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To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts. In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers' data are computed and transmitted without complicate encryption. To further reduce each worker's overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers' perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads.

In opportunistic mobile crowdsensing, participants (workers) accept to carry out the requested sensing tasks only if they are already close to or within the regions of interest. Thus, the existence of an assignment opportunity between a workertask pair strictly depends on whether or not the worker will visit the task region. However, when worker trajectories are uncertain and hence not known in advance, existing solutions fail to produce an effective task assignment. Besides, a satisfactory task assignment should respect the preferences and capacity constraints of workers and task requesters, which are generally neglected in literature. In this study, we address all of these issues together and propose novel task assignment algorithms for different settings, which we prove to be optimal in terms of preference-awareness (or stability). Extensive simulations performed on both synthetic and real data sets validate our theoretical results, and demonstrate that the proposed algorithms significantly outperform the existing solutions in terms of preference-awareness and average quality of sensing attained in the final task assignment in almost all scenarios.

With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms (CAP) have appeared. In the mean time, CAP-related models and frameworks based on different research hypotheses are rapidly emerging, and they usually address specific issues from a certain perspective. Due to different settings and conditions, different models are not compatible with each other. However, CAP urgently needs to combine these techniques to form a unified framework. In addition, these models needs to be learned and updated online with the extension of crowdsourced data and task types, thus requiring a unified architecture that integrates lifelong learning concepts and breaks down the barriers between different modules. This paper draws on the idea of ubiquitous operating systems and proposes a novel OS (CrowdOS), which is an abstract software layer running between native OS and application layer. In particular, based on an in-depth analysis of the complex crowd environment and diverse characteristics of heterogeneous tasks, we construct the OS kernel and three core frameworks including Task Resolution and Assignment Framework (TRAF), Integrated Resource Management (IRM), and Task Result quality Optimization (TRO). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further reveals TRO brings enormous improvement in efficiency and a reduction in energy consumption.

Sparse mobile crowdsensing (MCS) requires participants to collect data from partial cells and then intelligently infer the data of the rest cells. Since collecting data from different cells will probably result in different data inference quality, cell selection (i.e., which cells need to be selected to collect data) is a critical issue in Sparse MCS. Currently, state-of-the-art cell selection algorithms are implemented based on reinforcement learning. These algorithms ignore the problem that the urban environment is usually time dependent, and the cell selection model needs to be kept up-to-date to adapt to the time-dependent environment. However, Sparse MCS applications require participants to collect data only in a few cells, which makes it hard to obtain suitable training data for continuous cell selection model learning. To solve this problem, we model the spatiotemporal correlations in the collected sparse data, and then design various methods to update training data based on it. Particularly, these methods make full use of the gradual changes of data in time and space, and reasonably transform and splice sparse data at different moments. Finally, updated training data is fed to the cell selection model to keep it up-to-date. We conduct experimental evaluations by performing several sensing tasks in air quality monitoring. The results show that our proposed methods can effectively update training data as well as the cell selection model. Compared with several baselines, our best method can reduce inference error by more than 10% on average.

Most existing research about task allocation in mobile crowdsensing mainly focus on requester-centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker-centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks.

Mobile crowd sensing is a technique where a crowd sensing server outsources sensing tasks to the crowd for mobile data collection. In mobile crowd sensing, some tasks require location information to achieve their objectives, such as road monitoring, indoor floor plan reconstruction, and smart transportation. This required information incurs severe concerns on location privacy leakage and threatens workers’ properties as well as public safety. In some cases, even sensing data itself can be used as auxiliary information resulting in location privacy breaches. Many existing works apply differential privacy mechanisms for location privacy preservation to tackle this problem, but they cannot efficiently fulfill privacy goals because each worker only considers his own privacy. As a consequence, the accumulated privacy budget will lower down the composed privacy level of all the workers’ locations. In addition, deploying differential privacy is costly for workers and it will degrade the quality of data required in crowd sensing tasks. How to balance the cost and provide accurate aggregated data while fulfilling privacy objectives becomes a challenging issue. In this paper, we propose a group-differentially-private game-theoretical solution, which addresses these limitations in a privacy-preserving and efficient way. Our scheme enables the indistinguishability of workers’ locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks. The effectiveness and efficiency of our scheme are thoroughly evaluated based on real-world datasets.

