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EARS: Collaborative Research: Crowdsourcing-Based Spectrum
Etiquette Enforcement in Dynamic Spectrum Access
Sponsored by the U.S. National Science Foundation (Awards # CNS-1619728 and CNS-1444059)
Duration: 01/01/2015-12/31/2019
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Welcome to the website of our research project: "EARS: Collaborative Research: Crowdsourcing-Based Spectrum Etiquette Enforcement in Dynamic Spectrum Access". This project is a collaborative effort among three institutions: University of Arizona, Colorado School of Mines, and Virginia Tech. This website is created and maintained to disseminate and share research results and other information related to the project.
Project Description
The radio spectrum is becoming an increasingly valuable natural resource nowadays, while it has been shown that much of the spectrum is underutilized in existing licensed bands. To enhance spectrum utilization, dynamic spectrum access (DSA) has been envisioned as a set of promising new spectrum management paradigms, such as spectrum trading/auction and opportunistic spectrum access. While DSA and programmable cognitive radios enable a much higher flexibility of spectrum access, due to the openness of wireless medium, it is also susceptible to various forms of misuse or abuse. For example, unauthorized transmissions without a valid license, or secondary transmissions that intentionally disobey the interference constraints set by the primary users (radios). The misusers will not only gain higher throughput for themselves, but also harm the efficiency of spectrum access operations of normal users (radios). Therefore, enforcing spectrum access rules or etiquettes is crucial to ensuring the ultimate success of the DSA paradigm.
This project develops a framework for etiquette and rule enforcing in dynamic spectrum sharing environments. The main idea of the proposed research is to engage community users (radios) to detect misuse, and identify and punish unruly devices. By crowdsourcing the tasks of monitoring neighborhood radio access behaviors to many cognitive radio devices, multiple benefits can be gained: 1) the potentially large number of participating devices can result in much larger detection coverage and accuracy; 2) no pervasive dedicated trusted infrastructure or hardware is needed; and 3) the fact that every device could possibly be a monitoring device leads to a much stronger deterrence to misbehaviors. The interdisciplinary research plan consists of four major components: 1) an optimized crowdsourced passive radio traffic monitoring framework to detect access misbehavior in the vast DSA spectrum; 2) techniques to identify misbehaving cognitive radio devices using physical layer identification, even when the signal waveform can be adaptively modified; 3) techniques for immediate punishment of spectrum misuse through adaptive friendly jamming which exploits multi-functional re-configurable antennas; and 4) incentive mechanism design via auctions to ensure user participation in each task of crowdsourced etiquette enforcement. The success of this project will benefit multiple current and future application domains deploying DSA, especially those that require critical information protection, such as healthcare, transportation, energy, public services, emergency, and military services.
Personnel
Principal Investigators
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Dr. Ming Li (Lead PI)
Associate Professor
Department of Electrical and Computer Engineering
The University of Arizona
Email: ming.li@arizona.edu
Homepage: http://wiser.arizona.edu/mingli/ |
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Dr. Dejun Yang (PI)
Ben L. Fryrear Assistant Professor
Department of Computer Science
Colorado School of Mines
Email: djyang@mines.edu
Homepage: http://inside.mines.edu/~djyang/ |
Co-Principal Investigators
Current Graduate Students
Publications
- "ROBin: Known-Plaintext Attack Resistant Orthogonal Blinding via Channel Randomization",
Yanjun Pan, Yao Zheng and Ming Li,
IEEE INFOCOM 2020, Apr. 2020, Beijing, China (Acceptance rate: 19.8%)
Orthogonal blinding based schemes for wireless physical layer security aim to achieve secure communication by injecting noise into channels orthogonal to the main channel and corrupting the eavesdropper's signal reception. These methods, albeit practical, have been proven vulnerable against multi-antenna eavesdroppers who can filter the message from the noise. The vulnerability is rooted in the fact that the main channel state remains static in spite of the noise injection, which allows an eavesdropper to estimate it promptly via known symbols and filter out the noise. Our proposed scheme leverages a reconfigurable antenna for Alice to rapidly change the channel state during transmission and a compressive sensing based algorithm for her to predict and cancel the changing effects for Bob. As a result, the communication between Alice and Bob remains clear, whereas randomized channel state prevents Eve from launching the known-plaintext attack. We formally analyze the security of the scheme against both single and multi-antenna eavesdroppers and identify its unique anti-eavesdropping properties due to the artificially created fast-changing channel. We conduct extensive simulations and real-world experiments to evaluate its performance. Empirical results show that our scheme can suppress Eve's attack success rate to the level of random guessing, even if she knows all the symbols transmitted through other antenna modes.
