PAS: Prediction-Based Actuation System for City-Scale Ridesharing Vehicular Mobile Crowdsensing

Chen, Xinlei; Xu, Susu; Han, Jun; Fu, Haohao; Pi, Xidong; Joe‐Wong, Carlee; Li, Yong; Zhang, Lin; Noh, Hae Young; Zhang, Pei

doi:10.1109/jiot.2020.2968375

Cited by 67 publications

(18 citation statements)

References 57 publications

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“…The kernel part of semi-Markov is defined in (5). W uh i (k) denotes the probability that a participant i contributes high quality sensing data in nth time at a timeslot k, given he/she contributed unusable quality sensing data in the (n − 1)th time.…”

Section: Task Recommendation Methodsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

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Mobile crowd sensing (MCS) with human participants has been proposed as an efficient way of collecting data for smart cities applications. However, there often exist situations where humans are not able or reluctant to reach the target areas, due to for example traffic jams or bad road conditions. One solution is to complement manual data collection with autonomous data collection using unmanned aerial vehicles (UAVs) equipped with various sensors. In this paper, we focus on the scenarios of UAV-assisted MCS and propose a task allocation method, called "UMA" (UAV-assisted Multi-task Allocation method) to optimize the sensing coverage and data quality. The method incentivizes human participants to contribute sensing data from nearby points of interest (PoIs), with a limited budget. Meanwhile, the method jointly considers the optimization of task assignment and trajectory scheduling. It schedules the trajectories of UAVs, considering the locations of human participants, other UAVs and PoIs which are rarely visited by human participants. In detail, UAVs take care of two tasks in our proposal. One is to calibrate the data collected by the human participants whom the UAVs come across along their trajectories. The other is to collect data from the PoIs which are not covered by other UAVs or human participants. We apply deep reinforcement learning to schedule UAVs moving trajectories and sensing activities in order to minimize the overall energy cost. We evaluate the proposed scheme via simulation using two real data sets. The results show that our proposal outperforms the compared methods, in terms of coverage completed ratio, calibrating ratio, energy efficiency, and task fairness.

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Section: Task Recommendation Methodsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

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“…For city and area-scale systems, high-cost devices make it dicult to achieve high density or large coverage of the deployment. The state-of-the-art approaches including utilizing the physics model to enhance the data-driven estimation with limited high-resolution sensors [48], utilizing the mobility of the platform to enhance the coverage of the system with limited devices [49,50]. However, the challenges remain.…”

Section: (C4) System Cost and Data Qualitymentioning

confidence: 99%

Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable Systems

Pan

Nguyen

2020

Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing

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This is a position paper that discusses the challenges of emerging new sensing modalities for both device-free and wearable sensing systems, as well as opportunities lying in the combination of them across multiple information scales. With the development of the Internet of Things (IoT), many devices with sensing-ability have entered people's life. These systems mainly fall into two categories: wearables and infrastructure sensing (device-free). In this paper, we rst briey summarize the state-of-the-art sensing modalities of these two categories, then we discuss the challenges faced by them. We envision a future of IoT human sensing systems that achieves seamless sensing across multiple scales through collaborative information inference by both categories of modalities. Finally, we discuss the opportunities to expand the boundaries of sensing modalities that lie in their collaborative adaptation.

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“…However, it is usually difficult to directly apply collected real data to a simulation platform for multi-agent systems with incentive schemes. This is because the incentives can continuously change the behaviors (e.g., direction to move) and statues (e.g., locations) of agents while the real-world data does not include any incentive information and thus the changed behavioral information [14], making it difficult to simulate the agents behavior onward using the real-world data. Therefore, the simulation platform should be able to produce new data to continuously emulate behaviors of agents.…”

Section: Simulation Platformmentioning

confidence: 99%

A generative simulation platform for multi-agent systems with incentives

Zhang

et al. 2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Multi-agent systems have attracted much attention in the recent years due to their capabilities to handle complex and computation-heavy tasks and compatibility with incentive schemes. Considering the difficulty of creating an actual prototype and environment for evaluation, a simulation platform is a cheap and efficient way in analyzing and testing, prior to real environmental implementations. Existing simulators for multi-agent systems are inadequate to analyze the effects of different customized incentive schemes on agents' behavior patterns due to two reasons: 1) They lack the functionality to support various types of complex incentives, e.g., mixture of monetary incentives and non-monetary incentives, which influences agents' behaviors explicitly and implicitly; 2) They are not able to emulate heterogeneous agents' realtime behaviors that are influenced by complex incentives and deviate from their original behavior patterns shown in historical traces. In this paper, we focus on mobile agents that can move in a patio-temporal space, and we present a physical knowledge aided multi-agent simulation platform considering the influence of both direct and indirect incentives unified through a general utility-driven agent reaction function. The behaviors of agents are then emulated in three * These three authors contribute equally to this work. Xinlei Chen is corresponding author.

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PAS: Prediction-Based Actuation System for City-Scale Ridesharing Vehicular Mobile Crowdsensing

Cited by 67 publications

References 57 publications

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable Systems

A generative simulation platform for multi-agent systems with incentives

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