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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Tuan Anh Nguyen Dinh Anh Duy Nguyen Truong Thao Nguyen Thanh Hung Nguyen Phi Le Nguyen Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (纸本)9781665442664

In this paper, we address vehicle-based mobile crowdsensing for air quality monitoring applications. We tackle a novel issue that asks to determine monitoring frequencies for maximizing spatial-temporal coverage while reducing the monitoring costs and balancing load across the vehicles. We begin by theoretically formulating the problem and proposing an objective function that considers the three goals. We then leverage the evolutionary approach to develop an algorithm for determining the optimal monitoring frequency. We conduct comprehensive experiments to evaluate the performance of the proposed approach and compare it to the other methods. The results indicate that our approach can enhance the objective function by a factor of 1.33 to 4 compared to the others.

关键词： Vehicle-based Mobile Crowdsensing spatial-temporal coverage air quality monitoring cost minimization monitoring frequency optimization

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Probabilistic Control of Dynamic Crowds Toward Uniform spatial-temporal coverage

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第2期23卷 1050-1065页

作者： Ogawa, Yukio Hasegawa, Go Murata, Masayuki Muroran Inst Technol ICT Educ Ctr Muroran Hokkaido 0508585 Japan Tohoku Univ Res Inst Elect Commun Sendai 9808577 Japan Osaka Univ Grad Sch Informat Sci & Technol Osaka 5650871 Japan

Vehicular mobility and connectivity vary significantly over space and time when vehicular crowd sensing covers a city-wide area for a long time period, but it is important to achieve sufficiently uniform data coverage to satisfy the requirements of an environmental monitoring scenario. Our goal is thus to ensure uniform spatial-temporal coverage of sensed data over a city-wide area despite such vehicle dynamics. For a large area, trajectory-based approaches must deal with a great number and variety of participant mobility patterns. Hence, we propose a probabilistic control mechanism that adaptively adjusts the incentive to each participant, without using any prior information about participants. We provide a mathematical analysis that ensures stability of the number of participants with assigned tasks (called workers), and we evaluate the mechanism's robustness by using 24-hr vehicle trace data from a city-wide area. Our results demonstrate that, when the number of participants is up to 1500 times higher than the required number of workers, sensing actions result in a distribution with a mean of about 1 and an interquartile range of around 4 for a required sensing interval;moreover, the mean increases by 2% when 30% of communication messages are randomly lost.

关键词： Environmental monitoring probabilistic control spatial-temporal coverage vehicular crowd sensing

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Maximizing spatial-temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2018年第8期14卷

作者： Wang, Chaowei Li, Chensheng Qin, Cai Wang, Weidong Li, Xiuhua Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

Mobile crowd-sensing is a prospective paradigm especially for intelligent mobile terminals, which collects ubiquitous data efficiently in metropolis. The existing crowd-sensing schemes based on intelligent terminals mainly consider the current trajectory of the participants, and the quality highly depends on the spatial-temporal coverage which is easily weakened by the mobility of participants. Nowadays, public transports are widely used and affordable in many cities around the globe. Public transports embedded with substantial sensors act as participants in crowd-sensing, but different from the intelligent terminals, the trajectory of public transports is schedulable and predictable, which sheds an opportunity to achieve high-quality crowd-sensing. Therefore, based on the predictable trajectory of public transports, we design a novel system model and formulate the selection of public transports as an optimization problem to maximize the spatial-temporal coverage. After proving the public transport selection is non-deterministic polynomial-time hardness, an approximation algorithm is proposed and the coverage is close to 1. We evaluate the proposed algorithm with samples of real T-Drive trajectory data set. The results show that our algorithm achieves a near optimal coverage and outperforms existing algorithms.

关键词： Mobile crowd-sensing schedulable trajectory spatial-temporal coverage approximation algorithm performance guarantee

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Critical Location spatial-temporal coverage Optimization in Visual Sensor Network

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SENSORS 2019年第19期19卷 4106-4106页

作者： Xiong, Yonghua Li, Jing Lu, Manjie China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

coverage and network lifetime are two fundamental research issues in visual sensor networks. In some surveillance scenarios, there are some critical locations that demand to be monitored within a designated period. However, with limited sensor nodes resources, it may not be possible to meet both coverage and network lifetime requirements. Therefore, in order to satisfy the network lifetime constraint, sometimes the coverage needs to be traded for network lifetime. In this paper, we study how to schedule sensor nodes to maximize the spatial-temporal coverage of the critical locations under the constraint of network lifetime. First, we analyze the sensor node scheduling problem for the spatial-temporal coverage of the critical locations and establish a mathematical model of the node scheduling. Next, by analyzing the characteristics of the model, we propose a Two-phase spatial-temporal coverage-enhancing Method (TSCM). In phase one, a Particle Swarm Optimization (PSO) algorithm is employed to organize the directions of sensor nodes to maximize the number of covered critical locations. In the second phase, we apply a Genetic Algorithm (GA) to get the optimal working time sequence of each sensor node. New coding and decoding strategies are devised to make GA suitable for this scheduling problem. Finally, simulations are conducted and the results show that TSCM has better performance than other approaches.

