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: 22 June 2025

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Journal articles on the topic "Crowdsensing system"

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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 signature. The proposed scheme protects the participants’ privacy by decoupling contributions and rewarding claims. Our experiment results show that the proposed mechanism is feasible and efficient.
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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 execution time simultaneously. To tackle this challenge, an efficient allocation mechanism for tasks with precedence constraints is first proposed, which can minimize the total execution time. Then, a case study is given to show how to fit our mechanism in the UAV crowdsensing system. Finally, the simulation results show that the proposed mechanisms have good approximate optimal ratios under different parameter settings and are efficient for the UAV crowdsensing system as well.
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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 of task performers who can access the task by utilizing attribute-based encryption technology. T he untrusted crowdsensing platform cannot obtain any sensitive information concerning the requester or the task, while the qualified task performers are capable of retrieving tasks within 0.85 ms. Security analysis and experimental results are presented to show the feasibility and efficiency of the proposed system.
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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 simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.
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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 of the participants and detection overhead. In this paper, we propose an effective privacy-preserving Sybil attack detection mechanism that distributes observer role to the users. To demonstrate the performance of our mechanism, we implement a Wi-Fi-connection-based Sybil attack detection model and show its feasibility by evaluating the detection performance.
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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 data processing and the reward payment process. An arbitrary node in a sensing data processing pool consisting of user nodes is selected for sensing data processing, and only the processing results are sent to the service provider server to reward the data providing node. The proposed user-participatory crowdsensing system is implemented on the Kaa Internet of things (IoT) platform to evaluate its performance and demonstrate its feasibility.
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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 is often delayed. In this paper, compared with offloading data via WiFi APs, we investigate the communication and sharing of crowdsensing data by vehicles near the event (such as a pothole on the road), termed as a local crowd. In such crowd, a vehicle can transmit the data to each other by vehicle-to-vehicle (V2V) communication. The crowd-based approach has a lower delay than the offloading-based approach, by considering the quality of truth discovery. We define a utility function related to the crowdsensing data shared by the local crowd in order to quantify the trade-off between the quality of the truth discovery and the user satisfaction. Our extensional simulations verify the effectiveness of our proposed schemes.
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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’s confidence, the participant’s confidence, and its own benefit. Traditional allocating algorithms regard the privacy preservation, which may lose the confidence of participants. In this paper, we propose a novel three-step algorithm which allocates tasks to participants with privacy consideration. It maximizes the benefit of the crowdsensing platform and meanwhile preserves the privacy of participants. Evaluation results on both benefit and privacy aspects show the effectiveness of our proposed algorithm.
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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 sensing system because anyone can contribute data. This article proposes a privacy-preserving collaborative reputation system that preserves privacy and ensures data trustworthiness of the sensor readings for mobile crowdsensing applications. The proposed work also counters a number of possible attacks that might occur in mobile crowdsensing applications. We provide a detailed security analysis to prove the effectiveness of privacy-preserving collaborative reputation system against a number of attacks. We conduct an extensive simulation to investigate the performance of our schema. The obtained results show that the proposed schema is practical; it succeeds in identifying malicious users in most scenarios. In addition, it tolerates a large number of colluding adversaries even if their number surpass 65%. Moreover, it detects on-off attackers even if they report trusted data with high probability (0.8).
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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 paper, we propose a decentralized crowdsensing architecture based on blockchain technology which will help improve the attack resistance. Furthermore, we present a hybrid incentive mechanism, which integrates the data quality, reputation, and monetary factors to encourage participants to contribute their sensing data while discouraging malicious behaviors. The effectiveness our of proposed incentive model is verified through a combination of the theory of mechanism design. The performance analysis and simulation results illustrate that the proposed hybrid incentive model is a reliable and efficient mean to promote data security and incentivizing positive conduct on the crowdsensing application.
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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 propose SenSquare, a Crowdsensing approach based on smartphones and a central coordination server for time-and-space homogeneous data collecting. SenSquare relies on technologies such as CoAP lightweight protocol, Geofencing and the Military Grid Reference System.
