BETA: From Behavior Sequentializing to Task Mapping in 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.

Mobile crowdsensing (MCS) depends on mobile users to collect sensing data, whose quality highly depends on the expertise/experience of the users. It is critical for MCS to identify right persons for a given sensing task. A commonly-used strategy is to “teach-before-use”, i.e., training users with a set of questions and selecting a subset of users who have answered the questions correctly the most of times. This method has large room for improvement if we consider users’ learning curve during the training process. As such, we propose an interactive learning pattern recognition framework, Goldilocks, that can filter users based on their learning patterns. Goldilocks uses an adaptive teaching method tailored for each user to maximize her learning performance. At the same time, the teaching process is also the selecting process. A user can thus be safely excluded as early as possible from the MCS tasks later on if her performance still does not match the desired learning pattern after the training period. Experiments on real-world datasets show that compared to the baseline methods, Goldilocks can identify suitable users to obtain more accurate and more stable results for multi-categories classification problems.

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.

With the pervasiveness of mobile devices, satisfying spatial-temporal coverage requirements in interested regions while considering the quality of the sensing data and the budget constraint is a major research challenge in mobile crowdsensing (MCS). In this paper, we define a novel coverage metric, quality coverage, which considers both the fraction of subareas covered by sensor readings and the quality of sensing data in each covered subarea, for diversified location-based MCS tasks. In our system, as participants with high reliability level can contribute a lot to sensing tasks, we design an incentive model that provides higher bonus to more reliable participants. After knowing the reward for the per amount of sensing data, each worker can submit an interested task set that contains several unit tasks. The platform assigns MCS tasks to these mobile workers by solving the task allocation optimization problems, which select the optimal mobile worker set under the budget of data requesters to maximize diverse and spatial coverage level in the whole sensing area. As the task allocation problems are proven to be NP-hard, we propose a heuristic and a polynomialtime greedy approximation algorithm to solve optimization problems in two different application scenarios. Simulations using both real and synthetic data sets show that our algorithms outperform existing approaches.

This paper aims to explore the problem of energy economy of mobile devices in the Mobile Crowdsensing (MCS) scenario. The neighbor scanning mechanism of mobile devices usually consumes most of the energy in multi-hop message transmission. Traditional mechanisms such as chaotic neighbor detection and continuous neighbor discovery, can easily exhaust the limited energy. Since they are actually unnecessarily considering the strong correlation between the transmission opportunity and the social characteristics. Therefore, diminishing energy consumption is of key importance toward energy-efficient MCS. Sleeping strategy stands out as a promising approach to improve energy efficiency and hold the the network metrics in MCS applications, while challenges still lie in achieving effective scheduling given the changing environment. In this study, we proposed a novel self-adapt sleeping scheduling approach based on the correlation between the pedestrian's own historical trajectory and geographic grid information for energy saving (ESGeo). As the grid-based method can well record the encounter characteristics of nodes, ESGeo is able to pick flexible duty-cycling strategies for mobile devices in each distinguishable grid. This enables, mobile devices to avoid excessive scanning in low-probability encounter areas. Extensive simulation results further demonstrated that the proposed approach can significantly outperform the typical routing approaches in terms of energy-efficiency, without largely affecting the overall networking performance.

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.

Mobile crowd sensing (MCS) is a novel example of urban sensing, which uses smart phones, smart wearable devices, on-board sensors and other mobile sensor devices to collect data, and is widely used in intelligent transportation, medical care, environmental monitoring and other fields. A growing number of studies show that a good task allocation mechanism is an indispensable key factor in determining whether MCS applications can obtain good service quality. In this paper, we introduce the realistic background of MCS and combine some specific applications to exhaustively classify task allocation problems in MCS. Then, the typical algorithm for MCS task allocation is classified and illustrated. Finally, the current research challenges and future research directions are presented.

The pervasiveness of mobile phones and the increasing sensing capabilities of their built-in sensors have made mobile crowdsensing a promising approach for large-scale data collection. In mobile crowdsensing, a specific situation is that the service provider (SP) needs to recruit contributors to fulfill sensing tasks requested by consumers. There are several challenges for contributor selection and task allocation in mobile crowdsensing. Firstly, since some of the contributors may need to travel a certain distance to complete the sensing task, they may need to be compensated for their contributions, in proportion to the distance they need to travel. Secondly, the diversity in sensing devices and contributor behavior means that the sensing data from different contributors will have different Quality of Information (QoI). Moreover, the SP usually only has a limited budget to compensate the contributors. Thus, how to handle the trade-offs between traveling distances of the contributors and QoI of the sensing data in order to maximize the sensing revenue based on limited budget is an important consideration in mobile crowdsensing applications. In practice, the QoI from different contributors are usually unknown to the SP in advance, which makes contributor selection and task allocation even more challenging. In this paper, we propose a QoI-aware task allocation scheme (QOATA) for contributor selection and task allocation in mobile crowdsensing applications. Using an online learning approach to learn the QoI from different contributors, the scheme aims to achieve the highest sensing revenue under a limited budget. The effectiveness of QOATA is evaluated through extensive simulations.