Mobility models, which reproduce traces with basic crowd mobility patterns, are crucial for realistic mobile network simulation and performance evaluation of the planning strategies used for urban networks (such as transit network, communication network, and crowdsensing network). However, trajectories generated by traditional models are often perceived as not realistic for urban context or lack of scalability and universality. This article presents a data-driven trajectory generating technique, named as TraG, that produces synthetic trajectories with the help of some real-world trajectories. Our technique can automatically extract the context features and statistical mobility features, which characterize the mobility of a specific urban crowd from the input empirical traces and, then, regenerate more trajectories based on demand. Moreover, we also summarize the power-law scale correlation of crowd mobility based on four real-world open datasets, including public bicycle traces in New York City and Washington, D.C., taxicab traces in San Francisco and Shenzhen. Finally, we validate the proposed TraG model via the continuous San Francisco taxicab traces, and the result demonstrates that the trajectories simulated by TraG not only inherit the fundamental statistical features of crowd mobility from real traces, but also reflect the features of urban context.

The proliferation of mobile smart devices with ever improving sensing capacities means that human-centric Mobile Crowdsensing Systems (MCSs) can economically provide a large scale and flexible sensing solution. The use of personal mobile devices is a sensitive issue, therefore it is mandatory for practical MCSs to preserve private information (the user's true identity, precise location, etc.) while collecting the required sensing data. However, well intentioned privacy protection techniques also conceal autonomous, or even malicious, behaviors of device owners (termed as self-interested), where the objectivity and accuracy of crowdsensing data can therefore be severely threatened. The issue of data quality due to untruthful reporting in privacy-preserving MCSs has been yet to produce solutions. Bringing together game theory, algorithmic mechanism design, and truth discovery, we develop a mechanism to guarantee and enhance the quality of crowdsensing data without jeopardizing the privacy of MCS participants. Together with solid theoretical justifications, we evaluate the performance of our proposal with extensive real-world MCS trace-driven simulations. Experimental results demonstrate the effectiveness of our mechanism on both enhancing the quality of the crowdsensing data and eliminating the motivation of MCS participants, even when their privacy is well protected, to report untruthfully.

The sensing quality is critical for crowdsensing networks. However, it is still challenging to achieve reliable and accurate quantification on sensing quality. As most existing methods only quantify individual-level sensing quality, they cannot be simply extended to measure network-level sensing quality. Meanwhile, there are two critical challenges for the non-trivial quantifications of network-level sensing quality. First, it is quite daunting to accurately measure the sensing quality, due to the uncertainties in crowdsensing, such as random noise in sensing data and complicated inference of information. Second, it is incredibly difficult to conduct repeatable experiments for multiple times, with the natural dynamics of crowdsensing networks as well as the uncontrollable behaviors of crowdsensing participants. To address the above challenges, in this work, we devise a novel quantification metric to measure the uncertain sensing quality of crowdsensing networks. The proposed metric is based on the confidence interval, exploiting the asymptotic normality property of unbiased estimations. We further leverage the Fisher information in crowdsensing data and successfully derive the confidence interval without redundant multiple computations on repeated experiments. The trace-driven evaluations demonstrate that the proposed method can achieve remarkably accurate quantifications on the network-level sensing quality, outperforming the existing works.

Vehicular crowdsensing is attracting more and more attention because of its wide sensing coverage and diverse usage in smart cities. However, privacy issues that stem from traditional vehicular crowdsensing scenarios, violate the participant's privacy. Although some privacy-preserving schemes have been designed that aim to protect the sensitive information of sensed data, the reliability cannot be guaranteed because of the system's centralized structure. The introduction of blockchain in crowdsensing applications provides reliable data storage, however, the reliability of data sources remains an open challenge. Under these circumstances, the crowdsourcing service requester may not be able to obtain quality data. To solve these problems, we propose a novel P rivacy-preserving and R eliable V ehicular crowdsensing via B lockchain oracle, called PRVB. More specifically, a privacy-preserving vehicular data aggregation scheme is presented to protect the data privacy and unlinkability between participant vehicles and sensed data. Besides, two protocols are designed to protect data privacy and to achieve fair rewards for data providers. Thorough theoretical analysis and experimental evaluations have proved that the proposed PRVB achieves privacy protection, reliability, and fairness with significant computation & communication efficiency.

Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet, another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos’ spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of $33\%$ 33 % and $18.7\%$ 18 . 7 % on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8 percent improvement on the photo coverage performance.

International audience; The proliferation of the Internet of Things (IoT) has paved the way for the future of smart cities. The large volume of data over the IoT can enable decision-making for various applications such as smart transportation, smart parking, and smart lighting. The key to the success of smart cities is data collection and aggregation over the IoT. Recently, crowdsensing has become a new data collection paradigm over the IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with microphones on smartphones. Despite the advantages of crowdsensing and the IoT, there are many challenges to utilize crowdsensing over the IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users.

The rapid advance of the Internet of Things (IoT) has enabled a new paradigm of the sensing network, i.e., mobile crowdsensing (MCS). Primarily, in MCS systems, a crowd of participating mobile users, namely, workers, are allocated by the MCS platforms to outsource their sensory data for specific tasks. Obviously, the reliability of workers and the trustability of their sensing data play significant roles in the service quality, thus the worker selection becomes crucial for the success of MCS applications. However, due to either a large number of candidates or their dynamic natures, selecting reliable workers poses big challenges to the MCS platform. Evidently, workers’ reputation-based characteristics, such as trustability and credibility, are also pivotal for the worker selection in MCS, but they were often neglected in previous literature. In this article, aiming at addressing the above challenges, we propose a new privacy-preserving worker selection scheme based on the probabilistic skyline computation technique. Specifically, our proposed scheme is characterized by: 1) assigning a trustability score to each worker based on his/her past performance without revealing his/her sensitive information and 2) efficiently selecting a subset of reliable workers for a particular task. Detailed security analysis shows that our proposed scheme can preserve workers’ privacy. In addition, performance evaluations via extensive simulations are conducted, and the results also demonstrate its effectiveness and efficiency for reliable worker selection in MCS applications.

Mobile crowdsensing (MCS) is a paradigm that exploits the presence of a crowd of moving human participants to acquire, or generate, data from their environment. As a part of the Internet-of-Things (IoT) paradigm, MCS serves the quest for a more efficient operation of a smart city. Big data techniques employed on this data produce inferences about the participants’ environment, the smart city. However, sufficient amounts of data are not always available. Sometimes, the available data are scarce as it is obtained at different times, locations, and from different MCS participants who may not be present. As a consequence, the scale of data acquired may be small and susceptible to errors. In such scenarios, the MCS system requires techniques that acquire reliable inferences from such limited data sets. To that end, we resort to small data (SD) techniques that are relevant for scarce and erroneous scenarios. In this article, we discuss SD and propose schemes to tackle the problems associated with such limited data sets, in the context of the smart city. We propose two novel quality metrics: 1) MAD quality metric (MAD-Q) and 2) MAD bootstrap quality metric (MADBS-Q), to deal with SD, focusing on evaluating the quality of a data set within MCS. We also propose an MCS-specific coverage metric that combines the spatial dimension with MAD-Q and MADBS-Q. We show the performance of all the presented techniques through closed-form mathematical expressions, with which simulation results were found to be consistent.

The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants’ involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones.

