Towards Reliability in Online High-Churn Edge Computing: A Deviceless Pipelining Approach

Vance, Nathan; Rashid, Tahmid; Zhang, Dongke; Wang, Dong

doi:10.1109/smartcomp.2019.00066

Cited by 32 publications

(18 citation statements)

References 28 publications

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“…First, the unpredictable nature of the human users in SPS applications makes it difficult to ensure that the users will strictly abide Figure 22: Example of trading-off between privacy and sensing quality in SPS by policies to protect their privacy. Second, given the unique data and device heterogeneity in SPS applications, designing a unified framework to enforce individual privacy policies across all the devices is a challenging task [249]. Third, regardless of the robustness of privacy-preserving schemes, the complementary aspects of the contributed data through social and physical data platforms can be exploited to steal sensitive user information [213].…”

Section: A Uncertainty Quantification In Spsmentioning

confidence: 99%

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

Rashid¹,

Wei²,

Wang³

2021

Preprint

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Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for spontaneously capturing and interpreting the real-world phenomenon. Despite its virtues, various limitations (e.g., high application specificity, partial autonomy, and sparse coverage) hinder physical sensing's effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm that leverages the observations from human participants equipped with portable devices and ubiquitous Internet connectivity (i.e., through social media or crowdsensing apps) to perceive the occurrences in the environment. While social sensing possesses a plethora of benefits, it also inherently suffers from a few drawbacks (e.g., inconsistent reliability, uncertain data provenance, and limited sensing availability). Motivated by the complementary virtues of both physical and social sensing, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that tightly integrates the social and physical sensors at an unprecedented scale. The vision of SPS centers on mitigating the individual weaknesses of physical and social sensing while exploiting their collective strengths in reconstructing the "state of the world", both physically and socially. While a good amount of interesting SPS applications has been explored, several important unsolved challenges and open research questions prevail in the way of developing innovative and practical SPS systems, which require careful scrutiny and research to address. In this paper, we provide a comprehensive survey of SPS, with an emphasis on its definition and key enablers, state-of-the-art applications, potential research challenges, and the road-map for future work. While existing survey papers related to sensing have discussed sensing schemes that are partly related to SPS, they do not explicitly provide an extensive overview of the SPS paradigm or the state-of-the-art SPS schemes. This paper intends to bridge this knowledge gap by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.

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Section: A Uncertainty Quantification In Spsmentioning

confidence: 99%

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

Rashid¹,

Wei²,

Wang³

2021

Preprint

Self Cite

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show abstract

“…Task allocation with sparse resources has been well studied in mobile crowdsensing literature [3,9,10,28,29,11,12,13,14,15,30,31]. Those techniques can be classified into two main categories based on their primary objectives: 1) Resource and Cost Reduction: Tong et al proposed a two-phased-based online task allocation scheme to reduce the task allocation cost in real-time crowdsourcing systems [10].…”

Section: Task Allocationmentioning

confidence: 99%

“…Task allocation with sparse resources has been well studied in mobile crowdsensing literature [3,9,10,28,29,11,12,13,14,15,30,31]. Those techniques can be classified into two main categories based on their primary objectives: 1) Resource and Cost Reduction: For example, Wang et al developed a data quality aware task allocation scheme that leverages active learning and Bayesian inference techniques to allocate sensing tasks to a limited number of crowd sensors to reduce the overall sensing cost [3].…”

Section: Task Allocationmentioning

confidence: 99%

An online reinforcement learning approach to quality-cost-aware task allocation for multi-attribute social sensing

Zhang

Vance

et al. 2019

Pervasive and Mobile Computing

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Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world. This paper focuses on a quality-cost-aware task allocation problem in multi-attribute social sensing applications. The goal is to identify a task allocation strategy (i.e., decide when and where to collect sensing data) to achieve an optimized tradeoff between the data quality and the sensing cost.While recent progress has been made to tackle similar problems, three important challenges have not been well addressed: (i) "online task allocation": the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables in social sensing; (ii) "multi-attribute constrained optimization": minimizing the overall sensing error given the dependencies and constraints of multiple attributes of the measured variables is a non-trivial problem to solve; (iii) "nonuniform task allocation cost": the task allocation cost in social sensing often has a nonuniform distribution which adds additional complexity to the optimized task allocation problem. This paper develops a Quality-Cost-Aware Online Task Allocation (QCO-TA) scheme to address the above challenges using a principled on-line reinforcement learning framework. We evaluate the QCO-TA scheme through a real-world social sensing application and the results show that our scheme significantly outperforms the state-of-the-art baselines in terms of both sensing accuracy and cost.

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“…Social sensing has emerged as a new sensing paradigm by using humans as sensors [18], [19]. Social sensing has been widely applied in various application domains [20]- [25] including damage assessment in disaster response [22], multi-modal data fusion [23], crowd video sharing [24], and environment and urban infrastructure monitoring [25]. Abnormal traffic event localization using social sensing remains to be an important challenge that has not been well-addressed in intelligent transportation systems.…”

Section: A Social Sensingmentioning

confidence: 99%

A syntax-based learning approach to geo-locating abnormal traffic events using social sensing

Zhang

Dong

Zhang

et al. 2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Social sensing has emerged as a new sensing paradigm to observe the physical world by exploring the "wisdom of crowd" on social media. This paper focuses on the abnormal traffic event localization problem using social media sensing. Two critical challenges exist in the state-of-the-arts: i) "content-only inference": the limited and unstructured content of a social media post provides little clue to accurately infer the locations of the reported traffic events; ii) "informal and scarce data": the language of the social media post (e.g., tweet) is informal and the number of the posts that report the abnormal traffic events is often quite small. To address the above challenges, we develop SyntaxLoc, a syntax-based probabilistic learning framework to accurately identify the location entities by exploring the syntax of social media content. We perform extensive experiments to evaluate the SyntaxLoc framework through real world case studies in both New York City and Los Angeles. Evaluation results demonstrate significant performance gains of the SyntaxLoc framework over state-of-the-art baselines in terms of accurately identifying the location entities that can be directly used to locate the abnormal traffic events.

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Towards Reliability in Online High-Churn Edge Computing: A Deviceless Pipelining Approach

Cited by 32 publications

References 28 publications

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

An online reinforcement learning approach to quality-cost-aware task allocation for multi-attribute social sensing

A syntax-based learning approach to geo-locating abnormal traffic events using social sensing

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