Title: NeTS: Small: Collaborative Research: Network Economics for Secondary Spectrum Ecosystem, funded by NSF under grant CNS-1659965, 10/01/2016 – 09/30/2019.

 

Project Summary:

The emerging cognitive radio (CR) technology provides new solutions to the wireless service business, whereby a CR-capable service provider can acquire spectrum on demand from the secondary spectrum market (SpecM) to avoid paying the huge upfront spectrum licensing fee. This significantly lowers the threshold of entry to the wireless service business. As a result, it is foreseeable that a large number of small secondary wireless service providers (SSPs) will appear, transforming the wireless service business from one dictated by few giant carriers to a much more competitive service market (ServM), bringing better benefits to end users (EUs), e.g., more choices of SPs and lower premium, analogous to the situation of eBay. Although the allocation and utilization of secondary spectrum by CRs has been extensively studied from an engineering perspective, their economic implications are far less well understood. An optimal engineering solution may no longer be optimal when the economic factor is concerned. Because in reality the economic factor always dominates the actual deployment of technology, there is a need to revisit the secondary spectrum issues from a network economics perspective to ensure the successful deployment of CR technology in the real world. This project investigates efficient secondary spectrum market mechanisms to ensure efficient secondary spectrum allocation and utilization in an economically feasible and sustainable market setup. In particular, based on the observation that the secondary spectrum market is mainly comprised of small radio-resource-constrained SSPs, the PIs introduce SSPs’ collaboration to improve the market efficiency. Unlike the existing competition-based market mechanisms that tend to exclude the resource-insufficient SSPs from the market, SSPs’ collaboration ensures sufficient resource for the SSPs to service EU, and thus allows every SSP to retain a share of the market (better profitability). Collaboration also increases the competitiveness, and henceforth, the efficiency, of the market.

Figure 1. Three-tier market hierarchy

 

Project Goals:

The overarching goal of this project is to explore the network economics of secondary spectrum market under the competition-and-collaboration market framework. The PI aims to develop a set of high-fidelity secondary service models to reflect realistic service structures, spatial and temporal service correlations, QoS requirements, secondary service provider (SSP) behaviors, and human factors in the secondary spectrum market (ServM). Under this overarching goal, the PI aims to achieve the following three aims:

·        AIM 1: Optimal secondary spectrum market mechanisms in rational spot market: For short-term service, assuming perfect rationality for every player in the market, we first model ServM as a spot market, where the commodities are traded for immediate delivery. The PI will study the optimal market mechanisms under the competition-and-collaboration market model for single-hop and multi-hop services, respectively. Strategyproof and group-strategyproof situations for independent and colluded SSPs will be considered, respectively. The PI will also study SSP’s service quality control and online market learning for large-scale markets.

·        AIM2: Rational futures market: For long-term service, the project will study the potential and implication of the so-called futures market, i.e., the trading of spectrum and service in the future is exploited at an earlier time to reduce the long-term average spectrum cost and service price. The PI will investigate how service correlation in the space and time domains affects SSP’s profitability, with an emphasis on the financial risk factor caused by the random supply-and-demand structure over space and time.

·        AIM 3: Irrational player and human factors: We will release the rational-player assumption and study the impact of irrationality and human factors on SSP’s profitability. Using methods from Behavioral Economics, we modify the rational ServM models by incorporating psychological and social factors such as player’s willingness to pay, status quo bias, loss aversion, and preference reversals, and study optimal solutions to these modified market models.

Project Personnel (Auburn Site)

 

PI: Tao Shu, Ph.D.

 

Graduate Students

·        Li Sun

·        Jing Hou

·        Tian Liu

·        Jian Chen

·        Rui Zhu (Ph.D. at Oakland University, co-advised with Prof. Huirong Fu)

 

Past Graduate Students

·        Dong Han (Oakland University, graduated in Dec. 2016)

·        Ye Yan (Oakland University, graduated in Dec. 2016)

 

 

Project Outcomes

1. Privacy-preserving network economics for collaboration in wireless service market

We consider a participatory sensing scenario, whereby smartphones, which are nowadays pervasively distributed, are recruited by a service provider (SP) to perform certain sensing task over an area of interest. Such a sensing task could represent a wide range of applications, e.g., spectrum sensing over the service area of a cognitive radio network, traffic sensing in a highway network, or environment sensing in a neighborhood. In order for the SP to recruit the right smartphones to cover its area of interest, the SP has to have certain information on the geographic location (i.e., traces) of smartphone user movements. On the other hand, to incentivize smartphone users to participate the sensing task, the SP makes a payment to each recruited smartphone. The value of such payment depends on the actual route the smartphone is going to travel along. Because there may be several candidate routes for a smartphone user between his desired origin and destination, the user may ask for different prices for different candidate routes. 

