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Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker's moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers' historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers' and tasks' spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.

There have been many studies focusing on edge server deployment and service placement in mobile edge computing (MEC) respectively, but rare works took both of them into consideration. However, edge server deployment and service placement are coupling issues in practice, where the former affects the latter. Besides, the economic benefit of the MEC platform is also a consideration. Due to different service request rates and prices, appropriate service placement solutions are needed to increase the overall profit. In this paper, we propose a complete process combining edge server and service placement, where service placement explicitly takes into account the structure of current edge server placement and different service request rates and prices. We design a joint edge server deployment and service placement model with the goal of maximizing the overall profit of all edge servers under the constraints of the number of edge servers, the relationship among edge servers and base stations, the storage capacity and the computing capacity of each edge server. We propose a two-step method including the clustering algorithm and nonlinear programming to solve the formulated problem. Extensive evaluations based on the real-world dataset demonstrate that the proposed algorithm outperforms the baseline methods.

Reverse auction-based incentive mechanisms have been widely adopted to stimulate mobile workers to participate in mobile crowdsensing (MCS), where workers need to provide location information for winner selection. However, most existing mechanisms rely on trusted platforms. The worker’s location data uploaded to platforms thus can be easily exposed. Recent works start to incorporate location privacy in designing incentive mechanisms, but they have not considered the task’s location privacy and the worker’s location privacy simultaneously. Therefore, we propose a bilateral location privacy-preserving mechanism for MCS on untrusted platforms. In our model, each worker adopts differential privacy to obfuscate his location locally and then submits the obfuscated location together with the bid information. Besides, instead of the exact location, the task requester is only required to upload the task profile and the task’s obfuscated location. Then, we propose the lowest-cost winner selection mechanism which aims to minimize the social cost of winner selection under the location constraint while ensuring task quality requirements, and adopt the critical payment determination mechanism to determine the payments for the winners, which satisfies truthfulness, individual rationality, and computational efficiency. Theoretical analysis and extensive experiments on real-world datasets show the effectiveness of the proposed mechanisms.

Currently, Mobile Edge Computing (MEC) becomes a burgeoning paradigm to tackle the contradiction between delay-sensitive tasks and resource-limited mobile/IoT devices. However, a single MEC server is usually not able to satisfy the heavy computation tasks considering its limited storage and computation capability. Thus, the cooperation of MEC servers provides an effective way to accommodate this issue. In this paper, we study the joint task offloading and resource allocation problem in the scenario with cooperative MEC servers. We first define resource fairness among IoT devices from the user experience perspective. Then we formulate a joint optimization problem by taking into account the system efficiency and fairness, which is shown to be NP-hard and thus intractable. To solve this problem, we propose a two-level algorithm: The upper-level algorithm, inspired by evolutionary strategies, is able to search superior offloading schemes globally; While the lower-level algorithm, taking into account fairness among all tasks, is able to generate resource allocation schemes that make full use of server resources. Comprehensive evaluation results demonstrate the efficiency and fairness of the proposed algorithm compared to baselines.

Crowdsensing is regarded as a critical component of the Internet of Things (IoT) and has been widely applied in smart city services. Incentive mechanism design, data reliability evaluation, and privacy preservation are the research focuses of crowdsensing. However, most existing incentive mechanisms fail to protect data privacy and evaluate data credibility, simultaneously. Moreover, traditional privacy and reliability-aware incentive schemes are usually challenging to realize real-time reward distribution. To this end, we first point out a single time slice of failure problem in real-time incentive mechanisms and propose a two-layer truth discovery model (TLTD) to resolve this problem. Then, a reliability-aware real-time incentive mechanism (RRIM) is designed based on the proposed TLTD. In order to evaluate data reliability in a privacy-preserving manner, we build a privacy-preserving truth discovery solution (PriTD) based on secure computation protocols. Finally, our proposed system (PRICE) integrating the aforementioned protocols realizes real-time reward distribution, data reliability evaluation, and privacy protection, simultaneously. Theoretical analysis and experimental evaluations on a synthetic and real-world dataset demonstrate the feasibility and efficiency of the proposed PRICE.