关键词： visual sensor network spatial-temporal coverage sensor node scheduling two-phase coverage-enhancing method

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Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

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JOURNAL OF TRANSPORT GEOGRAPHY 2020年 88卷 102861-102861页

作者： Yang, Lin Zhang, Fayong Kwan, Mei-Po Wang, Ke Zuo, Zejun Xia, Shaotian Zhang, Zhiyong Zhao, Xinpei China Univ Geosci Sch Geog & Informat Engn 388 Lumo Rd Wuhan 430074 Peoples R China Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Fok Ying Tung Remote Sensing Sci Bldg Hong Kong Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Shatin Fok Ying Tung Remote Sensing Sci Bldg Hong Kong Peoples R China Univ Utrecht Dept Human Geog & Spatial Planning NL-3584 CB Utrecht Netherlands

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the "China Optics Valley" in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields 'better' configuration in meeting real-world bike riding needs.

关键词： Bike-sharing systems Location optimization Spatiotemporal dynamics Space-time demand cube spatial-temporal coverage Genetic algorithms

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A Framework Towards Solving the Mobile Crowd-sourcing coverage Question (Demo Paper) 22

A Framework Towards Solving the Mobile Crowd-sourcing Covera...

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30th ACM SIGspatial International Conference on Advances in Geographic Information Systems (ACM SIGspatial GIS)

作者： Chen, Shu Ahmed, Fahim Bai, Fan Grimm, Donald Gen Motors Warren MI 48092 USA

ISBN: (纸本)9781450395298

Nowadays, Mobile Crowdsourcing (MCS) systems, which leverage crowdsourced data to enable large-scale sensing, has become a popular research area. A persistent question has been whether the number of deployed sensing assets is sufficient to provide enough spatial and temporal coverage in particular areas and whether there is sufficient data to enable individual crowdsensing use cases within an area. We present a framework to provide such analysis. We also propose a pipeline for processing millions of vehicle telemetry data points into coverage assessment results. A set of novel geospatial algorithms for data processing are presented as well. Our analysis models support multiple types of coverage analysis including Segment mode, Node mode, Unique Mileage mode, and Categorized Road mode. Analysis table results and heatmap results are presented in the Demonstration section.

关键词： crowd-sensing spatial-temporal coverage vehicle telemetry

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Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective

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IEEE ACCESS 2019年 7卷 37334-37341页

作者： Wang, Chaowei Gaimu, Xiga Li, Chensheng Zou, He Wang, Weidong Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

Mobile crowdsensing (MCS) is a promising sensing paradigm based on the mobile node which provides the solution with cost-effectiveness to perform urban data collection. To monitor the urban environment and facilitate the municipal administration, more and more applications adopt vehicles as participants to carry out MCS tasks. The performance of the applications highly depends on the sensing data which is influenced by the recruiting strategy on vehicles. In this paper, we propose a novel vehicle selection algorithm to maximize the sensing range with limited cost while the vehicle selection problem was proved to be NP-complete. Specifically, we modeled the interaction between MCS server and candidate vehicles as a Markov decision process and formulated the maximum spatial temporal coverage (STC) optimization as a deep reinforcement learning process. The performance of our deep reinforcement learning-based vehicle selection (i.e., DRLVS) algorithm is evaluated with real trajectory dataset. The numerical result indicates that the proposed algorithm achieves an optimal solution and maximizes the STC.

关键词： Mobile crowdsensing spatial-temporal coverage deep reinforcement learning

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Performance Indexes Analysis for Multi-Observation Platform System 29

Performance Indexes Analysis for Multi-Observation Platform ...

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第29届中国控制与决策会议

作者： Tianxiang Chen Quanbo Ge Weifeng Liu Zhuyun Niu Jinyan Ma Institute of Systems Science and Control Engineering School of AutomationHangzhou Dianzi University North Automatic Control Technology Institute

ISBN: (纸本)9781509046584

Because of the more and more complex requirements of tracking environment,single-observation platform is difficult to deal with the challenges of the future *** with it,the multi-observation platform can overcome the uncertainty and limitation through coordination and complementarity between the *** to optimize the allocation of the limited sensor resources is a key problem in the platform management *** existing evaluation indexes of multi-observation platform are based on its operating parameters and are designed under the precondition of given network *** is less research on the optimal allocation of multi-observation *** this paper we put forward three kinds including four evaluation indexes,for example the time-spatial coverage(time coverage and spatial coverage),the observation completeness and the platform *** performance evaluation of the platform is obtained,by defining and relationship analysis of platform *** try to provide some thoughts and ideas for the performance evaluation,construction and optimization of the multi-platform observation system.

关键词： Multi-observation system Performance indexes spatial-temporal coverage Completeness Security

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