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.

Mobile crowdsensing is a promising service paradigm utilizing the pervasive mobile devices to cooperatively perform large-scale sensing tasks. Data uploading which aims to deliver the sensed data to the control center, is an important problem in mobile crowdsensing. Existing works assume that the devices participate in device-to-device (D2D) cooperation voluntarily to assist other devices for data uploading. In reality, to assist other devices to upload data, the devices are requirement of consuming their own resources (e.g., memory, channel and time), which affects their willingness to participate in D2D cooperation. To this limitation, we propose an incentive mechanism to stimulate efficient D2D cooperation among devices. With this incentive mechanism, the reward of each device is determined by the reduced data uploading latency. According to the incentive mechanism, we design a chain formation algorithm, where the nearby devices can cooperate to construct a D2D chain by employing bottom to top mode. Finally, we corroborate the effectiveness of our incentive mechanism in reducing the average data uploading latency by extensive simulations.

Mobile crowdsensing (MCS) has a huge potential to provide societal benefits by effectively utilizing the sensing, computing, and networking capability of mobile devices, which have become ubiquitous especially in the developed world. However, many challenges come along with MCS such as energy cost, privacy issues, and the data integrity of crowdsensed data. In this paper, we focus on analyzing the data integrity of mobile crowdsensed data in a user study of 60 people who participate in a crowdsensing campaign that collects barometric pressure data from various locations of our campus. Each set of 20 users runs one of the three different crowdsensing frameworks, one of which, called SENSE-AID, is an energy-efficient framework designed by us. We analyze the characteristics of the data with respect to their integrity. From our analysis, we find that for 90 MCS tasks there is a surprisingly high number of outlier percentage (close to 20%), only about 10% of the participants report data for the entire duration of 7 days of the MCS campaign, and using a reputation system to filter out spurious data values helps in getting within 0.8% of the ground truth barometric pressure, whereas not using the reputation system leads to a discrepancy of about 20%. We hope our analysis will provide a useful reference to MCS researchers and developers in their future work running MCS campaigns.

With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing worker participation. Different from existing work, we propose an incentive framework for MCS systems, named Thanos, that incorporates a crucial metric, called workers' quality of information (QoI). Due to various factors (e.g., sensor quality and environment noise), the quality of the sensory data contributed by individual workers varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, our design of Thanos is based on reverse combinatorial auctions. We investigate both the single- and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational, and computationally efficient mechanism that ensures a close-to-optimal social welfare. For the latter, we design an iterative descending mechanism that satisfies individual rationality and computational efficiency, and approximately maximizes the social welfare with a guaranteed approximation ratio. Through extensive simulations, we validate our theoretical analysis on the various desirable properties guaranteed by Thanos.

Mobile crowd sensing (MCS) takes advantage of pervasive mobile devices that are equipped with multi-sensors to collect rich data of a certain geographic area. Because of the importance of incentivizing users to participate, auction-based open MCS markets have been proposed in past literature. Note that their focus is to achieve critical economic properties but fail to protect bid privacy. Although there are limited schemes dealing with this issue, they are designed only for single-side auctions and are unsuitable for double-side auctions whose properties are quite different. In this paper, inspired by uniform pricing and ex-ponential mechanism, we propose a differentially private double auction (DPDA) scheme for MCS to protect bid privacy for both auction sides. In addition, the traditional economic properties, such as γ-truthfulness, individual rationality and budget balance, are guaranteed as well. Besides, we derive closed forms over the computation complexity and the approximate optimal platform revenue achieved by the scheme. Extensive simulations have been conducted on real-world datasets to validate the efficiency and effectiveness of DPDA.

Mobile crowdsensing (MCS) is a new and promising paradigm of data collection in large-scale sensing and computing. A large group of users with mobile devices are recruited in a specific area to accomplish sensing tasks. An essential aspect of an MCS application is task allocation, which aims to efficiently assign sensing tasks to the recruited workers. Due to various resource and quality constraints, the MCS task allocation problem is often an NP-hard optimization problem. Traditional greedy or heuristic approaches are usually subject to performance loss in a certain degree so as to maintain tractability or accommodate special requirements such as incentive constraints. In this paper, we attempt to employ a deep reinforcement learning method to search for a more efficient task allocation solution. Specifically, we use a double deep Q-network (DDQN) to solve the task allocation problem as a path-planning problem with time windows. Our formulated problem takes into account location-dependency and time-sensitivity of sensing tasks, as well as the resource limits of workers in terms of maximum travelling distances. Simulations are conducted to compare the DDQN-based solution with two standard baseline solutions. The results show that our proposed solution outperforms the baseline solutions in terms of the platform’s profit and the coverage of tasks.