- "Crowdsourced measurements for device fingerprinting",
Seth Andrews, Ryan Gerdes and Ming Li,
The 12th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec '19), Miami, FL, May 15-17, 2019.
Physical layer identification allows verifying a user’s identity based
on their transmitter hardware. In contrast with digital identifiers
at higher protocol layers, physical layer identification or device
fingerprinting can identify unique signal characteristics at the physical
layer introduced by manufacturing variability in each device.
Recently, dynamic spectrum access has been proposed to allow a
larger number of devices to efficiently access wireless spectrum.
In such a system using many low-cost devices may be distributed
over a large area with spectrum allocated and managed by a central
authority. Traditional authentication methods may not be secure,
or adequate to identify existing users in a backwards compatible
way: Identifiers such as MAC addresses can be impersonated, and
the number of devices and their distributed nature may make key
distribution and revocation difficult. Consequently, physical layer
identification can be used to augment other security measures.
We consider a crowdsourced scenario where individual users
observe a signal using their own receiver, and report their measurements
to an enforcement authority which then identifies malicious
devices. Three types of measurements that can be crowdsourced are
considered: actual signal observations, feature values, and fingerprinter
output. Several methods for combining these measurements
are considered. Performance is demonstrated on data collected from
three wireless channels, used to simulate multiple receivers, from a
total of twelve transmitters. The methods are evaluated in terms of
required computational resources receivers, bandwidth to report
measurements, and how they are affected by mismatch in receiver
characteristics. It is found that the crowdsourcing measurements
can provide an improvement over individual receivers, with the
best method dependent on the features and receivers used.
- “Enhance Physical Layer Security via Channel Randomization with Reconfigurable Antennas”,
Yanjun Pan, Ming Li, Yantian Hou, Ryan Gerdes, Bedri Cetiner,
invited chapter in book Proactive and Dynamic Network Defense, Springer, Cliff Wang and Zhuo Lu (Editors), ISBN: 978-3-030-10596-9, to appear, 2019.
Summary: Secure wireless communication techniques based on physical (PHY) layer properties are promising alternatives or complements to traditional upper-layer cryptography-based solutions, due to the capability of achieving message confidentiality or integrity and authentication protection without pre-shared secrets. While many theoretical results are available, there are few practical PHY-layer security schemes, mainly because the requirement of channel advantage between the legitimate users versus the attacker's is hard to satisfy in all cases. Recent research shows that channel randomization, which proactively and dynamically perturbs the physical channel so as to create an artificial channel advantage, is helpful to enhance certain PHY-layer security goals such as secrecy. However, a systematic study of the foundations of such an approach and its applicability is needed. In this chapter, we first survey the state-of-the-art in PHY-layer security and identify their main limitations as well as challenges. Then we examine the principles of channel randomization and explore its application to achieve in-band message integrity and authentication. Especially, we focus on preventing active signal manipulation attacks and use reconfigurable antennas to systematically randomize the channel such that it is unpredictable to the active attacker. Both theoretical and experimental results show that it is a feasible and effective approach. Other applications and future directions are discussed in the end.
- “Sybil-Proof Online Incentive Mechanisms for Crowdsensing”,
Jian Lin, Ming Li, Dejun Yang, and Guoliang Xue,
IEEE International Conference on Computer Communications (INFOCOM), 2018
Summary: Crowdsensing leverages the rapid growth of sensor embedded smartphones and human mobility for pervasive information collection. To incentivize smartphone users to participate in crowdsensing, many auction-based incentive mechanisms have been proposed for both offline and online scenarios. It has been demonstrated that the Sybil attack may undermine these mechanisms. In a Sybil attack, a user illegitimately pretends multiple identities to gain benefits. Sybil-proof incentive mechanisms have been proposed for the offline scenario. However, the problem of designing Sybil-proof online incentive mechanisms for crowdsensing is still open. Compared to the offline scenario, the online scenario provides users one more dimension of flexibility, i.e., active time, to conduct Sybil attacks, which makes this problem more challenging. In this paper, we design Sybilproof online incentive mechanisms to deter the Sybil attack for crowdsensing. Depending on users’ flexibility on performing their tasks, we investigate both single-minded and multi-minded cases and propose SOS and SOM, respectively. SOS achieves computational efficiency, individual rationality, truthfulness, and Sybilproofness. SOM achieves individual rationality, truthfulness, and Sybil-proofness. Through extensive simulations, we evaluate the performance of SOS and SOM.
