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- Liu, Xiuming, et al.
(författare)
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Secure Information Fusion using Local Posterior for Distributed Cyber-Physical Systems
- 2020
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE. - 1536-1233 .- 1558-0660. ; 20:5, s. 2041-2054
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Tidskriftsartikel (refereegranskat)abstract
- In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated "detection + fusion" solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general chi(2) distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation.
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| 4. |
- Jiang, Zhihan, et al.
(författare)
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Leveraging Machine Learning for Disease Diagnoses based on Wearable Devices : A Survey
- 2023
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:24, s. 21959-21981
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Tidskriftsartikel (refereegranskat)abstract
- Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson’s disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and machine learning. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, machine learning techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multi-modality, multi-sensor, multi-devices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.
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| 5. |
- Leung, Victor C. M., et al.
(författare)
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Editorial for QShine 2014 Special Issue
- 2016
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Ingår i: Mobile Networks and Applications. - : Springer Science and Business Media LLC. - 1383-469X .- 1572-8153. ; 21:3, s. 387-389
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Lin, Chuang, et al.
(författare)
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Special Issue on Cloud Computing for IoT
- 2016
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 3:3, s. 254-256
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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