Dynamic pricing incentive for participatory sensing

User participation is one of the most important elements in participatory sensing application for providing adequate level of service quality. However, incentive mechanism and its economic model for user participation have been less addressed so far in this research domain. This paper studies the economic model of user participation incentive in participatory sensing applications. To stimulate user participation, we design and evaluate a novel reverse auction based dynamic pricing incentive mechanism where users can sell their sensing data to a service provider with users’ claimed bid prices. The proposed incentive mechanism focuses on minimizing and stabilizing the incentive cost while maintaining adequate level of participants by preventing users from dropping out of participatory sensing applications. Compared with random selection based fixed pricing incentive mechanism, the proposed mechanism not only reduces the incentive cost for retaining the same number of participants but also improves the fairness of incentive distribution and social welfare. It also helps us to achieve the geographically balanced sensing measurements and, more importantly, can remove the burden of accurate price decision for user data that is the most difficult step in designing incentive mechanism.     

Price differentiation and control in the Kelly mechanism

The design and implementation of resource allocation and pricing for computing and network resources are crucial for system and user performance. Among various designing objectives, we target on maximizing the social welfare, i.e., the summation of all user utilities. The challenge comes from the fact that users are autonomous and their utilities are unknown to the system designer. Under the Kelly mechanism, users bid and proportionally share resources. When user population is large and “price-taking” can be assumed, the Kelly mechanism maximizes the social welfare; however, under oligopolistic competitions, this mechanism might induce an efficiency loss up to 25% of the welfare optimum.     

The role of aesthetic,cultural, utilitarian and branding factors in young Chinese consumers' repurchase intention of smartphone brands

Despite the exponential growth of smartphone consumption, to date, very few studies have investigated the factors that influence consumers' repurchase intention of smartphone brands. China has become the world's largest consumer markets for smartphones, therefore understanding young Chinese consumers' repurchase intention in the smartphone market is of crucial importance to smartphone companies. A preliminary qualitative study based on 30 face-to-face interviews has led to the development of a new conceptual framework including aesthetic (design appeal), functional (perceived quality), brand value (brand popularity), social (subjective norm) and cultural influences (mianzi). The newly developed framework has been tested through partial least squares structural equation modeling with a sample of 321 young Chinese smartphone users. The results show that young Chinese customer's smartphone repurchase intention is mainly determined by mianzi, perceived quality, brand popularity, and design appeal. Furthermore, findings also highlight that subjective norm, perceived quality and design appeal affect Chinese people's mianzi.     

Privacy-preserving computation of participatory noise maps in the cloud

This paper presents a privacy-preserving system for participatory sensing, which relies on cryptographic techniques and distributed computations in the cloud. Each individual user is represented by a personal software agent, deployed in the cloud, where it collaborates on distributed computations without loss of privacy, including with respect to the cloud service providers. We present a generic system architecture involving a cryptographic protocol based on a homomorphic encryption scheme for aggregating sensing data into maps, and demonstrate security in the Honest-But-Curious model both for the users and the cloud service providers. We validate our system in the context of NoiseTube, a participatory sensing framework for noise pollution, presenting experiments with real and artificially generated data sets, and a demo on a heterogeneous set of commercial cloud providers. To the best of our knowledge our system is the first operational privacy-preserving system for participatory sensing. While our validation pertains to the noise domain, the approach used is applicable in any crowd-sourcing application relying on location-based contributions of citizens where maps are produced by aggregating data – also beyond the domain of environmental monitoring.     

Cloud Computing System Management Under Flat Rate Pricing

Success of cloud computing service depends on an acceptable pricing mechanism both by users and the service provider. Piece rate pricing by counting work load should be favorable for the service provider due to the QoS control and finite resource, such as computing and, communication powers. Though the pricing mechanism based on counting work load is reasonable and fair, the experiences learned from ADSL, 3G and Wi-Fi show a different story. The flat rate pricing mechanism is the winner all the way. This study proposes a flat rate pricing mechanism with congestion control, called FRPCC. In the cloud computing system, allocation of resources can be formulated as an optimization problem seeking to maximize the sum of the utility function of each user under the constraints of fairness. The piece rate pricing mechanism is easy to achieve the social welfare but is not easy to be acceptable for customers. Consequently, we propose a congestion control scheme to reach the same goal with a flat rate pricing mechanism. The proposed FRPCC approach can achieve social welfare in the cloud computing environment. Performance evaluations show efficacy of, FRPCC approach in providing social welfare under fairness and preventing congestion.     

