Sparse Mobile CrowdSensing has emerged as a practical method for data collection, recruiting mobile users to collect partial data and leveraging spatiotemporal correlations to infer the missing data. To improved the QoS of crowdsourced data, existing methods typically assume that the scales of collected data are similar. However, in real-world scenarios, the diversity of user devices results in data collections that vary in scale. More importantly, the collected coarser-scale data points often do not perfectly correspond to multiple finer-scale data points, resulting in highly complex compositional relationships and posing significant challenges for multi-scale data completion. To address these challenges, this paper proposes a multi-scale data completion framework designed to process and integrate multi-scale data with non-aligned compositional relationships. We first align features across scales using the least common multiple scaling, then enhance the interaction and integration of data across scales through a bidirectional processing strategy and modified Mamba architectures, specifically ST-Mamba and Cross-Mamba. Evaluated on six real-world datasets, our study demonstrates the effectiveness of the proposed framework in handling multi-scale data completion challenges, particularly when dealing with non-aligned compositional relationships.

Spatial crowdsourcing is drawing much attention with the rapid development of mobile Internet. Achieving efficient crowdsourcing task assignment involves not only maximizing the earnings of workers but also balancing the preferences of users or customers. Users often express preferences for specific workers or conditions, such as particular drivers, delivery personnel, or service providers. To address this challenge, we investigate the future-aware balanced preference (FABP) problem. This problem aims to maximize the profits of global workers while simultaneously considering the preferences of both parties to ensure bilateral satisfaction. To address the FABP problem, we propose the learning to match (LTM) algorithm. This algorithm utilizes online reinforcement learning that considers both immediate profits and long-term rewards. It acknowledges the significance of task assignment decisions in relation to the spatial distribution of future drivers, which in turn affects subsequent decisions. The LTM algorithm generates future-aware preference lists using learned driver state values and guides the subsequent matching. Additionally, we present the real-time preference-based matching (RTPM) algorithm, which is a real-time matching algorithm that enables substitutions based on preference lists when a more preferred matching pair becomes available. This enhances the efficiency and fairness of real-time task assignment in dynamic environments, while simultaneously meeting the needs of passengers and drivers. Our extensive experiments on both real and synthetic datasets validate the effectiveness of our proposed algorithms, demonstrating a noteworthy improvement of up to 11.8% and an average increase of 4.7% compared to benchmark algorithms.

Mobilecrowdsensing (MCS) is a powerful technique that enables a variety of urban tasks, including temperature monitoring, location-based services, and urban path recommendations. However, these tasks often face the challenge of sparse and incomplete sensing data, undermining their effectiveness and reliability.Sparsedatacompletion (SDC) methods have been developed to infer missing or unobserved data by leveraging spatio-temporal correlations to tackle this issue. This forms the core concept of thesparsemobilecrowdsensing problem (SMCS), which aims to improve the performance of downstream tasks through inferred data. Despite the potential benefits, most existing SMCS methods fail to consider the trade-off between the cost of SDC and the benefits for downstream tasks. These methods often treat SDC and downstream tasks as independent modules, resulting in suboptimal outcomes. In this paper, we investigate the impact of SDC on the SMCS paradigm, both qualitatively and quantitatively. We establish the upper bound of performance achievable when applying SDC in SMCS under different levels of sensing data sparsity. Based on these studies and findings, we propose a practical and flexible framework calledSDC-EVA,SensingDataCompletionEVAluation framework. This framework allows for applying different SDC methods in SMCS, considering factors such as computing complexity, storage space, and associated costs. Our proposed framework allows researchers to assess the necessity and feasibility of integrating SDC into SMCS systems before designing and deploying them in real-world scenarios. This assessment can be tailored to specific data sparsity and contextual information. To validate the effectiveness of our proposed evaluation framework, we conduct experiments in various real-world scenarios involving different combinations of SDC and downstream tasks. The results demonstrate the superiority of our framework in improving the performance of SMCS. By presenting these findings, we aim to contribute to developing SMCS techniques and provide valuable insights for researchers and practitioners.

