Towards Emulating Internet-of-Vehicles on a Single Machine

Jin, Wangkai; Ming, Xiaoxing; Song, Zilin; Xiong, Zeyu; Peng, Xiangjun

doi:10.1145/3473682.3480275

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…There are many examples of applications, which require data aggregations in centralized servers. For instance, Transportation Scheduling demands all vehicles to update their coordinates and potential routines, to perform scheduling decisions [29]; also, there are many examples for applications, which demand more computational power in centralized servers (e.g., [14,27,28]). For instance, emerging trends of Deep Neural Networks demand significant computational power for model training, which are usually maintained in the centralized servers.…”

Section: Classifying Application Of Iov : Processing Inside or Outsid...mentioning

confidence: 99%

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Duan¹,

Liu²,

Jin³

et al. 2022

Preprint

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Recent developments of advanced Human-Vehicle Interactions rely on the concept Internet-of-Vehicles (IoV ), to achieve large-scale communications and synchronizations of data in practice. The concept of IoV is highly similar to a distributed system, where each vehicle is considered as a node and all nodes are grouped with a centralized server. In this manner, the concerns of data privacy are significant since all vehicles collect, process and share personal statistics (e.g. multi-modal, driving statuses and etc.). Therefore, it's important to understand how modern privacy-preserving techniques suit for IoV .We present the most comprehensive study to characterize modern privacy-preserving techniques for IoV to date. We focus on Differential Privacy (DP), a representative set of mathematicallyguaranteed mechanisms for both privacy-preserving processing and sharing on sensitive data. The purpose of our study is to demystify the tradeoffs of deploying DP techniques, in terms of service quality. We first characterize representative privacy-preserving processing mechanisms, enabled by advanced DP approaches. Then we perform a detailed study of an emerging in-vehicle, Deep-Neural-Network-driven application, and study the upsides and downsides of DP for diverse types of data streams. Our study obtains 11 key findings and we highlight FIVE most significant observations from our detailed characterizations. We conclude that there are a large volume of challenges and opportunities for future studies, by enabling privacy-preserving IoV with low overheads for service quality.

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Section: Classifying Application Of Iov : Processing Inside or Outsid...mentioning

confidence: 99%

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Duan¹,

Liu²,

Jin³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…more detailed facial expression [25,32,45]) and customization-friendly predictors [48] inside vehicles. It also of great pracical values to integrate them into certain platforms (e.g., IoV Emulation Platform [27]) to improve the overall Quality-of-Service.…”

Section: Takeaways From Brook Dataset and Example Studiesmentioning

confidence: 99%

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Jin¹,

Duan²,

Liu³

et al. 2022

Preprint

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Emerging Autonomous Vehicles (AV) breed great potentials to exploit data-driven techniques for adaptive and personalized Human-Vehicle Interactions. However, the lack of high-quality and rich data supports limits the opportunities to explore the design space of data-driven techniques, and validate the effectiveness of concrete mechanisms. Our goal is to initialize the efforts to deliver the building block for exploring data-driven Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a multi-modal dataset with facial video records. We first brief our rationales to build BROOK dataset. Then, we elaborate how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present three example studies using BROOK to justify the applicability of BROOK dataset. We also identify key learning lessons from building BROOK dataset, and discuss about how BROOK dataset can foster an extensive amount of follow-up studies. INTRODUCTIONRecent advances in data-driven techniques (e.g. Neural Networks) breed an extensive amount of opportunities, to enable adaptive and personalized Human-Vehicle Interaction (HVI). More interestingly, the emerging trends of Autonomous Vehicles relax the conventional burdens of driving, and in-vehicle drivers/passengers are capable to obtain better user experiences through more complex HVI. Incorporating data-driven approaches can greatly improve user experiences during driving processes. For instance, unobtrusive monitors of multi-modal statuses (i.e. by taking alternative data sources as input), rather than directly equipping biosensors, are more user-friendly as data sources for personalized interaction between drivers and vehicles. Therefore, combining data-driven techniques with HVI becomes promising in the near future, and relevant datasets become essential and highly demanded.Though there are already several datasets for HVI, there are four major limitations of existing datasets. First, the design purposes of existing datasets focus on a specific type of study purposes (e.g. Stress Detection [20], Driving Workload [40] and etc.), which naturally limits their potentials due to the narrowly-selected types of data streams. Second, existing datasets only collect information

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Face2Statistics: User-Friendly, Low-Cost and Effective Alternative to In-vehicle Sensors/Monitors for Drivers

Xiong

Wang

Jin

et al. 2022

Lecture Notes in Computer Science

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Towards Emulating Internet-of-Vehicles on a Single Machine

Cited by 10 publications

References 18 publications

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Face2Statistics: User-Friendly, Low-Cost and Effective Alternative to In-vehicle Sensors/Monitors for Drivers

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