Mobile Crowdsourcing Systems Research Articles

Eliciting Joint Truthful Answers and Profiles From Strategic Workers in Mobile Crowdsourcing Systems

Due to the openness of mobile crowdsourcing, workers may yield low-quality task answers. To alleviate this problem, substantial efforts have been devoted to eliciting truthful data from workers. On the other hand, to facilitate task assignment, workers are required to upload to the platform their profiles, such as locations and expertise. Therefore, task assignment outcomes and thus mobile crowdsourcing service accuracy are subject to the quality of worker's self-reported profiles. In this paper, we leverage incentive design to motivate workers to honestly reveal both task answers and their profiles. The challenge is to design one incentive payment for truth elicitation in two kinds of submissions. For this, we first derive the sufficient and necessary conditions for answer truthfulness and profile truthfulness separately. We then construct an incentive optimization problem that incorporates these conditions as constraints. Its optimal solution lists the payment to each worker that elicits answers and profiles jointly. Our proposed mechanism, with formally proved bounded approximation ratio, ensures that truth-telling is a Bayesian Nash equilibrium. We prototype the mechanism and conduct a series of experiments that involve 30 volunteers to validate the efficacy and efficiency of the proposed mechanism

Trends for Mobile IoT Crowdsourcing Privacy and Security in the Big Data Era

From tracking pandemics to applications, such as Google Maps, Uber, environmental monitoring, journalism, healthcare, crisis/disaster response, air quality control, noise and traffic monitoring, urban planning, etc., mobile crowdsourcing (MCS) systems are interweaved with the society and daily lives. This survey outlines major security and privacy challenges in MCS systems along with solutions and approaches. Comprehensive countermeasures, leveraging the capabilities of blockchains, smart contracts, machine learning, games, incentives, spatiotemporal cloaking, etc., are presented to preserve privacy and security of mobile workers, task requestors, and other aspects of crowdsourcing systems. Security recommendations for use cases, such as Industrial IoT, Internet of Vehicles, wireless crowdsensed systems, social crowdsourcing, edge computing, personalized and privacy-preserving recommendation, and mobile worker recruitment are further elaborated.

An Intelligent Approach of Intrusion Detection in Mobile Crowd Sourcing Systems in the Context of IoT Based SMART City

ABSTRACT The recent era of pervasive computing has evolved with various applications and has ground-breaking realities in mobile crowdsourcing (MCS). Multiple attempts have been devoted to integrating MCS with IoT-based smart cities where crowdsensing has played a crucial role in the recent past. Despite having potential features, MCS devices lack efficiency when security aspects are concerned. The current security approaches exercised in MCS operations imply limited features and are not intelligent enough to deal with different types of attacks in IoT smart cities. On the other hand, as MCS communications involve radio environmental mapping functional blocks from communication, it is an obvious situation that leads to a vulnerable situation of which adversarial modules can take advantage of it. There are different types of active and passive modes of attacks that can degrade the Quality-of-Service (QoS) aspects in IoT-driven smart city operations. This study’s prime aim and the appealing theme is to realize the need for resilient approaches to intelligent intrusion detection in MCS to mitigate different attacks. The study also introduces a theoretical approach of cluster-enabled multi-task (CeMT) based on bio-inspired learning modeling of the genetic approach to identify the maximum possible threats and misbehaving devices in the smart city-based MCS operations. The study also evaluated the model’s performance based on the processing time of identifying malicious events and showed the accuracy of detecting misbehaving working associate (WA) modules.

A Task Recommendation Model in Mobile Crowdsourcing

With the development of the Internet of Things and the popularity of smart terminal devices, mobile crowdsourcing systems are receiving more and more attention. However, the information overload of crowdsourcing platforms makes workers face difficulties in task selection. This paper proposes a task recommendation model based on the prediction of workers’ mobile trajectories. A recurrent neural network is used to obtain the movement pattern of workers and predict the next destination. In addition, an attention mechanism is added to the task recommendation model in order to capture records that are similar to candidate tasks and to obtain task selection preferences. Finally, we conduct experiments on two real datasets, Foursquare and AMT (Amazon Mechanical Turk), to verify the effectiveness of the proposed recommendation model.

