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Welcome to the website of our research project: "Modeling and Analysis of Green Mobile Crowd Sensing". This website is created and maintained to disseminate and share research results and other information related to the project.

Project Description

Mobile crowd sensing (MCS) arises as a new sensing paradigm based on the power of the crowd together with the ever-increasing sensing capabilities of various mobile devices. As carrying out sensing tasks can deplete the energy of battery-powered mobile devices quickly, this concern largely affects the wide deployment of MCS. Therefore, how to enhance the energy efficiency in MCS is an imperative and challenging task. Inspired by recent advances in wireless networking and energy harvesting techniques, the proposed research aims to develop joint sensing task computation and communication framework to achieve green MCS for various sensing tasks. The research project and activities have significant potential to better support newly emerging MCS applications such as healthcare, environment monitoring, traffic monitoring, social behavior monitoring, etc. The research results are expected to inspire other theoretical and systematic studies to contribute to the networking design and energy management aspects of developing energy-efficient MCS. The project plans to engage female and under-represented minority students in the research activities. The results of the project will be disseminated through publications and talks.

This project has an exciting two-year research plan focusing on fundamental challenges associated with modeling and analyzing green MCS. Observing that the energy consumed in task processing and its distribution correlates to each other, a unified framework to jointly model the energy consumption in computation and communication is proposed to strike a balance between the two to achieve energy efficiency. As renewable energy has emerged as a feasible alternative to the traditional energy sources, it is incorporated in the sensing crowd so as to decrease the on-grid energy demand from sensing devices. Dynamic energy optimization problems are then investigated to minimize energy expenditure in supporting performance-guaranteed sensing tasks, by comprehensively considering time-varying computing resource allocation, renewable energy supply, and wireless channel conditions. Moreover, some sensing devices are envisioned to be capable of transferring extra harvested renewable energy to others nearby, so as to fully explore the vacant energy and computation resources in MCS. Finally, in order to stimulate mobile devices to join MCS, incentive mechanisms and heterogeneous auction markets are developed.

Figure: General architecture of the mobile crowd sensing network.

 

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Personnel

Principal Investigator

Dr. Ming Li (Lead PI) Assistant Professor Department of Computer Science and Engineering The University of Texas at Arlington Email: ming.li@uta.edu Homepage: {{ site.baseurl }}/

Current Graduate Students

Mingyan Xiao Ph.D. student Department of Computer Science and Engineering The University of Texas at Arlington Email:mingyan.xiao@mavs.uta.edu Homepage:

Wenqiang Jin Ph.D. student Department of Computer Science and Engineering The University of Texas at Arlington Email: wenqiang.jin@mavs.uta.edu Homepage:

 

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Publications

1. STEP: A Spatio-Temporal Fine-Granular User Traffic Prediction System for Cellular Networks ,
   Lixing Yu, Ming Li, Wenqiang Jin, Yifan Guo, Qianlong Wang, Feng Yan, and Pan Li,
   IEEE Transactions on Mobile Computing (TMC), 2020.
   
   Summary: While traffic modeling and prediction are at the heart of providing high-quality telecommunication services in cellular networks and attract much attention, they have been approved as an extremely challenging task. Due to the diverse network demand of Internet-based apps, the cellular traffic from an individual user can have a wide dynamic range. Most existing methods, on the other hand, model traffic patterns as probabilistic distributions or stochastic processes and impose stringent assumptions over these models. Such assumptions may be beneficial at providing closed-form formula in evaluating prediction performances, but fall short for practice use. In this paper we propose STEP, a spatio-temporal fine-granular user traffic prediction mechanism for cellular networks. A deep graph convolution network, called GCGRN, is constructed. It is a novel combination of the graph convolution network (GCN) and gated recurrent units (GRU), which exploits graph neural network to learn an efficient spatio-temporal model from a user's massive dataset for traffic prediction. Extensive experimental results demonstrate that our model outperforms the state-of-the-art time-series based approaches. Besides, STEP merely incurs mild energy consumption, communication overhead and system resource occupancy to mobile devices. NS-3 based simulations validate the efficacy of STEP in reducing session dropping ratio in cellular networks.
   
