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Journal articles on the topic 'Crowdsensing system'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Wu, Xiaocan, Yu-E. Sun, He Huang, Yang Du, and Danlei Huang. "Time-Efficient Allocation Mechanisms for Crowdsensing Tasks with Precedence Constraints." Sensors 19, no. 11 (2019): 2456. http://dx.doi.org/10.3390/s19112456.

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Crowdsensing has emerged as an efficient and inexpensive way to perform specialized tasks by leveraging external crowds. In some crowdsensing systems, different tasks may have different requirements, and there may be precedence constraints among them, such as the Unmanned Aerial Vehicle (UAV) crowdsensing systems. Moreover, minimizing the total execution time is a regular target for finishing the crowdsensing tasks with precedence constraints. As far as we know, only a few existing studies consider the precedence constraints among crowdsensing tasks, and none of them can minimize the total exe

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Zhang, Zhong, Dae Hyun Yum, and Minho Shin. "PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems." Sensors 21, no. 21 (2021): 7045. http://dx.doi.org/10.3390/s21217045.

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Crowdsensing systems have been developed for wide-area sensing tasks because humancarried smartphones are prevailing and becoming capable. To encourage more people to participate in sensing tasks, various incentive mechanisms were proposed. However, participating in sensing tasks and getting rewards can inherently risk the users’ privacy and discourage their participation. In particular, the rewarding process can expose the participants’ sensor data and possibly link sensitive data to their identities. In this work, we propose a privacy-preserving reward system in crowdsensing using the blind

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Wang, Jingwei, Xinchun Yin, and Jianting Ning. "Fine-Grained Task Access Control System for Mobile Crowdsensing." Security and Communication Networks 2021 (February 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/6682456.

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Mobile crowdsensing enables people to collect and process a massive amount of information by using social resources without any cost on sensor deployment or model training. Many schemes focusing on the problems of task assignment and privacy preservation have been proposed so far. However, the privacy-preserving of requesters and task access control, which are vital to mobile crowdsensing, is barely considered in the literature. To address the aforementioned issues, a fine-grained task access control system for mobile crowdsensing is proposed. In particular, the requester can decide the group

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Hirth, Matthias, Michael Seufert, Stanislav Lange, Markus Meixner, and Phuoc Tran-Gia. "Performance Evaluation of Hybrid Crowdsensing and Fixed Sensor Systems for Event Detection in Urban Environments." Sensors 21, no. 17 (2021): 5880. http://dx.doi.org/10.3390/s21175880.

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Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a sim

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Yun, Junhyeok, and Mihui Kim. "SybilEye: Observer-Assisted Privacy-Preserving Sybil Attack Detection on Mobile Crowdsensing." Information 11, no. 4 (2020): 198. http://dx.doi.org/10.3390/info11040198.

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Mobile crowdsensing is a data collection system using widespread mobile devices with various sensors. The data processor cannot manage all mobile devices participating in mobile crowdsensing. A malicious user can conduct a Sybil attack (e.g., achieve a significant influence through extortion or the generation of fake IDs) to receive an incentive or destroy a system. A mobile crowdsensing system should, thus, be able to detect and block a Sybil attack. Existing Sybil attack detection mechanisms for wireless sensor networks cannot apply directly to mobile crowdsensing owing to the privacy issues

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Peng, Yadong, Tianxi Wang, and Qing Lang. "A Survey on Truth Discovery in Crowdsensing." Computers and Artificial Intelligence 2, no. 2 (2025): 1–8. https://doi.org/10.70267/cai.25v2n2.0108.

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In recent years, the rapid proliferation of smartphones and wearable devices has significantly propelled the development of crowdsensing. As a prerequisite for ensuring the secure operation of crowdsensing services, data quality has emerged as a critical issue that demands urgent resolution. This paper first introduces the main components and system workflows of crowdsensing, followed by an account of the basic concepts, principles, and key research focuses of truth discovery methods in crowdsensing. By categorizing truth discovery approaches based on modeling paradigms, this study systematica

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Kim, Mihui, and Junhyeok Yun. "Development of User-Participatory Crowdsensing System for Improved Privacy Preservation." Future Internet 12, no. 3 (2020): 56. http://dx.doi.org/10.3390/fi12030056.