Parking violation is a common urban problem in major cities all over the world. Traditional approaches for detecting parking violations mainly rely on fixed deployed sensors and enforcement agencies, which suffer from high deployment costs and limited coverage. With the rapid development of mobile networks, Mobile CrowdSensing (MCS) has been an effective sensing paradigm. The crowdsensing data can help predict the future parking violation distribution, and the prediction results can provide guidance for user scheduling, i.e., sending the mobile users to patrol areas where many parking violation events may occur. Inspired by this idea, we propose a comprehensive data-driven crowdsensing framework, which incorporates the nested design of a generative model for spatial-temporal data and a user scheduling model. The generative model extracts parking violation hotspots via a data completion module and violation prediction module. Since crowdsensing data is usually temporally sparse and unevenly distributed, a data completion module is proposed to infer the missing statistics in unsensed areas. The violation prediction module then predicts the parking violation distribution. Given the predicted results, the deep reinforcement learning-based user scheduling model coordinates users to visit hotspots for violation detection. Iteratively, the newly collected data can be used to predict the future violation distribution. Finally, we conduct extensive simulations based on two real-world datasets from two large urban cities. The simulation verifies the prediction accuracy and scheduling effectiveness of the proposed framework compared with the baselines.

Mobile CrowdSensing (MCS) is a promising sensing paradigm that leverages users’ mobile devices to collect and share data for various applications. A key challenge in MCS is task allocation, which aims to assign sensing tasks to suitable users efficiently and effectively. Existing task allocation approaches are mostly centralized, requiring users to disclose their private information and facing high computational complexity. Moreover, centralized approaches may not satisfy users’ preferences or incentives. To address these issues, we propose a novel distributed task allocation scheme based on route navigation systems. We consider two scenarios: time-tolerant tasks and time-sensitive tasks, and formulate them as potential games. We design distributed algorithms to achieve Nash equilibria while considering users’ individual preferences and the platform’s task allocation objectives. We also analyze the convergence and performance of our algorithm theoretically. In the time-sensitive task scenario, the problem becomes even more intricate due to temporal conflicts among tasks. We prove the task selection problem is NP-hard and propose a distributed task selection algorithm. In contrast to existing distributed approaches that require users to deviate from their regular routes, our method ensures task completion while minimizing disruption to users. Trace-based simulation results validate that the proposed algorithm attains a Nash equilibrium and offers a total user profit performance closely aligned with that of the optimal solution.

Vehicular CrowdSensing (VCS) has become a powerful sensing paradigm by selecting users driving vehicles to perform tasks. In most existing research, the platform centrally allocates tasks according to the collected user information. We argue that the information collection process results in user privacy leakage, and the centralized allocation leads to a heavy computation complexity. We propose to apply a distributed task allocation method in the widely-used route navigation system. The navigation system recommends several routes to a user and each route may cover some tasks. Then, the user distributively selects a route according to the route profit (task reward minus route cost). Since the task reward is shared by users, the route selections of users may influence each other. Hence, it remains unclear how to design a distributed route navigation approach to reach an equilibrium state (i.e., each user is satisfied with the selected route), while guaranteeing a good total profit. To this end, we formulate the problem as a multi-user potential game and propose a distributed route navigation algorithm. The trace-based simulation results verify that the proposed algorithm achieves a Nash equilibrium, while achieving a total user profit performance close to that of the optimal solution.

Mobile crowdsensing has become a significant sensing technique which takes advantage of mobile devices to collect information about the surrounding. The traditional cloud-based centralized mobile crowdsensing architecture generates significant traffic on networks and computation burden on the cloud. In this paper, we investigate the edge-based mobile crowdsensing architecture, where a group of mobile edge servers is deployed at network edge as the bridge between the central server and mobile users for data filtering and aggregation. Each user may collect multiple types of data in mobile crowdsensing. To facilitate data aggregation, the same type of data carried by different users is supposed to be uploaded to the same mobile edge server. In this scenario, a problem emerges: which server should be activated for processing each type of data in order to minimize the total cost? The cost consists of the facility cost (activating server and processing data) and the service cost (the users' movement cost for uploading data). Furthermore, the problem is formulated as a variant of the uncapacitated multi-commodity facility location problem. In particular, two situations of the problem are studied in our work: (1) for the situation where each user carries at most two types of data, we propose a relaxation based approximation algorithm, which is proved to have a bound to the optimal solution; (2) for a more generalized situation where each user can carry multiple types of data, we propose a connected multi-agent simulated annealing algorithm. Finally, we conduct extensive simulations based on the widely-used real-world datasets: roma/taxi, epfl/mobility and geolife trajectory. The simulation results show that the proposed algorithms demonstrate their superiority over baseline methods and are consistent with the theoretical analysis.