   
2. Data-Driven Spectrum Trading with Secondary Users' Differential Privacy Preservation ,
   Jingyi Wang, Qixun Zhang, Ming Li, Yuanxiong Guo, Zhiyong Feng, and Miao Pan,
   IEEE Transactions on Dependable and Secure Computing (TDSC), 2019.
   
   Summary: Mobile crowd sensing (MCS) is a technique where sensing tasks are outsourced to a crowd of mobile users. Since most of sensing tasks are location-dependent, workers are required to embed their locations into sensing reports, which incurs location privacy vulnerabilities. Realizing that workers perceive their location privacy differently, in this work we construct an auction-based trading market, facilitating location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform based on its own location privacy leakage budget $\xi$. The higher $\xi$ is, the less secrecy its reported location preserves. As a result, it receives higher payment from the platform as a compensation to its privacy loss. Besides, our mechanism enables the platform to select a suitable set of winning workers to achieve desirable service accuracy. For this purpose, a heuristic algorithm is devised, with polynomial-time complexity and bounded optimality gap. As formally proved in this manuscript, our proposed mechanism guarantees a series of nice properties, including $\xi$-privacy, $(\alpha, \beta)$-accuracy, and budget feasibility.
   
   
3. If You Do Not Care About It, Sell It: Trading Location Privacy in Mobile Crowd Sensing ,
   Wenqiang Jin, Mingyan Xiao, Ming Li, and Linke Guo,
   Proceedings of IEEE International Conference on Computer Communications (INFOCOM'19).
   
   Summary: Mobile crowd sensing (MCS) is a technique where sensing tasks are outsourced to a crowd of mobile users. Since most of sensing tasks are location-dependent, workers are required to embed their locations into sensing reports, which incurs location privacy vulnerabilities. Realizing that workers perceive their location privacy differently, in this work we construct an auction-based trading market, facilitating location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform based on its own location privacy leakage budget $\xi$. The higher $\xi$ is, the less secrecy its reported location preserves. As a result, it receives higher payment from the platform as a compensation to its privacy loss. Besides, our mechanism enables the platform to select a suitable set of winning workers to achieve desirable service accuracy. For this purpose, a heuristic algorithm is devised, with polynomial-time complexity and bounded optimality gap. As formally proved in this manuscript, our proposed mechanism guarantees a series of nice properties, including $\xi$-privacy, $(\alpha, \beta)$-accuracy, and budget feasibility.
   
   
4. Securing Task Allocation in Mobile Crowd Sensing: An Incentive Design Approach,
   Mingyan Xiao, Ming Li, Linke Guo, Miao Pan, Zhu Han and Pan Li,
   Proceedings of IEEE Conference on Communications and Network Security (CNS'19).
   
   Summary: As a critical component of mobile crowd sensing (MCS), task allocation has been extensively investigated. In general, it addresses how to wisely distribute sensing tasks among sensing workers. Yet, the security threat involved therein has hardly been studied. In an ideal scenario, workers are trusted to report their accurate parameters to the platform, so that task allocation optimization problems can be correctly formulated and calculated. Nonetheless, malicious workers can explore illegal benefit gain by simply uploading falsified parameters. Even worse, such an attack is difficult to detect. In this paper, we start from a simplified case in which workers report erroneous objective functions to gain extra utility. To defend this attack, we novelly leverage incentive mechanism design. Workers are motivated to report desirable ''indicators'', based on which the platform can still obtain the accurate task allocation profile even without workers' genuine parameters. The effectiveness and efficiency of our mechanism is validated through both formal analysis and extensive simulation results.
   
   
5. Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing
   Lei Yang, Mengyuan Zhang, Shibo He, Ming Li, and Junshan Zhang,
   ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'18).
   