- “Frameworks for Privacy-Preserving Mobile Crowdsensing Incentive Mechanisms”,
Jian Lin, Dejun Yang, Ming Li, Jia Xu, and Guoliang Xue
IEEE Transactions on Mobile Computing (TMC), vol. 17, no. 8, pp. 1851-1864, 2018.
Summary: With the rapid growth of smartphones, mobile crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in the mobile crowdsensing applications and systems. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. In this paper, we design two frameworks for privacy preserving auction-based incentive mechanisms that also achieve approximate social cost minimization. In the former, each user submits a bid for a set of tasks it is willing to perform; in the latter, each user submits a bid for each task in its task set. Both frameworks select users based on platform-defined score functions. As examples, we propose two score functions, linear and log functions, to realize the two frameworks. We rigorously prove that both proposed frameworks achieve computational efficiency, individual rationality, truthfulness, differential privacy, and approximate social cost minimization. In addition, with log score function, the two frameworks are asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance of the two frameworks and demonstrate that our frameworks achieve bid-privacy preservation although sacrificing social cost.
- “Online Incentive Mechanism for Mobile Crowdsourcing based on Two-tiered Social Crowdsourcing Architecture”,
Jia Xu, Chengcheng Guan, Haobo Wu, Dejun Yang, Lijie Xu, and Tao Li
IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2018.
Summary: Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem in the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. In the two-tiered social crowdsourcing system, the tasks are associated with different end times, and both the registered users and their social neighbors have different arrival/departure times. An online incentive mechanism, MTSC, which consists of two steps: Agent Selection and Online Reverse Auction, is proposed for this novel mobile crowdsourcing system. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanism achieves computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness.
- “Pay On-demand: Dynamic Incentive and Task Selection for Location-dependent Mobile Crowdsensing Systems”,
Zhibo Wang, Jiahui Hu, Jing Zhao, Dejun Yang, Honglong Chen, and Qian Wang
IEEE International Conference on Distributed Computing Systems (ICDCS), 2018.
Summary: With the rich sensing capacity and ubiquitous usage of smartphones, crowdsensing leveraging the power of the crowd of mobile users has become an effective technique to collect data for various sensing applications. Many incentive mechanisms have been proposed to encourage people to participate in crowdsensing. However, most of them set unchangeable rewards for sensing tasks, while the inherent inequality and on-demand feature of sensing tasks have been long ignored, especially for location-dependent sensing tasks. In this paper, we focus on location-dependent crowdsensing systems and propose a demand-based dynamic incentive mechanism that dynamically changes the rewards of sensing tasks at each sensing round in an on-demand way to balance their popularity. A demand indicator is introduced to characterize the demand of each sensing task by considering its deadline, completing progress, and number of potential participants. At each sensing round, we use the Analytic Hierarchy Process to calculate the relative demands of all sensing tasks and then determine their rewards accordingly. Moreover, we prove that the distributed task selection problem with time budget is NP-hard. We propose an optimal dynamic programming based solution and a greedy solution to help each user select tasks while maximizing its profit. Extensive experiments show that the demand-based dynamic incentive mechanism outperforms existing incentive mechanisms.
- "SpecWatch: A Framework for Adversarial Spectrum Monitoring with Unknown Statistics",
Ming Li, Dejun Yang, Jian Lin, Ming Li and Jian Tang,
Elsevier Computer Networks (COMNET), accepted, Jul. 2018.
Summary: In cognitive radio networks (CRNs), dynamic spectrum access has been proposed to improve the spectrum utilization, but it also generates spectrum misuse problems. One common solution to these problems is to deploy monitors to detect misbehaviors on certain channel. However, in multi-channel CRNs, it is very costly to deploy monitors on every channel. With a limited number of monitors, we have to decide which channels to monitor. In addition, we need to determine how long to monitor each channel and in which order to monitor, because switching channels incurs costs. Moreover, the information about the misuse behavior is not available a priori. To answer those questions, we model the spectrum monitoring problem as a combinatorial adversarial multi-armed bandit problem with switching costs (MAB-SC), propose an effective framework, and design two online algorithms, SpecWatch-II and SpecWatch-III, based on the same framework. To evaluate the algorithms, we use weak regret, i.e., the performance difference between the solution of our algorithm and optimal (fixed) solution in hindsight, as the metric. We prove that the expected weak regret of SpecWatch-II is O(T2/3), where T is the time horizon. Whereas, the actual weak regret of SpecWatch-III is O(T2/3) with probability 1−δ, for any δ ∈ (0, 1). Both algorithms guarantee the upper bounds matching the lower bound of the general adversarial MAB-SC problem. Therefore, they are all asymptotically optimal.