The minimum travel distance of task participants is one of the significant optimization objectives of privacy-preserving task assignment in mobile crowdsensing (MCS). However, when the travel distance is minimized, most of the previous schemes only focus on the task participant privacy and disregard the task requester privacy. Moreover, existing solutions usually only support the constraint of a single type, such as equality constraints or range constraints. In this paper, we propose a bilateral privacy-preserving Task Assignment mechanism for MCS (iTAM), which protects not only the task participants privacy but also the task requesters privacy and can minimize the travel distance. Furthermore, iTAM provides both equality and range constraints of task assignment by utilizing the Paillier cryptosystem. To accommodate the multiple relations between the task participants and the task, we propose the single/multiple task participants selection problems for a task requiring task participants to compete and cooperate. Experimental evaluations over synthetic and real-world data illustrate that iTAM is feasible and effective. Compared with the state-of-the-art, iTAM positively solves the optimal problem of travel distance. The complexities of iTAM are $\mathcal {O}(n)$ O ( n ) and $\mathcal {O}(n\log n)$ O ( n log n ) for a single and multiple task participants selection problems, respectively.

Task assignment is a critical issue in mobile crowdsensing (MCS), an emerging sensing paradigm applied to realize various sensing applications for smart cities. Existing task assignment schemes mostly require the exact location information of tasks and workers for optimization, which inevitably brings the issue of location privacy leakage. Therefore, researchers have started investigating privacy-preserving task assignment schemes. However, most of these works either make inaccurate assignments or only concentrate on workers’ privacy. In this article, we propose a novel bilateral privacy-preserving and accurate task assignment framework in fog-assisted MCS, called BRAKE. Specifically, we utilize the multisecret sharing scheme to preserve location privacy in the MCS task assignment, where tasks and workers only need to provide the secret shares of their real location information to fog nodes. Moreover, we consider distance-oriented and time-oriented tasks for assignment optimization and propose an adaptive top-k worker selection algorithm to accurately select the most suitable workers. The security analysis proves that BRAKE can resist collusion attacks, and the extensive evaluation results demonstrate the efficiency and accuracy of BRAKE.

Providing appropriate monetary rewards is an efficient way for mobile crowdsensing to motivate the participation of task participants. However, a monetary incentive mechanism is generally challenging to prevent malicious task participants and a dishonest task requester. Moreover, prior quality-aware incentive schemes are usually failed to preserve the privacy of task participants. Meanwhile, most existing privacy-preserving incentive schemes ignore the data quality of task participants. To tackle these issues, we propose a privacy-preserving and data quality-aware incentive scheme, called PACE. In particular, data quality consists of the reliability and deviation of data. Specifically, we first propose a zero-knowledge model of data reliability estimation that can protect data privacy while assessing data reliability. Then, we quantify the data quality based on the deviation between reliable data and the ground truth. Finally, we distribute monetary rewards to task participants according to their data quality. To demonstrate the effectiveness and efficiency of PACE, we evaluate it in a real-world dataset. The evaluation and analysis results show that PACE can prevent malicious behaviors of task participants and a task requester, and achieves both privacy-preserving and data quality measurement of task participants.

Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.

In aeromagnetic surveys, poor aircraft heading and weather may frequently cause a classical optically-pumped sensor to enter into or close to its dead zone, which results in unavoidable outliers that seriously reduce aeromagnetic compensation. To address these problems, a method to identify rapidly and pre-cull magnetic outliers based on the gray system theory is proposed to reduce their negative influence during the estimation of coefficients and target detection robustness. By constructing a gray region of aeromagnetic data and then checking whether the data at the end points of the region are normal, aeromagnetic outliers can be culled. The simulation results show that even if the outlier rate is increased to 20%, the average of the correct culling rate of the proposed method can still reach 99.67%, at which the culling effect is highly robust. We constructed an experimental survey platform and conducted a flight test. The results show that the improvement ratio of the proposed method can reach 4.36, which is 10.38 times higher than the conventional method.

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition.