   Summary: We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for a sensing task. In this framework, the workers are allowed to report privacy-preserving versions of their data to protect their data privacy; and the platform selects workers based on their sensing capabilities, which addresses the drawbacks of game-theoretic models that cannot ensure the accuracy level of the aggregated result, due to the existence of multiple Nash Equilibria. Observe that in this auction based framework, there exists externalities among workers’ data privacy, because the data privacy of each worker depends on both her injected noise and the total noise in the aggregated result that is intimately related to which workers are selected to fulfill the task. To achieve adesirable accuracy level of the data aggregation in a cost-effective manner, we explicitly characterize the externalities, i.e., the impact of the noise added by each worker on both the data privacy and the accuracy of the aggregated result. Further, by exploring the problem structure, we discover the hidden monotonicity property of the problem and determine the critical bid of workers, which makes it possible to design a truthful, individually rational and computationally efficient incentive mechanism. The proposed incentive mechanism can find a set of workers to approximately minimize the cost of purchasing private sensing data from workers subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.
   
   
6. Motivating Human-Enabled Mobile Participation for Data Offloading,
   Xiaonan Zhang, Linke Guo, Ming Li, and Yuguang Fang,
   IEEE Transactions on Mobile Computing (TMC), Vol. 17, No. 7, pp. 1624-1637, July 2018.
   
   Summary: The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps. However, the ever-increasing data traffic has exacerbated the congestion on current cellular networks, which results in users’ dissatisfaction, especially in crowded areas. Hence, how to alleviate data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic originally targeted to cellular networks, such as the small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of users and the social features among a group of users. A mobile caching user, who pre-caches a certain amount of contents, will roam around congested areas to participate in content dissemination in order to satisfy users’ requests, which is expected to benefit both himself and users in the crowd simultaneously. To motivate such human-enabled mobile participation for data offloading, a Stackelberg game is deployed with joint considerations on social effect and delay effect. Based on detailed performance analysis, we demonstrate the feasibility and efficiency of the proposed approach.
   
   
7. DPDA: A Differentially Private Double Auction Scheme for Mobile Crowd Sensing,
   Wenqiang Jin, Ming Li, Linke Guo, and Lei Yang,
   IEEE Conference on Communications and Network Security (CNS'18).
   
   Summary: 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 exponential 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.
   
   
8. Economic-Robust Transmission Opportunity Auction for D2D Communications in Cognitive Mesh Assisted Cellular Networks,
   Ming Li, Weixian Liao, Xuhui Chen, Jinyuan Sun, Xiaoxia Huang, and Pan Li,
   IEEE Transactions on Mobile Computing (TMC), pp. 1-1, December 2017.
   
   Summary: Device-to-device (D2D) communications can potentially alleviate cellular network congestion by utilizing local available links, and have attracted intensive attention recently. Cognitive radio (CR) allows users to opportunistically access unused licensed spectrum. It thus serves as a great candidate technology for D2D communications, but has not been widely employed in cellular networks due to hardware development limitations. In this paper, we propose a new architecture, called cognitive mesh assisted cellular network (CMCN), in which several secondary service providers (SSPs) deploy CR routers to facilitate D2D communications among wireless users. To address the competition among the SSPs, we further construct a secondary spectrum auction market. Although a few works have studied spectrum auction, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Uncertain spectrum availability is not considered in previous schemes either. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can address these problems. Extensive simulations are conducted to validate the efficiency of the CMCN architecture and that of the TOA scheme.
   
   
9. Pricing, Caching Selection, and Content Delivery in Wireless Networks: A Hierarchical Approach,
   Heli Zhang, Bowen Liu, Ming Li, Hong Ji, Xi Li, and Victor Leung,
   IEEE Global Communications Conference (GLOBECOM'17).
   
   Summary: Caching content at the edge of the network is a promising way to improve content delivery efficiency. In most existed research, content caching strategies are typically designed to maximize local hit rates, improve energy efficiency or reduce network cost. However, this metric cannot guarantee the utility of content providers (CPs). To encounter with this challenge, we construct a hierarchical content distribution problem, within this which, two layers are included called caching selection and content delivery, respectively. The former layer intends to find content caching places, i.e. service providers(SPs) for contents while guaranteeing CP’s utility, and the latter layer utilizes multiseller multi-buyer multi-content trading auction (MMMTA) to characterize the competition between the SPs and users. To solve the proposed problem, a hierarchical content distribution iteration (HCDI) method is designed. Various simulation results show the property of the proposed scheme.
   
