Crowdsensing Systems Research Articles

Intelligent Pandemic Surveillance via Privacy-Preserving Crowdsensing.

Intelligently responding to a pandemic like Covid-19 requires sophisticated models over accurate real-time data, which is typically lacking at the start, e.g., due to deficient population testing. In such times, crowdsensing of spatially tagged disease-related symptoms provides an alternative way of acquiring real-time insights about the pandemic. Existing crowdsensing systems aggregate and release data for pre-fixed regions, e.g., counties. However, the insights obtained from such aggregates do not provide useful information about smaller regions - e.g., neighborhoods where outbreaks typically occur - and the aggregate-and-release method is vulnerable to privacy attacks. Therefore, we propose a novel differentially private method to obtain accurate insights from crowdsensed data for any number of regions specified by the users (e.g., researchers and a policy makers) without compromising privacy of the data contributors. Our approach, which has been implemented and deployed, informs the development of the future privacy-preserving intelligent systems for longitudinal and spatial data analytics.

Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

Air pollution has become a global concern for many years. Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity. To better utilize the sensory data with varying credibility, truth discovery frameworks are introduced. However, in urban cities, there is a significant difference in traffic volumes of streets or blocks, which leads to a data sparsity problem for truth discovery. Protecting the privacy of participant vehicles is also a crucial task. We first present a data masking-based privacy-preserving truth discovery framework, which incorporates spatial and temporal correlations to solve the sparsity problem. To further improve the truth discovery performance of the presented framework, an enhanced version is proposed with anonymous communication and data perturbation. Both frameworks are more lightweight than the existing cryptography-based methods. We also evaluate the work with simulations and fully discuss the performance and possible extensions.

Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Mobile Crowdsensing Systems

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers’ sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers’ different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets.

Multi-task versus consecutive task allocation with tasks clustering for Mobile Crowd Sensing Systems

In this paper, we address the problem of multi-task allocation for dense mobile crowd sensing systems (MCS). The multi-task allocation problem is well-known to be time-consuming as the number of tasks or workers increases. However, the problem can be decomposed into small subproblems based on the location of both tasks and workers. For this reason, we propose to investigate two ways of reducing the complexity of the multi-task allocation: i) grouping tasks based on their proximity and their expected QoI such that the quality of information is maximized and ii) a meta-heuristic algorithm based on Particle Swarm Optimization (PSO) to solve the selection of workers for several tasks or sequentially solve the workers selection for single task which gives a consecutive allocation of the tasks for each worker. Simulation results shows that the consecutive allocation provides better quality of information for the allocated tasks in comparison with the multi-tasks allocation for a given cluster configuration while guaranteeing the delivery of the requested sensed data on time.

Disguised as Privacy: Data Poisoning Attacks against Differentially Private Crowdsensing Systems

Although crowdsensing has emerged as a popular information collection paradigm, its security and privacy vulnerabilities have come to the forefront in recent years. However, one big limitation of previous research is that the security domain and the privacy domain are typically considered separately. Therefore, it is unclear whether the defense methods in the privacy domain will have unexpected impact on the security domain. To bridge this gap, in this paper, we propose a novel Disguise-based Data Poisoning Attack (DDPA) against the differentially private crowdsensing systems empowered with the truth discovery method. Specifically, we propose a novel stealth strategy, i.e., disguising the malicious behavior as privacy behavior, to avoid being detected by truth discovery methods. With this stealth strategy, the shortcoming of failing to maximize the attack effectiveness is avoided naturally through structuring a bi-level optimization problem, which can be solved with the alternating optimization algorithm. Moreover, we show that the differentially private crowdsensing systems are vulnerable to data poisoning attacks, and enhancing the level of privacy will bring more serious security threats. Finally, the evaluation results on the real-world dataset Emotion and the synthetic dataset SynData demonstrate that DDPA can not only achieve maximum utility damage but also remain undetected.

Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques, such as the internet of things (IoT) and mobile crowdsensing (MCS). The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively, with each mobile user completing much simpler micro-tasks. This paper discusses the task assignment problem in mobile crowdsensing, which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals. The goal is to minimize aggregate sensing time for mobile users, which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality. This paper introduces a two-phase task assignment framework called location time-based algorithm (LTBA). LTBA is a framework that enhances task assignment in MCS, whereas assigning tasks requires overlapping time intervals between tasks and mobile users’ tasks and the location of tasks and mobile users’ paths. The process of assigning the nearest task to the mobile user's current path depends on the ant colony optimization algorithm (ACO) and Euclidean distance. LTBA combines two algorithms: (1) greedy online allocation algorithm and (2) bio-inspired travel-distance-balance-based algorithm (B-DBA). The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user. B-DBA was location-based and worked on maximizing total task quality. The results demonstrate that the average task quality is 0.8158, 0.7093, and 0.7733 for LTBA, B-DBA, and greedy, respectively. The sensing time was reduced to 644, 1782, and 685 time units for LTBA, B-DBA, and greedy, respectively. Combining the algorithms improves task assignment in MCS for both total task quality and sensing time. The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time, and the greedy algorithm follows it then B-DBA.

Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems.

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.

Understanding Driver-Passenger Interactions in Vehicular Crowdsensing

Smart city projects collect data on urban environments to identify problems, inform policymaking, and boost citizen engagement. Typically, this data is collected by static sensors placed around the city, which is not ideal for spatiotemporal needs of certain sensing applications such as air quality monitoring. Vehicular crowdsensing is an upcoming approach that addresses this problem by utilizing vehicles' mobility to collect fine-grained city-scale data. Prior work has mainly focused on designing vehicular crowdsensing systems and related components, including incentive schemes, vehicle selection, and application-specific sensing, without understanding the motivations and challenges faced by drivers and passengers, one of the two key stakeholders of any vehicular crowdsensing solution. Our work aims to fill this gap. To understand drivers' and passengers' perspectives, we developed Turn2Earn, a generic vehicular crowdsensing system that incentivizes drivers to take specific routes for data collection. Turn2Earn system was deployed with 13 auto-rickshaw drivers for two weeks in Bangalore, India. Our drivers took 709 trips using Turn2Earn covering 79.2% of the city's grid cells. Interviews with 13 drivers and 15 passengers revealed innovative information-based strategies adopted by the drivers to convince passengers in taking alternative routes, and passengers' altruism in supporting the drivers. We uncovered novel insights, including viability of offered routes due to road closure, issues with electric vehicles, and selection bias among the drivers. We conclude with design recommendations to inform the future of vehicular crowdsensing, including engaging and incentivizing passengers, and criticality-based reward structure.

Biobjective Robust Incentive Mechanism Design for Mobile Crowdsensing

In recent years, mobile crowdsensing has become an effective method for large-scale data collection. Incentive mechanism is fundamentally important for mobile crowdsensing systems. Many mobile crowdsensing systems expect to optimize multiple objectives simultaneously. Most of the existing works transform the multiobjective problem into a single objective problem through constraints or scalarization method. However, due to the uncertain importance (weights) of objectives and the instable quality of crowdsensed data, such transformation is usually unrealizable. In this article, we aim to optimize the worst performance of two objective functions in mobile crowdsensing in order to improve the system robustness. We model an auction-based biobjective robust mobile crowdsensing system, and design two independent objective functions to maximize the expected profit and coverage, respectively. We formulate the robust user selection (RUS) problem, and design an incentive mechanism, which utilizes the combination of binary search and greedy algorithm, to solve the RUS problem. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed incentive mechanisms satisfy desirable properties of computational efficiency, individual rationality, truthfulness, and constant approximation to the tightened RUS problem. Moreover, the proposed incentive mechanism can be easily extended to multiobjective robust mobile crowdsensing systems, and all desirable properties still hold. The simulation results reveal that our incentive mechanism achieves 11% improvement of the platform’s utility, compared with the greedy algorithm for biobjective mobile crowdsensing systems on average.

Enhanced privacy preserving and truth discovery method for 5G and beyond vehicle crowd sensing systems

As 5G networks are being deployed the key constraints of 5G networks have been highlighted, which encourages the probing research of 5G and beyond networks as the next-generation solutions. Especially for vehicular networks which require a network topology with very low latency and high reliability. 5G Vehicular networks offer higher reliability, lower latency, and secure and efficient communication services but these advancements come at the cost of new security and privacy concerns. Especially in the case of a high number of connected vehicles which leads to the concept of Vehicle Crowd Sensing System (VCS) in 5G enabled Vehicular Ad hoc networks (VANETS). Hence, due to this, the number of connected vehicle nodes is also soaring drastically which is directly proportional to the expansion of the Vehicle Crowd Sensing System (VCS). These vehicles are equipped with the latest gadgetries and a large number of sensors, which works simultaneously for processing at end-user utility applications e.g. navigation, predictions, and traffic monitoring. Therefore, in this scenario, all vehicle nodes upload their sole respective data to the cloud for computing which arise the need for reliability and privacy. Thus, Privacy-Preserving Truth Discovery (PPTD) is one such scheme that distills reliable information with privacy from raw signals acquired from different sources. Currently, PPTD brings substantial overhead in terms of consumed data, computation at the Vehicle level and requires each vehicle to remain online till the completion of uploading at the cloud server. So, in this paper, we explain a newly proposed novel Efficient Privacy-Preserving Truth Discovery (EPTD) entailing double masking for VCS and allowing Vehicles to go offline anytime during upload processing while ensuring privacy and accuracy at the same time. Simulation results have shown that the proposed work has outperformed all the previously existing methods.

Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

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.

An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint

The adoption of smart device technologies is steadily increasing. Most of the smart devices in use today have built-in sensors which measure motion, direction, and various environmental conditions. Sensors are able to provide raw data with different quality and accuracy. A large group of smart devices forms a mobile crowdsensing system which is capable of sensing, collecting and sharing the environmental data to perform large scale sensing jobs. This paper aims to study and design an incentive mechanism for a mobile crowdsensing system based on a one-leader multi-follower Stackelberg game. A platform provider, as proponent of the sensing job, will act as the leader, while the mobile users will act as the followers. The final goal is to devise an efficient mechanism able to motivate the smart device users to participate in the sensing activity. Different from existing approaches, we propose a centralized method where the platform provider can estimate users’ parameters very efficiently sending and receiving a few messages. We formulate the optimization problem on the platform provider side as a mixed integer nonlinear program with time constraints for each job and a budget constraint. Finally, a heuristic algorithm based on the derivative-free directional direct search method is designed to solve the platform optimization problem and achieve a close-to-optimal solution for the game. Results show that our Stackelberg game solution is much more scalable than the approach proposed in the work by other authors Zhan et al. (2018) as we can decrease the average number of messages by a factor between 53 to 80 and the average running time between 23 and 650 times. Furthermore, we compared our heuristic algorithm with BARON, a state of the art commercial tool for mixed integer global optimization, to solve the platform optimization problem. Results demonstrated that our proposed algorithm converges to a near-optimal solution much faster especially in large scale systems.

Privacy Enhancement on Unilateral Bluetooth Authentication Protocol for Mobile Crowdsensing

As an open standard for the short-range radio frequency communications, Bluetooth is suitable for Mobile Crowdsensing Systems (MCS). However, the massive deployment of personal Bluetooth-enabled devices also raises privacy concerns on their wielders. Hence, we investigate the privacy of the unilateral authentication protocol according to the recent Bluetooth standard v5.2. The contributions of the paper are twofold. (1) We demonstrate that the unilateral authentication protocol suffers from privacy weakness. That is, the attacker is able to identify the target Bluetooth-enabled device once he observed the device’s previous transmitted messages during the protocol run. More importantly, we analyze the privacy threat of the Bluetooth MCS, when the attacker exploits the proposed privacy weakness under the typical Internet of Things (IoT) scenarios. (2) An improved unilateral authentication protocol is therefore devised to repair the weakness. Under our formal privacy model, the improved protocol provably solves the traceability problem of the original protocol in the Bluetooth standard. Additionally, the improved protocol can be easily adapted to the Bluetooth standards because it merely employs the basic cryptographic components available in the standard specifications. In addition, we also suggest and evaluate two countermeasures, which do not need to modify the original protocol.

Trustworthy and Cost-Effective Cell Selection for Sparse Mobile Crowdsensing Systems

Cell selection is a critical issue in sparse mobile crowdsensing (MCS) systems. However, the sensing cost heterogeneity among different cells (subareas) has long been ignored by existing works. Moreover, the data provided by participants are not always trustworthy, and some malicious participants may intend to launch data positioning attacks, which raises a new challenge for cell selection. In this paper, to address these issues, we propose a trustworthy and cost-effective cell selection (TCECS) framework that takes cell heterogeneity and malicious participants into consideration simultaneously. To this end, we first offer to utilize an iterative statistical spatial interpolation technique to identify trustworthy participants with the help of a small portion of dedicated sensors. Furthermore, we employ the regularized mutual coherence (RMC) in compressive sensing (CS) theory to characterize the contribution to inference accuracy of measurements submitted by different trustworthy participants. Finally, the cell selection strategy, which consumes the least sensing cost while satisfying a given sensing quality, is determined via an RMC-constrained optimization problem. Extensive experiments on a real-world taxi GPS dataset demonstrate that the proposed approach can mitigate the adverse effects of malicious participants and outperforms the baselines with less sensing cost for the same required sensing quality.

On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

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.