Evaluating package delivery crowdsourcing using location traces in different population densities

Crowdsourcing package delivery is an emerging business activity that utilizes people's existing travel for delivering packages and mail. While crowdsourcing can address the growing need for faster and cheaper package delivery without deploying the system, it is unclear which types of local crowdsourcing can be effective and perform in urban, suburban, and rural areas. This study suggests a method for simulating package delivery architectures using anonymized mobile location traces, comparing prominent architectures, and elevating the success rates, duration periods, and other measures in different geographical locales. Based on a sample of 1.8 million cellular phone users in Israel, we show that both systems have performance challenges in areas with low population density. Our analysis also indicates that multi-hop architectures allow couriers to transfer packages to their destination by leaving them at intermediate stop points until the next courier picks them up, outperforming one-hop architectures, in which packets can be transferred between origin and destination stop points by one courier. We conclude the paper by discussing how mobility-based simulation methods can be used to assess the performance of mobile crowdsourcing systems before they are built.

Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model

With the advent of mobile crowd sourcing (MCS) systems and its applications, theselectionof the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

SocialRecruiter: Dynamic Incentive Mechanism for Mobile Crowdsourcing Worker Recruitment With Social Networks

Worker recruitment is an important problem in mobile crowdsourcing (MCS), which aims to find sufficient and suitable participants to perform tasks. However, existing worker recruitment approaches mainly focus on how to select the most suitable workers for tasks from a large worker pool, while the recruitment problem under insufficient workers (e.g., a new MCS system) has not been well addressed. In this paper, we focus on the insufficient participation problem of MCS systems with limited number of workers, and propose to leverage social network to recruit workers for task completion as well as expanding the worker pool. To this end, we propose a dynamic incentive mechanism, called SocialRecruiter, to encourage workers on the MCS platform to propagate tasks through social networks, so that inviting friends to join in the MCS platform to further propagate and complete tasks. Motivated by the SIR epidemic model, we propose a novel task-specific epidemic model to characterize the status change of users for task propagation and completion through social networks. In order to encourage task completion and propagation, the propagating reward and completing reward are provided according to workers’ actions. In particular, in order to maximize the task completion within the financial budget, the propagating and completing rewards are dynamically updated at each cycle according to real-time worker recruitment progress. The extensive experimental results on two real-world datasets demonstrate that SocialRecruiter outperforms the state-of-the-art approaches in terms of worker recruitment and task completion.

Multistrategy Repeated Game‐Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real‐time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long‐term participation of users and high quality of tasks. This paper designs a long‐term incentive mechanism based on game theory. The long‐term incentive mechanism is to encourage participants to provide long‐term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game‐based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long‐term participation and high‐quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long‐term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright‐Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long‐term incentive effect for participants in mobile crowdsourcing systems.

Walrasian Equilibrium-Based Multiobjective Optimization for Task Allocation in Mobile Crowdsourcing

With the rapid development of Industry 5.0 and mobile devices, the research of mobile crowdsensing networks has become an important research focus. Task allocation is an important research content that can inspire crowd workers to participate in crowd tasks and provide truthful sensed data in mobile crowdsourcing systems. However, how to inspire crowd workers to participate in crowd tasks and provide truthful sensed data still has many challenges. In this article, based on the Markov model and collaborative filtering model, the similarities, trajectory prediction, dwell time, and trust degree are considered to propose the Markov and Collaborative filtering-based Task Recommendation (MCTR) model. Then, based on the Walrasian equilibrium, the optimum solution is researched to maximize the social welfare of mobile crowdsourcing systems. Finally, the comparison experiments are carried out to evaluate the performance of the proposed multiobjective optimization and the Markov-based task allocation with other methods. Through comparison experiments, the efficiency and adaptation of mobile crowdsourcing systems could be improved by the proposed task allocation.

A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing systems

With the development of Mobile Crowd Sensing Networks (MCSN), more and more Mobile Crowdsourcing applications emerge. The mobile sensing technologies and theories have become the research hotspot. How to design an effective incentive mechanism to maximize social welfare is the key research in mobile crowdsourcing. This paper proposes an online auction algorithm through combining multi-attribute auction and reverse auction to select the candidate set of crowd workers dynamically. In order to further optimize the utility of platform-centric mobile crowdsourcing, the candidate set of crowd workers is optimized through improving the Discrete Particle Swarm Optimization (DPSO), which can determine the winner set of crowd workers. Through comparison experiments on simulation dataset and real dataset, the effectiveness and performance of the proposed worker-selection incentive mechanism are verified. From experimental results, it can be inferred that the proposed worker-selection incentive mechanism can inspire users to participate in crowd tasks and maximize the utility of mobile crowdsourcing systems effectively.

