehensive Database Creation for Potential Fish Zones Using IoT and ML with Assimilation of Geospatial Techniques

Kimothi, Sanjeev; Thapliyal, Asha; Singh, Rajesh; Rashid, Mamoon; Gehlot, Anita; Akram, Shaik Vaseem; Javed, Abdul Rehman

doi:10.3390/su15021062

Cited by 8 publications

(4 citation statements)

References 42 publications

(42 reference statements)

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“…In the development of innovative smart city solutions, Internet of things (IoT) technologies has played a vital role in shaping the life of citizens by addressing issues such as traffic congestion, theft detection, geospatial farming, telemedicine, remote healthcare monitoring, and many more [61,62]. In recent times, smart city problems have been addressed using various data sensed via smart sensing devices.…”

Section: Xai For Iotmentioning

confidence: 99%

A Survey of Explainable Artificial Intelligence for Smart Cities

et al. 2023

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The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities.

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Section: Xai For Iotmentioning

confidence: 99%

A Survey of Explainable Artificial Intelligence for Smart Cities

et al. 2023

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“…In 2023, Kimothi, S., et al [22] have used a data exchange sensor through IoT, for location identifications GPS was installed, and finally grabbed information through Geo-Information System (GIS). Initially, data was collected and monitored through IoT.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Driven Distributed Deep Learning for Aqua Status Prediction in IoT

Rajeshwar¹

2023

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The continuous screening of the quality and characteristics of water in IoT (Internet of Things) is essential due to the increasing requirement on aquaculture in order to maximize the yields. There are various physicochemical parameters used in water quality monitoring, but the analysis of these parameters are needed to obtain the final decision with experts. This paper proposes an aqua status prediction model in IoT using the Fractional Gravitational Search Algorithm based distributed Deep Convolutional Neural network (FGSA-based distributed Deep CNN). Initially, the aqua parameters are analysed using the distributed IoT nodes in the aqua environment. The loss and delay in the transmission of the data related to the aqua status is controlled with the selection of cluster head optimally using the FGSA. The prediction of the aqua status is done with the distributed DeepCNN in the final step. The performance of the proposed method is analyzed with the evaluation metrics, namely accuracy, energy, and throughput. The accuracy, energy, and throughput of the proposed FGSA-based distributed Deep CNN classifier is obtained as 95.4758, 99.4293, and 99.9571, respectively, which is high as compared to the existing methods. This shows the effectiveness of the proposed method in the prediction of the aqua status.

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“…Based on that, researchers can easily access the target information and use GIS analysis tools for ecosystem service research [40,41], pasture dynamics research [42], etc. There is additional research using geospatial databases in sustainable agroecosystems [37], digitalized aqua farming [43], and sustainable foods [44].…”

Section: Related Work 21 Sustainability In Geospatial Databasesmentioning

confidence: 99%

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Sun

Jin

2023

Sustainability

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Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

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Comprehensive Database Creation for Potential Fish Zones Using IoT and ML with Assimilation of Geospatial Techniques

Cited by 8 publications

References 42 publications

A Survey of Explainable Artificial Intelligence for Smart Cities

A Survey of Explainable Artificial Intelligence for Smart Cities

Optimization Driven Distributed Deep Learning for Aqua Status Prediction in IoT

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

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