With the rapid development of wireless networks and mobile devices, mobile crowdsensing (MCS) has enabled many smart city applications, which are key components in the Internet of Things. In an MCS system, the sufficient participation of mobile workers plays a significant role in the quality of sensing services. Therefore, researchers have studied various incentive mechanisms to motivate mobile workers in the literature. The existing works mostly focus on optimizing one objective function when selecting workers. However, some sensing tasks are associated with more than one objective inherently. This motivates us to investigate biobjective incentive mechanisms in this article. Specifically, we consider the scenario where the MCS system selects workers by optimizing the completion reliability and spatial diversity of sensing tasks. We first formulate the incentive model with two optimization goals and then design two online incentive mechanisms based on the reverse auction. We prove that the proposed mechanisms possess desirable properties, including computational efficiency, individual rationality, budget feasibility, truthfulness, and constant competitiveness. The experimental results indicate that the proposed incentive mechanisms can effectively optimize the two objectives simultaneously.

In mobile edge computing (MEC), it is challenging to offload tasks to appropriate edge nodes due to the heterogeneity in both tasks and edge nodes. Most existing task offloading mechanisms mainly aim at optimizing the global system performance, e.g., social welfare, while ignoring the personal preferences of the individual tasks and edge nodes. However, in an open MEC system, a task offloading decision is prone to be unstable if edge nodes or task owners have incentives to deviate from the decided allocation, and seek for alternative choices to improve their own utilities. In addition, to win the competition, task owners may gradually adjust their payments, which brings new challenge in achieving the stability of the system. To address the above issues, this paper constructs a distributed many-to-many matching model to capture the interaction between mobile tasks and edge nodes, with the consideration of their diverse resource requirements and availabilities. Based on this, we design both distributed and centralized stable matching based algorithms to jointly offload the tasks to edge nodes, and determine their payments. We prove that the proposed mechanisms achieve several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed schemes can get optimal social welfare, when the considered tasks are homogeneous in terms of their resource requirements. Finally, we conduct simulations to validate the effectiveness of the proposed work.

In mobile crowd sensing, task allocation is of vital importance, and it has attracted much attention in recent years. Though there have been many studies focusing on task allocation, rare works took sensing duration of tasks into consideration. However, sensing duration plays a key role for the success of many sensing tasks. For example, when the crowd sensing system needs to monitor the crowd flow in locations of interest, it is better to allocate this task to workers who can record a video of certain duration rather than those who can only take a picture. In this article, we try to solve this problem by designing a duration-sensitive task allocation model, where each task is associated with a specific sensing duration. The model aims at maximizing the number of completed tasks under the constraints of sensing duration and task capacity of each worker. To find an efficient task allocation scheme for the model, we design a utility function that can reflect the probability of task completion by using the exponential distribution. Then, an efficient greedy heuristic is proposed based on the utility function. Extensive evaluations based on the simulated and real-world datasets demonstrate that the proposed algorithm outperforms the baseline methods.

A reliable mobile crowdsensing (MCS) application usually relies on sufficient participants and trustworthy data. However, privacy concerns reduce participants’ willingness to participate in sensing tasks. The uncertainty of participant behavior and heterogeneity of sensing devices result in the unreliability of sensing data and further bring unreliable MCS services. Hence, it is crucial to estimate the reliability of sensing data and protect privacy. Unfortunately, most existing privacy-preserving data estimation solutions are designed for single-type data. In practice, however, heterogeneous sensing data are ubiquitous in data integration tasks. To this end, we propose a privacy-preserving reliability estimation solution of heterogeneous data for MCS, called IronM, which is effective for text, number, and multimedia data (e.g., image, audio, and video). Specifically, IronM first formulates the reliability assessment of text, number, and multimedia data as equality and range constraints, and then estimates the reliability of heterogeneous data through our proposed privacy-preserving hybrid constraints assessment mechanism. Privacy analysis demonstrates that IronM can not only evaluate the reliability of heterogeneous data but also protect data confidentiality. The experimental results in real-world datasets show the effectiveness and efficiency of IronM.