   
10. Energy-efficient Autonomic Offloading in Mobile Edge Computing,
    Changqing Luo, Sergio Salinas, Ming Li, and Pan Li,
    IEEE Digital Avionics Systems Conference (DASC'17).
    
    Summary: The booming growth and popularity of mobile devices have led to the surge of various mobile applications. Many mobile applications, such as online video, gaming, are essentially computation-intensive, and hence can quickly deplete mobile devices’ battery energy. To address this issue, academia and industry have proposed mobile edge computing (MEC) that can enable mobile devices to automatically offload computations to the edge servers located within the radio access networks of cellular operators. However, energy-hungry wireless communications incur extra energy consumption that may offset the energy saving due to computation offloading. To this end, we design an energy-efficient autonomic offloading scheme by jointly considering the physical layer design and application running latency. Specifically, we first mathematically model the energy consumption of a mobile application in MEC environment by taking into account the energy consumption incurred by the interactions among the tasks for the same application, which is largely ignored by previous studies. Then, we identify task execution flows based on a task interaction matrix, and formulate the maximum of the taskflow’s latencies as the application’s latency. Finally, we formulate an energy efficient offloading problem, which is generally NP-hard, and develop an efficient heuristic method to solve the problem. We present extensive simulation results to show that our proposed scheme can achieve significant reduction (up to 20% around) in energy consumption compared with previous schemes.
    
    
11. Energy-source-aware cost optimization for green cellular networks with strong stability,
    Weixian Liao, Ming Li, Sergio Salinas, Pan Li, and Miao Pan,
    IEEE Transactions on Emerging Topics in Computing (TETC), Vol. 4, No. 4, pp. 541-555, October 2016.
    
    Summary: Last decade witnessed the explosive growth in mobile devices and their traffic demand, and thereby the significant increase in the energy cost of the cellular service providers. One major component of the service providers’ operational expenditure comes from the operation of cellular base stations using grid power or diesel generators when grid power is absent, which also causes adverse environmental impact due to enormous carbon footprint. Therefore, from the service providers’ perspective, how to effectively reduce the energy cost of base stations while satisfying cellular users’ soaring traffic demands has become an imperative and challenging problem. In this paper, we investigate the minimization of the long-term time-averaged expected energy cost of cellular service providers while guaranteeing the strong stability of the network. In particular,we firstformulate the problemby jointly considering flow routing, link scheduling, and energy (i.e.,renewable energy resource, energy storage unit,and soon) constraints. Since the formulated problem is a time-coupling stochastic mixed-integer nonlinear programming problem, which is prohibitively expensive to solve, we reformulate the problem by employing Lyapunov optimization theory. A decomposition-based algorithm is developed to solve the problem and the network strong stability is proven. We then derive and prove both the lower and the upper bounds on the optimal result of the original problem. Simulation results demonstrate the tightness of the obtained bounds and the efficacy of the proposed scheme.
    
    
12. Social-enabled data offloading via mobile participation-a game-theoretical approach,
    Xiaonan Zhang, Linke Guo, Ming Li, and Yuguang Fang,
    IEEE Global Communication Conferences (GLOBECOM'16).
    
    Summary: The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps, such as social networking, mobile video services, and location-based services, etc. However, the ever-increasing data traffic has exacerbated congestion on current cellular networks, which results in users’ dissatisfaction, especially in crowded areas. Hence, how to deal with the explosive data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic targeted to cellular networks, such as small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users will still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of mobile users and the social features among a group of users. A mobile caching user, who precaches certain amount of contents, can roam around congested areas to participate in data dissemination in order to satisfy users’ requests, which can benefit both herself and users in the crowd simultaneously. Therefore, we propose a game theoretical approach to analyze the data offloading via mobile participation with joint considerations on users’ content requests, network effect brought by their social features, congestion effect, and pricing strategy. Based on detailed performance analysis, we show the feasibility and efficiency of the proposed approach.
    