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Recently, crowdsensing, which can provide various sensing services using consumer mobile devices, is attracting considerable attention. The success of these services depends on active user participation and, thus, a proper incentive mechanism is essential. However, if the sensing information provided by a user includes personal information, and an attacker compromises the service provider, participation will be less active. Accordingly, personal information protection is an important element in crowdsensing services. In this study, we resolve this problem by separating the steps of sensing dat

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8

Song, Chao, Ming Liu, and Xili Dai. "Share the Crowdsensing Data with Local Crowd by V2V Communications." Mobile Information Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/6406981.

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With an increase in the number of mobile applications, the development of mobile crowdsensing systems has recently attracted significant attention from both academic researchers and industries. In mobile crowdsensing system, the remote cloud (or back-end server) harvests all the crowdsensing data from the mobile devices, and the crowdsensing data can be uploaded immediately via 3G/4G. To reduce the cost and energy consumption, many academic researchers and industries investigate the way of mobile data offloading. Due to the sparse distribution of the WiFi APs, offloading the crowdsensing data

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Tang, Wenyi, Qi Jin, Xu Zheng, Guangchun Luo, Guiduo Duan, and Aiguo Chen. "A Novel Task Allocation Algorithm in Mobile Crowdsensing with Spatial Privacy Preservation." Wireless Communications and Mobile Computing 2019 (April 1, 2019): 1–13. http://dx.doi.org/10.1155/2019/3154917.

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The Internet of Things (IoT) has attracted the interests of both academia and industry and enables various real-world applications. The acquirement of large amounts of sensing data is a fundamental issue in IoT. An efficient way is obtaining sufficient data by the mobile crowdsensing. It is a promising paradigm which leverages the sensing capacity of portable mobile devices. The crowdsensing platform is the key entity who allocates tasks to participants in a mobile crowdsensing system. The strategy of task allocating is crucial for the crowdsensing platform, since it affects the data requester

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10

Alamri, Bayan Hashr, Muhammad Mostafa Monowar, and Suhair Alshehri. "A privacy-preserving collaborative reputation system for mobile crowdsensing." International Journal of Distributed Sensor Networks 14, no. 9 (2018): 155014771880218. http://dx.doi.org/10.1177/1550147718802189.

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Mobile crowdsensing is an emerging technology in which participants contribute sensor readings for different sensing applications. This technology enables a broad range of sensing applications by utilizing smartphones and tablets worldwide to improve people’s quality of life. Protecting participants’ privacy and ensuring the trustworthiness of the sensor readings are conflicting objectives and key challenges in this field. Privacy issues arise from the disclosure of the participant-related context information, such as participants’ location. Trustworthiness issues arise from the open nature of

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11

Wei, Lijun, Jing Wu, and Chengnian Long. "A Blockchain-Based Hybrid Incentive Model for Crowdsensing." Electronics 9, no. 2 (2020): 215. http://dx.doi.org/10.3390/electronics9020215.

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Crowdsensing is an emerging paradigm of data aggregation, which has a pivotal role in data-driven applications. By leveraging the recruitment, a crowdsensing system collects a large amount of data from mobile devices at a low cost. The critical issues in the development of crowdsensing are platform security, privacy protection, and incentive. However, the existing centralized, platform-based approaches suffer from the single point of failure which may result in data leakage. Besides, few previous studies have addressed the considerations of both the economic incentive and data quality. In this

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Agarwal, Dhruv, Srishti Agarwal, Vidur Singh, et al. "Understanding Driver-Passenger Interactions in Vehicular Crowdsensing." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–24. http://dx.doi.org/10.1145/3479869.

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Smart city projects collect data on urban environments to identify problems, inform policymaking, and boost citizen engagement. Typically, this data is collected by static sensors placed around the city, which is not ideal for spatiotemporal needs of certain sensing applications such as air quality monitoring. Vehicular crowdsensing is an upcoming approach that addresses this problem by utilizing vehicles' mobility to collect fine-grained city-scale data. Prior work has mainly focused on designing vehicular crowdsensing systems and related components, including incentive schemes, vehicle selec

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Luo, Xiaoling, Che Chen, Wenjie Zhang, Chunnian Zeng, Chengtao Li, and Jing Xu. "Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks." Electronics 11, no. 19 (2022): 3224. http://dx.doi.org/10.3390/electronics11193224.