The economically-optimal privacy-preserving participatory sensing problem is for the SP, without knowing the geographic location of the candidate routes of any smartphone user, to recruit a set of smartphones and decide their routes, such that the total payment from the SP is minimized given a full coverage of the area of interest, or the coverage is maximized subject to a constraint on the budget of the SP. The location privacy requirement in the problem is bilateral. While the trace of smartphone movement is not disclosed to the service provider, the location information of the sensing area is not disclosed to any smartphone.

Without the location privacy requirement, the plain version of the mobile phone recruitment problem is simply a set covering problem and the solutions are readily available, e.g., those 2-approximation algorithms, such as LP-rounding, primal-dual methods, and combinatorial methods such as greedy algorithms. Different from the existing set-covering work, in this research we develop a solution for the privacy-preserving version of the problem. Our problem can be considered as a privacy-preserving combinatorial optimization. We show that the technique of homomorphic encryption, which has been extensively used for privacy-preserving computation, does not achieve satisfactory performance for our privacy-preserving optimization problem. Instead of relying on homomorphic encryption, our solution is based on a modified bloom-filter method. We show our method can achieve differential privacy under low communication and computation overhead. Extensive simulations have been performed to verify the privacy strength and the efficiency cost of the proposed method. This work has been published and presented in the following IEEE ICDCS 2017 Conference.

Ye Yan, Dong Han, and Tao Shu, “Privacy-preserving optimization of participatory sensing in mobile cloud computing,” IEEE ICDCS 2017.

2.  Trace-driven machine-learning-based wireless channel spatial correlation analysis, inference attack modeling, and countermeasures

Spatial correlation of spectrum at different locations is critical to the statistical modeling and sharing of spectrum among users and service providers. These correlation information also has many applications in physical layer security. For instance, link-signature-based (LSB) secret key extraction techniques have received many interests in recent years. It is believed that these mechanisms are secure, based on the fundamental assumption that wireless signals received at two locations separated by more than half a wavelength apart are uncorrelated. However, recently it has been observed that in some circumstances this assumption does not hold, rendering LSB key extraction mechanisms vulnerable to attacks. In the past year, we have studied empirical statistical inference attacks (SIA) to LSB key extraction, whereby an attacker infers the signature of a target link, and henceforce recovers the secret key extracted from that link signature, by observing the surrounding links. Different from prior work that assumes a theoretical link correlation model for the inference, our study does not make any assumption on link correlation. Instead, ours is taking a machine learning method for link inference based on empirically measured link data. We have developed three statistical inference attack (SIA) models to estimate the link signature of a target channel by exploiting its correlation with surround channels. Our SIA models consider three different link cases: disjoint links, links sharing the same transmitter, and historical links. The proposed SIA attacks have been launched based on a variety of machine learning algorithms, including ANN, SVM, ensemble methods, and multivariate linear regressions. Based on the CRAWDAD data set, a database that contains over 9300 link signatures measured over a 44-node wireless network, our comprehensive experiments indicated that the proposed SIA attacks can estimate the link signature of a target channel with high accuracy, and thus reduces the key search space by many orders of magnitude. For example, in the learning from historical link signature case, our experiment shows that over 90% of the secret key bits can be hit in just one round of estimation when ANN is used.

In light of the above vulnerability, we have proposed a novel multilink Forward-backward Cooperative Key Generation protocol with helpers as a countermeasure to the aforementioned statistical inference attack (SIA) attacks, aiming to make the LSB key extraction more secure. In particular, by introducing a set of helpers (these are legitimate nodes assisting the key generation process), FBCH allows two communicating terminals to extract symmetric secret keys based on the combined signatures of several randomly selected links. This is in sharp contrast to the conventional method where the key extraction is only dependent on the particular link between the transmitter and the receiver. Consequently, the resulting key extraction becomes less dependent on a particular fixed channel, making the aforementioned SIA attacks, which mainly target the channel between the two communicating terminals, less effective.

Our work has generated a published conference paper in IEEE Milcom 2017 and another paper under journal submission.

Rui Zhu, Tao Shu, and Huirong Fu, “Empirical statistical inference attack against PHY-layer key extraction in real environments,” IEEE MILCOM 2017.

Rui Zhu, Tao Shu, and Huirong Fu, “Statistical inference attack agains PHY-layer key extraction and countermeasures,” under journal submission.