    
13. Privacy-Preserving Data Aggregation over Incomplete Data for Crowdsensing,
    Iman Vakinilia, Jiajun Xin, Ming Li, and Linke Guo,
    IEEE Global Communications Conference (GLOBECOM'16).
    
    Summary: Crowdsensing recently attracts great attention from both industry and academia. By fusing and analyzing multidimensional sensing data collected from crowdsensing users, it is possible to support health caring, environment mentoring, traffic mentoring and social behavior mentoring. Nonetheless, how to preserve users’ data privacy during data fusing, e.g., data aggregation, has been rarely discussed for crowdsensing before. Besides, due to the dynamics of sensing environments and available resources at users, there will be missing elements from users’ sensing results. In this paper we aim to achieve privacy-preserving data aggregation over incomplete data for crowdsensing. A novel scheme is developed based on linear transformation and homomorphic encryption scheme. It enables the server to obtain aggregation results over recovered sensing results without learning their individual details. Security analysis and performance evaluation are conducted showing the effectiveness and efficiency of our scheme.
    
    
14. Privacy-preserving Verifiable Data Aggregation and Analysis for Cloud-assisted Mobile Crowdsourcing,
    Gaoqiang Zhuo, Qi Jia, Linke Guo, Ming Li, and Pan Li,
    IEEE International Conference on Computer Communications (INFOCOM'16).
    
    Summary: Crowdsourcing is a crowd-based outsourcing, where a requester (task owner) can outsource tasks to workers (public crowd). Recently, mobile crowdsourcing, which can leverage workers’ data from smartphones for data aggregation and analysis, has attracted much attention. However, when the data volume is getting large, it becomes a difficult problem for a requester to aggregate and analyze the incoming data, especially when the requester is an ordinary smartphone user or a start-up company with limited storage and computation resources. Besides, workers are concerned about their identity and data privacy. To tackle these issues, we introduce a three-party architecture for mobile crowdsourcing, where the cloud is implemented between workers and requesters to ease the storage and computation burden of the resource-limited requester. Identity privacy and data privacy are also achieved. With our scheme, a requester is able to verify the correctness of computation results from the cloud. We also provide several aggregated statistics in our work, together with efficient data update methods. Extensive simulation shows both the feasibility and efficiency of our proposed solution.
    
    
15. Privacy-Preserving Verifiable Set Operation in Big Data for Cloud-assisted Mobile Crowdsourcing,
    Gaoqiang Zhuo, Qi Jia, Linke Guo, Ming Li, and Pan Li,
    IEEE Internet of Things Journal (IoT), Vol. 4, No. 2, pp. 572-582, June 2016.
    Summary: The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester (task owner) can crowdsource data from the workers (smartphone users) by using their sensor-rich mobile devices. However, data collection, data aggregation, and data analysis have become challenging problems for a resource constrained requester when data volume is extremely large, i.e., big data. In particular to data analysis, set operations, including intersection, union, and complementation, exist in most big data analysis for filtering redundant data and preprocessing raw data. Facing challenges in terms of limited computation and storage resources, cloud-assisted approaches may serve as a promising way to tackle big data analysis issue. However, workers may not be willing to participate if the privacy of their sensing data and identity are not well preserved in the untrustedcloud.Inthiswork,weproposetousecloudtocompute set operation for the requester, at the same time workers’ data privacy and identities privacy are well preserved. Besides, the requester can verify the correctness of set operation results. We also extend our scheme to support data preprocessing, with which invalid data can be excluded before data analysis. By using batch verification and data update methods, the proposed scheme greatly reduces the computational cost. Extensive performance analysis and experiment-based on real cloud system have shown both the feasibility and efficiency of our proposed scheme.
    
    

Disclaimer: The papers here are made available for timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders.

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Curriculum Development and Outreach

At University of Nevada,Reno:

• CPE 600: Computer Communication Networks,
• CPE 601: Computer Network Systems,
• CS 791: Special Topics in Networking: Mobile Cloud Computing

At The University of Texas at Arlington:

• CS 2315: Discrete Structure,
• CS 5349/6349: Special Topic on Internet of Things.

Note: Any opinions, findings and conclusions or recommendations expressed on this website are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).