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Mobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of the decentralized wireless sensor nodes. Typically, the quality of information obtained from crowdsensing is largely affected by various factors, such as the diverse requirements of crowdsensing tasks, the varying quality of information across different crowd workers, and the dynamic changes of channels conditions and the sensing environment. In this paper, considering the dynamics’ of the crowd workers, we focus on a spatial-temporal crowdsensing model and aim to maximize the value

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14

Song, Chengqun, Jun Cheng, and Wei Feng. "A Crowdsensing-Based Real-Time System for Finger Interactions in Intelligent Transport System." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/7385052.

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Crowdsensing leverages human intelligence/experience from the general public and social interactions to create participatory sensor networks, where context-aware and semantically complex information is gathered, processed, and shared to collaboratively solve specific problems. This paper proposes a real-time projector-camera finger system based on the crowdsensing, in which user can interact with a computer by bare hand touching on arbitrary surfaces. The interaction process of the system can be completely carried out automatically, and it can be used as an intelligent device in intelligent tr

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Kim, Sungwook. "New Bargaining Game Model for Collaborative Vehicular Network Services." Mobile Information Systems 2019 (March 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/6269475.

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The wireless industry’s evolution from fourth generation (4G) to fifth generation (5G) will lead to extensive progress in new vehicular network environments, such as crowdsensing, cloud computing, and routing. Vehicular crowdsensing exploits the mobility of vehicles to provide location-based services, whereas vehicular cloud computing is a new hybrid technology that instantly uses vehicular network resources, such as computing, storage, and Internet for decision-making. In this study, novel crowdsensing, cloud computing, and routing algorithms are developed for a next-generation vehicular netw

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Luo, Renfei, João Alexandre Lôbo Marques, Kok-Leong Ong, and Simon Fong. "Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living." International Journal of Extreme Automation and Connectivity in Healthcare 2, no. 2 (2020): 54–64. http://dx.doi.org/10.4018/ijeach.2020070106.

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Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encourage data sharing and mutual assistance is proposed. The game is called “Lemmings,” which stands for location-based mutual and mobile information navigation system; it is based on a classical video game where a group of creatures have to work and win through the puzzle game together. This game include

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Kim, Mihui, and Youngmin Kim. "Multi-Blockchain Structure for a Crowdsensing-Based Smart Parking System." Future Internet 12, no. 5 (2020): 90. http://dx.doi.org/10.3390/fi12050090.

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As a representative example for the construction of a smart city, a smart parking system has been developed in past research and implemented through IoT and cloud technologies. However, the initial installation cost of IoT sensor devices is preventing the spread of this technology, and thus as an alternative, a crowdsensing-based system, operating through data from publicly owned mobile devices, has been proposed. In this paper, we propose a multi-blockchain structure (i.e., constructed with public chain and private chain) in a crowdsensing-based smart parking system. In this structure, many s

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Hu, Xiping, Terry H. S. Chu, Henry C. B. Chan, and Victor C. M. Leung. "Vita: A Crowdsensing-Oriented Mobile Cyber-Physical System." IEEE Transactions on Emerging Topics in Computing 1, no. 1 (2013): 148–65. http://dx.doi.org/10.1109/tetc.2013.2273359.

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19

Li, Zan, Xiaohui Zhao, Fengye Hu, Zhongliang Zhao, Jose Luis Carrera Villacres, and Torsten Braun. "SoiCP: A Seamless Outdoor–Indoor Crowdsensing Positioning System." IEEE Internet of Things Journal 6, no. 5 (2019): 8626–44. http://dx.doi.org/10.1109/jiot.2019.2921561.

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Zhao, Xuefeng, Niannian Wang, Ruicong Han, et al. "Urban infrastructure safety system based on mobile crowdsensing." International Journal of Disaster Risk Reduction 27 (March 2018): 427–38. http://dx.doi.org/10.1016/j.ijdrr.2017.11.004.