With the advancement of smartphones, mobile crowdsourcing has become a new computing paradigm to efficiently support novel mobile applications. Achieving good service quality of these applications, however, necessitates the participation of large number of smartphones, which can be obtained via providing suitable incentives to smartphone users. Nonetheless, most existing incentive mechanisms assume a centralized platform for recruiting smartphones, which is prone to expose the privacy of both smartphones and task requesters. In this context, this article studies a distributed truthful incentive mechanism (DTIM) for mobile crowdsourcing, where multiple auction rounds can be conducted locally in each smartphone and task requester. Specifically, in each auction round, the participating smartphones act as the sellers and submit bids to compete for their intended crowdsourcing tasks. The task requesters, on the other hand, act as the buyers that decide on the sellers and corresponding payments, depending on their submitted bids. Finally, based on the offered payment, each smartphone selects a buyer for trading to optimize its utility. It is shown that the proposed incentive mechanism is strategy-proof, budget balanced, individually rational, and computationally efficient. Numerical results provided corroborate the beneficial properties of the scheme.

Recently, cellular networks face a huge challenge to satisfy large increment of user demands for higher-speed and lower-latency communication service. One promising solution is to apply the cache technology in edge cache networks to reduce redundant data transmission. The cache technology is effective, but the cache capacity is limited. Researchers have proposed various cache strategies to efficiently utilize the limited cache capacity. However, most existing solutions have not taken into account the service capacity limitation of mobile devices. In this paper, we propose a cache replacement strategy for heterogeneous networks considering the limitation of service capacity and user mobility. In the cache replacement strategy, we utilize the user characteristics, such as user mobility and file popularity, to estimate the user demands, and then define the system cost. We formulate the cache strategy design as a mixed integer linear programming problem to minimize the system cost, and use Lagrangian relaxation and hierarchical primal-dual decomposition method to solve this problem. Numerical results show that the proposed cache strategy can significantly reduce the system cost and increase the cache hit ratio compared to the cache strategy that does not consider the limitation of user service capacity.

Facial appearance transfer (FAT) is a critical component of various facial editing tasks. It aims to transfer the facial appearance of a reference into a target with good visual consistency. When there are considerable visual differences between a reference and a target, however, it may introduce visual artifacts into the results. To tackle this problem, we propose a facial appearance map with illumination-aware and region-aware properties that allows seamless FAT. We formulate the appearance-map generation as label propagation (LP) on a similarity graph, and propose a new regularization structure to facilitate the adaptive appearance-map diffusion. Solving the original LP model of appearance map in general requires on the order $O(kn^2)$ time for an $n$ -nodes graph where each node has $k$ neighbors. It may be computationally prohibitive for an image with a large spatial resolution. To tackle this problem, we mathematically analyze the graph-based LP model and propose a fast algorithm with smart subset sampling. It selects a subset with $m$ nodes of the graph with $n$ nodes ( $m\ll n$ ) to approximate the solution to the original system, which significantly reduces its computational requirements from $O(kn^2)$ to $O(m^2n)$ . Based on the adaptive LP-based appearance map, we construct a framework to achieve various editing effects with FAT, including face replacement, face dubbing, face swapping, and transfiguring. Comparisons with related methods show the effectiveness of the adaptive LP model for FAT. Qualitative and quantitative evaluations verify the computational improvements of the approximation algorithm.

Online social networks (OSNs) are becoming more and more prevalent in people’s life, but they face the problem of privacy leakage due to the centralized data management mechanism. The emergence of distributed OSNs (DOSNs) can solve this privacy issue, yet they bring inefficiencies in providing the main functionalities, such as access control and data availability. In this article, in view of the above-mentioned challenges encountered in OSNs and DOSNs, we exploit the emerging blockchain technique to design a new DOSN framework that integrates the advantages of both traditional centralized OSNs and DOSNs. By combining smart contracts, we use the blockchain as a trusted server to provide central control services. Meanwhile, we separate the storage services so that users have complete control over their data. In the experiment, we use real-world data sets to verify the effectiveness of the proposed framework.

