IoT Data Provenance Implementation Challenges

Alkhalil, Adel; Ramadan, Rabie A.

doi:10.1016/j.procs.2017.05.436

Cited by 47 publications

(20 citation statements)

References 14 publications

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“…The early stage of such aspired world has already begun when the concept of Internet of Things (IoT) was introduced for the first time in 1999 where the phrase was originally started as the title of a presentation by Kevin Ashton [1] at Procter & Gamble (P&G). The definition by Alkhalil & Ramadan [2] for what we call as the IoT today may describe and generally fits what is needed in this study: ‗A worldwide network of interconnected entities'. The core concept of the term describes itself as a global infrastructure for the information society, with enabled advanced services by interconnecting physical and virtual things based on existing and evolving interoperable information and communication technologies [3].…”

Section: Introduction 1internet Of Things and Industrial Big Datamentioning

confidence: 61%

Internet of Things (IoT) in Agriculture Industries

Adesta

Agusman

Avicenna

2017

IJEEI

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The IoT applications in the agriculture industry are intertwined in many ways, creating a hybrid construct of traditional businesses with digital connectivity implementations. Strategic issues and principles on the assimilation of IoT solutions are prevail through time: investment costs, data interconnectivity, management and analysis, as well as data security. The agriculture industry is expected to be always alerted for the changes that may apply, with all the contemporary challenges and future directions responding to the situations, so it can reap the optimum use of the IoT connectivity.

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Section: Introduction 1internet Of Things and Industrial Big Datamentioning

confidence: 61%

Internet of Things (IoT) in Agriculture Industries

Adesta

Agusman

Avicenna

2017

IJEEI

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“…For instance, the work reported by Bauer et al identified data provenance requirements to integrate it in an IoT environment using a proposed conceptual architecture model. The work by Alkhalil et al tried to address the challenges associated with data provenance in IoT. The authors reviewed the most recent data provenance techniques applied to the IoT.…”

Section: Related Workmentioning

confidence: 99%

Data provenance and trust establishment in the Internet of Things

Elkhodr

Alsinglawi

2019

Security and Privacy

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The Internet of Things (IoT) is a network of heterogeneous networks encompassing various forms of communications form the current traditional communication models to ubiquitous and pervasive machine to machine communications. In such an ever expanding, dynamic, and complex environment, it becomes vital to know the origin or the source of data and whether this data can be trusted or no. This requires not only accurate, secure, and correct data collection processes; but also provisioning of data provenance throughout the life-cycle of an IoT device and the data it produces. To this end, this work introduces a provenance-based trust management solution, which provides a trust establishment mechanism amongst communicating devices in the IoT. It provides a way to quantitatively assess the trustworthiness of data supplied by a given IoT device. Thus, providing a novel solution to preserve data provenance of IoT devices. This IoT data provenance solution extends the capabilities of our previously proposed IoT platform known as the IoT-management platform, which aimed at preserving the privacy of users in the IoT. K E Y W O R D SInternet of Things, data provenance, privacy, trust 1 Security Privacy. 2020;3:e99.wileyonlinelibrary.com/journal/spy2

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“…Adel Alkhalil et al recommended the usage of information provenance as an imperative instrument that can improve the security and protection of IoT frameworks and reviewed the most difficult issues in IoT information provenance. Seven issues have been talked about including provenance security, monstrous measure of information, ordering, different customers, change, question, and interoperability [4]. Nallapaneni Manoj Kumar et al expounded the conceivable security and protection issues considering the segment cooperation in IoT and concentrates how the Distributed Ledger based Block Chain (DL-BC) innovation add to it [5].…”

Section: Related Workmentioning

confidence: 99%

TPR, PPV and ROC based Performance Measurement and Optimization of Human Face Recognition of IoT Enabled Physical Location Monitoring

Shitole¹,

Devare²

2019

IJRTE

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 Abstract: This paper describes the construction of Internet of Things (IoT) enabled system which not only captures the sensors data in textual and numeric form but also performs live human face recognition to monitor physical location effectively. The dataset used in order to apply supervised machine learning algorithms is the combination of automatically captured live sensor data along with name of the human face recognized or unknown and additional manually introduced class label. Performance measurement of face recognition is done with the help of Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Logistic Regression (LR). The results show that DT gives the best performance with respect to classifier's accuracy; True Positive Rate, Positive Predictive Value and area under curve of Receiver Operating Characteristics (ROC) for face recognition prediction whether the recognized face is true or false.

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IoT Data Provenance Implementation Challenges

Cited by 47 publications

References 14 publications

Internet of Things (IoT) in Agriculture Industries

Internet of Things (IoT) in Agriculture Industries

Data provenance and trust establishment in the Internet of Things

TPR, PPV and ROC based Performance Measurement and Optimization of Human Face Recognition of IoT Enabled Physical Location Monitoring

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