The Experts below are selected from a list of 2688 Experts worldwide ranked by ideXlab platform

Dong Wang - One of the best experts on this subject based on the ideXlab platform.

  • an online reinforcement learning approach to quality cost aware task allocation for multi attribute Social Sensing
    Pervasive and Mobile Computing, 2019
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    Abstract 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 online 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.

  • an integrated top down and bottom up task allocation approach in Social Sensing based edge computing systems
    International Conference on Computer Communications, 2019
    Co-Authors: Daniel Yue Zhang, Dong Wang
    Abstract:

    With the advance of mobile computing, Internet of Things, and 5G networks, Social Sensing based edge computing (SSEC) systems have emerged as a new computation paradigm where people and their personally owned devices collect and process Sensing measurements about the physical world at the edge of networks. In this paper, we focus on the task allocation problem in SSEC where rational edge devices are motivated by incentives to collectively accomplish the computation tasks in the system. Several unique challenges exist to solve this problem: (i) the edge devices often do not share the complete context information (e.g., CPU, memory usage) in the task allocation process due to privacy concerns; (ii) the edge devices are rational actors who may have competing objectives with the Application; (iii) the Application server and edge devices are usually owned by different entities, making the coordination in task allocation more challenging. This paper develops a novel integrated Top-Down and Bottom-Up (TDBU) task allocation framework to address these challenges. In particular, TDBU incorporates abottom-up game-theoretic model that allows the edge devices to specify their task preferences in a way that maximizes their payoffs. It also incorporates atop-down control model that ensures the performance of the Applications using control theory. The TDBU was implemented on a real-world edge computing testbed that consists of heterogeneous devices (Jetson TX1, TK1 boards, Raspberry Pi3). We compared the performance of TDBU with state-of-the-art baselines through a real-world Social Sensing Application. The results showed that our solution significantly outperformed the baselines in various Application settings.

  • optimizing online task allocation for multi attribute Social Sensing
    International Conference on Computer Communications and Networks, 2018
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    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 an optimized task allocation problem in multi- attribute Social Sensing Applications where the goal is to effectively allocate the tasks of collecting multiple attributes of the measured variables to human sensors while respecting the Application's budget constraints. While recent progress has been made to tackle the optimized task allocation problem, two important challenges have not been well addressed. The first challenge is "online task allocation": the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables (e.g., temperature, noise, traffic) in Social Sensing. Delayed task allocation may lead to inaccurate Sensing results and/or unnecessarily high Sensing costs. The second challenge is the "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. To address the above challenges, this paper develops an Online Optimized Multi-attribute Task Allocation (OO-MTA) scheme inspired by techniques from machine learning and information theory. We evaluate the OO-MTA scheme using an urban Sensing dataset collected from a real-world Social Sensing Application. The evaluation results show that OO- MTA scheme significantly outperforms the state-of-the-art baselines in terms of the Sensing accuracy.

  • exploitation of physical constraints for reliable Social Sensing
    Real-Time Systems Symposium, 2013
    Co-Authors: Dong Wang, Tarek Abdelzaher, Lance Kaplan, Raghu K Ganti, H Liu
    Abstract:

    This paper develops and evaluates algorithms for exploiting physical constraints to improve the reliability of Social Sensing. Social Sensing refers to Applications where a group of sources (e.g., individuals and their mobile devices) volunteer to collect observations about the physical world. A key challenge in Social Sensing is that the reliability of sources and their devices is generally unknown, which makes it non-trivial to assess the correctness of collected observations. To solve this problem, the paper adopts a cyber-physical approach, where assessment of correctness of individual observations is aided by knowledge of physical constraints on both sources and observed variables to compensate for the lack of information on source reliability. We cast the problem as one of maximum likelihood estimation. The goal is to jointly estimate both (i) the latent physical state of the observed environment, and (ii) the inferred reliability of individual sources such that they are maximally consistent with both provenance information (who claimed what) and physical constraints. We evaluate the new framework through a real-world Social Sensing Application. The results demonstrate significant performance gains in estimation accuracy of both source reliability and observation correctness.

Yang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • an online reinforcement learning approach to quality cost aware task allocation for multi attribute Social Sensing
    Pervasive and Mobile Computing, 2019
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    Abstract 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 online 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.

  • optimizing online task allocation for multi attribute Social Sensing
    International Conference on Computer Communications and Networks, 2018
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    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 an optimized task allocation problem in multi- attribute Social Sensing Applications where the goal is to effectively allocate the tasks of collecting multiple attributes of the measured variables to human sensors while respecting the Application's budget constraints. While recent progress has been made to tackle the optimized task allocation problem, two important challenges have not been well addressed. The first challenge is "online task allocation": the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables (e.g., temperature, noise, traffic) in Social Sensing. Delayed task allocation may lead to inaccurate Sensing results and/or unnecessarily high Sensing costs. The second challenge is the "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. To address the above challenges, this paper develops an Online Optimized Multi-attribute Task Allocation (OO-MTA) scheme inspired by techniques from machine learning and information theory. We evaluate the OO-MTA scheme using an urban Sensing dataset collected from a real-world Social Sensing Application. The evaluation results show that OO- MTA scheme significantly outperforms the state-of-the-art baselines in terms of the Sensing accuracy.