Crowdsensing systems collect various types of data from sensors embedded on mobile devices owned by individuals. These individuals are commonly referred to as workers that complete tasks published by crowdsensing systems. Because of the relative lack of control over worker identities, crowdsensing systems are susceptible to data poisoning attacks which interfering with data analysis results by injecting fake data conflicting with ground truth. Frameworks like TruthFinder can resolve data conflicts by evaluating the trustworthiness of the data providers. These frameworks somehow make crowdsensing systems more robust since they can limit the impact of dirty data by reducing the value of unreliable workers. However, previous work has shown that TruthFinder may also be affected by the data poisoning attack when the malicious workers have access to global information. In this article, we focus on partially observable data poisoning attacks in crowdsensing systems. We show that even if the malicious workers only have access to local information, they can find effective data poisoning attack strategies to interfere with crowdsensing systems with TruthFinder. First, we formally model the problem of partially observable data poisoning attack against crowdsensing systems. Then, we propose a data poisoning attack method based on deep reinforcement learning, which helps malicious workers jeopardize with TruthFinder while hiding themselves. Based on the method, the malicious workers can learn from their attack attempts and evolve the poisoning strategies continuously. Finally, we conduct experiments on real-life data sets to verify the effectiveness of the proposed method.

The popularization of mobile devices connected to the network promotes the rise and development of the emerging mobile Internet of Things (MIoT). Crowdsensing is a promising mode to perceive data in MIoT, where the collection of sensing data is outsourced to the public crowd carrying mobile devices. However, this crowdsensing mode inevitably makes privacy compromised, due to the workers’ sensitive information in the sensing data. As such, how to incentivize workers’ participation with privacy preservation becomes a challenge. To tackle this problem, in this article, we propose an auction-based privacy-preserving incentive scheme (APIS) for sensing task allocation in MIoT. Specifically, integrating the idea of software-defined network (SDN), we first present a cloud and edge cooperation-based crowdsensing framework, where the cloud is designed as the controller to collect sensing results from the distributed edge nodes and each edge node outsources sensing tasks to participating workers. To motivate workers’ participation, we devise a differential privacy-based auction mechanism, whereby each worker can utilize her privacy budget to control how much privacy can be leaked and decide the sensing precision by the sensing time. Moreover, to maximize the utility of the sensing platform, we design a greed-based algorithm to select the winning workers and determine payments to winners. Finally, we conduct extensive simulations to verify the effectiveness of APIS and demonstrate its superiority.

With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for special interest customers. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation from geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy re-encryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency on computation and communication., Comment: This paper has been submitted to IEEE TMC

A reliable mobile crowdsensing (MCS) application usually relies on sufficient participants and trustworthy data. However, privacy concerns reduce participants’ willingness to participate in sensing tasks. The uncertainty of participant behavior and heterogeneity of sensing devices result in the unreliability of sensing data and further bring unreliable MCS services. Hence, it is crucial to estimate the reliability of sensing data and protect privacy. Unfortunately, most existing privacy-preserving data estimation solutions are designed for single-type data. In practice, however, heterogeneous sensing data are ubiquitous in data integration tasks. To this end, we propose a privacy-preserving reliability estimation solution of heterogeneous data for MCS, called IronM, which is effective for text, number, and multimedia data (e.g., image, audio, and video). Specifically, IronM first formulates the reliability assessment of text, number, and multimedia data as equality and range constraints, and then estimates the reliability of heterogeneous data through our proposed privacy-preserving hybrid constraints assessment mechanism. Privacy analysis demonstrates that IronM can not only evaluate the reliability of heterogeneous data but also protect data confidentiality. The experimental results in real-world datasets show the effectiveness and efficiency of IronM.

In the face of a massive number of vehicular users, a data collection paradigm based on vehicular crowdsensing requires an effective means of attracting participants. Thus, the incentive mechanisms play a key role in crowdsensing procedure design, inevitably leading to problems related to user privacy leakage. In this article, to reduce the risk of privacy leakage in the implementation of incentive mechanisms, we propose a fog computing-based crowdsensing architecture specialized for vehicular crowdsensing and corresponding privacy-preserving solutions for the processes of data reporting, reward issuing, and trust management. Authentication and encryption technologies, such as zero-knowledge verification, one-way hashing, partially blind signature authentication, and homomorphic encryption, are utilized to achieve our goals. Finally, efficiency improvements in both privacy preservation and network response are proven through analysis and simulations. This work was supported in part by the National Natural Science Foundation of China under Grant 61571098, in part by the Sichuan Science and Technology Program under Grant 2019YGF0206, and in part by the 111 Project under Grant B14039.