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21

Yang, Mengmeng, Tianqing Zhu, Kaitai Liang, Wanlei Zhou, and Robert H. Deng. "A blockchain-based location privacy-preserving crowdsensing system." Future Generation Computer Systems 94 (May 2019): 408–18. http://dx.doi.org/10.1016/j.future.2018.11.046.

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22

Liang, Yihuai, Yan Li, and Byeong-Seok Shin. "FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment." Sensors 20, no. 11 (2020): 3172. http://dx.doi.org/10.3390/s20113172.

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Crowdsensing applications provide platforms for sharing sensing data collected by mobile devices. A blockchain system has the potential to replace a traditional centralized trusted third party for crowdsensing services to perform operations that involve evaluating the quality of sensing data, finishing payment, and storing sensing data and so forth. The requirements which are codified as smart contracts are executed to evaluate the quality of sensing data in a blockchain. However, regardless of the fact that the quality of sensing data may actually be sufficient, one key challenge is that mali

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Liu, Yang, Yong Li, Wei Cheng, Weiguang Wang, and Junhua Yang. "A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing." Applied Sciences 13, no. 10 (2023): 6040. http://dx.doi.org/10.3390/app13106040.

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Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we pr

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Toliopoulos, Theodoros, Nikodimos Nikolaidis, Anna-Valentini Michailidou, et al. "Sboing4Real: A real-time crowdsensing-based traffic management system." Journal of Parallel and Distributed Computing 162 (April 2022): 59–75. http://dx.doi.org/10.1016/j.jpdc.2022.01.017.

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Li, Xiao, and Daniel W. Goldberg. "Toward a mobile crowdsensing system for road surface assessment." Computers, Environment and Urban Systems 69 (May 2018): 51–62. http://dx.doi.org/10.1016/j.compenvurbsys.2017.12.005.

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Gu, Yiren, Hang Shen, Guangwei Bai, Tianjing Wang, and Xuejun Liu. "QoI-aware incentive for multimedia crowdsensing enabled learning system." Multimedia Systems 26, no. 1 (2019): 3–16. http://dx.doi.org/10.1007/s00530-019-00616-w.

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Zhong, Weida, Qiuling Suo, Fenglong Ma, et al. "A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 4 (2019): 1–26. http://dx.doi.org/10.1145/3369815.

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Ye, Hai-Bo, Xuan-Song Li, Li Sheng, and Kai Dong. "CBSC: A Crowdsensing System for Automatic Calibrating of Barometers." Journal of Computer Science and Technology 34, no. 5 (2019): 1007–19. http://dx.doi.org/10.1007/s11390-019-1957-1.

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Miguel-Santiago, David, Mario E. Rivero-Angeles, Laura I. Garay-Jiménez, Izlian Y. Orea-Flores, and Blanca Tovar-Corona. "Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario." International Journal of Distributed Sensor Networks 18, no. 11 (2022): 155013292211332. http://dx.doi.org/10.1177/15501329221133291.

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Crowdsensing systems are developed in order to use the computational and communication capabilities of registered users to monitor specific variables and phenomena in an opportunistic manner. As such, the Quality of Experience is not easily attained since these systems heavily rely on the user’s behavior and willingness to cooperate whenever an event with certain interest needs to be monitored. In this work, we analyze the data acquisition phase, where pedestrians opportunistically transmit to vehicles to further disseminate it in the city according to their trajectory. This highly dynamic env

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Kim, Sungwook. "A New Differential Privacy Crowdsensing Scheme Based on the Multilevel Interactive Game." Wireless Communications and Mobile Computing 2018 (October 17, 2018): 1–11. http://dx.doi.org/10.1155/2018/9867061.

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With the rapid growth of network of devices with embedded technology, mobile crowdsensing (MCS) has been gaining increasing popularity. The development of 5G network services is prompting further growth in crowdsensing applications. However, MCS participants risk their privacy when reporting data with their actual sensing positions. To address this issue, the concept of differential privacy (DP) can be adopted to provide a theoretical guarantee for participants’ privacy in MCS services. In this study, we design a new DP crowdsensing scheme with game theory. Based on the multilevel interactive

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Mathew, Sujith Samuel, May El Barachi, and Mohammad Amin Kuhail. "CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting." Applied Sciences 12, no. 21 (2022): 11156. http://dx.doi.org/10.3390/app122111156.