3. Spectrum sensing and measurement testbed development

The PI’s team has developed two testbeds for 3D spectrum sensing. The first testbed is based on several USRP (Universal Software Radio Peripheral) devices and GNU Radio, operating on the VHF bands, with a particular emphasis on the FM radio broadcast band (75-105 MHz). The second testbed consists of a modified TP-Link 802.11 WiFi router and several Google Nexus tablets running our self-developed channel sensing and measurement app. This testbed operates over the 2.4 GHz ISM band. The TP-Link router has been re-programmed, so that it can perform automatic frequency sweeping over the 11 802.11g WiFi channels according to a pre-defined pattern. Our android App can synchronize with the frequency sweeping of the WiFi router, measure the received signal strength (RSS) of the beacon broadcast by the WiFi router, and calculate their statistics such as max, min, mean, etc. The App also logs all measured data for future off-line analysis.

Figure 2. Programmable channel-sweeping AP

Figure 3. Android App for automatic channel measurement, recording, and analysis

 

Figure 4. Field tests

 

4. Contract-based strategic network infrastructure sharing through backup reservation in a competitive environment

This research activity is to address the competition-and-collaboration spot market model for service providers (operators) in AIM 1 of the project goals by taking a bargaining and contractual perspective. In transitioning to the next generation cellular systems (5G), the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades have triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. NIS generically refers to the sharing of network resources and elements among operators, such as spectrum, antennas, power supplies, computing and processing capacities at base stations (BSs), etc. To collaborate with competitors, NIS is a bargain whose terms and conditions need to be carefully determined to guarantee profitability in a market with uncertainties. In this research, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator of limited resources and uncertain demands, and one resourceful operator with potentially redundant capacity. The backup reservation agreement requires the local operator (say, operator A) to pay a fixed reservation fee to the resource-owning operator (say, operator B) at fixed time intervals. In return, the operator B guarantees availability of its resource (e.g., spectrum) up to a predetermined level. In such a way, a certain amount of backup resource capacity is reserved for future use under high traffic demand. We characterize the bargaining between the operators in terms of the optimal reservation prices and resource reservation quantities with considerations of the competitions between operators in market share. The conditions under which the competitive operators will cooperate are explored. The impacts of competition intensity, redundant capacity, and demand uncertainty on performance under backup reservation are also investigated.

Our work is under conference submission:

Jing Hou, Li Sun, Tao Shu, Yong Xiao, and Marwan Krunz, “Strategic network infrastructure sharing through backup reservation in a competitive environment,” under conference submission.

5. Multi-operator network sharing for massive IoT

This research activity is also to address AIM 1 of the project goals. Recent study predicts that by 2020 up to 50 billion IoT devices will be connected to the Internet, straining the capacity of wireless network that has already been overloaded with data-hungry mobile applications, such as high-definition video streaming and virtual reality(VR)/augmented reality(AR). How to accommodate the demands for both massive IoT and high-speed cellular services in the same spectrum without significantly increasing operational and infrastructure costs is one of the main challenges for operators. In this research, we introduce a new network sharing framework that supports coexistence of both massive IoT and high-speed cellular services in cellular spectrum. Our framework is based on the radio access network (RAN) sharing architecture recently introduced by 3GPP as a promising solution for operators to improve their resource utilization and reduce the system roll-out cost. In particular, we have considered two RAN sharing strategies: spectrum pooling and spectrum leasing. In spectrum pooling, operators s can merge their licensed (GSM and/or LTE) bands to form a common pool to be used by the shared RAN. In spectrum leasing, one of the operators serves as the master operator (MOP) to manage and control the resource allocation of the shared RAN. In this case, it is possible for one operator to lease a part of its BSs and the licensed band to be shared by other operators. To incentivize operators to participate sharing, we have considered two fairness criteria, namely proprtional fairness (PF) and shapley value (SV), for fair revenue division among operators. A non-orthogonal multiple access (NOMA) mechanism is also proposed to improve resource utilization efficiency for the coexistence between cellular UEs and massive IoT, whereby each BS can carefully choose different numbers of low-power IoT devices and high-power UEs at different locations to share the same channel. We have evaluated the performance of our proposed framework using the real base station location data in the city of Dublin collected from two major operators in Ireland.

Our work has been accepted by IEEE Communications Magazine:

Xiao Yong, Marwan Krunz, and Tao Shu, “Multi-operator network sharing for massive IoT,” IEEE Communications Magazine, Accepted, Jan. 2019. 

 

Broader Impacts

This project will significantly advance the state of the art of secondary spectrum market along several dimensions. Specifically, it will extend our knowledge on secondary spectrum market from a competition-only scenario to a more efficient competition-collaboration coexistence scenario. In addition, new knowledge will be generated regarding the entire secondary spectrum ecosystem, including the impacts from human-an important market factor that has been largely ignored in existing research. Moreover, the proposed research is inter-disciplinary, and draws methods from networking, stochastic optimization, machine learning, economics, and social science.