With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)—a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues of matching workers to their assigned tasks and processing the collected data. In this paper, we reexamine these issues by considering the spatio-temporal worker mobility and task arrivals, which more fit the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the non-linear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby, we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Finally, we propose an optimization framework to aggregate the sensing data which can estimate worker expertise and task truth simultaneously. Performance evaluations on real and synthetic data set validate the proposed algorithm, where 80 percent gain of fairness is achieved at the expense of 12 percent loss of sensing value on average.

Currently, one of the main challenges that the world's big cities are facing is the security problem of their citizens and specifically the disappearance of people (even more in Latin America countries). Thus, to address this problem, a software system called SiGPro has been developed, which allows notifications for the early location of missing persons, using some technological tools, being one of the most prominent application of the MQTT protocol frequently used in the Internet of Things (IoT). SiGPro applies a MQTT-Bridge for hierarchical server communication and developing the software system for real-time performance. SiGPro innovates a Progressive Notification Mechanism, not considered in other similar projects and being one of the main contributions of this research; it delivers notifications depending on the disappearance distance in order to not saturate the end user. In addition, SiGPro was deployed in three cities in Ecuador, (being Quito city the MQTT-broker), where more than 140 users participated in each city and in different test scenarios. Time and distances of disappearance solution were obtained, taking into account the total anonymity of the victim. Finally, SigPro has shown that increasing crowdsensing (number of users), reduces the distance at which the missing person has been located.

As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism.

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called $QnQ$ Q n Q , to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both ‘quality’ (accuracy of contribution) and ‘quantity’ (degree of participation) of their contributions. Specifically, $QnQ$ Q n Q exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT) , we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate $QnQ$ Q n Q experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Josang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that $QnQ$ Q n Q is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

This chapter provides an overview of applications and requirements for vehicular crowdsensing as well as anticipatory data transmission. As a case study, it presents an opportunistic, context‐predictive transmission scheme that relies on machine learning‐based data‐rate prediction for channel quality assessment, which is executed online on embedded devices. The chapter provides an overview of requirements and current work in mobile crowdsensing and anticipatory data transfer. It summarizes the work on machine learning channel‐aware transmission (ML‐CAT) and ML‐predictive CAT (ML‐pCAT), which extend the established transmission schemes CAT and pCAT with ML‐based data‐rate prediction. The proposed mechanisms implement client‐side anticipatory networking and bring together different research directions of anticipatory optimization such as ML‐based channel quality assessment, mobility prediction, and connectivity maps. The chapter presents the methodology setup for the real‐world performance evaluation, and discusses the results of the empirical performance evaluation about the proposed solutions.

Data sensing and gathering is essential for diverse information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffering from limited sensing coverage. On the other hand, data can be gathered dynamically by crowdsensing contributed from voluntary users but suffering from its unreliability and the lack of incentives for users' contributions. In this paper, we explore an integrated paradigm called "hybrid sensing" that aims to harness both IoT-sensing and crowdsensing in a complementary and privacy-preserving manner. We implemented our hybrid sensing system and conducted some initial empirical evaluations.

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular $k$k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.

Mobile crowdsensing is a useful technique to collect detailed information from mobile devices of the participants. The participants need to participate to sense and transmit valuable information to the servers. Due to the technological growth, various components of mobile devices such as accelerometer, gyroscope, camera and inertial, collect vast volumes of data in a quick, efficient, and cost-effective manner. However, a mobile crowdsensing paradigm may result in serious privacy and security breaches by exposing the mobile devices to various threats and vulnerabilities. This leakage of privacy has an adverse impact on the usage and participation of mobile devices. Motivated by these threats and privacy challenges, we investigate the current approaches used for preserving privacy in mobile crowdsensing applications. After a generic description of mobile crowdsensing systems and their components, we discuss critical issues related to privacy preservation, such as task management, task assignment models, and incentive mechanisms. We also discuss various mobile crowdsensing mechanisms available in the literature. Finally, we highlight numerous research challenges that need to be addressed to improve the performance of future privacy-preserving mechanisms for mobile crowdsensing applications.

