Mobile Crowdsensing Tasks Research Articles

Task Scheduling Optimization in Cloud-Fog-MCS Environment Using Genetic Algorithm and Game Theory

Fog-cloud computing is a promising platform for processing Mobile Crowdsensing (MCS) tasks that come with different requirements. A fog environment is more suitable for processing time-sensitive tasks due to its proximity to the MCS layer. On the other hand. the cloud environment provides powerful resources to handle large tasks. However, due to the heterogeneity of the computing nodes, scheduling MCS tasks in a fog-cloud environment is a challenging issue. This paper presents a non-cooperative game theoretical model for the task scheduling problem of MCS tasks in the fog-cloud environment. Then, the paper presents an improved genetic algorithm to efficiently solve the problem of task scheduling game model with main enhancements including a new strategy to generate a diverse initial population, incorporating the utility function of the game theoretical model with system fitness function, and finally, the paper introduces a new strategy for population sorting and grouping with applying adaptive crossover operator to meet the specific needs of each group. This improves the exploration of the unseen regions of the search space, as well as exploiting the already-found promising solutions, ultimately leading to a faster convergence toward the optimal solution. The experimental results demonstrate that the proposed approach has better performance in terms of reducing the makespan by 26%, decreasing the energy consumption by 32.4%, decreasing total system cost by 28%, and decreasing the degree of imbalance by 21.53% as compared with other scheduling approaches such as Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II), Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO, Time-Cost Aware Scheduling (TCaS), Moth Flame Optimization (MFO), and Bees Life Algorithm (BLA).

A Constrained Many-Objective Mobile Crowd Sensing Task Allocation Method Considering Latent Workers

A Constrained Many-Objective Mobile Crowd Sensing Task Allocation Method Considering Latent Workers

FRNet: An MCS Framework for Efficient and Secure Data Sensing and Privacy Protection in IoVs

Mobile Crowdsensing (MCS) is a kind of sensing mode based on mobile nodes, which provides a cost-effective solution for data collection of urban sensing. However, when participants collect and upload sensing data in MCS system, they often overlap in time and space. Therefore, MCS system obtains a lot of redundant data, and ultimately cannot meet the MCS task requirements. Meanwhile, existing schemes cannot realize the dynamic allocation of sensing tasks and the dynamic selection of task participants. In addition, task participants will generate and transfer a large amount of sensitive information, such as location, identity, personal habits, etc., in the process of uploading sensing data and completing sensing tasks, thus leading to the risk of privacy leakage. At present, many schemes use federated learning and other technologies to protect privacy, but there are still challenges that adversaries can extract parameters information during model training. Therefore, in order to the above problems, this paper puts forward an efficient and secure MCS framework FRNet in Internet of Vehicles (IoVs). Specifically, MCS task completion is described as a two-phase process. This paper not only proposes a method to dynamically select candidate participants in MCS vehicular networks to collect high-quality sensing data, but also establishes a data privacy protection mechanism for the whole process of model training in federated learning. Finally, the effectiveness of FRNet is verified through security analysis and experimental evaluation. Experimental results show that FRNet can effectively improve the quality of sensing data, maximize TCV, and enhance data privacy protection ability.

Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation

With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.

A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN.

With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform's development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.

Towards Robust Task Assignment in Mobile Crowdsensing Systems

Mobile Crowdsensing (MCS), which assigns outsourced sensing tasks to volunteer workers, has become an appealing paradigm to collaboratively collect data from surrounding environments. However, during actual task implementation, various unpredictable disruptions are usually inevitable, which might cause a task execution failure and thus impair the benefit of MCS systems. Practically, via reactively shifting the pre-determined assignment scheme in real time, it is usually impossible to develop reassignment schemes without a sacrifice of the system performance. Against this background, we turn to an alternative solution, i.e., proactively creating a robust task assignment scheme offline. In this work, we provide the first attempt to investigate an important and realistic RoBust Task Assignment (RBTA) problem in MCS systems, and try to strengthen the assignment scheme's robustness while minimizing the workers' traveling detour cost simultaneously. By leveraging the workers' spatiotemporal mobility, we propose an assignment-graph-based approach. Firstly, an assignment graph is constructed to locally model the assignment relationship between the released MCS tasks and available workers. And then, under the framework of evolutionary multi-tasking, we devise a population-based optimization algorithm, namely EMTRA, to effectively achieve adequate Pareto-optimal schemes. Comprehensive experiments on two real-world datasets clearly validate the effectiveness and applicability of our proposed approach.