The proposed research will advance their state of the art and their applications to CR. Furthermore, this project will also carry out a comprehensive education plan to further broaden its impact, including integrating research findings with undergraduate and graduate courses, recruiting and outreaching to minority and under-represented students, engaging undergraduates in doing research through REUs, disseminating research findings through open access, and open-lab days.

The following activities have been taken to broaden the impacts of this project so far:

·        The PI presented the research outcomes in several invited talks, including at the University of Miami, University of Central Florida, University of South Florida, and Kansas State University.

·        The PI presented the research outcomes to international audience in several invited talks, including Tsinghua University, South China University of Technology, and Guangxi University.

·        The PI presented the research outcomes to the UnCoRe REU students at the CSE department of Oakland University in the summer of 2016.

·        The two REU students under the PI’s supervision presented our research in the 2016 Mid-SURE symposium (Mid-Michigan Symposium for Undergraduate Research Experience) at Michigan State University.

·        The PI and his students presented their research outcomes in several reputed conferences, including IEEE ICDCS 2017, ICC 2017, and Milcom 2017.

·        The PI has integrated part of the research outcomes in the course materials he is teaching at Auburn University, including COMP 5320/6320/6326 (Design and Analysis of Computer Networks) and COMP 7970 (Cryptographic and non-cryptograhic methods for network security). 

·        This project was also introduced to over 1000 high-school students and their parents during the 2017 Open House Engineering Day of the Samuel Ginn College of Engineering at Auburn University. This helps to foster the high-school students' interests in taking science and technology as their future career.

Project Outcomes (underlined authors are/were my graduate students when the work was performed)

 

1. Jing Hou, Li Sun, Tao Shu, Yong Xiao, and Marwan Krunz, “Economics of strategic network infrastructure sharing: A backup reservation approach,” submitted to IEEE/ACM Transactions on Networking, Aug. 2019.

2. Jing Hou, Li Sun, and Tao Shu, “Incentive mechanism for crowdsourcing to consumers: Work for yourself and get reward,” submitted to ACM MobiHoc 2020, Nov. 2019.

3. Li Sun, Jing Hou, and Tao Shu, “Optimal handover policy for mmWave cellular networks: A multi-armed bandit approach, ” accepted by the 2019 IEEE Global Communications Conference (GLOBECOM), to appear in Dec. 2019.

4. Yong Xiao, Marwan Krunz, and Tao Shu, “Multi-operator Network sharing for massive IoT,” IEEE Communications Magazine, Vol. 57, No. 4, pp. 96 - 101, 2019.

5. Jing Hou, Li Sun, Tao Shu, and Husheng Li, “Target information trading – An economic perspective of security,” Proc. of the 15th International Conference on Security and Privacy in Communication Networks (SecureComm 2019), Orlando, FL, Oct., 2019.

6. Tian Liu and Tao Shu, “Adversarial false data injection attack against nonlinear AC state estimation with ANN in smart grid,” Proc. of the 15th International Conference on Security and Privacy in Communication Networks (SecureComm 2019), Orlando, FL, Oct., 2019.

7. Jing Hou, Li Sun, Tao Shu, Yong Xiao, and Marwan Krunz, “Strategic network infrastructure sharing through backup reservation in a competitive environment,” Proc. of the 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Boston, MA, June 2019.

8. Jingchao Bao, Tao Shu, and Husheng Li, “Handover prediction based on geometry method in mmWave Communications -- A sensing approach,” in Proc. of the 2018 IEEE International Conference on Communications (ICC), Workshop on Evolutional Technologies and Ecosystems for 5G Phase II (WDN-5G), May 2018.

9. Rui Zhu, Tao Shu, and Huirong Fu, “Empirical statistical inference attack against PHY-layer key extraction in real environments,” in Proc. of the 2017 IEEE Military Communications Conference (MILCOM), Oct. 2017.

10. Dong Han, Ye Yan, Tao Shu, Liuqing Yang, and Shuguang Cui, “Cognitive context-aware distributed storage optimization in mobile cloud computing: A stable matching based approach,” in Proc. of the 2017 IEEE International Conference on Distributed Computing Systems (ICDCS), June 2017.

11. Ye Yan, Dong Han, and Tao Shu, “Privacy preserving optimization of participatory sensing in mobile cloud computing,” in Proc. of the 2017 IEEE International Conference on Distributed Computing Systems (ICDCS), June 2017.

12. Tao Shu and Shuguang Cui, “Renovating location-based routing for integrated communication privacy and efficiency in IoT,” in Proc. of the 2017 IEEE International Conference on Communications (ICC), May 2017.