A mobile crowdsensing (MCS) platform motivates employing participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. In this article, we improve the profit via the data reconstruction method, which brings new challenges, because it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two Profit-driven Online Participant Selection (POPS) problems under different situations are studied in our work: (1) for S-POPS, the sensing cost of the different parts within the target area is the Same. Two mechanisms are designed to tackle this problem, including the ProSC and ProSC+. An exponential-based quality estimation method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different parts within the target area is Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets. The experimental results demonstrate that our proposed mechanisms are more effective and efficient than baselines, selecting the participants with a larger profit for the platform.

The information sensed by a mobile device is just data, while the aggregation of information sensed by thousands of devices is knowledge. This is the basic idea of Mobile CowdSensing (MCS). In MCS, the user selection problem has always been a key issue, where the task publisher attempts to recruit a suitable set of users to cooperatively sense the events or data at some particular time points and places. Hence, the temporal and spatial constraint becomes the main criterion to select the users. However, whether a user prefers to accomplish the sensing data not only depends on the temporal and spatial relationship, but also lies on the data property. In other words, a user is likely to take the sensing task if it is interested in the data to be sensed. In this paper, we propose a user Selection utilizing data properties in mobile crowdsensing (SPM), where a triple-layer structure considering not only the temporal and spatial probability, but also the data’s property is formulated, and task finishing probabilities are calculated in both the intentional and unintentional situations. Then, a greedy algorithm is proposed to select a suitable set of users for finishing the sensing tasks. We conduct extensive simulations based on three widely-used real-world traces: geolife, roma/taxi, epfl and a synthetic data set. The results show that, compared with other user selection strategies, SPM finishes the largest number of sensing tasks.

With the popularization of intelligent terminals, crowdsensing has become increasingly prominent because of its advantages, such as low cost, high convenience, and fast speed in conducting tasks. However, the quality of the data collected through crowdsensing is varied and is difficult to evaluate. Furthermore, the existing crowdsensing quality control methods are mostly based on a central platform, which is not completely trusted in reality and results in the existence of fraud and other problems. To solve these two questions, a crowdsensing quality control model based on a two-consensus blockchain is proposed in this paper. First, the idea of a blockchain is introduced into this model. The credit-based verifier selection mechanism and the two-consensus approach are proposed to realize the nonrepudiation and nontampering of information in crowdsensing. Then, to help task publishers obtain higher-quality sensing data, the methods of node matching and QGE are proposed. The former method uses the idea of the calculation of matching degree to select workers, and the latter uses the idea of clustering and fuzzy theories to evaluate the quality of the sensing data. Finally, the experiments show that the running time of the block generation in our model is acceptable, and comparing with the other methods, our model can acquire data of higher quality after the addition of malicious nodes.

The development and ubiquity of smart mobile devices have produced the idea of crowdsensing, where people report and request data in a community via a cloud server. Recently, fog is introduced to assist the cloud server by providing location-sensitive and latency-aware local data management. However, interaction between users and server without appropriate sanitation puts serious security threats to user’ privacy (e.g., data content and preference). While existing work already has a wide range of privacy-preserving schemes, they hardly consider collusion attacks (CAs) between the server and users, let alone CAs between fog nodes and users. To solve this problem, we first define four specific CAs in fog-assisted crowdsensing and propose a novel privacy-preserving data reporting and requesting (PARE) scheme with collusion resistance. PARE is constructed by leveraging one-way hash chains, marked mix-nets, and grouping-based secure searchable encryption to securely collect users’ reports and respond to users’ requests under CAs. Then, we consider one extreme scenario and provide a solution by introducing a role of sentry reporter while reducing computational costs and communication overhead. Thorough security and privacy analysis shows that PARE is secure and collusion resistant and we also quantitatively measure privacy with mutual information. Extensive performance evaluation results indicate that PARE is lightweight with respect to computational cost and communication overhead. To the best of our knowledge, this paper is the first one that gives four formal definitions of CAs in fog-assisted crowdsensing and aim to defend them at the same time.