Mobile CrowdSensing (MCS) is a promising paradigm that recruits mobile users to cooperatively perform various sensing tasks. When assigning tasks to users, most existing works only consider the fairness of users, i.e., the user's processing ability, with the goal of minimizing the assignment cost. However, in this paper, we argue that it is necessary to not only give full use of all the users' ability to process the tasks (e.g., not exceeding the maximum capacity of each user while also not letting any user idle too long), but also satisfy the assignment frequency of all corresponding tasks (e.g., how many times each task should be assigned within the whole system time) to ensure a long-term, double-fair and stable participatory sensing system. Hence, to solve the task assignment problem which aims to reasonably assign tasks to users with limited task processing ability while ensuring the assignment frequency, we first model the two fairness constraints simultaneously by converting them to user processing queues and task virtual queues, respectively. Then, we propose a Fair Task Assignment Strategy (FTAS) utilizing Lyapunov optimization and we provide the proof of the optimality for the proposed assignment strategy to ensure that there is an upper bound to the total assignment cost and queue backlog. Finally, extensive simulations have been conducted over three real-life mobility traces: Changchun/taxi, Epfl/mobility, and Feeder. The simulation results prove that the proposed strategy can achieve a trade-off between the objective of minimizing the cost and the fairness of tasks and users compared with other baseline approaches.

Mobile crowdsensing recruits a group of mobile users to cooperatively perform a common sensing job with their smart devices. As a special issue, photo crowdsensing allows users to utilize the built-in cameras of mobile devices to take photos for an event or a target. Then, the photos can be used in numerous application areas, such as target reconstruction, scenario reduction, and so on. Therefore, photo crowdsensing has attracted considerable attention recently due to the rich information that can be provided by images. In this paper, we focus on using the photos to make reconstructions for specific targets. Furthermore, we develop a user recruitment system for efficient photo collecting in mobile crowdsensing (RSMC), where the task requesters publish a sensing task to the users, and the map is gridded according to the locations of the sensing targets. Then, we use a semi-Markov model to calculate the user's utility for the sensing task. Finally, a user recruitment strategy is devised to recruit the optimal k users for finishing the sensing task. We conduct extensive simulations based on three widely used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, RSMC takes the largest number of efficient photos for the sensing task.

Mobile crowdsensing emerges as a powerful sensing paradigm which utilizes a group of users with mobile devices to perform sensing tasks cooperatively. The traditional mobile crowdsensing architecture is centralized and cloud-based, where the users upload the sensing data directly to the central server. Due to the fact that the amount of sensing data is very large, it will bring much burden to upload and process data collected by mobile users on the central server. In this paper, we propose an edge-based mobile crowdsensing architecture, which introduces a new intermediate layer for data storage, processing and aggregation through deploying mobile edge servers between the traditional cloud server and the user layer. Considering the limited budget, a decision-maker has to decide which server is activated to process each data type at minimum cost, where the cost consists of the facility cost for activating server and processing data, and the service cost measured by the distance of mobile users' movement during the process of uploading data. Since a user may have multiple types of data, this problem is formulated as a variant of the uncapacitated multi-commodity facility location problem. Furthermore, an approximation algorithm is proposed to solve it for minimizing cost, which is proved to have a bound to the optimal solution. We conduct extensive simulations based on the widely-used real-world datasets: roma taxi set, epfl mobility set and geolife trajectory set. The experiment results show that the proposed approximation algorithm outperforms other baseline algorithms and accords with the theoretical bound results.

Mobile crowdsensing recruits a massive group of mobile workers to cooperatively finish a sensing task through their smart devices (mobile phones, ipads, etc.). In this paper, the communication in social network for delivering the sensing data of mobile crowdsensing is considered, where some requesters publish the sensing tasks to all the Point of Interests (PoIs), and the workers are recruited to take the sensing data in the PoI until they could communicate with the requester through an offline and online connection. We first use the semi-Markov model to predict the offline encounter situation. Then, the worker's utility is decided by both the offline encounter and social connection probabilities. The Worker Recruitment for Self-organized MSC (WEO) is further presented through recruiting a set of workers, who have the maximum communication probability with the requesters. We prove that the optimal recruitment problem is NP-hard, and we introduce a practical greedy heuristic method for this problem, the performance of the greedy method is also tested. Two real-world traces, roma/taxi and epfl are tested in our simulations, where WEO always achieves the highest delivery ratio of sensing tasks among different recruitment strategies.