云计算资源分配动态定价方法

云市场用户的资源需求往往会随着时间而波动变化,在资源分配与定价时若不充分考虑供需双方的内在激励,将难以获得理想的结果。基于市场策略,设计一个组合拍卖机制来平滑用户需求,以提高资源管理效率及服务收益;所提机制以动态定价的方式向用户分配资源,实现了无妒与可信两种属性,无妒属性保证了机制运行的稳定性,而可信属性可以使得服务收益最大化;此外所提方法具有较低的计算复杂度,易于实现。实验结果表明,在短缺与饱和市场下,所提方法均可获得近似最优的收益及相对较高的社会福利。     

Towards real-time dynamic spectrum auctions

In this paper, we propose a low-complexity auction framework to distribute spectrum in real-time among a large number of wireless users with dynamic traffic. Our design consists of a compact and highly expressive bidding format, two pricing models to control tradeoffs between revenue and fairness, and fast auction clearing algorithms to achieve conflict-free spectrum allocations that maximize auction revenue. We develop analytical bounds on algorithm performance and complexity to verify the efficiency of the proposed approach. We also use both simulated and real deployment traces to evaluate the auction framework. We conclude that pricing models and bidding behaviors have significant impact on auction outcomes and spectrum utilization. Any efficient spectrum auction system must consider demand and spectrum availability in local regions to maximize system-wide revenue and spectrum utilization.     

基于云平台的共享制造模式定价策略

在共享制造背景下,设备制造商可以通过两种方式向消费者提供服务:直接出售生产型设备给消费者,通过云平台提供制造服务:在购买模式下,讨论消费者的决策行为以及设备制造商的最优定价策略;在服务模式下,采用逆向归纳法分别求解基于商业性云平台(最大化自身收益)和基于公益性云平台(最大化社会总效益)的多阶段动态博弈问题.通过比较两种...     

A survey of approximability and inapproximability results for social welfare optimization in multiagent resource allocation

Multiagent resource allocation provides mechanisms to allocate bundles of resources to agents, where resources are assumed to be indivisible and nonshareable. A central goal is to maximize social welfare of such allocations, which can be measured in terms of the sum of utilities realized by the agents (utilitarian social welfare), in terms of their minimum (egalitarian social welfare), and in terms of their product (Nash product social welfare). Unfortunately, social welfare optimization is a computationally intractable task in many settings. We survey recent approximability and inapproximability results on social welfare optimization in multiagent resource allocation, focusing on the two most central representation forms for utility functions of agents, the bundle form and the k-additive form. In addition, we provide some new (in)approximability results on maximizing egalitarian social welfare and social welfare with respect to the Nash product when restricted to certain special cases.     

kNN processing with co-space distance in SoLoMo systems

With the increasing popularity of smart phones, SoLoMo (Social-Location-Mobile) systems are expected to be fast-growing and become a popular mobile social networking platform. A main challenge in such systems is on the creation of stable links between users. For each online user, the current SoLoMo system continuously returns his/her kNN (k Nearest Neighbor) users based on their geo-locations. Such a recommendation approach is simple, but fails to create sustainable friendships. Instead, it would be more effective to tap onto the existing social relationships in conventional social networks, such as Facebook and Twitter, to provide a “better” friend recommendations.To measure the similarity between users, we propose a new metric, co-space distance, by considering both the user distances in the real world (physical distance) and the virtual world (social distance). The co-space distance measures the similarity of two users in the SoLoMo system. We compute the social distances between users based on their public information in the conventional social networks, which can be achieved by a few MapReduce jobs. To facilitate efficient computation of the social distance, we build a distributed index on top of the key-value store, and maintain the users’ geo-locations using an R-tree. For each query on finding potential friends around a location, we return kNN neighbors to each user based on their co-space distances. We propose a progressive top-k processing strategy and an adaptive-caching strategy to facilitate efficient query processing. Experiments with Gowalla dataset¹ show the effectiveness and efficiency of our recommendation approach.     