Anomalies in road surface not only impact road quality but also affect driver safety, mechanic structure of the vehicles, and fuel consumption. Several approaches have been proposed to automatic monitoring of the road surface condition in order to assess road roughness and to detect potholes. Some of these approaches adopt a crowdsensing perspective by using a built-in smartphone accelerometer to sense the road surface. Although the crowdsensing perspective has several advantages as ubiquitousness and low cost, it has certain sensibility to the false positives produced by man-made structures, driver actions, and road surface characteristics that cannot be considered as road anomalies. For this reason, we propose a deep learning approach that allows us (a) to automatically identify the different kinds of road surface, and (b) to automatically distinguish potholes from destabilizations produced by speed bumps or driver actions in the crowdsensing-based application context. In particular, we analyze and apply different deep learning models: convolutional neural networks, LSTM networks, and reservoir computing models. The experiments were carried out with real-world information, and the results showed a promising accuracy in solving both problems. Fil: Varona, Braian Raúl. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Teyseyre, Alfredo Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina

While much research has been devoted to algorithm improvement of the machine learning model for multimedia applications, relatively little research has focused on the acquisition of massive multimedia datasets with strict data demands for model training. In this paper, we propose a Quality-of-Information (QoI) aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. We begin with a reverse auction incentive model to maximize social welfare while meeting the requirements in quality, timeliness, correlation, and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, we design an effective incentive mechanism to solve the auction problem, which is shown to be truthful, individually rational and computationally efficient. Our evaluation study is carried out using a real multimedia dataset. Extensive simulation results demonstrate that the proposed incentive mechanism produces close-to-optimal social welfare noticeably, while accompanied by accelerating the growth of the machine learning model with a high-QoI dataset.

With the support of portable electronic devices and crowdsensing, a new class of mobile applications based on the Internet of Things (IoT) application is emerging. Crowdsensing enables workers with mobile devices to travel to specified locations and collect data, then send it back to the requester for rewards. However, the majority of the existing crowdsensing systems are based on centralized servers, which are prone to a high chance of attack, intrusion, and manipulation. Further, during the process of transmitting information to and from the service server, the worker’s location is usually exposed. This raises the potential risk of a privacy infringement. In this paper, we first identify three ways locations can be disclosed in traditional crowdsensing systems. Then, we propose a novel solution, dubbed a blockchain privacy-preservation crowdsensing system, to address these privacy problems. The proposed system not only protects the privacy of worker locations but also increases the success rate of completing the assigned task. Specifically, the system entails a rewards-based task assignment process that, essentially, markets the given assignment and uses the anonymized characteristics of blockchain technology to hide the identity information of users. To prevent attacks through re-identification, we have introduced a private blockchain to distribute the worker’s transaction records.

With the proliferation of the Internet of Things (IoT), wireless sensor networks, mobile social networks, crowdsensing applications, and beyond, huge amounts of data are being explosively generated every day. While big data and IoT bring well-understood benefits to increase knowledge and improve the quality of our daily lives, they also face wide attacking surfaces, and thus raise critical concerns on notions of trust, security, and privacy. One of the fundamental questions is how to collect, store, and process the huge amounts of data in a secure and privacy-preserving manner, without compromising the potential of capitalization on big data and IoT.

Mobile crowdsensing (MCS) offers a promising paradigm for large-scale sensing with the rapid growth of mobile smart devices. Compared with traditional sensing methods, MCS is more effective and efficient in energy and cost. Task allocation is a key problem in MCS, which has a significant impact on the performance. It is challenging to design a generic solution to the task allocation problem because MCS applications typically consider distinct targets under specific constraints. However, there are many common interests such as data quality, budget, and energy consumption. In this paper, we analyze and formulate the task allocation problem from two perspectives, respectively. First, we focus on data quality and propose a genetic algorithm (GA) to maximize data quality. Then, we take the profit of workers into account and propose a detective algorithm (DA) to improve the profit. In the GA-based solution, only the platform is able to decide the task assignment. However, in the DA-based solution, the workers are allowed to determine and submit their task sets to the platform, which just needs to make a selection from these task sets. In addition, we consider the clustering effect of tasks and the influence caused by different geographic distributions of tasks. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm and there is a tradeoff between the data quality and the profit of workers.