A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

With the tremendous advances in ubiquitous computing, mobile crowd sourcing (MCS) has become an appealing part of the Internet of Things (IoT). In MCS systems, workers collect data with a certain quality, and get incentivized in return. However, MCS systems are vulnerable to misbehaving acts such as workers submitting multiple false or fake reports using multiple devices to affect the majority vote of the task. In addition, workers may try to maximize their profit by submitting multiple truthful data, a behavior that may prevent other potential workers to participate. The selection of such workers for a task has a negative impact on the decision making or the payoff of the task. Most of the current approaches aim to maximize the completion of tasks based on the reputation or the credibility of workers, but without consideration of tasks' payoff and the threat of misbehavior. In literature, a misbehaving act, where workers impersonate multiple identities using multiple devices to maliciously change the majority votes or selfishly increase their payment, has not been addressed during MCS recruitment. In this paper, a two-layer selection approach is proposed based on game-theory in which the payoff of the tasks is maximized based on the individual contributions of the workers. In addition, the proposed model detects and eliminates the misbehaving act where workers submit multiple reports using multiple devices, during the recruitment phase. Simulations using real-life datasets show that the proposed approach succeeds in detecting and eliminating misbehaving devices, and outperforms the benchmarks in terms of the payoff of the tasks.

A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

Mobile crowdsourcing has emerged as a promising collaboration paradigm in which each spatial task requires a set of mobile workers in near vicinity to the target location. Considering the desired privacy of the participating mobile devices, trust is considered to be an important factor to enable effective collaboration in mobile crowdsourcing. The main impediment to the success of mobile crowdsourcing is the allocation of trustworthy mobile workers to nearby spatial tasks for collaboration. This process becomes substantially more challenging for large-scale online spatial task allocations in uncertain mobile crowdsourcing systems. The uncertainty can mislead the task allocation, resulting in performance degradation. Moreover, the large-scale nature of real-world crowdsourcing poses a considerable challenge to spatial task allocation in uncertain environments. To address the aforementioned challenges, first, an optimization problem of mobile crowdsourcing task allocation is formulated to maximize the trustworthiness of workers and minimize movement distance costs. Second, for the uncertain crowdsourcing scenario, a Markov decision process-based mobile crowdsourcing model (MCMDP) is formulated to illustrate the dynamic trust-aware task allocation problem. Third, to solve large-scale MCMDP problems in a stable manner, this study proposes an improved deep Q-learning-based trust-aware task allocation (ImprovedDQL-TTA) algorithm that combines trust-aware task allocation and deep Q-learning as an improvement over the uncertain mobile crowdsourcing systems. Finally, experimental results illustrate that the ImprovedDQL-TTA algorithm can stably converge in a number of training iterations. Compared with the reference algorithm, our proposed algorithm achieves effective solutions on the experimental data sets.

An Optimization and Auction-Based Incentive Mechanism to Maximize Social Welfare for Mobile Crowdsourcing

Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is how to maximize social welfare through selecting appropriate sensing tasks for crowd workers and selecting appropriate workers for sensing tasks such that it can improve the effectiveness and efficiency of mobile crowdsourcing. This paper proposes an incentive mechanism to maximize social welfare for mobile crowdsourcing and, respectively, investigates worker-centric task selection and platform-centric worker selection. This paper applies an optimization algorithm in task selection for mobile crowdsourcing systems. A discrete particle swarm optimization (DPSO) algorithm for worker-centric task selection is designed to maximize the utilities of workers. In addition, a platform-centric worker selection method, which integrates multiattribute auction and two-stage auction, is proposed to maximize the utility of the platform. The performance of the proposed incentive mechanism is evaluated through experiments. The experimental results show that the proposed incentive mechanism can improve the efficiency and truthfulness of mobile crowdsourcing effectively.

Task-Bundling-Based Incentive for Location-Dependent Mobile Crowdsourcing

With the ubiquitous usage of mobile devices, we are witnessing the emergence of commercial crowdsourcing applications that leverage the power of the crowd (workers) to collect massive data. However, the participation unbalance problem commonly occurs in existing location-dependent mobile crowdsourcing applications as workers tend to select nearby tasks while far away tasks are ignored. In this article, we propose a novel task bundling based incentive mechanism that dynamically bundles tasks with different popularity together to solve the participation unbalance problem. We consider the continuous sensing scenarios and categorize tasks into high-popularity (hot) tasks and low-popularity (cold) tasks at each round according to the real-time participation situation of tasks at the last round. We then formulate the task bundling problem as a multi-objective optimization problem, and propose a dynamic task bundling algorithm that dynamically bundles cold tasks with hot tasks at each round. The experimental results demonstrate that the bundling incentive mechanism has a more balanced participation for location-dependent tasks in mobile crowdsourcing systems.