Smooth Gaze: a framework for recovering tasks across devices using eye tracking

A user task is often distributed across devices, e.g., a student listening to a lecture in a classroom while watching slides on a projected screen and making notes on her laptop, and sometimes checking Twitter for comments on her smartphone. In scenarios like this, users move between heterogeneous devices and have to deal with task resumption overhead from both physical and mental perspectives. To address this problem, we created Smooth Gaze, a framework for recording the user’s work state and resuming it seamlessly across devices by leveraging implicit gaze input. In particular, we propose two novel and intuitive techniques, smart watching and smart posting, for detecting which display and target region the user is looking at, and transferring and integrating content across devices respectively. In addition, we designed and implemented a cross-device reading system SmoothReading that captures content from secondary devices and generates annotations based on eye tracking, to be displayed on the primary device. We conducted a study that showed that the system supported information seeking and task resumption, and improved users’ overall reading experience.     

Recruiting the <Emphasis Type="Italic">K</Emphasis>-most influential prospective workers for crowdsourcing platforms

Viral marketing is widely used by businesses to achieve their marketing objectives using social media. In this work, we propose a customized crowdsourcing approach for viral marketing which aims at efficient marketing based on information propagation through a social network. We term this approach the social community-based crowdsourcing platform and integrate it with an information diffusion model to find the most efficient crowd workers. We propose an intelligent viral marketing framework (IVMF) comprising two modules to achieve this end. The first module identifies the K-most influential users in a given social network for the platform using a novel linear threshold diffusion model. The proposed model considers the different propagation behaviors of the network users in relation to different contexts. Being able to consider multiple topics in the information propagation model as opposed to only one topic makes our model more applicable to a diverse population base. Additionally, the proposed content-based improved greedy (CBIG) algorithm enhances the basic greedy algorithm by decreasing the total amount of computations required in the greedy algorithm (the total influence propagation of a unique node in any step of the greedy algorithm). The highest computational cost of the basic greedy algorithm is incurred on computing the total influence propagation of each node. The results of the experiments reveal that the number of iterations in our CBIG algorithm is much less than the basic greedy algorithm, while the precision in choosing the K influential nodes in a social network is close to the greedy algorithm. The second module of the IVMF framework, the multi-objective integer optimization model, is used to determine which social network should be targeted for viral marketing, taking into account the marketing budget. The overall IVMF framework can be used to select a social network and recruit the K-most influential crowd workers. In this paper, IVMF is exemplified in the domain of personal care industry to show its importance through a real-life case.     

基于移动感知的智能手机防盗软件的研究

得益于硬件技术的发展,智能手机感知信息的方式日益丰富。因而以智能手机自带的传感器件为出发点,以移动感知为基础,设计并开发了一款智能手机防盗软件。该软件针对智能手机在生活中的不同使用场景,利用手机自带的加速度传感器、环境光传感器、接近传感器以及手机内部的广播机制,通过分析从周围环境获取的实时数据来判断手机状态是否变化,从而实现不同的防盗模式。其中在通过加速度传感器输出值判断手机状态时,设计了三轴加速度从手机坐标系到参考坐标系的四元数转换算法,以利于客观统一地判断手机的运动状态。此外,通过手机内部短信广播的截获技术来判断设备是否收到短信以及短信内容是否为预设指令,实现对手机的远程控制。目前Android智能手机的市场占有率高达80%,因而以Android手机为例,实现了上述智能手机防盗软件。各项功能经过真机测试,均已达到预期效果。     

Spatial-aware interest group queries in location-based social networks

With the recent advances in positioning and smartphone technologies, a number of social networks such as Twitter, Foursquare and Facebook are acquiring the dimension of location, thus bridging the gap between the physical world and online social networking services. Most of the location-based social networks released check-in services that allow users to share their visiting locations with their friends. In this paper, users' interests are modeled by check-in actions. We propose a new type of Spatial-aware Interest Group (SIG) query that retrieves a user group of size k where each user is interested in the query keywords and they are close to each other in the Euclidean space. We prove that the SIG query problem is NP-complete. A family of efficient algorithms based on the IR-tree is thus proposed for the processing of SIG queries. Experiments on two real datasets show that our proposed algorithms achieve orders of magnitude improvement over the baseline algorithm.     