Nondeterministic Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing

Based on the Internet of Behavior (IoB), mobile crowd-sensing (MCS) utilizes the Internet of Things (IoT) to recruit users by analyzing behavioral patterns. MCS is widely used in numerous large-scale and complex monitoring services, but it cannot provide stable and high-quality services due to nondeterministic user mobility and behaviors, which has a vital impact on recruiting high-quality users. In this article, a stochastic semi-algebraic hybrid system (SSAHS) model is constructed to characterize the user mobility and behaviors of the MCS systems. Based on the definition of probabilistic path and task execution rate, a nondeterministic evaluation mechanism is proposed to measure nondeterministic user mobility and behaviors and to give the probability of the user completing the MCS task under the specified time bound and space conditions. The greater the probability is, the higher the quality of the user. Furthermore, a user recruitment scheme based on a nondeterministic evaluation mechanism (NUR) is developed. The NUR employs historical user data to predict user mobility and behaviors; high-quality users are recruited to quickly upload reliable sensing data. We conduct simulation experiments based on a real-world user trace dataset, Geolife.The results show that compared with competing recruitment strategies, NUR achieves a higher quality of service for the same MCS sensing tasks.

BETA: From Behavior Sequentializing to Task Mapping in Mobile Crowdsensing

Mobile crowdsensing (MCS) can be widely adopted for undertaking a variety of tasks for the smart city by recruiting distributed participants. With the rapid proliferation of MCS applications, tasks can be devised with different sensing scales. Hence, allocating the tasks to participants with variable spatiotemporal granularity would be urgent and challenging. To this end, we propose a behavior sequentializing and task mapping (BETA) framework, where MCS tasks can be flexibly mapped based on the moving behavior of a participant’s daily activities. A sequential behavior model is first proposed to describe the moving behavior of a participant’s daily activities. Specifically, the behavior of a daily activity is represented by the spatiotemporal trajectory distribution, and a sequential behavior graph is adopted to model the relationships among multiple activities. To extract the sequential behavior from historical trajectories, a projection-clustering (PC) algorithm and a behavior sequentializing approach are developed. Then, based on the extracted sequential behavior, a behavior-to-task mapping network is proposed to evaluate the fitness between tasks and a participant. Finally, extensive simulations are conducted to demonstrate the functionality and performance of BETA. The results show that our proposed method outperforms the state-of-the-art solutions in the mapping efficiency of tasks with different sensing scales.

PSARE: A RL-Based Online Participant Selection Scheme Incorporating Area Coverage Ratio and Degree in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a cost-effective paradigm for gathering real-time and location-related urban sensing data. To complete MCS tasks, MCS platform needs to exploit the trajectory of participants (vehicles or individuals, etc.) for effectively choosing participants. On one hand, the existing works usually assume that platform has possessed the abundant historical movement trajectory for participant selection, or can accurately predict the movement of participant before selection, but this assumption is impractical for many MCS applications, for some candidates have just arrived without sufficient mobility profiles, so-called trajectory from-scratch, or cold-trajectory issue. On the other hand, most of works only considers the coverage ratio of the sensing area, while some hotspots should be sensed frequently, so-called coverage degree of hotspots. To solve the issue, this paper proposes a reinforcement learning (RL) based, i.e., an improved Q-learning based online participant selection scheme to incorporate both coverage ratio and degree, PSARE. First, to solve the explosion of state-value table in traditional tabular Q-learning, an improved two-level Q-learning method is proposed to select participants in online way so as to achieve high long-term return. Specifically, in each selection round, PSARE dynamically compresses all the real participants (RPs) into several virtual participants (VPs) using the available historical trajectories of RPs, and the VP-based state-value table is constructed and constantly updated (i.e., the first level). Then, after selecting the VP through looking up the table, PARSE chooses the RP with the largest expected reward in this VP using epsilon-greedy way to balance the effect of exploration and exploitation (i.e., the second level). Moreover, the reward function is designed to measure the MCS coverage quality, including both coverage degree of hotspots and coverage ratio of target area. Thorough experiments on real-world mobility data set demonstrate that PSARE outperforms than other RL based online participant selection schemes (including deep Q-learning network) and traditional offline selection methods.

