A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

Mobile CrowdSensing (MCS), through employing considerable workers to sense and collect data in a participatory manner, has been recognized as a promising paradigm for building many large-scale applications in a cost-effective way, such as combating COVID-19. The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies assume that the qualities of workers are known in advance, or the platform knows the qualities of workers once it receives their collected data. In reality, to reduce their costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform. So, it is very hard for the platform to evaluate the authenticity of the received data. In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem, and design an UCB-based algorithm to separate the exploration and exploitation, considering the Sensing Rates (SRs) of recruited workers as the gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We prove that our SCMABA achieves truthfulness and individual rationality. Additionally, we exhibit outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.Comment: 18 pages, 14 figure

CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie

Computational Analysis of Urban Places Using Mobile Crowdsensing

In cities, urban places provide a socio-cultural habitat for people to counterbalance the daily grind of urban life, an environment away from home and work. Places provide an environment for people to communicate, share perspectives, and in the process form new social connections. Due to the active role of places to the social fabric of city life, it is important to understand how people perceive and experience places. One fundamental construct that relates place and experience is ambiance, i.e., the impressions we ubiquitously form when we go out. Young people are key actors of urban life, specially at night, and as such play an equal role in co-creating and appropriating the urban space. Understanding how places and their youth inhabitants interact at night is a relevant urban issue. Until recently, our ability to assess the visual and perceptual qualities of urban spaces and to study the dynamics surrounding youth experiences in those spaces have been limited partly due to the lack of quantitative data. However, the growth of computational methods and tools including sensor-rich mobile devices, social multimedia platforms, and crowdsourcing tools have opened ways to measure urban perception at scale, and to deepen our understanding of nightlife as experienced by young people. In this thesis, as a first contribution, we present the design, implementation and computational analysis of four mobile crowdsensing studies involving youth populations from various countries to understand and infer phenomena related to urban places and people. We gathered a variety of explicit and implicit crowdsourced data including mobile sensor data and logs, survey responses, and multimedia content (images and videos) from hundreds of crowdworkers and thousands of users of mobile social networks. Second, we showed how crowdsensed images can be used for the computational characterization and analysis of urban perception in indoor and outdoor places. For both place types, urban perception impressions were elicited for several physical and psychological constructs using online crowdsourcing. Using low-level and deep learning features extracted from images, we automatically inferred crowdsourced judgments of indoor ambiance with a maximum R2 of 0.53 and outdoor perception with a maximum R2 of 0.49. Third, we demonstrated the feasibility to collect rich contextual data to study the physical mobility, activities, ambiance context, and social patterns of youth nightlife behavior. Fourth, using supervised machine learning techniques, we automatically classified drinking behavior of young people in an urban, real nightlife setting. Using features extracted from mobile sensor data and application logs, we obtained an overall accuracy of 76.7%. While this thesis contributes towards understanding urban perception and youth nightlife patterns in specific contexts, our research also contributes towards the computational understanding of urban places at scale with high spatial and temporal resolution, using a combination of mobile crowdsensing, social media, machine learning, multimedia analysis, and online crowdsourcing

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

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

Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing

Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respective trajectories have different values, and we can select participants who can collect high-value data based on their trajectory predictions. In this paper, we study two aspects of user trajectory prediction and user recruitment. First, we propose an STGCN-GRU user trajectory prediction algorithm, which uses the STGCN algorithm to extract features related to temporal and spatial information from the trajectory map, and then inputs the feature sequences into GRU for trajectory prediction, and this algorithm improves the accuracy of user trajectory prediction. Second, an ADQN (action DQN) user recruitment algorithm is proposed.The ADQN algorithm improves the objective function in DQN on the idea of reinforcement learning. The action with the maximum input value is found from the Q network, and then the output value of the objective function of the corresponding action Q network is found. This reduces the overestimation problem that occurs in Q networks and improves the accuracy of user recruitment. The experimental results show that the evaluation metrics FDE and ADE of the STGCN-GRU algorithm proposed in this paper are better than other representative algorithms. And the experiments on two real datasets verify the effectiveness of the ADQN user selection algorithm, which can effectively improve the accuracy of data inference under budget constraints

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

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

Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establish a new and fastgrowing sensing paradigm to satisfy this need, which is called Mobile Crowd Sensing (MCS). The MCS uses different sensing abilities to acquire local knowledge through enhanced mobile devices. In MCS, it is very important to collect high-quality sensory data that satisfies the needs of all assigned tasks and the task organizers with a minimum cost for the participants. One of the most important factors which affect the MCS cost is how to schedule different sensing tasks which must be assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring high-quality sensory data. In this paper, the problem of task scheduling the which have mutual sensor is formulated and a scheduling method to minimize the energy consumption by reducing the sensor utilization is proposed. The proposed method will incentive the users to participate in multiple tasks at the same time, which minimizes the total cost of the performed tasks and increases his rewards. The experimental results by using synthetic and real data show that the proposed scheduling method can minimize the energy consumption and preserve the task requirements compared to existing algorithms