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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, as well as the voluntary participation of people willing to help. This thesis investigates how to incorporate wearables into an MCS. Furthermore, it explores how to utilise the gathered data and the participants in the system to manage alarming situations.The contributions of this thesis are twofold. First, we propose the exten- sion of a mobile crowdsensing system for managing alarming situations that allows integration of wearables. We base our work on CrowdS - an MCS that facilitates the distributed interactions between people and sensory devices. We integrate a commodity smartwatch into CrowdS using different techniques (i.e. Internet and Bluetooth). The smartwatch’s sensors enable the detection of various alarming situations and their transmission to the MCS. The mobile crowdsensing system then relays the data and finds volunteers willing to help. Our solution can be adapted to handle various types of dangerous situations. Moreover, the system can easily be integrated with other types of wearables.Second, to test the usefulness of an MCS without actually deploying it in real life, we create a simulation that models different scenarios that rep- resent dangerous events. It allows us to represent the event visually and to parametrise various factors that influence the effectiveness of the system. The simulation helps to identify how different parameters might affect the outcome of the alarming situation. Our results show that important attributes include but are not limited to the coverage of the system, the number of participants and their density, as well as distribution and means of transportation.We enhance the capabilities of CrowdS by enabling the integration of var- ious Bluetooth wearable devices. Thus we expand CrowdS into a prototype of a system for managing alarming situations. Moreover, through the MCS simulation, we identify essential parameters that need to be considered when building such a system. The simulation is a tool that can also be used to find the optimal configuration of the MCS.<br>Farliga händelser som till exempel oavsiktliga fall, allergiska reaktioner eller till och med panikångest kan inträffa utan förvarning och inom några sekun- der. Människor som upplever livsfarliga situationer som dessa behöver ofta hjälp. Bärbara enheter som smartphones eller smartwatches användas för att upptäcka dessa situationer genom att använda en mängd sensorer som är in- byggda i dem. Mobile Crowdsensing Systems (MCS) användas för att hantera upptäckten av dessa situationer och hjälpa människor få de hjälp som behövs. För att kunna hantera dessa situationer kräver en MCS integration mellan mo- bila sensoriska enheter, såväl som ett deltagande av människor som är villiga att hjälpa. Denna avhandling undersöker hur man kan integrera wearables i en MCS. Man undersöker även hur man kan samla in data och deltagare i systemet för att hantera farliga situationer.Bidragen i denna avhandling är tvåfaldiga. För det första föreslår vi utök- ning av en MCS för att hantera farliga situationer som möjliggör integrationen av bärbara enheter. Vi baserar vårt arbete på CrowdS - ett MCS som under- lättar distribuerade interaktioner mellan människor och sensoriska apparater. Vi integrerar en smartwatch med CrowdS med hjälp av olika metoder (exem- pelvis. Internet och Bluetooth). Smartwatch-sensorerna möjliggör upptäckt av olika farliga situationer och deras överföring till MCS. MCS vidarebefordrar sedan datan och försöker hitta frivilliga som är villiga att hjälpa. Vår lösning kan anpassas för att hantera olika typer av farliga situationer. Dessutom kan systemet enkelt integreras med andra typer av bärbara enheter.För att testa nyttan av MCS utan att distribuera den i verkliga livet, skapar vi en simulering av olika scenarier som representerar farliga händelser. I si- muleringen kan vi ändra parametrar och faktorer under händelseförloppet för att se hur det påverkar systemets effektivitet. Simuleringen hjälper till att iden- tifiera hur olika parametrar kan påverka resultatet av den farliga situationen. Våra resultat visar att en del viktiga attribut inkluderar men inte är begränsa- de till området som täcks av systemet, antalet deltagare och deras täthet, samt distribution av människor samt tillgång till transportmedel.Vi förbättrar förmågan hos CrowdS genom att möjliggöra integrationen av olika Bluetooth-bärbara enheter. Vi har utvecklat CrowdS till en prototyp av ett system för att hantera farliga situationer. Genom MCS-simuleringen iden- tifierar vi dessutom viktiga parametrar som måste uppmärksamma när man bygger ett sådant system. Simuleringen är ett verktyg som kan användas för att hitta den optimala konfigurationen av MCS.
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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, possibly virtualised, infrastructure of sensing (and actuation) resources. Apart from the flexibility of renting this kind of resources according to the utility model, by providing access at the lowest level where possible, the research path undertaken in this dissertation is meant to provide even bigger scope to the scenarios under consideration, from a platform for opportunistic and cooperative mobile crowd-sensing to a model for Software-Defined Smart Cities, where dynamic reconfiguration of the wiring among Things ultimately enables wide-area feedback control loops on demand. Yet none of these outcomes, and in particular shaping the surrounding environment, may be achievable without means to interact bidirectionally, and with the greatest control that may be exerted, with physical systems through remote sensors and actuators at one's own fingertips. The premise then lies in engaging the research from a "device-centric" perspective. The infrastructure and platform takes on IoT, the Stack4Things framework as well as other results are then outcomes of this approach. Analytical descriptions of the scenarios, details of the architectures and investigated case studies therefore are here provided, while also reporting and highlighting design choices.
