Relevant bibliographies by topics / Crowdsensing system

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Crowdsensing system'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Crowdsensing system"

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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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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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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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Dissertations / Theses on the topic "Crowdsensing system"

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Di, Chiappari Alain. "A Collaborative Mobile Crowdsensing System for Smart Cities." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11874/.

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Nowadays words like Smart City, Internet of Things, Environmental Awareness surround us with the growing interest of Computer Science and Engineering communities. Services supporting these paradigms are definitely based on large amounts of sensed data, which, once obtained and gathered, need to be analyzed in order to build maps, infer patterns, extract useful information. Everything is done in order to achieve a better quality of life. Traditional sensing techniques, like Wired or Wireless Sensor Network, need an intensive usage of distributed sensors to acquire real-world conditions. We prop
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Kutsarova, Viktoriya. "Managing Alarming Situations with Mobile Crowdsensing Systems and Wearable Devices." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280456.

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Dangerous events such as accidental falls, allergic reactions or even severe panic attacks can occur spontaneously and within seconds. People experienc- ing alarming situations like these often require assistance. On the one hand, wearable devices such as smartphones or smartwatches can be used to detect these situations by utilising the plethora of sensors built into them. On the other hand, mobile crowdsensing systems (MCS) might be used to manage the detection and mitigation of alarming situations. To be able to handle these events, an MCS requires integration with mobile sensory devices, a
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Merlino, Giovanni. "Sensing and Actuation as a Service, a device-centric paradigm for the IoT: analysis, design and case studies." Doctoral thesis, Università di Catania, 2016. http://hdl.handle.net/10761/3959.

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The huge and steady growth in the number of distributed devices connected to the global network as a so-called Internet of Things (IoT) calls for infrastructure management techniques able to deal with this overwhelming complexity, especially in light of the growing impact of the sharing economy and the role played by the so-called "long tail". In this context, the as-a-Service approach provides well investigated mechanisms for infrastructure and service provisioning, and an interesting challenge lies in evaluating its application to the instantiation and lifecycle management of a dynamic, poss
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Chu, Yu-Chi, and 許育淇. "Crowdsensing System with Server-Centric Incentive Mechanism." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7zywzk.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>105<br>Crowdsensing is an approach to collect human activities and surrounding environment which takes the benefit of the pervasive smartphones and their powerful sensors. In a crowdsensing system, a large number of users in the sensing tasks collect and send data through their mobile devices to a data collection server. The performance of the system heavily depends on the crowd participation. Thus, incentive mechanisms are important in crowdsensing. We focus on the server-centric model, in which the server has more control over the payment. We design four incentive
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KUO, JUN-HONG, and 郭峻鴻. "Credibility Verification Mechanism for Remote Sensed Data in Mobile Crowdsensing System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/82x767.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>106<br>In recent years, the maturity of related technologies such as big data, cloud com- puting and mobile applications has promoted the development of various integrate systems. In all kinds of social public issues, it is necessary to rely on collecting a large amount of environmental data as a basis. However, if the method of deploying the sensing device is adopted, it may face implementation problems such as deploy- ment and operation and maintenance, and lack of high mobility. In recent years, the concept of Crowdsensing has gradually emerged, relying on the hig
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Liao, Chen-Chih, and 廖宸誌. "Detour Planning Problem on Mobile Crowdsensing Systems." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/26654700016373633516.

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碩士<br>國立清華大學<br>資訊工程學系<br>103<br>Crowdsensing is a popular paradigm that outsources sensory/multimedia tasks to mobile workers. In the crowdsensing systems, workers perform diverse tasks such as detecting sensory data and taking pictures by employing (using) their smartphones, which are equipped with sensing and multimedia functions. We provide a crowdsensing system to efficiently delegate sensory/multimedia tasks to mobile workers, and we focus on spatial-temporal tasks that must be conducted at specific locations and time. Each worker supplies his/her destination with a deadline to our syste
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Simões, João Pedro Fernandes. "Análise de dados e Machine Learning na mobilidade urbana." Master's thesis, 2018. http://hdl.handle.net/10400.26/29858.

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A mobilidade tornou-se num dos desafios mais difíceis que as cidades têm de enfrentar. Mais de metade da população mundial reside em áreas urbanas e com o contínuo aumento da população é imperativo que as cidades usem os seus recursos de forma eficiente. Exige-se por isso, que cada vez mais, a gestão e o planeamento da oferta de transportes, tenha de ser realizada de uma forma racional e eficaz de modo a satisfazer as necessidades dos cidadãos. Obter e reunir dados a partir de diferentes fontes de dados pode ser extretamente importante para apoiar novas soluções que podem ajudar a construir um
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Books on the topic "Crowdsensing system"

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Borcea, Cristian, Manoop Talasila, and Reza Curtmola. Mobile Crowdsensing. Taylor & Francis Group, 2020.

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Borcea, Cristian, Manoop Talasila, and Reza Curtmola. Mobile Crowdsensing. Taylor & Francis Group, 2016.

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Borcea, Cristian, Manoop Talasila, and Reza Curtmola. Mobile Crowdsensing. Taylor & Francis Group, 2016.

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Borcea, Cristian, Manoop Talasila, and Reza Curtmola. Mobile Crowdsensing. Taylor & Francis Group, 2016.

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Borcea, Cristian, Manoop Talasila, and Reza Curtmola. Mobile Crowdsensing. Taylor & Francis Group, 2016.

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Mobile Crowdsensing. Taylor & Francis Group, 2016.