Privacy Preserving Participant Recruitment for Coverage Maximization in Location Aware Mobile Crowdsensing

Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participants, which requires participants’ precise locations and thus raises privacy concerns. In this paper, we are motivated to develop a privacy preserving participant recruitment scheme for mobile crowdsensing, which maximizes the spatial coverage of the sensing range while protecting participants’ location privacy against an untrusted crowdsensing platform. Briefly, we propose a utility-assured location obfuscation mechanism operated in a hexagonal grid system, which the participants can follow to locally perturb their locations with personalized privacy demands. Given the obfuscated locations, we efficiently solve a coverage-maximized participant recruitment problem with the budget constraint by using a deterministic rounding algorithm. Considering the existence of biased sensing data incurred by location obfuscation, we further develop a fault-aware crowdsensing framework to improve the robustness of the recruitment strategy, where a constant-approximation algorithm is applied to select participants against any number of unqualified sensing results. Extensive simulations on real-world location datasets and Uber’s geospatial indexing system validate the efficacy of our location obfuscation mechanism and participant recruitment schemes in mobile crowdsensing systems.

A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

In this paper, an efficient and fault-proof active node selection approach for localization tasks in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems is proposed. The proposed approach is resilient to the presence of anomalous nodes. Localization is the process of fusing data readings from multiple sensing nodes with the aim of finding the location of a specific target, such as radiation source, forest fires and noisy areas. Current active node selection systems for localization tasks perform algorithms like greedy and genetic methods over the whole Area of Interest (AoI). As such, a system that considers anomalous data is required to detect anomalies and perform localization over a large number of active nodes, which usually takes multiple iterations and is computationally costly. To overcome this, we propose a resilient localization approach which a) uses the median filter based image filtering technique to level out anomalous readings, b) uses the filtered readings to reduce the AoI to be around the target location without being influenced by anomalous nodes, c) detects and eliminates anomalies in the new AoI based on the deviation between filtered readings and original readings, and d) selects remaining nodes in new AoI for localization. As a result, there is a huge reduction in the complexity of active node selection and thus reduction in time taken by the system to perform the task of source localization. The efficacy of the proposed system is evaluated for radiation source localization tasks using simulated radiation dataset, by performing experiments for several test scenarios. The results demonstrate that the system is able to perform localization tasks in significantly reduced time and therefore generate near real-time results while also maintaining low localization error.

Detecting Fake Mobile Crowdsensing Tasks: Ensemble Methods Under Limited Data

The nondedicated sensing capabilities of smart mobile devices contribute to Internet of Things (IoT) ecosystems with integral building blocks called mobile crowdsensing (MCS) systems. The distributed and nontrusted nature of MCS systems leads to various threats for devices and MCS platforms as well as for end users. Out of the many threats, fake tasks may lead to drained resources at the participating devices and clogged resources at the MCS platforms. Furthermore, when limited data are available, it becomes a further challenge to identify maliciously submitted fake tasks. In this article, we introduce possible solutions that leverage ensemble learning against fake tasks submitted to MCS platforms. More specifically, boosting-based solutions, namely adaptive boosting for binary classification (AdaBoost), gentle adaptive boosting (GentleBoost), and random under-sampling boosting (RUSBoost), form the basis for learning the legitimacy of tasks submitted to MCS platforms. Over a six-day observation window, one day was used for training while the remaining five days were used for testing to evaluate the performance under limited data in terms of training the machine learning (ML) models. Through extensive simulations, we have shown that GentleBoostbased ensemble learning can achieve promising performance in detecting fake/illegitimate tasks submitted to an MCS platform.

A Crowd-Sensing Framework for Allocation of Time-Constrained and Location-Based Tasks

Thanks to the capabilities of the built-in sensors of smart devices, mobile crowd-sensing (MCS) has become a promising technique for massive data collection. In this paradigm, the service provider recruits workers (i.e., common people with smart devices) to perform sensing tasks requested by the consumers. To efficiently handle workers' recruitment and task allocation, several factors have to be considered such as the quality of the sensed data that the workers can deliver and the different tasks locations. This allocation becomes even more challenging when the MCS tries to efficiently allocate multiple tasks under limited budget, time constraints, and the uncertainty that selected workers will not be able to perform the tasks. In this paper, we propose a service computing framework for time constrained-task allocation in location based crowd-sensing systems. This framework relies on (1) a recruitment algorithm that implements a multi-objective task allocation algorithm based on Particle Swarm Optimization, (2) queuing schemes to handle efficiently the incoming sensing tasks in the server side and at the end-user side, (3) a task delegation mechanism to avoid delaying or declining the sensing requests due to unforeseen user context, and (4) a reputation management component to manage the reputation of users based on their sensing activities and task delegation. The platform goal is to efficiently determine the most appropriate set of workers to assign to each incoming task so that high quality results are returned within the requested response time. Simulations are conducted using real datasets from Foursquare <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and Enron email social network. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Simulation results show that the proposed framework maximizes the aggregated quality of information, reduces the budget and response time to perform a task and increases the average recommenders' reputation and their payment.