An IoT-based resource utilization framework using data fusion for smart environments

Fawzy, Dina; Moussa, Sherin M.; Badr, Nagwa L.

doi:10.1016/j.iot.2022.100645

Cited by 12 publications

(4 citation statements)

References 52 publications

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“…On the other hand, a lot of efforts have been recently devoted to increasing data quality with the help of automated pipelines, data engineering frameworks, and prototypes. The implementation of technical solutions like data lakes [105], low-latency data infrastructure [106], feature stores [107], data warehouses [108,109], data branching [110], AutoML for data management [111], data strew-ships [112], data fusion techniques [113], data taxonomies [114], data-quality enhancement pipelines [115], data mesh and fabric [116], addressing imbalances in data [117], smart bots for data quality enhancement [118], data ontologies [119], data quality evaluation metrics [120], synthetic data generation tools [120], data profiling [121], reference stores for data quality [122], and data validation pipelines [123,124], to name a few, are vastly contributing in the feasibility and affordability of DC-AI-based solutions. In the future, more developments are expected in data quality enhancement, leading to the realization of DC-AI across many enterprises.…”

Section: Analysis Of the Feasibility And Affordability Of Dc-ai-based...mentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

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Section: Analysis Of the Feasibility And Affordability Of Dc-ai-based...mentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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“…This model uses trapezoidal membership functions in conjunction with Mamdani inference rules to fuzzify the equation (1).…”

Section: Fuzzificationmentioning

confidence: 99%

“…IoT enables the data transfer between humans and physical items, which is carried out via IoT-based user activities' services and gadgets [1]. Using complimentary elements and Internet Protocol (IP), atmospheric bodies or objects actively participate in data sharing across wired or wireless networks [2].…”

Section: Introductionmentioning

confidence: 99%

Smart-Fcd: Iot Data Interoperability Using Sensor Based Fuzzy Linked Rules for Cross Domain Applications

Anitha,

Balasubramani,

Prasad K S

2023

MJCS

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Internet of Things (IoT) is connected everywhere and enables massive information exchange between objects and people. The unified global IoT must manage a massive amount of data generated by these connected smart which arises the interoperability challenges such as lack of communication protocols, device support, and accepted open standards in smart environments. To combat these issues, a novel Smart-Fuzzy linked rules for Cross Domain application (Smart-FCD) framework is proposed to ensure interoperability by enabling efficient communication and data exchange between multiple platforms and systems. Initially, the heterogenous data from the sensors such as temperature sensors and humidity sensors, and the descriptions are implemented according to the Sensor Measurement List (SenML) language. After composing, semantic modelling will occur which converts the relational data into Resource Description Framework (RDF) format. The Linked Open Vocabulary for the Internet of Things (LOV4IoT) dataset is used to extract interoperable domain knowledge, which is then fed into the related sensor-based fuzzy rules. The IoT preset semantic models which utilise interoperable datasets and domain instances, which are updated via fuzzy association rules. Semantic Web of Things (SWoT) is a technology that assists in creating semantic-based IoT applications, thus IoT developers may use it to create intelligent applications. The sensor data, RDF simulation, evolution time and latency are some of the parameters that are used to evaluate the effectiveness of the proposed Smart-FCD methodology.

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“…Despite of the enormous data prediction processes have been presented for different IoT domains, but still they have some limitations to cope all new features of IoT data [9]. Hence, data fusion has become a popular method for maintaining and enhancing data for further data analysis [5,[10][11][12][13][14][15]. In this context, data fusion helps reducing data amount, maintaining data faults, and extracting useful data [16].…”

Section: Introductionmentioning

confidence: 99%

DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES

Fawzy

Moussa

Badr

2023

IJICIS

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Recently with the high implementation of numerous Internet of Things (IoT) based systems, it becomes a crucial need to have an effective data prediction approach for IoT data analysis that copes with sustainable smart city services. Nevertheless, IoT data add many data perspectives to consider, which complicate the data prediction process. This poses the urge for advanced data fusion methods that would preserve IoT data while ensuring data prediction accuracy, reliability, and robustness. Although different data prediction approaches have been presented for IoT applications, but maintaining IoT data characteristics is still a challenge. This paper presents our proposed approach the domain-independent Data Fusion for Data Prediction (DFDP) that consists of: (1) data fusion, which maintains IoT data massive size, faults, spatiotemporality, and freshness by employing a data input-data output fusion approach, and (2) data prediction, which utilizes the K-Nearest Neighbor data prediction technique on the fused data. DFDP is validated using IoT data from different smart cities datasets. The experiments examine the effective performance of DFDP that reaches 91.8% accuracy level.

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An IoT-based resource utilization framework using data fusion for smart environments

Cited by 12 publications

References 52 publications

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Smart-Fcd: Iot Data Interoperability Using Sensor Based Fuzzy Linked Rules for Cross Domain Applications

DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES

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