Path-vector contracting: Profit maximization and risk management

We consider an Internet Service Provider’s (ISP’s) problem of providing end-to-end (e2e) services with bandwidth guarantees, using a path-vector based approach. In this approach, an ISP uses its edge-to-edge (g2g) single-domain contracts and vector of contracts purchased from neighboring ISPs as the building blocks to construct, or participate in constructing, an end-to-end “contract path”. We develop a spot-pricing framework for the e2e bandwidth guaranteed services utilizing this path contracting strategy, by formulating it as a stochastic optimization problem with the objective of maximizing expected profit subject to risk constraints. In particular, we present time-invariant path contracting strategies that offer high expected profit at low risks, and can be implemented in a fully distributed manner. Simulation analysis is employed to evaluate the contracting and pricing framework under different network and market conditions. An admission control policy based on the path contracting strategy is developed and its performance is analyzed using simulations.     

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P (http://smartp2p.cs.ucy.ac.cy/) system deployed over our SmartLab (http://smartlab.cs.ucy.ac.cy/) testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95 %, with one order of magnitude less time and two orders of magnitude less energy than its competitors.     

Android fine-grained permission control system with real-time expert recommendations

In current Android architecture design, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to prevent unnecessary privacy breach. However, inexperienced users may not be able to decide correctly. To assist inexperienced users to make a right permission granting decisions, we propose RecDroid. RecDroid is a crowdsourcing recommendation framework that facilitates a user-help-user environment regarding smartphone permission control. In this framework, the responses from expert users are aggregated and recommended to other users. We implement our prototype on Android platform and evaluated the system through simulation and real user study.     

TracSim: Simulating and scheduling trapped power capacity to maximize machine room throughput

The power supplied to machine rooms tends to be over-provisioned because it is specified in practice not by workload demands but rather by high energy LINPACK runs or nameplate power estimates. This results in a considerable amount of trapped power capacity—excess power infrastructure. Instead of being wasted, this trapped power capacity should be reclaimed to accommodate more compute nodes in the machine room and thereby increase system throughput. But to do this we need the ability to enforce a system-wide power cap. In this paper, we present TracSim, a full-system simulator that enables users to measure trapped power capacity and evaluate the performance of different policies for scheduling parallel tasks under a power cap. TracSim simulates the execution environment of a production HPC cluster at Los Alamos National Laboratory (LANL). TracSim enables users to specify the system topology, hardware configuration, power cap, and task workload and to develop resource configuration and task scheduling policies aimed at maximizing machine-room throughput while keeping power consumption under a power cap by exploiting CPU throttling techniques. We use real measurements from the LANL cluster to set TracSim’s configuration parameters. We leverage TracSim to implement and evaluate four resource scheduling policies. Simulation results indicate the performance of those policies and quantify the amount of trapped capacity that can effectively be reclaimed.     

Where and what: Using smartphones to predict next locations and applications in daily life

This paper investigates the prediction of two aspects of human behavior using smartphones as sensing devices. We present a framework for predicting where users will go and which app they will use in the next ten minutes by exploiting the rich contextual information from smartphone sensors. Our first goal is to understand which smartphone sensor data types are important for the two prediction tasks. Secondly, we aim at extracting generic (i.e., user-independent) behavioral patterns and study how generic behavior models can improve the predictive performance of personalized models. Experimental validation was conducted on the Lausanne Data Collection Campaign (LDCC) dataset, with longitudinal smartphone data collected over a period of 17 months from 71 users.     

Viral marketing for dedicated customers

Viral marketing has attracted considerable concerns in recent years due to its novel idea of leveraging the social network to propagate the awareness of products. Specifically, viral marketing first targets a limited number of users (seeds) in the social network by providing incentives, and these targeted users would then initiate the process of awareness spread by propagating the information to their friends via their social relationships. Extensive studies have been conducted for maximizing the awareness spread given the number of seeds (the Influence Maximization problem). However, all of them fail to consider the common scenario of viral marketing where companies hope to use as few seeds as possible yet influencing at least a certain number of users. In this paper, we propose a new problem, called J-MIN-Seed, whose objective is to minimize the number of seeds while at least J users are influenced. J-MIN-Seed, unfortunately, is NP-hard. Therefore, we develop an approximate algorithm which can provide error guarantees for J-MIN-Seed. We also observe that all existing studies on viral marketing assume that all users in the social network are of interest for the product being promoted (i.e., all users are potential consumers of the product), which, however, is not always true. Motivated by this phenomenon, we propose a new paradigm of viral marketing where the company can specify which types of users in the social network are of interest when promoting a specific product. Under this new paradigm, we re-define our J-MIN-Seed problem as well as the Influence Maximization problem and design some algorithms with provable error guarantees for the new problems. We conducted extensive experiments on real social networks which verified the effectiveness of our algorithms.