- "LTE Misbehavior Detection in Wi-Fi/LTE Coexistence Under the LAA-LTE Standard",
Islam Samy, Loukas Lazos, Yong Xiao, Ming Li and Marwan Krunz,
11th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec 2018), Stockholm, Sweden, Jun. 18-20, 2018 (Full Paper Acceptance rate: 25.6%)
Summary: In this paper, we consider the fair coexistence between LTE and Wi-Fi systems in unlicensed bands. We focus on the misbehavior opportunities that stem from the heterogeneity of the coexisting systems and the lack of explicit coordination mechanisms. We show that a selfishly behaving LTE can gain an unfair share of the spectrum resources through the manipulation of the parameters defined in the LAA-LTE standard, including the manipulation of the backoff mechanism of LAA, the traffic class, the clear channel assignment threshold and others. We develop a detection mechanism for the Wi-Fi system that can identify a misbehaving LTE system. Our mechanism advances the state of the art by providing an accurate monitoring method of the LTE behavior under various topological scenarios, without explicit cross-system coordination. Deviations from the expected behavior are determined by computing the statistical distance between the protocol-specified and estimated distributions of the LAA-LTE protocol parameters. We analytically characterize the detection and false alarm probabilities and show that our detector yields high detection accuracy at very low false alarm rate, for a wise choice of statistical parameters.
- "On the Privacy and Utility Tradeoff in Database-Assisted Dynamic Spectrum Access" (Full paper),
Ahmed Salama, Ming Li, Loukas Lazos, Yong Xiao, and Marwan Krunz,
The IEEE International Symposium on Dynamic Spectrum Access Networks (IEEE DySPAN 2018), Seoul, South Korea, Oct. 22-25, 2018
Summary: In dynamic spectrum access, commercially-operated database servers are often used to assist the opportunistic users
(OUs) to query and access spectrum vacancies of incumbent users
(IUs). The query and answer process in such DSA architectures
introduce significant privacy concerns for potential leakage of the
sensitive operational details of the IUs, especially their locations.
Existing privacy-preserving mechanisms such as location cloaking
or spatial obfuscation can be used to hide IUs’ locations. They
however cannot guarantee the interference from all surrounding
access-allowed OUs staying under a given limit. In addition,
the spectrum utilization (i.e., transmission opportunities) of OUs
should also be considered in the design of mechanisms. The
complex three-way tradeoff among privacy, interference and
utility has not been systematically studied in the literature. In this
paper, we first endeavor to tackle this challenge, by introducing
a privacy zone within the exclusion zone. In the privacy zone
the IU’s location is indistinguishable, while the exclusion zone
guarantees the interference limit within the privacy zone. Under
two variants of the system model (either known OU locations
or probabilistic locations with known density), we formulate and
solve corresponding optimization problems to find the optimal
tradeoff of one versus the other two objectives. Simulation results
with real-world maps/parameters show that the IU’s privacy
increases with decreasing OUs’ utility given a fixed allowable
interference for the IUs.
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“ Towards Physical Layer Identification of Cognitive Radio Devices”,
Seth Andrews, Ryan Gerdes and Ming Li,
the 5th IEEE Conference on Communications and Network Security (IEEE CNS 2017), Las Vegas, NV, Oct. 2017.
Summary: Increasing demand has led to wireless spectrum shortages, and many parts of the existing spectrum are heavily
used. Dynamic spectrum access (DSA) has been proposed to allow cognitive radio networks to use existing spectrum more efficiently. It will allow secondary users to transmit on already allocated spectrum on a non-interference basis. Cognitive radios are able to change bandwidth and other transmission characteristics to take advantage of this spectrum. To enforce spectrum access rules it is necessary to uniquely identify all devices on the network. Manufacturing variation cause every device to have minute differences. Physical layer identification (also called device fingerprinting) techniques allow identification of devices based on small but unique variation due to these imperfections. Fingerprinting is very sensitive to any changes in the capture setup or device’s environment. The changes in bandwidth that would occur in a DSA system cause device fingerprinting to fail. In this paper, we extend current device identification methods to include identification of devices with changing bandwidth. Experimental results are demonstrated on a collection of over 50 transmitters, with a significant improvement over current methods.