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning.

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.

Dynamic link prediction method of task and user in Mobile Crowd Sensing

To address the problem that the existing Mobile Crowd Sensing task allocation methods only considering the perspective of sensing users, ignoring the potential relationship between tasks and users, this paper proposes a task allocation method based on link prediction. First, we analyze the characteristics of sensing users and sensing tasks in Mobile Crowd Sensing, especially the common attribute characteristics of both, and establish the Mobile Crowd Sensing knowledge graph. Then the deep link relationship between sensing users and sensing tasks is mined by the link prediction method based on knowledge graph reasoning. Finally, according to the predicted fit between users and tasks, high-quality sensing users that meet the requirements of the task are selected, to improve the perceptual quality. The experiment results on different datasets show that the method proposed in this paper is high-quality to other comparison methods in sensing data quality and coverage.

Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario.

Mobile crowdsensing (MCS) is a sensing paradigm that allows ordinary citizens to use mobile and wearable technologies and become active observers of their surroundings. MCS services generate a massive amount of data due to the vast number of devices engaging in MCS tasks, and the intrinsic mobility of users can quickly make information obsolete, requiring efficient data processing. Our previous work shows that the Bloom filter (BF) is a promising technique to reduce the quantity of redundant data in a hierarchical edge-based MCS ecosystem, allowing users engaging in MCS tasks to make autonomous informed decisions on whether or not to transmit data. This paper extends the proposed BF algorithm to accept multiple data readings of the same type at an exact location if the MCS task requires such functionality. In addition, we thoroughly evaluate the overall behavior of our approach by taking into account the overhead generated in communication between edge servers and end-user devices on a real-world dataset. Our results indicate that using the proposed algorithm makes it possible to significantly reduce the amount of transmitted data and achieve energy savings up to 62% compared to a baseline approach.

A Biometrics-Based Behavioral Trust Framework for Continuous Mobile Crowd Sensing Recruitment

The emergence of mobile crowd sensing (MCS) platforms makes it possible to collect data in a time and cost efficient manner. However, one of the challenges in MCS systems is obtaining reliable information especially in the presence of impersonators who could provide false reports without getting detected. User recruitment mechanisms adopted in MCS systems hire users such that the Quality of Information (QoI) of the submitted sensing reports is maximized. Despite that, there is still a risk of recruiting impersonators to the system. This problem is even more prominent in the case of continuous mobile sensing tasks, since multiple false sensing could be submitted by the impersonator during the sensing period, which can impact the quality of the sensing outcome and hence the performance of the system. Therefore, to ensure the reliability of the continuously submitted data, a more robust recruitment mechanism, which detects and eliminates impersonators during the sensing task, is needed. This work proposes a biometrics-based behavioral trust framework that can support a reliable recruitment process in continuous MCS tasks. Behavioral biometrics are unique behavioral traits that can be used to profile users based on how they naturally perform a specific activity. By leveraging machine learning techniques, these behavioral traits can be used in order to detect impersonators in the system. In this work, a unique model for each MCS worker is built based on their unique interaction patterns with the smartphone’s touching screen. The proposed approach integrates the trained machine learning models with a dynamic continuous recruitment system, which continuously monitors the QoI of the submitted sensing reports and changes the recruited participants as needed. Simulation results of the proposed approach show its efficacy in detecting and eliminating impersonators in continuous sensing recruitment.

