Ruozhou Yu

Truthful incentive mechanisms for crowdsourcing

With the prosperity of smart devices, crowdsourcing has emerged as a new computing/networking paradigm. Through the crowdsourcing platform, service requesters can buy service from service providers. An important component of crowdsourcing is its incentive mechanism. We study three models of crowdsourcing, which involve cooperation and competition among the service providers. Our simplest model generalizes the well-known user-centric model studied in a recent Mobicom paper. We design an incentive mechanism for each of the three models, and prove that these incentive mechanisms are individually rational, budget-balanced, computationally efficient, and truthful.

Network function virtualization in the multi-tenant cloud

With more and more tenants launching their applications on the cloud, various requirements have been posed regarding the clouds performance, security, and management. In the face of tenant demands, the cloud provider deploys different hardware middleboxes, carrying out different network functions, and enhancing the clouds capability in serving tenant requirements. While middleboxes are crucial to the cloud, concerns have been raised regarding their costs, manageability, and performance overhead. To tackle these problems, researchers have proposed an alternative to hardware middleboxes: network function virtualization. Software applications are deployed in place of hardware middleboxes, offering equivalent functionalities while greatly improving flexibility, manageability, and cost-efficiency. In this paper we discuss opportunities and challenges that network function virtualization brings to the multi-tenant cloud. We also propose a cloud architecture that exploits virtual network functions. Our contributions can serve as an enlightener for future efforts in this area.

Joint Scheduling and Beamforming Coordination in Cloud Radio Access Networks With QoS Guarantees

The cloud radio access network (C-RAN) is a promising architecture for future radio access networks (RANs) due to its advantages in cost efficiency, flexibility, and utilization efficiency. To fully reap these benefits, this paper focuses on joint optimization of user grouping, virtual base station (VBS) clustering, and transmit beamforming in C-RAN downlink networks for maximizing the system utility, subject to the diverse quality-of-service (QoS) requirements of users and the power constraints of distributed remote radio heads (RRHs). To tackle the high computational complexity in solving the nonconvex combinatorial optimization problem, a two-stage solution is proposed. Specifically, a dynamic user-centric scheduling algorithm is developed to form user groups and cluster RRHs into VBSs by exploiting the nonuniform distribution of users. Then, an iterative transmit beamformer optimization algorithm is devised to coordinate the transmit beamforming among the VBSs to mitigate the intracell and intercell interference, hence further enhancing the overall system utility. Evaluation results demonstrate that the proposed algorithm achieves significant performance gain over various reference algorithms in terms of system utility, system throughput, and energy efficiency.

Enhancing software-defined RAN with collaborative caching and scalable video coding

The ever increasing video demands from mobile users have posed great challenges to cellular networks. To address this issue, video caching in radio access networks (RANs) has been recognized as one of the enabling technologies in future 5G mobile networks, which brings contents near the end-users, reducing the transmission cost of duplicate contents, meanwhile increasing the Quality-of-Experience (QoE) of users. Inspired by the emerging software-defined networking technology, recent proposals have employed centralized collaborative caching among cells to further increase the caching capacity of the RAN. In this paper, we explore a new dimension in video caching in software-defined RANs to expand its capacity. We enable the controller with the capability to adaptively select the bitrates of videos received by users, in order to maximize the number and quality of video requests that can be served, meanwhile minimizing the transmission cost. To achieve this, we further incorporate Scalable Video Coding (SVC), which enables caching and serving sliced video layers that can serve different bitrates. We formulate the problem of joint video caching and scheduling as a reward maximization (cost minimization) problem. Based on the formulation, we further propose a 2-stage rounding-based algorithm to address the problem efficiently. Simulation results show that using SVC with collaborative caching greatly improves the cache capacity and the QoE of users.

Keep Your Promise: Mechanism Design Against Free-Riding and False-Reporting in Crowdsourcing

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 semi-truthful, 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.

