Towards Reliable IoT: Fog-Based AI Sensor Validation

Russell, Luke; Kwamena, Felix; Goubran, Rafik

doi:10.1109/cloudsummit47114.2019.00013

Cited by 9 publications

(15 citation statements)

References 13 publications

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“…We identified papers that propose techniques or algorithms to improve dependability characteristics [39], [96][97][98], or techniques related to trust management mechanisms [99], [100].…”

Section: ) Algorithms/techniquesmentioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

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“…We identified papers that propose techniques or algorithms to improve dependability characteristics [39], [96][97][98], or techniques related to trust management mechanisms [99], [100].…”

Section: ) Algorithms/techniquesmentioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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“…As the reliability of systems starts with their input, reliable IoT sensor data is an important enabling factor of EI. One approach towards reliable sensor data uses fog-based validation by combining the output of several physically clustered sensors of different types to detect unreliable outputs [122]. The algorithm is applied to a scenario in which AI detects people through a security camera, showing that false negatives of the AI can be corrected through sensory substitution.…”

Section: Reliabilitymentioning

confidence: 99%

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Goethals¹,

Volckaert

Turck

2021

IEEE Open J. Commun. Soc.

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The use of AI in Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with "Enabling Technology" for Edge Intelligence, "Organization" of the edge using AI, and AI "Applications" in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The "Organization" aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work.

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“…User sits upright and does not touch the armrest for extended periods. 4. User leans to the left and leans on the left armrest for extended periods.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…2. Data Analytics [4], [5], [6], [7], [8], [9], [10]: A) Sensory Validation: Designed a method of validating sensor inputs: ambient physical sensors for AI camera analytic validation and performed an analysis of sensing security implications. B) Classification and Adaptation: Proposed an urban sensing method using imaging analytics with acoustic signals for UAV signature classification and designed an environmental and physical sensor system for smart environments to perform human detection and develop personalization in an IoT environment.…”

Section: Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Sensory Substitution: Situational Awareness and Resilience using Available Sensors

Russell¹

Self Cite

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I would like to take this opportunity to thank my amazing and incredible supervisors, Dr. Rafik Goubran and Dr. Felix Kwamena. What an astounding honour it has been for me to have each of these superb advisors. You have unceasingly provided keen and diligent feedback and much appreciated detailed input, from not only your immense knowledge, but also from your quick thinking. Your steadfast support and abiding dedication has been instrumental in the journey through this program. I sincerely extend to you with profound respect, and with heartfelt appreciation, an epic, mammoth, and gigantic thank you. Next, I greatly appreciate and thank each person who has participated on my committees. Thank you to my talented and outstanding colleagues in the lab, and to my friends, and to other great researchers and impressive students with whom I have collaborated throughout campus and all around the world. Also, thank you to all the many admirable professors with whom I have worked or sought advice, especially my former supervisor Dr. Alan Steele, and thank you to those other professionals with whom I have worked. Thank you very much to all of the administrative staff, technical staff, and support staff who have helped me in any way during my time at Carleton. I especially thank my awesome Mom, Norma, for standing with me every step of the way, from well before my first day of Kindergarten through to the preparation of this document, and of course for her love and support in all of my life. Above all, I thank God for everything!! I am ever so grateful.

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Towards Reliable IoT: Fog-Based AI Sensor Validation

Cited by 9 publications

References 13 publications

A Survey on Trustworthiness for the Internet of Things

A Survey on Trustworthiness for the Internet of Things

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Sensory Substitution: Situational Awareness and Resilience using Available Sensors

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