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Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating

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Zhu, Yi, Abhishek Gupta, Shaohan Hu, Weida Zhong, Lu Su, and Chunming Qiao. "Driver Behavior-aware Parking Availability Crowdsensing System Using Truth Discovery." ACM Transactions on Sensor Networks 17, no. 4 (2021): 1–26. http://dx.doi.org/10.1145/3460200.

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Spot-level parking availability information (the availability of each spot in a parking lot) is in great demand, as it can help reduce time and energy waste while searching for a parking spot. In this article, we propose a crowdsensing system called SpotE that can provide spot-level availability in a parking lot using drivers’ smartphone sensors. SpotE only requires the sensor data from drivers’ smartphones, which avoids the high cost of installing additional sensors and enables large-scale outdoor deployment. We propose a new model that can use the parking search trajectory and final destinat

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Zou, Shihong, Jinwen Xi, Honggang Wang, and Guoai Xu. "CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System." IEEE Transactions on Industrial Informatics 16, no. 6 (2020): 4206–18. http://dx.doi.org/10.1109/tii.2019.2957791.

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El-Wakeel, Amr S., Jin Li, Aboelmagd Noureldin, Hossam S. Hassanein, and Nizar Zorba. "Towards a Practical Crowdsensing System for Road Surface Conditions Monitoring." IEEE Internet of Things Journal 5, no. 6 (2018): 4672–85. http://dx.doi.org/10.1109/jiot.2018.2807408.

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Wang, En, Yongjian Yang, Jie Wu, Kaihao Lou, Dongming Luan, and Hengzhi Wang. "User Recruitment System for Efficient Photo Collection in Mobile Crowdsensing." IEEE Transactions on Human-Machine Systems 50, no. 1 (2020): 1–12. http://dx.doi.org/10.1109/thms.2019.2912509.

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Agate, Vincenzo, Pierluca Ferraro, Giuseppe Lo Re, and Sajal K. Das. "BLIND: A privacy preserving truth discovery system for mobile crowdsensing." Journal of Network and Computer Applications 223 (March 2024): 103811. http://dx.doi.org/10.1016/j.jnca.2023.103811.

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Wang, Pengfei, and Ruiyun Yu. "SMF-GA: Optimized Multitask Allocation Algorithm in Urban Crowdsourced Transportation." Wireless Communications and Mobile Computing 2019 (March 17, 2019): 1–13. http://dx.doi.org/10.1155/2019/8035167.

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Urban crowdsourced transportation, which can solve traffic problem within city, is a new scenario where citizens share vehicles to take passengers and packages while driving. Differing from the traditional location based crowdsourcing system (e.g., crowdsensing system), the task has to be completed with visiting two different locations (i.e., start and end points), so task allocation algorithms in crowdsensing cannot be leveraged in urban crowdsourced transportation directly. To solve this problem, we first prove that maximizing the crowdsourcing system’s profit (i.e., maximizing the total sav

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Wang, Kun, Zhigang Chen, Lizhong Zhang, Jiaqi Liu, and Bin Li. "Incentive Mechanism for Improving Task Completion Quality in Mobile Crowdsensing." Electronics 12, no. 4 (2023): 1037. http://dx.doi.org/10.3390/electronics12041037.

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Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure the stability of the sensing data quality. Most of the existing incentive mechanisms for data quality in crowdsensing are based on traditional economics, which believe that the decision of participants to complete a task depends on whether the benefit of the task is greater than the cost of completing the tas

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Domaszewicz, Jaroslaw, and Dariusz Parzych. "Intra-Company Crowdsensing: Datafication with Human-in-the-Loop." Sensors 22, no. 3 (2022): 943. http://dx.doi.org/10.3390/s22030943.

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Every day employees learn about things happening in their company. This includes plain facts witnessed while on the job, related or not to one’s job responsibilities. Many of these facts, which we call “occurrence data”, are known by employees but remain unknown to the company. We suppose that some of them are valuable and may improve the company’s situational awareness. In the spirit of mobile crowdsensing, we propose intra-company crowdsensing (ICC), a method of “extracting” occurrence data from employees. In ICC, an employee occasionally responds to sensing requests, each about one plain fa

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Wang, Jionghua, Haowen Luo, Wenyu Li, and Bo Huang. "Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China." Remote Sensing 13, no. 23 (2021): 4751. http://dx.doi.org/10.3390/rs13234751.