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“ Message Integrity Protection over Wireless Channel: Countering Signal Cancellation via Channel Randomization,“
Yanjun Pan, Yantian Hou, Ming Li, Ryan Gerdes, Kai Zeng, Md Asaduzzaman Towfiq, Bedri Cetiner,
IEEE Transactions on Dependable and Secure Computing (TDSC), accepted, pp. 1-14, Sept. 2017.
Summary: Physical layer message integrity protection and authentication by countering signal-cancellation has been shown as a promising alternative to traditional pure cryptographic message authentication protocols, due to the non-necessity of neither pre-shared secrets nor secure channels. However, the security of such an approach remained an open problem due to the lack of systematic security modeling and quantitative analysis. In this paper, we frst establish a novel signal cancellation attack framework to study the optimal signal-cancellation attacker's behavior and utility using game-theory, which precisely captures the attacker's knowledge using its correlated channel estimates in various channel environments as well as the online nature of the attack. Based on theoretical results, we propose a practical channel randomization approach to defend against signal cancellation attack, which exploits state diversity and swift reconfigurability of reconfigurable antenna to increase randomness and meanwhile reduce correlation of channel state information. We show that by proactively mimicking the attacker and placing restrictions on the attacker's location, we can bound the attacker's knowledge of channel state information, thereby achieving a guaranteed level of message integrity protection in practice. Besides, we conduct extensive experiments and simulations to show the security and performance of the proposed approach.
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Optimal Crowdsourced Channel Monitoring in Cognitive Radio Networks
Ahmed M. Salama, Ming Li, Dejun Yang
2017 IEEE Global Communications Conference (Globecom 2017), Cognitive Radio and Networks Symposium, Singapore, Dec. 4-8, 2017.
Summary: Crowdsourcing is an emerging paradigm for spectrum
access rule enforcement in dynamic spectrum sharing,
which leverages a large number of mobile users to help monitoring
and detect spectrum violations and misuse. Its main
advantages compared with traditional dedicated monitoring architecture
includes enhanced coverage, effectiveness and lower
costs. However, how to optimally assign mobile users to monitor
the channel usage has not been studied in the crowdsourced
setting. The main challenges are: the large number of channels to
monitor while mobile users may not be available all the time, the
need to consider monitoring costs and incentives, as well as the
uncertainty of each channel’s traffic patterns. In this paper, we
tackle such challenges by formulating a stochastic optimization
problem to optimize the spectrum monitoring task assignment for
crowdsourced mobile users. We consider the availability pattern
of the mobile users and we give them payments as an incentive
to participate in monitoring. Simulations show that our method
outperforms the risk-averse scenario and has a small gap with
the solution under perfect information.
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Jian Lin, Ming Li, Dejun Yang, Guoliang Xue, and Jian Tang
Sybil-Proof Incentive Mechanisms for Crowdsensing
IEEE International Conference on Computer Communications (INFOCOM), 2017.
Summary: The rapid growth of sensor-embedded smartphones has led to a new data sensing and collecting paradigm, known as crowdsensing. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in crowdsensing. However, none of them have taken into consideration the Sybil attack where a user illegitimately pretends multiple identities to gain benefits. This attack may undermine existing inventive mechanisms. To deter the Sybil attack, we design Sybilproof auction-based incentive mechanisms for crowdsensing in this paper. We investigate both the single-minded and multi-minded cases and propose SPIM-S and SPIM-M, respectively. SPIM-S achieves computational efficiency, individual rationality, truthfulness, and Sybil-proofness. SPIM-M achieves individual rationality, truthfulness, and Sybil-proofness. We evaluate the performance and validate the desired properties of SPIM-S and SPIM-M through extensive simulations.
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Xiang Zhang, Guoliang Xue, Ruozhou Yu, Dejun Yang, and Jian Tang
Robust Incentive Tree Design for Mobile Crowdsensing
IEEE International Conference on Distributed Computing Systems (ICDCS), 2017.