The emerging mobile crowdsensing applications are able to facilitate people's life in various aspects. A key factor to ensure that these applications can provide high-quality service is the sufficient participation of normal smartphone users. Therefore, a lot of effort has been made to design incentive mechanisms to motivate users to participate. Most of these works assume that users are associated with homogeneous costs across the whole sensing area, based on which many utility optimization models are proposed. In this paper, we consider the scenario where smartphone users have heterogeneous costs across the sensing area, i.e., users in different regions have different cost distributions. In this scenario, traditional mechanisms may generate sensing holes and recruit insufficient users in some regions with higher costs, which may lead to unsatisfactory service. To accommodate this issue, we propose two optimization models, which aim at maximizing the user cardinality and the sensing utility function for each region of the whole sensing area, respectively. We then design effective incentive mechanisms, which possess the desirable properties, including computational efficiency, individual rationality, budget feasibility, truthfulness, and good competitiveness. By conducting extensive experiments on the real-world geographical dataset, we demonstrate the effectiveness of the proposed mechanisms in achieving the good quality of service.

The large quantity of mobile devices equipped with various built-in sensors and the easy access to the high-speed wireless networks have made spatial crowdsourcing receive much attention in the research community recently. Generally, the objective of spatial crowdsourcing is to outsource location-based sensing tasks (e.g., traffic monitoring and pollution monitoring) to ordinary mobile workers (e.g., users carrying smartphones) efficiently. In this paper, we study a reliable task assignment problem for spatial crowdsourcing in a large worker market. Specifically, we use worker confidence to represent the reliability of successfully completing the assigned sensing tasks, and we formulate two optimization problems, maximum reliability assignment (MRA) under a recruitment budget and minimum cost assignment (MCA) under a task reliability requirement. We reveal the special structure properties of these problems, based on which we design effective approaches to assign tasks to the most suitable workers. The performances of the proposed algorithms are verified by theoretic analysis and experimental results on both real and synthetic datasets.

Mobile crowdsourcing is an emerging complex problem solving paradigm that makes use of pervasive mobile devices equipped with multi-functional sensors. Recently, vehicles have also been increasingly adopted for mobile crowdsourcing, as the vehicles, as well as drivers, can provide diverse sensing capability and predictable mobility. Existing mobile crowdsourcing algorithms mostly recruit workers to complete one kind of sensing tasks, i.e., location-based query tasks or automatic sensing tasks. In this paper, we investigate the possibility of recruiting a set of vehicles to simultaneously complete these two categories of tasks, so as to maximize the sensing utility of each participant. We first model the worker recruitment for vehicle-based crowdsourcing as a bi-objective optimization problem with respect to the sensing capability and predictable mobility of vehicles. The recruitment problem is proven to be NP-hard, and we design two heuristic algorithms based on the bi-objective greedy strategy and the multi-objective genetic algorithm to find the solutions. The experimental results with a real-world traffic trace data set show that the proposed algorithms outperform some existing algorithms in finding solutions that maximize both objectives.

Ubiquitous smartphones with various built-in sensors have boosted development of many crowdsensing applications in recent years. To achieve good performance with this class of applications, a large number of participating users with sensed data are needed. Two important considerations for improving user participation in crowdsensing are the incentive mechanism for motivating users to contribute their sensing capabilities, and the techniques for protecting user privacy. In this article, we survey the works that address these issues by integrating privacy techniques with incentive mechanisms. We also advocate two future research directions to enhance incentive mechanisms for mobile crowdsensing.

While location is one of the most important context information in mobile and pervasive computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose MPiLoc, a multi-floor indoor localization system that utilizes data contributed by smartphone users through participatory sensing for automatic floor plan and radio map construction. Our system does not require manual calibration, prior knowledge, or infrastructure support. The key novelty of MPiLoc is that it clusters and merges walking trajectories annotated with sensor and signal strengths to derive a map of walking paths annotated with radio signal strengths in multi-floor indoor environments. We evaluate MPiLoc over five different indoor areas. Evaluation shows that our system can derive indoor maps for various indoor environments in multi-floor settings and achieve an average localization error of 1.82 m.