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Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were

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Kim, Yungeun, Yohan Chon, and Hojung Cha. "Mobile Crowdsensing Framework for a Large-Scale Wi-Fi Fingerprinting System." IEEE Pervasive Computing 15, no. 3 (2016): 58–67. http://dx.doi.org/10.1109/mprv.2016.50.

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Yang, Guang, Xiufang Shi, Li Feng, Shibo He, Zhiguo Shi, and Jiming Chen. "CEDAR: A Cost-Effective Crowdsensing System for Detecting and Localizing Drones." IEEE Transactions on Mobile Computing 19, no. 9 (2020): 2028–43. http://dx.doi.org/10.1109/tmc.2019.2921962.

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Mowafi, Moad, Fahed Awad, and Fida’a Al-Quran. "Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments." Sensors 22, no. 15 (2022): 5467. http://dx.doi.org/10.3390/s22155467.

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Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers’ interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo

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Chang, Sha, Yahui Wu, Su Deng, Wubin Ma, and Haohao Zhou. "Task-Importance-Oriented Task Selection and Allocation Scheme for Mobile Crowdsensing." Mathematics 12, no. 16 (2024): 2471. http://dx.doi.org/10.3390/math12162471.

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In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance o

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Xie, Zhenzhen, Liang Hu, Yan Huang, and Junjie Pang. "A Semiopportunistic Task Allocation Framework for Mobile Crowdsensing with Deep Learning." Wireless Communications and Mobile Computing 2021 (February 15, 2021): 1–15. http://dx.doi.org/10.1155/2021/6643229.

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The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high-quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the s

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Fan, Xiaochen, Xiangjian He, Chaocan Xiang, et al. "Towards System Implementation and Data Analysis for Crowdsensing Based Outdoor RSS Maps." IEEE Access 6 (2018): 47535–45. http://dx.doi.org/10.1109/access.2018.2867578.

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Chen, Xinlei, Susu Xu, Jun Han, et al. "PAS: Prediction-Based Actuation System for City-Scale Ridesharing Vehicular Mobile Crowdsensing." IEEE Internet of Things Journal 7, no. 5 (2020): 3719–34. http://dx.doi.org/10.1109/jiot.2020.2968375.

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Xu, Chang, Xiaodong Shen, Liehuang Zhu, and Yan Zhang. "A Collusion-Resistant and Privacy-Preserving Data Aggregation Protocol in Crowdsensing System." Mobile Information Systems 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3715253.

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Abstract:

With the pervasiveness and increasing capability of smart devices, mobile crowdsensing has been applied in more and more practical scenarios and provides a more convenient solution with low costs for existing problems. In this paper, we consider an untrusted aggregator collecting a group of users’ data, in which personal private information may be contained. Most previous work either focuses on computing particular functions based on the sensing data or ignores the collusion attack between users and the aggregator. We design a new protocol to help the aggregator collect all the users’ raw data

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Owoh, Nsikak, Jackie Riley, Moses Ashawa, Salaheddin Hosseinzadeh, Anand Philip, and Jude Osamor. "An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing." Sensors 24, no. 7 (2024): 2353. http://dx.doi.org/10.3390/s24072353.

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Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenari

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Shou, Tieqi, Zhuohan Ye, Yayao Hong, et al. "CrowdQ." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 3 (2023): 1–28. http://dx.doi.org/10.1145/3610875.

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Abstract:

Hospital Emergency Departments (EDs) are essential for providing emergency medical services, yet often overwhelmed due to increasing healthcare demand. Current methods for monitoring ED queue states, such as manual monitoring, video surveillance, and front-desk registration are inefficient, invasive, and delayed to provide real-time updates. To address these challenges, this paper proposes a novel framework, CrowdQ, which harnesses spatiotemporal crowdsensing data for real-time ED demand sensing, queue state modeling, and prediction. By utilizing vehicle trajectory and urban geographic environ

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