Summary: With the proliferation of smart mobile devices (smart phone, tablet, and wearable), mobile crowdsensing becomes a powerful sensing and computation paradigm. It has been put into application in many fields, such as spectrum sensing, environmental monitoring, healthcare, and so on. Driven by promising incentives, the power of the crowd grants crowdsensing an advantage in mobilizing users who perform sensing tasks with the embedded sensors on the smart devices. Auction is one of the commonly adopted crowdsensing incentive mechanisms to incentivize users for participation. However, it does not consider the incentive for user solicitation, where in crowdsensing, such incentive would ease the tension when there is a lack of crowdsensing users. To deal with this issue, we aim to design an auction-based incentive tree to offer rewards to users for both participation and solicitation. Meanwhile, we want the incentive mechanism to be robust against dishonest behavior such as untruthful bidding and sybil attacks, to eliminate malicious price manipulations. We design RIT as a Robust Incentive Tree mechanism for mobile crowdsensing which combines the advantages of auctions and incentive trees. We prove that RIT is truthful and sybil-proof with probability at least H, for any given H in (0, 1). We also prove that RIT satisfies individual rationality, computational efficiency, and solicitation incentive. Simulation results of RIT further confirm our analysis.
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Xiang Zhang, Guoliang Xue, Ruozhou Yu, Dejun Yang, and Jian Tang
Countermeasures against False-Name Attacks on Truthful Incentive Mechanisms for Crowdsourcing
IEEE Journal on Selected Areas in Communications (JSAC), vol 35, no. 2, pp. 478-485, Feb 2017.
Summary: The proliferation of crowdsourcing brings both opportunities and challenges in various fields, such as environmental monitoring, healthcare, and so on. Often, the collaborative efforts from a large crowd of users are needed in order to complete crowdsourcing jobs. In recent years, the design of crowdsourcing incentive mechanisms has drawn numerous interests from the research community, where auction is one of the commonly adopted mechanisms. However, few of these auctions consider the robustness against false-name attacks (a.k.a. sybil attacks), where dishonest users generate fake identities to increase their utilities without devoting more efforts. To provide countermeasures against such attacks, we design TAFA as an auction-based incentive mechanism for crowdsourcing. We prove that TAFA is truthful, individually rational, budget-balanced, and computationally efficient. We also prove that TAFA provides countermeasures against false-name attacks, such that each user is better off not generating any false name. Extensive performance evaluations are conducted and the results further confirm our theoretical analysis.
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Ming Li, Jian Lin, Dejun Yang, Guoliang Xue, and Jian Tang
QUAC: Quality-Aware Contract-Based Incentive Mechanisms for Crowdsensing
IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), 2017.
Summary: Crowdsensing is a sensing method which involves participants from general public to collect sensed data from their mobile devices, and also contribute and utilize a common database. To ensure a crowdsensing system to operate properly, there must be certain effective and efficient incentive mechanism to attract users and stimulate them to submit sensing data with high quality. Intuitively, the agreement on the qualities and payments in crowdsensing systems can be best modeled as a contract. However, none of existing incentive mechanisms consider data quality through effective contract design. In this paper, we design two quality-aware contract-based incentive mechanisms for crowdsensing, named QUAC-F and QUAC-I, under full information model and incomplete information model, respectively, which differ in the level of users’ information known to the system. Both QUAC-F and QUAC-I are guaranteed to maximize the platform utility while satisfying individual rationality and incentive compatibility. We evaluate the performance of our designed mechanisms based on a real dataset.
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Jia Xu, Zhengqiang Rao, Lijie Xu, Dejun Yang, and Tao Li
Mobile Crowd Sensing via Online Communities: Incentive Mechanisms for Multiple Cooperative Tasks
IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), 2017.
Summary: Mobile crowd sensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Many incentive mechanisms for mobile crowd sensing have been proposed. However, none of them has taken into consideration the cooperative compatibility of users for multiple cooperative tasks. In this paper, we design truthful incentive mechanisms to minimize the social cost such that each of the cooperative tasks can be completed by a group of compatible users. We consider that the mobile crowd sensing is launched in an online community. We study two bid models and formulated the Social Optimization Compatible User Selection (SOCUS) problem for each model. We also define three compatibility models and explore the compatible relation via the social relation of online communities. We design two reverse auction based incentive mechanisms, MCT-M and MCT-S. Both of them consist of two steps: compatible user grouping and reverse auction. Through both rigid theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality and truthfulness. In addition, MCT-M can output the optimal solution.