In recent years, the boom of mobile-network-measurement apps has stimulated the growth of publicly available datasets, which contain up to billions of measurements conducted by anonymous end-users. Although the crowdsensing has proven its value in different areas, in the context of performance monitoring in cellular mobile networks, convincing applications are scarce. A portion of already published research focuses on modeling and predicting achievable throughput as a function of observed signal indicators. Due to the system complexity and a large number of possible predictors, the performance of these models is low since the throughput varies greatly even at a constant signal power level. In this paper, we introduce a new method for evaluating an empirical network-centric upper bound on the throughput-performance of different cellular mobile networks. Based on simulations and reference measurements, we propose a model function that characterizes throughput as a function of signal power. The critical point that increases the quality of fit when processing the crowdsourced measurements is the removal of the system-inherent high noise level by finding a method to exclude biased samples systematically. We apply our method to crowdsourced measurements provided by several national regulatory bodies, and benchmark the performance of LTE networks of different mobile operators in different countries.

The cost of dedicated sensors has hampered the collection of the high-quality seismic response data required for real-time health monitoring and damage assessment. The emergence of crowdsensing technology, where a large number of mobile devices collectively share data and extract information of common interest, may help remove such obstacles and mitigate the seismic hazard. The present study proposes a crowdsensing-oriented vibration acquisition and identification method based on time–frequency characteristics and deep transfer learning. It can distinguish the responses during an earthquake event from vibration under serviceability conditions. The core classification process is performed using a combination of wavelet transforms and deep transfer networks. The latter were pre-trained using finite element models calibrated with the monitored seismic responses of the structures. The validation study confirmed the superior identification accuracy of the proposed method.

International audience; Crowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies "anonymizing" the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the-completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with − 1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure-completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application.

Incentive mechanisms are an essential method to encourage users to participate in the mobile crowdsensing task. However, most incentive mechanisms based on data quality do not consider users’ security and privacy protection. To overcome the above problems, we propose a privacy protection incentive mechanism based on truth discovery, named PAID. Specifically, we use the secure truth discovery scheme to calculate ground truth and the weight of users’ data while protecting their privacy. Besides, to ensure the accuracy of the MCS results, a data eligibility assessment is proposed to remove unreliable user data before the truth discovery scheme. Finally, we distribute rewards to users based on their data quality. The analysis and evaluation demonstrate the security and effectiveness of our PAID.

Incentive mechanisms are crucial for motivating adequate users to provide reliable data in mobile crowdsensing (MCS) systems. However, the privacy leakage of most existing incentive mechanisms leads to users unwilling to participate in sensing tasks. In this paper, we propose a privacy-preserving incentive mechanism based on truth discovery. Specifically, we use the secure truth discovery scheme to calculate ground truth and the weight of users’ data while protecting their privacy. Besides, to ensure the accuracy of the MCS results, a data eligibility assessment protocol is proposed to remove the sensing data of unreliable users before performing the truth discovery scheme. Finally, we distribute rewards to users based on their data quality. The analysis shows that our model can protect users’ privacy and prevent the malicious behavior of users and task publishers. In addition, the experimental results demonstrate that our model has high performance, reasonable reward distribution, and robustness to users dropping out.

Based on the popularity of intelligent embedded devices, mobile crowdsensing emerged as a new paradigm of crowdsourcing perception. However, it often faces the problem of low data quality caused by the insufficient number of participators. In this paper, we propose an incentive mechanism for mobile crowdsensing considering social networks. Our mechanism takes into account the social networks of mobile users, which is not only to reward users for data contribution, but also for solicitation behavior, so as to expand the number of participators under budget constraints, and to select users with high-capability. We prove by the theory that the proposed mechanism satisfies individual rationality, truthfulness, solicitation incentives, and budget effectiveness. Besides, the simulation results show that the mechanism can effectively motivate users' long-term participation and high-quality data collection.