Yanru Zhang

A Social-Aware Framework for Efficient Information Dissemination in Wireless Ad Hoc Networks

In wireless ad hoc networks, each node participates in routing by forwarding data to other nodes without a pre-existing infrastructure. Particularly, with the wide adoption of smart devices, the concept of smartphone ad hoc networks (SPANs) has evolved to enable alternate means for information sharing. Using unlicensed frequency spectrum and short-range wireless technologies, a SPAN enables a new paradigm of applications and thus is seen as an attractive component in future wireless networks. In a SPAN, smartphones form local peer-to-peer networks to cooperate and share information efficiently. Recent studies have shown that if the users’ social relations are considered while designing cooperation schemes and protocols in SPANs, the cooperation initialization and content dissemination can be notably improved to increase the overall network efficiency and communications reliability. In this article, we present a social-aware framework for optimizing SPANs by exploiting two layers: users’ relationships in the online social network layer and users’ offline connections and interactions in the physical wireless network layer. The online content popularity distribution is also studied as a result of the users’ online interaction profiles. In the end, we integrate both online and offline layers, and discuss possible applications to further enhance the network performance.

Incentive Mechanism for Mobile Crowdsourcing Using an Optimized Tournament Model

With the wide adoption of smart mobile devices, there is a rapid development of location-based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivizes a large group of mobile users to participate. In this paper, we investigate the problem of designing a crowdsourcing tournament to maximize the principal’s utility in crowdsourcing and provide continuous incentives for users by rewarding them based on the rank achieved. First, we model the user’s utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Finally, we present numerical results to show the parameters’ impact on the tournament design and compare the system performance under the different proposed incentive mechanisms. We show that by using the tournament, the principal successfully maximizes the utilities, and users obtain the continuous incentives to participate in the crowdsourcing activity.

A Survey of Contract Theory-Based Incentive Mechanism Design in Wireless Networks

The prevalence of high performance mobile devices such as smartphones and tablets has brought fundamental changes to existing wireless networks. The growth of multimedia and location-based mobile services has exponentially increased network congestion and the demands for more wireless access. This has led to the development of advanced techniques to address the resulting challenges based on the concept of cooperation in various heterogeneous network scenarios. Thus, innovative incentive mechanisms in wireless networks are needed to ensure the participation of third party nodes, such as access points, small cells, and users. In this tutorial, we demonstrate the effectiveness of contract theory to design incentive mechanisms for a wide range of application scenarios in wireless networks. In contract theory, participants are offered properly designed rewards based on their performances to encourage better participation. First, we present an overview of basic concepts and models of contract theory, with comparisons to other related methods from economics. We then discuss incentive mechanisms, with a focus on the design of rewards in a contract. We demonstrate how contract theory can be utilized for developing effective incentive mechanisms for emerging wireless network scenarios such as traffic offloading, mobile crowdsourcing, and spectrum trading.

Non-Cash Auction for Spectrum Trading in Cognitive Radio Networks: Contract Theoretical Model With Joint Adverse Selection and Moral Hazard

In cognitive radio networks (CRNs), spectrum trading is an efficient way for secondary users (SUs) to achieve dynamic spectrum access and to bring economic benefits for the primary users (PUs). Existing methods require full payment from SU, which blocked many potential “buyers,” and thus limited the PU’s expected income. To better improve PUs’ revenue from spectrum trading in a CRN, we introduce a financing contract, which is similar to a sealed non-cash auction that allows SU to do financing. Unlike previous mechanism designs in CRN, the financing contract allows the SU to only pay part of the total amount when the contract is signed, known as the down payment. Then, after the spectrum is released and utilized, the SU pays the rest of payment, known as the installment payment, from the revenue generated by utilizing the spectrum. The way the financing contract carries out and the sealed non-cash auction works similarly. Thus, contract theory is employed here as the mathematical framework to solve the non-cash auction problem and form mutually beneficial relationships between PUs and SUs. As the PU may not have the full acknowledgment of the SU’s transmission status, the problems of adverse selection and moral hazard arise in the two scenarios, respectively. Therefore, a joint adverse selection and moral hazard model is considered here. In particular, we present three situations when either or both adverse selection and moral hazard are present during the trading. Furthermore, both discrete and continuous models are provided in this paper. Through simulations, we show that the adverse selection and moral hazard cases serve as the upper and lower bounds of the general case where both problems are present.

Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs’ idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs’ participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.

LTE-Unlicensed Coexistence Mechanism: A Matching Game Framework

Integrating LTE-A into unlicensed spectrum using carrier aggregation, LTE-Unlicensed is considered as a promising solution with the benefits of enhanced transmission and seamless user experience. However, it is still facing some major design challenges, especially the coexistence of multiple systems (LTE/Wi-Fi) within the shared unlicensed spectrum. In this work, a matching game framework is presented to tackle the coexistence issues. Specifically, wireless users (i.e., cellular users and Wi-Fi users), which interfere with each other when they operate on the same unlicensed band, are modeled as matching players. The coexistence issues between the LTE/Wi-Fi systems can thus be modeled as the interactions between LTE/Wi-Fi users using suitable matching games. In addition, the matching framework allows definition of the preference lists and matching optimality as ways to interpret various system requirements and objectives. Together with the low-complexity and semi-distributive implemented algorithms, the matching framework can accommodate many application-oriented coexistence issues in LTE-Unlicensed. Specific implementation scenarios, as well as experimental results, are analyzed to demonstrate the potential of the matching-based approaches in the LTE-Unlicensed design.

Incentive mechanism in crowdsourcing with moral hazard

With the widely adoption of smart mobile devices, there is a rapidly development of location based services. One key feature in providing the service is the crowdsourcing in which the principal obtains essential data from a large group of users, and inversely sharing the data based service with everyone for free. In this paper, we investigate the problem of how to provide continuous incentives for users to participate in the crowdsourcing activity, which can be referred to the moral hazard problem in the contract theory. First, a performance related incentive mechanism is proposed. Then, the utility maximization problem of the principal is formulated, under the constraint that each user maximizes its own utility by choosing the optimal effort in the crowdsourcing activity. Finally, the numerical results show that by using the proposed incentive mechanism, the users obtains the continuous incentives to participate in the crowdsourcing activity, and the principal successfully maximize the utilities.

Student admission matching based content-cache allocation

As a support to the backend storage, the content caching technique is of great importance to online social networks (e.g., Facebook), in reducing the request service latency and improving user satisfaction. However, the limited caching capacity and booming user data pose great challenges for the content-cache allocation. In this paper, we propose a three-layer content caching model, and focus on how to efficiently allocation contents to caches in order to minimize the overall service latency. We try to tackle this issue by utilizing both centralized Mix Integer Linear Programming (MILP) optimization and by modeling it as a distributed student admission (SA) stable matching problem. In the SA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to yield a stable matching between contents and cache centers. We compare the performance between the centralized and distributed algorithms in terms of system welfare and computation analysis. Through numerical results, we prove the effectiveness of our proposed methods.

Incentive design for collaborative jamming using contract theory in physical layer security

Cooperative jamming is a promising technology for improving information secrecy at the physical layer. The increase of secrecy capacity heavily depends on the participation of the jammer and its geographical locations. However, the jammers geographical location might not be known to others such as the source node due to privacy, and the asymmetric information will lead to inefficiency in jamming. Thus, incentive mechanisms for cooperative jamming are on demand. In this paper, a solution based on contract theory is proposed to solve the problem of motivating efficient friendly jamming. First, we classify the diverse potential locations of the jammer into a finite number of types under the framework of contract theory. Second, we analyze the feasible conditions which guarantee that a jammer will provide service and select the contract bundle designed only for its corresponding type. Finally, an optimal contract is proposed targeting to maximize the source nodes utility. Numerical results verify the performances of our proposed scheme.

Tournament Based Incentive Mechanism Designs for Mobile Crowdsourcing

With the wide adoption of smart mobile devices, there is rapid development of location based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivize a large group of mobile users to participate. In this paper, we investigate the problem of designing a tournament to provide continuous incentives for users by rewarding them based on the rank achieved in crowdsourcing. First, we model the users utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Furthermore, we show that, the tournament can approximate the optimal contract under full information by step function. Finally, we present numerical results to compare the system performance under the different proposed incentive mechanisms; we show that by using the tournament, the users obtain the continuous incentives to participate in the crowdsourcing activity.