1st International Workshop on Intelligent Data Collection in Cyber-Physical Systems

Session IDC


8:00 AM — 10:40 AM GMT
Dec 13 Mon, 3:00 AM — 5:40 AM EST

2prong: Adaptive Video Streaming with DNN and MPC

Yipeng Wang, Tongqing Zhou, Zhiping Cai (National University of Defense Technology, China)

Adaptive bitrate (ABR) algorithms are often used to optimize the quality of user experience (QoE) during video playback. In the client-side video player, the buffer size and predicted throughput are mainly used to improve user's QoE. However, due to the randomness of mobile network traffic and the heavy-tail effect of the network, it is very difficult to predict throughput. We innovatively use Bayesian neural network to dynamically evaluate video signals. Unlike previous neural network solutions, we use probability distributions instead of point estimates to predict throughput, which can effectively evaluate QoE metrics. Our contributions are to first (i) use of Bayesian neural network to guide video adaptive bitrate adaptation, and then (ii) propose a bitrate adaptive algorithm denoted BMPC, which utilizes high-dimensional contextual information such as buffer occupancy, predicted throughput and video quality to find the most valuable information for quality adaption in real-time. We use an emulation testbed to demonstrate the advancement of BMPC compared to state-of-the-art algorithms. In terms of improving QoE metrics, the effectiveness of the proposed framework is validated by comparison with different approaches.

FDataCollector: A Blockchain Based Friendly Web Data Collection System

Jing Wang, Weiping Zhu, Jianqiao Lai, Zhu Wang (Wuhan University, China)

In the last decade, a growing number of people use web crawlers to collect the data from the Internet for data analysis. The web crawlers greatly increase the workload of web servers and hence hinder normal accesses of the websites located in the servers. The accesses from web crawlers also affect the effectiveness of web mining, which assumes that the accesses are all from normal users. Moreover, the un-licensed collection of data from websites are often prohibited by laws and regulations of government and commercial organizations. To restrict the data collection from web crawlers, currently anti-crawler technique is applied to the websites. The behaviors of web crawler are recognized and their accesses are denied. This overcome the aforementioned problem, however, become a big obstacle for data exchange, considering that the large volume of data in the Internet could be useful for many data analysis applications. The dilemma of data collection using web crawlers and anti-crawler techniques demand a better solution. In this study, we propose to a friendly data sharing system FDataCollector to allow the data collection and also alleviate the workload of web servers by using blockchain techniques. We first make the data uploaded to the data sharing system by a few trustful users and then sell to public users in a traceable and P2P sharing way. The other accesses of web web crawlers are prohibited. On the user side, this design not only enable a convenient search of data but also improve the download efficiency. On the data holder side, this traceable and benefit way encourages them to share the data. We implement the system to demonstrate our idea. The results show that the system has high efficiency even when many transactions occur at the same time.

Mobile Unattended-Operation Detector for Bulk Dangerous Goods Handling

Nicola Zingirian (University of Padova, Italy), Federico Botti (Click & Find s.r.l, Italy)

The paper presents the prototype of an innovative system, called the “Unattended-Operation Detector” (UOD), developed on top of an Oil & Gas Transportation IoT platform managing a sensor network installed on over 3,000 tank trucks. The system, integrated as a new sensor, runs a real-time Computer Vision algorithm to detect, through a camera, whether the operator attends the dangerous goods unloads. The paper introduces the UOD application and technology contexts, shows the main design and implementation choices, reports the experimental results of the first prototype mounted on a tanker, and discusses the product perspectives.

The Design and Implementation of an Efficient Quaternary Network Flow Watermark Technology

Lusha Mo, Gaofeng Lv, Baosheng Wang, Guanjie Qiao, Jing Tan (National University of Defense Technology, China)

With the increasing demand for network security, passive traffic analysis technology has the characteristics of low efficiency, high overhead, and susceptibility to interferences, and network flow watermark technology has emerged. As an active traffic analysis method, network flow watermark technology can effectively track malicious anonymous communication users and real attackers behind the stepping-stone chain, which has the advantages of high accuracy and low overhead. However, the existing flow watermark codec is embedded in the software, which is only suitable for low-rate or small-sample network traffic. In the face of modern network high-rate data stream (such as 10gbps per port), it has exceeded the software and traditional switch processing power. In order to improve the coding efficiency and processing capacity of network flow watermark, this paper combines flow watermark technology with smart NIC(Network Interface Card), and proposes an efficient quaternary network flow watermark technology, deployed in the switch to form an efficient dynamic watermark mechanism. Theoretical analysis and experimental results show that the network flow watermark technology can efficiently process high-rate data stream, with higher coding efficiency and good robustness to disturbances.

TSCF: An Efficient Two-Stage Cuckoo Filter for Data Deduplication

Tao Liu (Peking University, China), Qinshu Chen (Guangdong Communications & Networks Institute, China), Hui Li, Bohui Wang, Xin Yang (Peking University, China)

The rapid growth of data on the Internet has brought huge challenges to storage systems. Data deduplication technology is proposed to solve the problem of data redundancy. As one of the data deduplication technologies, the memory-assisted method uses an approximate membership data structure to greatly reduce the space consumption of membership determination. The approximate membership data structures represented by the cuckoo filter have been widely used. However, there is a lack of efficient ways to solve the problem that the insertion time increases exponentially with the load rate of the cuckoo filter. In this paper, an efficient cuckoo filter named TSCF is proposed with a two-stage insertion algorithm. The TSCF balances the load of the filter through active relocations in the first stage, laying the foundation for the second stage. Through the experiments, the cumulative relocation times of the TSCF are reduced to 37% and 46% respectively compared with the SCF and the CFBF, indicating that the TSCF greatly reduces the relocation times and insertion time of the entire insertion process, and improves the performance of the cuckoo filter.

Session Chair

Weiping Zhu (Wuhan University, China) Junbin Liang (Guangxi University, China) Xuefeng Liu (Beihang University, China)

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