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Jia Xu, Hui Li, Yanxu Li, Dejun Yang, and Tao Li
Incentivizing the Biased Requesters: Truthful Task Assignment Mechanisms in Crowdsourcing
IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2017.
Summary: Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. In the integrated crowdsourcing systems, the requesters are non-monopolistic and may show preferences over the workers. We are the first to design the incentive mechanisms, which consider the issue of stimulating the biased requesters in the competing crowdsourcing market. In this paper, we explore truthful task assignment mechanisms to maximize the total value of accomplished tasks for this new scenario. We present three models of crowdsourcing, which take the preferences of the requesters and the workload constraints of the workers into consideration. We design a task assignment mechanism, which follows the matching approach to solve the Valuation Maximizing Assignment (VMA) problem for each of the three models. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed assignment mechanisms achieve computational efficiency, workload feasibility, preference (universal) truthfulness and constant approximation.
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Towards Energy-efficient Task Scheduling on Smartphones in Mobile Crowd Sensing Systems
Jing Wang, Jian Tang, Guoliang Xue, and Dejun Yang
Elsevier Computer Networks ( COMNET), to appear.
Summary: In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as two effective polynomial-time heuristic algorithms, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve significant energy savings, compared to a widely-used baseline approach; moreover, the proposed heuristic algorithms produce close-to-optimal solutions.
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Game Theoretical Analysis of Coexistence in MIMO-Empowered Cognitive Radio Networks
Yantian Hou, Ming Li and Dejun Yang,
International Conference on Computing, Networking and Communication ( ICNC), Silicon Valley, USA, January 26-29, 2017.
Summary: In Cognitive Radio Networks (CRNs), the spectrum underlay approach enables primary and secondary networks to
transmit simultaneously, as long as the interference from the
secondary network to the primary network is below certain
threshold. As the recent advancement of the underlay approach,
the transparent coexistence exploiting MIMO interference cancellation
was proposed. Previous works assume that the secondary
networks will completely follow the spectrum access rules
by restricting their interference towards the primary network.
However, this assumption might be invalid in practice due to
the selfish nature of CRN users. In this work, we study the
multi-hop MIMO-empowered secondary network’s incentives of
following or violating this rule through compliantly canceling
or non-compliantly ignoring its interferences towards the primary
network. Specifically, we model the coexistence between
the primary and secondary networks as a Stackelberg game.
The equilibriums reveal the secondary network’s non-compliant
incentives. These insights help in developing the methodology to
deal with such type of selfish secondary networks.
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BidGuard: A Framework for Privacy-Preserving Crowdsensing Incentive Mechanisms
Jian Lin, Dejun Yang, Ming Li, Jia Xu, and Guoliang Xue
IEEE Conference on Communications and Network Security ( CNS), 2016.
Summary: With the rapid growth of smartphones, crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Auction has been widely used to design mechanisms to stimulate smartphone users to participate in the crowdsensing applications and systems. Many auction-based incentive mechanisms have been proposed for crowdsensing. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. To the best of our knowledge, we are the first to study the design of privacy-preserving incentive mechanisms that also achieve approximate social cost minimization. In this paper, we design BidGuard, a general privacy-preserving framework for incentivizing crowdsensing. This framework works with different score functions for selecting users. In particular, we propose two score functions, linear and log functions, to realize the framework. We rigorously prove that BidGuard achieves computational efficiency, individual rationality, truthfulness, differential privacy and approximate social cost minimization. In addition, the BidGuard with log score function is asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance and validate the desired properties of BidGuard.
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Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing
Jing Wang, Jian Tang, Dejun Yang, Erica Wang, and Guoliang Xue
IEEE International Conference on Distributed Computing Systems ( ICDCS), 2016.
Summary: Limited research efforts have been made for Mobile CrowdSensing (MCS) to address quality of the recruited crowd, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing, which is the main focus of the paper. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. In this paper, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine-grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. We conducted trace-driven simulation using the mobility dataset of San Francisco taxis. Extensive simulation results show the proposed incentive mechanism achieves noticeable expenditure savings compared to two well-designed baseline methods, and moreover, it produces close-to-optimal solutions.
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SpecWatch: Adversarial Spectrum Usage Monitoring in CRNs with Unknown Statistics
Ming Li, Dejun Yang, Jian Lin, Ming Li and Jian Tang
IEEE International Conference on Computer Communications ( INFOCOM), 2016.
