Learn from IoT

Solmaz, Gürkan; Berz, Everton Luís; Dolatabadi, Marzieh; Aytaç, Samet; Fürst, Jonathan; Cheng, Bin; Ouden, Jos den

doi:10.1145/3349622.3355446

Cited by 8 publications

(1 citation statement)

References 7 publications

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“…In [21], a vehicular interface notation has been developed to help older customers of smart vehicles to control their driving experience better and improve their cognitive ability. Similarly, in [41], a machine learning system has been proposed to exploit data from autonomous vehicles and external IoT data sources to predict pedestrian's next movement steps using real-time trajectory. The suggested solution ensures safety in urban environments by enhancing autonomous driving efficiently in local public transport services.…”

Section: Introductionmentioning

confidence: 99%

Improving Big Data Clustering for Jamming Detection in Smart Mobility

Bangui

Bühnová

2020

IFIP Advances in Information and Communication Technology

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Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.

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Section: Introductionmentioning

confidence: 99%

Improving Big Data Clustering for Jamming Detection in Smart Mobility

Bangui

Bühnová

2020

IFIP Advances in Information and Communication Technology

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A review on deep learning for future smart cities

Bhattacharya

Somayaji

Gadekallu

et al. 2020

Internet Technology Letters

104

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The advancements in Information and Communication Technologies (ICT) made the concept of Smart Cities into reality. In a smart city several Internet of Things (IoT) sensors are deployed across several locations to collect the data about traffic, drainage, mobility of citizens etc. and the insights gained from these data are used to manage resources, assets etc. effectively. Deep Learning has been used extensively on the data generated by IoT sensors in a smart city by several researchers. In this article an attempt is made to survey several state-of-the art on usage of Deep Learning on Smart City data. Several future research directions are suggested at the end of the article.

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