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user’s perceived quality or cost value, how to establish a suitable user recruitment mechanism is the critical issue that this article needs to solve. It is necessary to learn the user’s perceived quality in the execution p. It also needs to try its best to ensure the efficiency and profit maximization of the mobile group intelligence perception platform. Therefore, this paper proposes a mobile crowdsensing user recruitment algorithm based on Combinatorial Multiarmed Bandit (CMAB) to solve the recruitment problem with known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multiarm bandit model. Each rocker arm represents the selection of different users, and the income obtained represents the user’s perceived quality. Secondly, it proposes the upper confidence bound (UCB) algorithm, which updates the user’s perceptual quality according to the completion of the task. This algorithm sorts the users’ perceived quality values from high to low, then selects the most significant ratio of perceived quality to recruitment costs under the budget condition, assigns tasks, and updates their perceived quality. Finally, this paper introduces the regret value to measure the efficiency of the user recruitment algorithm and conducts many experimental simulations based on real data sets to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recruitment algorithm with known user cost is close to the optimal algorithm, and the recruitment algorithm with unknown user cost is more than 75% of the optimal algorithm result, and the gap tends to decrease as the budget cost increases, compared with other comparisons. The algorithm is 25% higher, which proves that the proposed algorithm has good learning ability and can independently select high‐quality users to realize task assignments.

The Game Selection Model for the Payoff Strategy Optimization of Mobile CrowdSensing Task

The Game Selection Model for the Payoff Strategy Optimization of Mobile CrowdSensing Task

Accurate and Privacy-Preserving Task Allocation for Edge Computing Assisted Mobile Crowdsensing

Mobile crowdsensing (MCS) has heated up and has become a new paradigm of data collection. In the process of the task allocation of MCS, users are often required to provide their own location information with the server to conveniently dispatch some suitable tasks to them. However, it is possible for malicious servers to infer some sensitive information based on the user’s location such as the user’s home address or the user’s trajectory and so on which will cause serious privacy issues. Recently, differential privacy (DP) has become a promising privacy protection scheme. However, the existing DP schemes in location privacy protection for MCS do not pay attention to the accuracy of task allocation as equally as the effects of privacy protection, which often results in the task allocation to be inaccurate and inefficient. In order to overcome the shortcoming, we propose a novel MCS task allocation scheme integrating the mapping accuracy of task-worker with the privacy-preserving effect on the user’s location. Besides edge devices, its implementation system consists of a task allocation server and a third party, both of which are semitrusted, that is, both of them only know about the user’s rough location information but cannot obtain the user’s exact location. At first, to improve the response speed of MCS task requests and the location privacy protection of MCS users, our scheme can exploit more than one edge-computing node nearby a user to cooperatively participate in MCS task allocation by aggregating him/her and the near users into groups. Then, our scheme can further enhance the location privacy protection effect based on the Johnson–Lindenstrauss (JL) transformation, which can achieve accurate task allocation and hold the merits of DP. Finally, we verify the feasibility by some experiments based on two data sets. The performance is compared with that of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> he typical DP. The results show that our scheme not only provides strict privacy guarantees but also has higher performance.

Verifiable, Reliable, and Privacy-Preserving Data Aggregation in Fog-Assisted Mobile Crowdsensing

Fog-assisted mobile crowdsensing (FA-MCS) alleviates challenges with respect to computation, communication, and storage from the traditional model of mobile crowdsensing (MCS) “requester-server-users.” Data aggregation, as a specific MCS task, has attracted a lot of attentions in mining the potential value of the massive crowdsensing data. However, the process of data aggregation in FA-MCS may threaten the privacies of both users' data and aggregation results. The untrusted server and fog nodes (FNs) may damage the correctness of aggregation results. Moreover, bad FNs, which do not upload data to server or fail to verify successfully, can endanger the reliability of FA-MCS and the accuracy of aggregation results. To tackle these problems, we propose a verifiable, reliable, and privacy-preserving data aggregation scheme for FA-MCS. Specifically, the proposed scheme preserves privacies of both users' data and aggregation results, enables requester to verify the correctness of aggregation result, and is able to tolerate several bad FNs without affecting the data aggregation result. Through formal security analysis, the proposed scheme is shown to be secure and privacy preserving. Extensive experiments also show the proposed scheme is efficient and reliable.