QoS-aware service selection in virtualization-based Cloud computing

Cloud computing is one of the most significant latest efforts in the field of information technology, which may change the way how information services are provisioned. In a Cloud environment, different types of resources need to be virtualized as a collection of Cloud services using virtualization technology. End-users in the Cloud are usually provided with customized Cloud services that involve not only different kinds of computing services but also the networks interconnecting those computing services. Therefore, a set of Cloud computing services and the networking services should be modeled as a composite customized Cloud service. In this paper, we present an improved model for Cloud service provisioning based on our previous Network-Cloud proposal, and propose a procedure with several QoS-aware service selection algorithms for composing different services offered by a Cloud. Our analysis with numerical experiments show that the presented algorithms can select services appropriately that deal with different requirements of service provisioning.

You better be honest: Discouraging free-riding and false-reporting in mobile crowdsourcing

Crowdsourcing is an emerging paradigm where users can pay for the services they need or receive rewards for providing services. One example in wireless networking is mobile crowdsourcing, which leverages a cloud computing platform for recruiting mobile users to collect data (such as photos, videos, mobile user activities, etc) for applications in various domains, such as environmental monitoring, social networking, healthcare, transportation, etc. However, a critical problem arises as 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 aim to design schemes to tackle these problems, so that each individual in the system is better off being honest. 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 free-riding and false-reporting. We prove that EFF eliminates the existence of free-riding and false-reporting, while guaranteeing truthfulness, individual rationality, budget-balance, and computational efficiency. We also prove that DFF is semi-truthful, which discourages dishonest behavior such as free-riding and false-reporting when the rest of the individuals are honest, while guaranteeing budget-balance and computational efficiency. Performance evaluation shows that within our mechanisms, no dishonest behavior could bring extra benefit for each individual.

An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications

Abstract-Internet of Things (IoT) is emerging as part ofthe infrastructures for advancing a large variety of applicationsinvolving connection of many intelligent devices, leadingto smart communities. Due to the severe limitation on thecomputing resources of IoT devices, it is common to offloadtasks of various applications requiring substantial computingresources to computing systems with sufficient computingresources, such as servers, cloud systems, and/or data centersfor processing. However, the offloading method suffers fromthe difficulties of high latency and network congestion in theIoT infrastructures. Recently edge computing has emergedto reduce the negative impacts of these difficulties. Yet, edgecomputing has its drawbacks, such as the limited computingresources of some edge computing devices and the unbalancedload among these devices. In order to effectively explorethe potential of edge computing to support IoT applications,it is necessary to have efficient task management in edgecomputing networks. In this paper, an approach is presented toperiodically distributing incoming tasks in the edge computingnetwork so that the number of tasks, which can be processedin the edge computing network, is increased, and the qualityof-service (QoS) requirements of the tasks completed in theedge computing network are satisfied. Simulation results arepresented to show the improvement of using this approach onthe increase of the number of tasks to be completed in the edgecomputing network.

TSA: A framework of truthful spectrum auctions under the physical interference model

Auction is an effective method of allocating scarce spectrum resources in cognitive radio networks, where the primary users are sellers and the secondary users are buyers. In order for the buyers and sellers to act honestly during the auction, truthfulness has been identified as an important property. Current research focuses on the truthfulness and spatial reusability by either assuming that a conflict graph is given under the protocol model, or assuming that the grouping result is given under the physical interference model without power control. To fill this void, we design a framework of truthful double auctions, named TSA, for spectrum sharing in cognitive radio networks. TSA finds a feasible grouping profile such that users in the same group can be assigned to the same channel while each gets a satisfactory SINR value by an appropriate transmitting power allocation. We prove that TSA guarantees all the desired economic properties: individual rationality, budget-balance, computational efficiency, and truthfulness. Extensive performance evaluation also supports our theoretic analysis.

Robust Incentive Tree Design for Mobile Crowdsensing

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.