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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 mechanisms using Stackelberg game, where the server is the leader while the users are the followers. We study the original scenario in which the total reward is fixed and a different scenario in which the total reward is proportional to the effort spent or information collected. The reward is distributed to the users in proportion to either the amount of time spent or quantity of information. We assume that the ability of information collect is the same for all users, but the value of resources may be different. Based on the above reward models and distribution methods, we formulate four different models called TR-T, TR-Q, DR-T, DR-Q models respectively. We study the cases with homogeneous and heterogeneous users. For homogeneous users, we can prove the existence and uniqueness of pure strategy Stackelberg equilibrium in TR-T, TR-Q, DR-Q models. For heterogeneous users, there is a unique Stackelberg equilibrium in TR-T and DR-Q models. We compute the efficiency which measured by PoA and PoS for homogeneous models and DR-Q model with heterogeneous users. The PoA is bounded by a constant except for some special cases in TR-T and TR-Q models.
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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 high mobility and large quantity of the people, and can obtain a large amount of environmental infor- mation. However, before using the environmental data provided by the masses, it is necessary to ensure that the information is credible. If the information is wrong or falsified, which leads to mistakes in decision-making or mishandling, it may cause great losses. Therefore, how to effectively verify the collected information. In order to solve this problem, this paper proposes a framework for verifying the perceptual data of the masses. By deploying a sensor that covers the number of sensing areas, the sensor is used to collect environmental data and match the Fuzzy Vault. The mathematical characteristics help to verify the trustworthiness of the mass percep- tion of the data, thereby improving the quality of the data, saving costs and solving the effects of public issues.
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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 system and does not mind taking detour paths to maximize profits. Once workers submit their profiles to our system, they will receive detour paths, which consist of tasks in particular orders. Workers execute tasks by following their detour paths and receive maximal profits. We formulate this problem as a detour planning problem, and the advanced problem is multi-users detour planning problem. The difference between these two problems is that the detour planning problem just considers a worker at a time. In this thesis, we develop a detour planning algorithm (DP) and a multi-users detour planning algorithm (MDP) to solve problems respectively. We simulate the extensive trace-driven scenarios and demonstrate the effectiveness and efficiency of our algorithms. Developing a working prototype on Android OS and addressing other challenging aspects of the considered systems are our future tasks.
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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 uma melhor mobilidade. O crowdsensing tornou-se uma conhecida forma de partilhar dados extraídos por dispositivos, que capturam dados através dos seus sensores, como o smartphone com o objetivo de atingir um bem comum. Nesta tese de mestrado é proposta uma metodologia que analisa dos dados extraídos, identifica áreas de maior procura e as possíveis razões pra este fenómeno. Esta metodologia pretende auxiliar o melhoramento da gestão e oferta da rede de transportes de uma dada cidade em estudo, neste caso a área metropolitana do Porto, considerando dados recolhidos da utilização da técnica de crowdsensing.
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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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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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Abstract:
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 crowdsensing techniques target structural dynamic problems. Indeed, the accurate extraction of dynamic information beyond the resonant frequencies of the structure is still an open issue and requires tackling the problem considering metrological aspects. Including effects like different sensitivities, sampling frequencies of the devices, as well as all the other interfering inputs that might affect a structural dynamic measurement, will provide the basis for extracting enriched structural data like mode shapes and damping. This may pave the way to a paradigm shift in monitoring civil infrastructures, as fixed installations could be correlated, compensated, or even substituted by mobile solutions. To prove these potentials, this paper discusses the main challenges arising in this application field of crowdsensing, such as the need of synchronizing the devices if targeting operational deflection shapes (ODSs) reconstructions, the lower sensitivity of sensors incorporated in mobile/wearable devices (typically based on micro electro-mechanical system – MEMS – technology), etc. These issues are demonstrated and tackled by performing a three-step analysis. At first, the metrological characterization of the selected hardware is carried out. In the second step, a first experiment is conducted on a laboratory case study, to verify whether the proposed system can capture the dynamic response of the structure. The final step consists of an experimental campaign conducted on a pedestrian bridge. The results obtained clearly demonstrate the need for a multidisciplinary approach which includes metrology science when targeting crowdsensing for the assessment of the dynamic behavior of a structure.
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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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Jezdović, Ivan, Nevena Nedeljković, Zorica Bogdanović, Aleksandra Labus, and Božidar Radenković. "Smart Cities - System for Monitoring Microclimate Conditions based on Crowdsensing." In 14th International Conference on e-Business. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006430701080115.

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