Multi-worker multi-task selection framework in mobile crowd sourcing

In this paper, we address the problem of multi-worker multi-task allocation for mobile crowd sourcing systems (MCS), known to be hard to solve. The existing solutions for multi-task selection are mainly sequential assignments and/or focus on solely minimizing the traveling distance for the workers. Hence, these solutions fall short in allocating the workers that maximize the Quality of Service (QoS) of the tasks. In this work, we propose a Group-based multi-task Worker Selection (GMWS) model that allocates multiple tasks for workers while maximizing the QoS of the tasks, and minimizing their completion time. The proposed approach relies on – 1) clustering tasks based on their geographic locations using k-mediods algorithm, 2) selecting workers based on genetic algorithm (GA), that assigns a group of workers to a cluster of tasks, and 3) delegating workers to individual tasks within a cluster using tabu search algorithm. Simulations based on real-life dataset show that the proposed model outperforms other benchmarks in terms of the total distance traveled and the QoS achieved.

Pro-social behaviour in crowdsourcing systems: Experiences from a field deployment for beach monitoring

Abstract The paper presents experiences from the rapid introduction and deployment of a data crowdsourcing and data sharing system, motivated by an urgent civic need arising due to the appearance of jellyfish in the swimming coastal areas of western Greece during the summer season. The system was tailored for mobile use and although the pressing need for its deployment negated the time for thorough design, a rich set of lessons and findings emerge from its public use by 13,340 users, over a period of 2 months, reporting over 1,800 times on the condition of 189 local beaches, of which 157 were added to the system by the users themselves. This work touches on issues of usability, motivation, data reliability and public utility of mobile participatory systems and demonstrates that effective outcomes for public bodies may rise when systems are designed for the immediate benefit of citizens, by openly exposing the collected data. Most importantly, participation in mobile crowdsourced systems where the data is openly shared between participants is found to be strongly driven by altruistic motives and not by financial or ethical awards. Additionally, the altruistic motives behind participation overcome the added difficulty of participating from a purely mobile use context, and safeguard the quality of the contributed data, reducing the need for complex quality monitoring and safeguarding mechanisms. Finally, the paper identifies barriers and opportunities for the opportunistic participation in mobile crowdsourcing systems during leisure time.

DTCS: An Integrated Strategy for Enhancing Data Trustworthiness in Mobile Crowdsourcing

Mobile crowdsourcing systems (MCSs) are important sources of information for the positioning services in Internet-of-Things such as gathering location information through employing citizens to participate in data collection. Although MCSs have attracted significant research and development efforts, there are salient open issues and challenges in security and privacy for MCS, which is an essential factor for its success. This paper proposes an integrated strategy named data trustworthiness enhanced crowdsourcing strategy (DTCS) to enhance data trustworthiness and defend against the internal threats for mobile crowdsourcing. The DTCS integrates effective methods including an evaluation scheme for the attribute relevancy and familiarity of participants, a trust relationship establishment method, a group division strategy based on attributes and metagraph, and a core-selecting-based incentive mechanism. The simulation results show that the DTCS improves the performance of the crowdsourcing strategy compared to the state-of-the-art including the TSCM and PPPCM. The DTCS can effectively defend against internal conflicting behavior attacks and collusion attacks to enhance data trustworthiness for mobile crowdsourcing.

CampusWatch

Community policing to collaboratively maintain community safety and order in conjunction with law enforcement is becoming increasingly popular and efficient with the use of mobile technologies. Beyond sharing information about local problems such as crime via online discussion forums, there has been an increased focus on the impact of mobile crowdsourcing systems on community policing. In this study, we designed a novel crowdsourced patrolling campaign in which community members schedule their own patrol times and routes, then perform bike-based patrolling with video capturing using their smartphones. We conducted a four-week field study (n=20) on a university campus to verify the campaign's feasibility and observe users' behavior. Our results show key findings about users' task scheduling, event capturing and reporting behaviors, factors affecting task selection and execution and user motivation and engagement. Finally, we discuss several practical design implications in building crowdsourcing systems for community policing.

Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems

With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. In order to improve the efficiency and truthfulness of mobile crowdsourcing systems, this paper proposes a truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems. The improved two-stage auction algorithm based on trust degree and privacy sensibility (TATP) is proposed. In addition, the k−ɛ-differential privacy-preserving is proposed to prevent users’ location information from being leaked. Through comparison experiments, the effectiveness of the proposed incentive mechanism is verified. The proposed incentive mechanism with location privacy-preserving can inspire users to participate sensing tasks, and protect users’ location privacy effectively.

A novel task recommendation model for mobile crowdsourcing systems

With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. However, how to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, the trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE datasets to verify the effectiveness of the proposed recommendation model.