Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job. One of the users' main concerns is the cost of data uploading, which affects their willingness to participate in a crowdsensing task. In this paper, we propose an efficient Prediction-based User Recruitment for mobile crowdsEnsing (PURE), which separates the users into two groups corresponding to different price plans: Pay as you go (PAYG) and Pay monthly (PAYM). By regarding the PAYM users as destinations, the minimizing cost problem goes to recruiting the users that have the largest contact probability with a destination. We first propose a semi-Markov model to determine the probability distribution of user arrival time at points of interest (PoIs) and then get the inter-user contact probability. Next, an efficient prediction-based user-recruitment strategy for mobile crowdsensing is proposed to minimize the data uploading cost. We then propose PURE-DF by extending PURE to a case in which we address the tradeoff between the delivery ratio of sensing data and the recruiter number according to Delegation Forwarding. 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, PURE achieves a lower recruitment payment and PURE-DF achieves the highest delivery efficiency.

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.

With the boosting of neural networks, recommendation methods become significantly improved by their powerful ability of prediction and inference. Existing neural-network based recommender systems (NN-RSs) usually first employ matrix embedding (ME) as a pre-process to learn users’ and items’ representations (latent vectors), then input these representations to a specific modified neural network framework to make accurate Top-k recommendations. Obviously, the performance of ME has a significant effect on RS models. However, most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same items should be embedded into similar representations), which is an ideological disadvantage. On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage. In this paper, we propose a supervised collaborative representation learning model - Magnetic Metric Learning (MML) - to map users and items into a unified latent vector space, enhancing the representation learning for NN-RSs. First, MML utilizes dual triplets to model not only the observed relationships between users and items, but also the underlying relationships between users as well as items to overcome the ideological disadvantage. Specifically, a modified metric-based dual loss function is proposed in MML to gather similar entities and disperse the dissimilar ones. With MML, we can easily compare all the relationships (user to user, item to item, user to item) according to the weighted metric, which overcomes the methodological disadvantage. We conduct extensive experiments on four real-world datasets with large item space. The results demonstrate that MML can learn a proper unified latent space for representations from the user-item matrix with high accuracy and effectiveness, and lead to a performance gain over the state-of-the-art RS models by an average of 17 percent.

Mobile Crowdsensing (MCS) is a powerful sensing paradigm, which provides sufficient social data for cognitive analytics in industrial sensing, and industrial manufacturing. Considering the sensing costs, sparse MCS, as a variant, only senses the data in a few subareas, and then infers the data of unsensed subareas by the spatio-temporal relationship of the sensed data. Existing works usually assume that the sensed data are linearly spatiotemporal dependent, which cannot work well in real-world nonlinear systems, and thus, result in low data inference accuracy. Moreover, in many cases, users not only require inferring the current data, but also have an interest in predicting the near future, which can provide more information for users’ decision making. Facing these problems, we propose a deep learning-enabled industrial sensing, and prediction scheme based on sparse MCS, which consists of two parts: matrix completion and future prediction. Our goal is to achieve high-precision prediction of future moments under the hypothesis of sparse historical data. To make full use of the sparse data for prediction, we first propose a deep matrix factorization method, which can retain the nonlinear temporal-spatial relationship, and perform high-precision matrix completion. In order to predict the subareas’ data in several future sensing cycles, we further propose a nonlinear autoregressive neural network, and a stacked denoising autoencoder to obtain the temporal–spatial correlation between the data from different cycles or subareas. According to the results gained by experiments on four real-world industrial sensing datasets consisting of six typical tasks, it can be seen that the method in this article improves the accuracy of prediction using sparse data.

Sparse Mobile CrowdSensing is a practical paradigm for large sensing systems, which recruits a small number of users to sense data from only a few subareas, and then infers the data of unsensed subareas. In order to provide high quality sensing services under a budget constraint, we would like to select the most effective users to collect useful sensing data to achieve the highest inference accuracy. However, due to the variable user mobility and complicated data inference, it is really challenging to directly select the best user set which helps the most with data inference. From the user’s side, we can obtain the probabilistic coverage according to the users’ mobilities, while the probabilistic coverage cannot indicate the data inference accuracy directly. From the subarea’s side, we may identify some more useful subareas under the current states (e.g., the previous sensed subareas and the current expected coverage), while these useful subareas may not be covered by the users. Moreover, both the user mobility and data inference introduce a lot of uncertainty, which yields non-monotonicity and thus non-submodularity in the user recruitment problem. Therefore, in this paper, we study the user recruitment problem on both the user’s and subarea’s sides, and propose a three-step strategy, including user selection, subarea selection and user-subarea-cross selection. We first select some candidate user sets, which may cover the most subareas under the budget constraint (user selection), then estimate which subareas are more useful on data inference according to the selected candidates (subarea selection), which finally guides us to recruit the best user set (user-subarea-cross selection). Extensive experiments on two real-world data sets with four types of sensing tasks verify the effectiveness of our proposed user recruitment algorithms, which can effectively enhance the data inference accuracy under a budget constraint.

Mobile Crowdsensing (MCS) is a promising paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user's probability or frequency of covering the task locations. However, we argue that the task completion effect depends not only on the user's objective ability, but also on their subjective collaboration likelihood with each other. Furthermore, even though we can find a well-behaved group of users in the single-round scenario, while in the multi-round scenario without enough prior knowledge, we still face the problem of recruiting previously well-behaved user groups (exploitation) or recruiting uncertain user groups (exploration). Additionally, we consider that each user has a different cost, and the platform recruits users under a cost budget; thus, the problem becomes more challenging: users may report fake costs to gain more profits. To address these problems, assuming that the user's information is known, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the approximate solution. Then, in the multi-round scenario where the user's information is estimated from the previous rounds, to balance the trade-off between exploration and exploitation, we propose the multi-round User Recruitment strategy under the budget constraint based on the combinatorial Multi-armed Bandit model (URMB), which is proven to achieve a tight regret bound. Next, we propose a graph-based payment strategy to achieve truthfulness and individual rationality of users. Finally, extensive experiments on three real-world datasets show that URMB always outperforms the state-of-th-art strategies.