Summary: In multi-channel cognitive radio networks, it is very costly to deploy monitors on every channel to monitor the spectrum misuse behaviors. With a limited number of monitors, we have to decide which channels to monitor. In addition, we need to determine how long to monitor each channel and in which order to monitor, because switching channels incurs costs. Moreover, the information about the misuse behavior is not available a priori. We model the spectrum usage monitoring problem as an adversarial multi-armed bandit problem with switching costs and design two effective online algorithms, SpecWatch and SpecWatch+, to decide the set of channels to monitor at each time slot in an online manner. In SpecWatch, we select strategies based on the monitoring history and repeat the same strategy for certain timeslots to reduce switching costs. We prove its expected weak regret, i.e., the performance difference between the solution of SpecWatch and optimal (fixed) solution, is O(T^{2/3}), where T is the time horizon. Whereas, in SpecWatch+, we select strategies more strategically to improve the performance. We show its actual weak regret is O(T^{2/3}) with probability 1−δ, for any δ ∈ (0, 1). Both algorithms are asymptotically optimal as the bounds on the weak regret match the lower bound.
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Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing
Jia Xu, Jinxin Xiang, and Dejun Yang
IEEE Transactions on Wireless Communications ( TWC), vol. 14, no. 11, pp. 6353-6364, 2015.
Summary: Crowdsourcing participants may have associated costs while performing crowdsourcing tasks. Therefore it is necessary to provide appropriate incentives to compensate their costs. To this end, incentive mechanisms are designed to determine who should be awarded and how much should be awarded. A typical crowdsourcing application provides a platform for the service requesters, who have crowdsourcing jobs that need to be done and are willing to pay for the service, and the service providers, who would like to perform crowdsourcing tasks in exchange for rewards. A lot of research efforts have been focused on developing such incentive mechanisms to encourage users to participate in crowdsourcing. However, the incentive mechanisms for time window dependent scenarios are still lacking. Under these scenarios, mobile crowdsensing applications have strong requirements of data integrity and the value of the fragmented data may decrease significantly. To fill this void, we consider a time window dependent task crowdsourcing scenario, i.e., the platform wants to collect the continuous data in a specific time interval, and design two incentive mechanisms, MST and MMT. In MST, we adopt a dynamic programming algorithm to select users and determine the payment by Vickrey-Clarke-Groves (VCG) auction. Since the general SOUS problem is NP-hard, we design MMT based on an approximation algorithm, which follows a greedy approach. We show that both two design incentive mechanisms satisfy the desirable properties (listed above). Moreover, the approximation ratio of MMT is ln|W| + 1, where |W| is the length of the sensing time window defined by the platform.
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Keep Your Promise: Mechanism Design against Free-riding and False-reporting in Crowdsourcing
Xiang Zhang, Guoliang Xue, Ruozhou Yu, Dejun Yang, and Jian Tang
IEEE Internet of Things Journal ( IoT-J), vol. 2, no. 2, pp. 562-572, 2015.
Summary: Crowdsourcing is an emerging paradigm where users can have their tasks completed by paying fees, or receive rewards for providing service. A critical problem that arises in current crowdsourcing mechanisms is how to ensure that users pay or receive what they deserve. Free-riding and false-reporting may make the system vulnerable to dishonest users. In this paper, we design schemes to tackle these problems, so that each individual in the system is better off being honest and each provider prefers completing the assigned task. We first design a mechanism EFF which eliminates dishonest behavior with the help from a trusted third party for arbitration. We then design another mechanism DFF which, without the help from any third party, discourages dishonest behavior. We also prove that DFF is semitruthful, which discourages dishonest behavior such as free-riding and false-reporting when the rest of the individuals are honest, while guaranteeing transaction-wise budget-balance and computational efficiency. Performance evaluation shows that within our mechanisms, no user could have a utility gain by unilaterally being dishonest.
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Curriculum Development and Outreach
At University of Arizona:
- ECE 478/578: Fundamentals of Computer Networks (covers wireless MAC protocols, and MIMO interference cancellation)
- ECE 471/571: Fundamentals of Information and Network Security (covers cryptography and network security protocol design)
At Colorado School of Mines:
- CSCI 555/455: Game Theory and Networks (covers the topic of incentive mechanism design)
At Virginia Tech:
- ECE 5930: Hardware Security (covers the topic of physical layer device identification)
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).
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