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Book chapters on the topic "Crowdsensing system"

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Sangeetha, S., K. Anitha Kumari, M. Shrinika, P. Sujaybharath, S. Muhil Varsini, and K. Ajith Kumar. "Ensuring Location Privacy in Crowdsensing System Using Blockchain." In Futuristic Communication and Network Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9748-8_4.

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Sato, Koji, Shigemi Ishida, Jumpei Kajimura, Shigeaki Tagashira, and Akira Fukuda. "Proposal of Acoustic Train Detection System for Crowdsensing." In Intelligent Transport Systems for Everyone’s Mobility. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7434-0_24.

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Wu, Xiaocan, Yu-E. Sun, Yang Du, Xiaoshuang Xing, Guoju Gao, and He Huang. "An Efficient Malicious User Detection Mechanism for Crowdsensing System." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59016-1_42.

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Wang, Zhen, Jinghua Zhu, and Doudou Li. "Prediction Based Reverse Auction Incentive Mechanism for Mobile Crowdsensing System." In Combinatorial Optimization and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36412-0_44.

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Huang, Min, and Xia Wu. "A Review of Air Quality Monitoring System Based on Crowdsensing." In International Symposium for Intelligent Transportation and Smart City (ITASC) 2019 Proceedings. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7542-2_28.

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Zhu, Jinghua, Yuanjing Li, Anqi Lu, and Heran Xi. "Worker Recruitment Based on Edge-Cloud Collaboration in Mobile Crowdsensing System." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95388-1_27.

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Labus, Aleksandra, Miloš Radenković, Stefan Nešković, Snežana Popović, and Svetlana Mitrović. "A Smart City IoT Crowdsensing System Based on Data Streaming Architecture." In Marketing and Smart Technologies. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9268-0_26.

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Isha, Anugrah Kamla Lakra, and Deepika Suhag. "CrisisConnect: A blockchain-driven edge computing approach in mobile crowdsensing system." In Data Science & Exploration in Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003589273-44.

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Arulprakash, M., and R. Jebakumar. "Enhanced Data Privacy Preservation Model for Mobile Crowdsensing System Using Blockchain Technology." In Ambient Communications and Computer Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7952-0_53.

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Miletić, Aleksa, Marijana Despotović-Zrakić, Zorica Bogdanović, Miloš Radenković, and Tamara Naumović. "A Prototype of the Crowdsensing System for Pollution Monitoring in a Smart City Based on Data Streaming." In Information Systems and Technologies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-45648-0_5.

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Conference papers on the topic "Crowdsensing system"

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Li, Zuxin, Fanhang Man, Xuecheng Chen, et al. "QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications. IEEE, 2024. http://dx.doi.org/10.1109/infocom52122.2024.10621374.

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Marry, Revathi, and Luis G. Jaimes. "A Probabilistic Incentive Mechanism for Vehicular Crowdsensing System Based on Utility." In 2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2025. https://doi.org/10.1109/percomworkshops65533.2025.00112.

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Maqour, Zaina, Hanan El Bakkali, Driss Benhaddou, Houda Benbrahhim, and Hajar Elgadi. "BlockCrowd: A Privacy-Preserving Trust Management Blockchain-Based System for Crowdsensing." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059661.

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Ortiz, Sebastian Luis S., Justin Gabriel R. Enriquez, Wilson M. Tan, and Cedric Angelo M. Festin. "Hotspotter: An Incentivized Crowdsensing System for WiFi and Cellular Network Coverage Visualization." In 2024 IEEE 29th Asia Pacific Conference on Communications (APCC). IEEE, 2024. https://doi.org/10.1109/apcc62576.2024.10767933.

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Ji, Sijie, Lixiang Lian, Yuanqing Zheng, and Chenshu Wu. "MuSAC: Mutualistic Sensing and Communication for Mobile Crowdsensing." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00031.

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Baljak, Luka, Natasa Bojkovic, Aleksandra Labus, Tamara Naumovic, and Aleksandra Maksimovic. "A system for crowdsensing vibration in public transportation." In 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI). IEEE, 2019. http://dx.doi.org/10.1109/ic-aiai48757.2019.00012.

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Peng, Tao, Jierong Liu, Jianer Chen, and Guojun Wang. "A Privacy-Preserving Crowdsensing System with Muti-blockchain." In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2020. http://dx.doi.org/10.1109/trustcom50675.2020.00265.

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She, RuYi. "Survey on Incentive Strategies for Mobile Crowdsensing System." In 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2020. http://dx.doi.org/10.1109/icsess49938.2020.9237745.

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IACUSSI, LEONARDO, MATTEO BRAMBILLA, PAOLO CHIARIOTTI, STEFANO MANZONI, and ALFREDO CIGADA. "EXPLORING CROWDSENSING POTENTIALS FOR STRUCTURAL HEALTH MONITORING APPLICATIONS: CHALLENGES AND OPPORTUNITIES." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36841.

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The use of devices equipped with sensing capabilities (ranging from smartphones to smartwatches) has become commonplace in our daily lives. These devices can measure various parameters such as acceleration and position, and have the potential to be leveraged for purposes beyond their intended use. This is the main goal of crowdsensing techniques. However, if on the one hand crowdsensing is spreading for its great appeal, on the other hand sometimes the importance of key concepts related to the robustness of the measurement data collected are underestimated. This is particularly true when crowd
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Radu, Valentin, Lito Kriara, and Mahesh K. Marina. "Pazl: A mobile crowdsensing based indoor WiFi monitoring system." In 2013 9th International Conference on Network and Service Management (CNSM). IEEE, 2013. http://dx.doi.org/10.1109/cnsm.2013.6727812.

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