Spatial crowdsourcing and crowdsensing are two emerging crowdsourcing paradigms, which enable a variety of location-based query and sensing tasks. In spatial crowdsourcing, mobile workers are required to travel physically to target locations in order to complete query tasks. Most existing work, hence, has focused on designing efficient query task assignment schemes to maximize the task completion rate under traveling constraints of workers for spatial crowdsourcing systems. In crowdsensing, on the other hand, sensor recordings of workers’ smartphones are of interest and have been collected to build various applications. Therefore, work concerning crowdsensing has strived to maximize the coverage area of sensor trajectories by selecting a set of workers. In this paper, we investigate the integration of these two paradigms. We notice a key link between these paradigms: While a worker is traveling to the target location of a query task, his trajectory may provide valuable coverage for a sensing task. Therefore, we propose a task management framework, named SpatialRecruiter, to efficiently match workers to the merged query and sensing tasks. We propose two coverage estimation functions to compute the coverage potential of a worker. Then, we design a greedy heuristic to select and assign workers. The experimental results on a real-world dataset demonstrate that the proposed strategies are efficient and effective in meeting the requirements of both paradigms.

Mobile crowdsensing systems aim to provide various novel applications by employing pervasive smartphones. A key factor to enable such systems is substantial participation of normal smartphone users, which requires effective incentive mechanisms. In this paper, we investigate incentive mechanisms for online scenarios, where users arrive and interact with a task requester in a random order, and they have preferences (e.g., photographing) or limits (e.g., travel distance) over the sensing tasks. In existing online mechanisms, the task requester has limited power in assigning tasks to the selected users, i.e., it has to pay for all of the tasks specified by the selected users, although some of these tasks are of little value. To accommodate this, we investigate a more flexible setting, where the requester can actively assign most valuable tasks to the selected users. We design two online incentive mechanisms motivated by a sampling-accepting process and weighted maximum matching. We prove that the designed mechanisms achieve computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness. By carrying out extensive experiments on two real-world geographical datasets, we demonstrate the practical applicability of the proposed mechanisms.

Destination prediction is very important in location-based services such as recommendation of targeted advertising location. Most current approaches always predict destination according to existing trip based on history trajectories. However, no existing work has considered the difference between the effects of passing-by locations and the destination in history trajectories, which seriously impacts the accuracy of predicted results as the destination can indicate the purpose of traveling. Meanwhile, the temporal information of history trajectories in destination prediction plays an important role. On one hand, the history trajectories in different periods also differ in the influence, e.g., the history trajectories from last week can reflect the status quo more accurately than the history trajectories two years ago. On the other hand, the history trajectories in different time slots reflect different facts of traffic and moving habits of people, e.g., visiting a restaurant in the daytime and visiting a bar at night. Although a huge amount of history trajectories can be achieved in the era of big data, it is still far from covering all the query trajectories since a road network is widely distributed and trajectory data is sparse. The temporal sensitivity of history trajectories highlights the sparsity problem even more. Therefore, we propose a novel model $\text{T-DesP}$ to solve the aforementioned problems. The model is comprised of two modules: trajectory learning and destination prediction. In the module of trajectory learning, a novel method called the mirror absorbing Markov chain model is proposed for modeling the trajectories for isolating the destination. We build a transition tensor to deduce the transition probability between each location pair in a particular time slot. To address the data sparsity problem, we fill the missing values in transition tensor through a context-aware tensor decomposition approach. In the module of destination prediction, an absorbing tensor is derived from the filled transition tensor, and the theoretical model is established for destination prediction. The experiments prove the effectiveness and efficiency of $\text{T-DesP}$ .

Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.

A great many online users, with diverse backgrounds, act as powerful resources that mobile social networks (MSNs) can utilize for crowdsourcing. Exploiting these online users as crowd workers is promising yet nontrivial. To efficiently leverage human intelligence or crowd wisdom, we need to address the following issues: 1) how to motivate users to participate and 2) how to discourage malicious behaviors, such as copying answers or making random guesses. Furthermore, as low-quality answers may degrade the accuracy of synthetic results sharply, the last issue is how to weed them out. In this paper, we present MacroWiz, a simple yet effective framework to manage crowd wisdom on MSNs. Given a crowdsourcing task, MacroWiz motivates online users to contribute their knowledge or opinions, and assists the task requester in collecting answers, selecting reliable ones, and making ultimate decisions. The platform consists of two functional units: wisdom collection and answer selection. The former estimates and gathers the minimum number of answers required to satisfy the task requirement conservatively, while the latter analyzes the accuracy, the effectiveness, and the cost of each answer, based on which it selects those with high accuracy and low cost by solving a dual-objective optimization problem. We validate the effectiveness of our platform by using MovieLens data sets, which contain over one million anonymous ratings of movies. The experimental results show that MacroWiz significantly reduces the latency in making decisions and provides high-quality answers with low cost.