Continuous Probabilistic Skyline Query for Secure Worker Selection in Mobile Crowdsensing

Worker selection is always one of the most fundamental problems in mobile crowdsensing (MCS), since the reliability of workers' sensing data is hugely significant to the service quality. In the worker selection process, it is inevitable for the workers to share some of their sensitive information. Consequently, numerous studies are conducted on the problem of privacy-preserving worker selection in MCS platforms. However, most of the existing methods focus on static and short-term situations. As a result, they are inapplicable to the highly dynamic environments where the MCS tasks are long term and the workers can continuously arrive at/leave the system. To solve these problems, in this article, we propose a privacy-preserving worker selection scheme based on the probabilistic skyline over sliding windows. Specifically, the proposed scheme can select reliable workers for each current sliding window in terms of working experience, expiry time, and trustability. Besides, we design an ElGamal encryption-based scheme for securely outsourcing and comparing workers' personal information without revealing their privacy. Detailed security analysis shows that the workers' sensitive information, e.g., working experience and trustability, are not revealed to any authorized parties during the process of MCS under our security model. Furthermore, extensive experiments on both real-world and simulated data sets demonstrate that our proposed scheme outperforms the baseline method in two application scenarios, i.e., 1) continuous worker arrival and 2) continuous worker departure.

ULPT: A User-Centric Location Privacy Trading Framework for Mobile Crowd Sensing

Mobile crowd sensing (MCS) arises as a promising data collection paradigm that leverages the power of ubiquitous mobile devices to acquire rich information regarding their surrounding environment. In many location-based sensing tasks, workers are required to associate their sensing reports with corresponding geographic coordinates. Such information leaves a trail of worker's historical location record which thus poses a severe threat to their location privacy. On the other hand, individual workers may perceive location privacy differently. Instead of following conventional solutions that aim to perfectly hide user privacy, this paper adopts a novel alternative approach. A <b>u</b> ser-centric <b>l</b> ocation <b>p</b> rivacy <b>t</b> rading framework, called ULPT, is constructed to facilitate location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform in an MCS task based on its own location privacy leakage budget <inline-formula><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> . The higher <inline-formula><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> is, the more privacy its reported location discloses. Accordingly, it receives higher payment from the platform as compensation. Besides, ULPT enables the platform to select a suitable set of winning workers to achieve desirable MCS service accuracy while taking into account of its budget limit and worker privacy requirements. For this purpose, a heuristic algorithm is devised with a bounded optimality gap. As formally proved in this manuscript, ULPT guarantees a series of nice properties, including <inline-formula><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> - <i>privacy</i> , <inline-formula><tex-math notation="LaTeX">$(\alpha, \beta)$</tex-math></inline-formula> - <i>accuracy</i> , <i>budget feasibility</i> . Moreover, both rigorous theoretical analysis and extensive simulations are conducted to evaluate tradeoffs among these three.

Optimizing Mobile Crowdsensing Platforms for Boundedly Rational Users

In participatory mobile crowdsensing (MCS) users repeatedly make <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">choices</i> among a finite set of alternatives, i.e., whether to contribute to a task or not and which task to contribute to. The platform coordinating the MCS campaigns often <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">engineers</i> these choices by selecting MCS tasks to recommend to users and offering monetary or in-kind rewards to motivate their contributions to them. In this paper, we revisit the well-investigated question of how to optimize the contributions of mobile end users to MCS tasks. However, we depart from the bulk of related literature by explicitly accounting for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bounded rationality</i> evidenced in human decision making. Bounded rationality is a consequence of cognitive and other kinds of constraints, e.g., time pressure, and has been studied extensively in behavioral science. We first draw on work in the field of cognitive psychology to model the way boundedly rational users respond to MCS task offers as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fast-and-Frugal-Trees (FFTs)</i> . With each MCS task modeled as a vector of feature values, the decision process in FFTs proceeds through sequentially parsing lexicographically ordered features, resulting in choices that are satisfying but not necessarily optimal. We then formulate, analyze and solve the novel optimization problems that emerge for both nonprofit and for-profit MCS platforms in this context. The evaluation of our optimization approach highlights significant gains in both platform revenue and quality of task contributions when compared to heuristic rules that do not account for the lexicographic structure in human decision making. We show how this modeling framework readily extends to platforms that present multiple task offers to the users. Finally, we discuss how these models can be trained, iterate on their assumptions, and point to their implications for applications beyond MCS, where end-users make choices through the mediation of mobile/online platforms.