Nathan Vance - One of the best experts on this subject based on the ideXlab platform.

  • an online reinforcement learning approach to quality cost aware task allocation for multi attribute Social Sensing
    Pervasive and Mobile Computing, 2019
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    Abstract 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 online 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.

  • optimizing online task allocation for multi attribute Social Sensing
    International Conference on Computer Communications and Networks, 2018
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    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 an optimized task allocation problem in multi- attribute Social Sensing Applications where the goal is to effectively allocate the tasks of collecting multiple attributes of the measured variables to human sensors while respecting the Application's budget constraints. While recent progress has been made to tackle the optimized task allocation problem, two important challenges have not been well addressed. The first challenge is "online task allocation": the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables (e.g., temperature, noise, traffic) in Social Sensing. Delayed task allocation may lead to inaccurate Sensing results and/or unnecessarily high Sensing costs. The second challenge is the "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. To address the above challenges, this paper develops an Online Optimized Multi-attribute Task Allocation (OO-MTA) scheme inspired by techniques from machine learning and information theory. We evaluate the OO-MTA scheme using an urban Sensing dataset collected from a real-world Social Sensing Application. The evaluation results show that OO- MTA scheme significantly outperforms the state-of-the-art baselines in terms of the Sensing accuracy.

Daniel Zhang - One of the best experts on this subject based on the ideXlab platform.

  • an online reinforcement learning approach to quality cost aware task allocation for multi attribute Social Sensing
    Pervasive and Mobile Computing, 2019
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    Abstract 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 online 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.

  • optimizing online task allocation for multi attribute Social Sensing
    International Conference on Computer Communications and Networks, 2018
    Co-Authors: Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang
    Abstract:

    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 an optimized task allocation problem in multi- attribute Social Sensing Applications where the goal is to effectively allocate the tasks of collecting multiple attributes of the measured variables to human sensors while respecting the Application's budget constraints. While recent progress has been made to tackle the optimized task allocation problem, two important challenges have not been well addressed. The first challenge is "online task allocation": the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables (e.g., temperature, noise, traffic) in Social Sensing. Delayed task allocation may lead to inaccurate Sensing results and/or unnecessarily high Sensing costs. The second challenge is the "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. To address the above challenges, this paper develops an Online Optimized Multi-attribute Task Allocation (OO-MTA) scheme inspired by techniques from machine learning and information theory. We evaluate the OO-MTA scheme using an urban Sensing dataset collected from a real-world Social Sensing Application. The evaluation results show that OO- MTA scheme significantly outperforms the state-of-the-art baselines in terms of the Sensing accuracy.

Wang Dong - One of the best experts on this subject based on the ideXlab platform.

  • An Online Reinforcement Learning Approach to Quality-Cost-Aware Task Allocation for Multi-Attribute Social Sensing
    2019
    Co-Authors: Zhang Yang, Zhang Daniel, Vance Nathan, Wang Dong
    Abstract:

    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 online 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.Comment: The paper has been accepted to Elsevier Pervasive and Mobile Computing (PMC) in September 201

  • Reliable Social Sensing with Physical Dependencies: Analytic Bounds and Performance Evaluation
    2026
    Co-Authors: Wang Dong, Abdelzaher, Tarek F., Kaplan Lance, Ganti, Raghu K., Hu Shaohan, Liu Hengchang
    Abstract:

    Correctness guarantees are at the core of cyber-physical computing research. While prior research addressed correctness of timing behavior and correctness of program logic, this paper tackles the emerging topic of assessing correctness of input data. This topic is motivated by the desire to crowd-source Sensing tasks, an act we henceforth call Social Sensing, in Applications with humans in the loop. A key challenge in Social Sensing is that the reliability of sources is generally unknown, which makes it difficult to assess the correctness of collected observations. To address this challenge, we adopt a cyber-physical approach, where assessment of correctness of individual observations is aided by knowledge of physical dependencies between sources and observed variables to compensate for the lack of information on source reliability. We cast the problem as one of Maximum Likelihood Estimation (MLE). The goal is to jointly estimate both (i) the latent physical state of the observed environment, and (ii) the inferred reliability of individual sources such that they are maximally consistent with both provenance information (who claimed what) and physical dependencies. We also derive new analytic bounds that allow the Social Sensing Applications to accurately quantify the estimation error of source reliability for given confidence levels. We evaluate the framework through both a real-world Social Sensing Application and extensive simulation studies. The results demonstrate significant performance gains in estimation accuracy of the new algorithms and verify the correctness of the analytic bounds we derived.unpublishedis peer reviewedU of I OnlyStill in submission for revie