Mobile sensing apps have proliferated rapidly over the recent years. Most of them rely on inference components heavily for detecting interesting activities or contexts. Existing work implements inference components using traditional models designed for balanced data sets, where the sizes of interesting (positive) and non-interesting (negative) data are comparable. Practically, however, the positive and negative sensing data are highly imbalanced. For example, a single daily activity such as bicycling or driving usually occupies a small portion of time, resulting in rare positive instances. Under this circumstance, the trained models based on imbalanced data tend to mislabel positive ones as negative. In this paper, we propose a new inference framework SLIM based on several machine learning techniques in order to accommodate the imbalanced nature of sensing data. Especially, guided under-sampling is employed to obtain balanced labelled subsets, followed by a similarity-based sampling that draws massive unlabelled data to enhance training. To the best of our knowledge, SLIM is the first model that considers data imbalance in mobile sensing. We prototype two sensing apps and the experimental results show that SLIM achieves higher recall (activity recognition rate) while maintaining the precision compared with five classical models. In terms of the overall recall and precision, SLIM is around $12$ percent better than the compared solutions on average.

Off-the-shelf smartphones have boosted large scale participatory sensing applications as they are equipped with various functional sensors, possess powerful computation and communication capabilities, and proliferate at a breathtaking pace. Yet the low participation level of smartphone users due to various resource consumptions, such as time and power, remains a hurdle that prevents the enjoyment brought by sensing applications. Recently, some researchers have done pioneer works in motivating users to contribute their resources by designing incentive mechanisms, which are able to provide certain rewards for participation. However, none of these works considered smartphone users’ nature of opportunistically occurring in the area of interest. Specifically, for a general smartphone sensing application, the platform would distribute tasks to each user on her arrival and has to make an immediate decision according to the user’s reply. To accommodate this general setting, we design three online incentive mechanisms, named TBA, TOIM and TOIM-AD, based on online reverse auction. TBA is designed to pursue platform utility maximization, while TOIM and TOIM-AD achieve the crucial property of truthfulness. All mechanisms possess the desired properties of computational efficiency, individual rationality, and profitability. Besides, they are highly competitive compared to the optimal offline solution. The extensive simulation results reveal the impact of the key parameters and show good approximation to the state-of-the-art offline mechanism.

Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations.

Due to slow advance in battery technology, power remains a bottleneck to limit wide applications of mobile ad hoc and wireless sensor networks. Among all extensive studies on minimizing power consumption, neighbor discovery is one of the fundamental components focusing on communication and access. This work surveys research literature on neighbor discovery protocols (NDPs). In general, they can be roughly classified by four underlying principles: randomness, over-half occupation, rotation-resistant intersection, and coprime cycles. The Birthday protocols act as representatives of NDPs using randomness, in which a node decides to listen, transmit, or sleep with probabilities. The original idea of over-half occupation is to be active over at least half of each period, though several refinements have been proposed to decrease its high duty cycle. Methods of rotation-resistant intersection formulate the problem of discovery using combinatorial characteristics of discrete time slots, and guarantee discovery at least once per period. Moreover, neighbor discovery can also be guaranteed within a worst-case bound, as shown by methods adopting coprime cycles. In this paper, we elaborate on these ideas and present several representative protocols, respectively. In particular, we give an integrative analysis of deterministic protocols via a generic framework. A qualitative comparison incorporating multiple criteria and a quantitative evaluation on energy efficiency are also included. Finally, we point out promising research directions towards energy-efficient neighbor discovery.