Spatial Data Quality in the IoT Era: Management and Exploitation

Li, Huan; Tang, Bo; Lu, Hua; Cheema, Muhammad Aamir; Jensen, Christian S.

doi:10.1145/3514221.3522568

Cited by 8 publications

(3 citation statements)

References 135 publications

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“…Penelitian oleh Jawad dkk. (Dogra et al, 2022;Lazarescu, 2017;Li et al, 2022) mengeksplorasi sinergi antara IoT dan WSN, menunjukkan bagaimana integrasi ini meningkatkan skalabilitas dan kemampuan jaringan sensor secara real-time, yang memungkinkan transmisi dan analisis data yang efisien. Kemampuan gabungan IoT dan WSN memfasilitasi pengumpulan data dalam jumlah besar.…”

Section: Integrasi Iot Dan Wsnunclassified

Mengoptimalkan Jaringan Sensor Nirkabel dalam Aplikasi Monitor Lingkungan dengan Teknologi IoT di Indonesia

Setiawan,

Hiswara,

Muthmainah

2023

JMWS

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Integrasi teknologi Internet of Things (IoT) dengan jaringan sensor nirkabel (WSN) telah mengantarkan era baru dalam pemantauan lingkungan dan pengumpulan data. Di Indonesia, negara yang kaya akan keanekaragaman ekologi dan tantangan lingkungan, penerapan teknologi IoT untuk mengoptimalkan WSN untuk pemantauan lingkungan menjadi sangat penting. Penelitian ini mengeksplorasi kondisi pemantauan lingkungan saat ini di Indonesia, mengidentifikasi tantangan dan peluang, dan mengevaluasi aspek teknis dari optimalisasi WSN. Survei dan wawancara mendalam dengan para pemangku kepentingan mengungkapkan terbatasnya adopsi IoT, tantangan infrastruktur, dan kekhawatiran mengenai privasi dan keamanan data. Evaluasi teknis penyebaran node sensor menunjukkan tantangan dalam efisiensi transmisi data dan konsumsi daya. Studi kasus menggambarkan tantangan kontekstual dan solusi inovatif dalam pengaturan lingkungan yang beragam. Untuk mengatasi masalah-masalah ini, rekomendasi yang diberikan termasuk mempromosikan adopsi IoT, standarisasi, peningkatan infrastruktur, keamanan data, efisiensi energi, inisiatif kolaboratif, dan pemantauan berkelanjutan. Rekomendasi-rekomendasi ini bertujuan untuk mendorong Indonesia menuju infrastruktur pemantauan lingkungan yang lebih efisien dan berkelanjutan, serta memastikan kelestarian ekosistem dan sumber dayanya yang unik.

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Section: Integrasi Iot Dan Wsnunclassified

Mengoptimalkan Jaringan Sensor Nirkabel dalam Aplikasi Monitor Lingkungan dengan Teknologi IoT di Indonesia

Setiawan,

Hiswara,

Muthmainah

2023

JMWS

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“…Currently, Data Quality Management (DQM) has emerged as a critical issue garnering significant attention from both academics and industry [1]. Data has become one of the most valuable assets for organizations [2].…”

Section: Introductionmentioning

confidence: 99%

Data Quality Assessment: A Case Study on Asset Valuation Comparison Data

Adi Wicaksana,

Hidayanto,

Mekkawati

et al. 2024

jitk

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To realize a data-driven organization, good data quality is needed as a foundation for solving various problems related to data management. The case study used in this research is asset valuation comparison data. The purpose of this research is to define dimensions, measure and analyze data quality on asset valuation comparison data. There are three dimensions used in measuring data quality in this study which are adjusted based on existing regulations at Ministry X, namely accuracy, completeness, and validity. This research uses the stages in the Total Data Quality Management (TDQM) framework to measure data quality. The results of measuring all dimensions, 29 out of 58 business rules cannot be fulfilled completely. The business rules that can be fulfilled in each dimension are 47.06% in the completeness dimension, 60% in the validity dimension, and 44.44% in the accuracy dimension. The main factor causing the existence of data attributes that have not met the data quality business rules is because the asset valuation comparison data comes from various data sources. In addition, there are methods or standards for recording data from data source units that are not uniform, so an evaluation of the uniformity of data standardization and the implementation of data governance is needed. The results of this study can be used as material for organizational consideration to be more aware of the current state of data quality. In addition, it can be used by organizations to design strategies and steps to improve data quality so that it can support leaders in making the right decisions.

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“…Artificial intelligence (AI) methods, like machine learning (ML) and deep learning (DL), are widely used in this context to extract practical knowledge from these data. DL has led to significant breakthroughs in distributed networks like the Internet of Things (IoT) [1,2] and cloud computing (CC). Specifically, DL has also been used to handle privacy and security problems, like network intrusion detection, in distributed machine learning (DML) or cloud-fog-edge computing (CFEC) frameworks.…”

Section: Introductionmentioning

confidence: 99%

A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation

Wang,

Zheng,

Liu

et al. 2023

Electronics

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The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the cloud–fog–edge computing system using mobile devices is essential. External attacks on network press organizations led to anomaly flow in network traffic. The network intrusion detection system (NIDS) has been an effective method for detecting anomaly flow. However, the NIDS is hard to deploy in distributed networks because network flow data are kept private. Existing methods cannot obtain an accurate NIDS under such a federated scenario. To construct an NIDS while preserving data privacy, we propose a combined model that integrates binary classifiers into a whole network based on simple classifier networks to specify the type of attack on anomalous data and offer instruction to other security system components. We also introduce federated learning (FL) methods into our system and design a new aggregation algorithm named vertical blocking aggregation (FedVB) according to our model structure. Our experiments demonstrate that our system can be more effective than simple multi-classifiers in terms of accuracy and significantly reduce communication and computation overhead when applying FedVB.

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Spatial Data Quality in the IoT Era: Management and Exploitation

Cited by 8 publications

References 135 publications

Mengoptimalkan Jaringan Sensor Nirkabel dalam Aplikasi Monitor Lingkungan dengan Teknologi IoT di Indonesia

Mengoptimalkan Jaringan Sensor Nirkabel dalam Aplikasi Monitor Lingkungan dengan Teknologi IoT di Indonesia

Data Quality Assessment: A Case Study on Asset Valuation Comparison Data

A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation

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