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Advanced Research in Mobile Crowd Sensing Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 4912

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
Interests: mobile intelligent computing; mobile big data; blockchain and privacy protection

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Guest Editor

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Guest Editor
College of Computer Science and Technology, China Three Gorges University, Yichang 443002, China
Interests: mobile crowdsensing;mobile edge computing;VANETs

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Guest Editor
School of Computer Science and Technology, Jilin University, Changchun 130012, China
Interests: Mobile Crowdsensing; Mobile Computing; Data Mining
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Special Issue Information

Dear Colleagues,

With the development of Internet of Things technologies and the proliferation of mobile smart devices, mobile crowdsensing (MCS) has attracted a great deal of attention from industry and academia. MCS utilizes large number of participants to perform various sensing tasks via the built-in sensors of mobile devices. Compared to traditional wireless sensor networks, MCS can make full use of mobile devices, participants, and networks to achieve strong sensing ability, large coverage, low deployment cost, and high scalability. In recent years, many interesting and useful MCS applications have already been presented and developed, such as air quality monitoring, target tracking, digital map updating, road condition detection, and so on.

Although many researchers have paid attention to MCS, it still faces many challenges. First of all, MCS has to face a great deal of uncertainty in terms of participants, sensing tasks, and even communication scenarios. Thus, how to adjust the strategies (participant selection, task allocation, etc.) to deal with these uncertainties is one of the most pressing challenges. Secondly, MCS aims to provide fine-grained and large-scale sensing services by utilizing widespread participants and large amounts of mobile devices. However, there might still be some data that cannot be sensed. Moreover, the sensing data might also have some errors caused by devices or some extreme events. Thus, how to deal with the missing, corrupted, and abnormal sensing data is another challenge in MCS. Finally, MCS also needs to provide secure and trustworthy data sensing services. In consideration of these points, this Special Issue of Sensors aims to present novel studies in terms of mobile crowdsensing, spatial crowdsourcing, data filling in sparse crowdsensing, and so on.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Mobile crowdsensing applications;
  • Machine learning for mobile crowdsensing;
  • Incentive mechanisms for mobile crowdsensing;
  • Security and trust of mobile crowdsensing;
  • Privacy preserving in mobile crowdsensing;
  • Truth discovery in mobile crowdsensing;
  • Blockchain-based mobile crowdsensing systems;
  • Federated learning for mobile crowdsensing;
  • Edge computing for mobile crowdsensing;
  • Crowdsensing data trading and sharing;
  • Big data spatial-temporal analysis in crowdsensing;
  • Crowdsensing for intelligent transportation applications.

We look forward to receiving your contributions.

Prof. Dr. Mingjun Xiao
Prof. Dr. Ning Wang
Prof. Dr. Huan Zhou
Prof. Dr. En Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mobile crowdsensing
  • machine learning
  • incentive mechanism
  • security and trust
  • privacy preserving
  • truth discovery
  • blockchain
  • federated learning
  • edge computing
  • data trading

Published Papers (2 papers)

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20 pages, 3918 KiB  
Article
A Personalized Task Allocation Strategy in Mobile Crowdsensing for Minimizing Total Cost
by Hengfei Gao and Hongwei Zhao
Sensors 2022, 22(7), 2751; https://doi.org/10.3390/s22072751 - 2 Apr 2022
Cited by 7 | Viewed by 1935
Abstract
Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works usually assume a static task allocation by [...] Read more.
Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works usually assume a static task allocation by sorting the cost of users to complete the tasks, where the cost is measured by the expense of time or distance. In this paper, we argue that the task allocation process is actually a dynamic combinational optimization problem because the previous allocated task will influence the initial state of the user to finish the next task, and the user’s preference will also influence the actual cost. To this end, we propose a personalized task allocation strategy for minimizing total cost, where the cost for a user to finish a task is measured by both the moving distance and the user’s preference for the task, then instead of statically allocating the tasks, the allocation problem is formulated as a heterogeneous, asymmetric, multiple traveling salesman problem (TSP). Furthermore, we transform the multiple-TSP to the single-TSP by proving the equivalency, and two solutions are presented to solve the single-TSP. One is a greedy algorithm, which is proved to have a bound to the optimal solution. The other is a genetic algorithm, which spends more calculation time while achieving a lower total cost. Finally, we have conducted a number of simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The simulation results could match the results of theoretical analysis. Full article
(This article belongs to the Special Issue Advanced Research in Mobile Crowd Sensing Systems)
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21 pages, 2373 KiB  
Article
Query Optimization for Distributed Spatio-Temporal Sensing Data Processing
by Xin Li, Huayan Yu, Ligang Yuan and Xiaolin Qin
Sensors 2022, 22(5), 1748; https://doi.org/10.3390/s22051748 - 23 Feb 2022
Cited by 1 | Viewed by 1998
Abstract
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so [...] Read more.
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively. Full article
(This article belongs to the Special Issue Advanced Research in Mobile Crowd Sensing Systems)
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