Social welfare maximization in participatory smartphone sensing

Participatory smartphone sensing has lately become more and more popular as a new paradigm for performing large-scale sensing, in which each smartphone contributes its sensed data for a collaborative sensing application. Most existing studies consider that smartphone users are strictly strategic and completely rational, which try to maximize their own payoffs. A number of incentive mechanisms are designed to encourage smartphone users to participate, which can achieve only suboptimal system performance. However, few existing studies can maximize a system-wide objective which takes both the platform and smartphone users into account. This paper focuses on the crucial problem of maximizing the system-wide performance or social welfare for a participatory smartphone sensing system. There are two great challenges. First, the social welfare maximization cannot be realized on the platform side because the cost of each user is private and unknown to the platform in reality. Second, the participatory sensing system is a large-scale real-time system due to the huge number of smartphone users who are geo-distributed in the whole world. A price-based decomposition framework is proposed in our previous work (Liu and Zhu, 2013), in which the platform provides a unit price for the sensing time spent by each user and the users return the sensing time via maximizing the monetary reward. This pricing framework is an effective incentive mechanism as users are motivated to participate for monetary rewards from the platform. In this paper, we propose two distributed solutions, which protect users’ privacy and achieve optimal social welfare. The first solution is designed based on the Lagrangian dual decomposition. A poplar iterative gradient algorithm is used to converge to the optimal value. Moreover, this distributed method is interpreted by our pricing framework. In the second solution, we first equivalently convert the original problem to an optimal pricing problem. Then, a distributed solution under the pricing framework via an efficient price-updating algorithm is proposed. Experimental results show that both two distributed solutions can achieve the maximum social welfare of a participatory smartphone system.     

移动群智感知中面向任务需求的用户选择激励机制

现有的移动群智感知激励机制大多以平台为中心或是以用户为中心进行设计，缺乏对感知任务需求的多维考虑，从而无法切实地以任务为中心进行用户选择，导致无法满足任务需求的最大化和多样化。针对上述问题，提出一种面向任务需求的用户选择激励机制TRIM，这是一种以任务为中心的设计方法。首先，感知平台根据任务需求发布感知任务，并从任务类型、时空特性以及感知报酬等多维度构建任务向量以最大化满足任务需求，而感知用户则基于意愿偏好、个人贡献值以及期望报酬等属性构建用户向量，实现个性化选择感知任务参与响应；然后，通过引入高效且隐私保护的余弦相似度计算协议（PCSC），计算任务和用户的相似度并根据相似度高低进行用户匹配筛选得到目标用户集，更好地满足感知任务需求的同时保护用户隐私不泄露；最后，通过仿真实验表明，在感知任务和感知用户的匹配过程中，与采用Paillier加密协议的激励机制相比，TRIM缩短了指数级增量的计算时间开销，提高了计算效率；与采用直接余弦相似度计算协议的激励机制相比，TRIM保证了感知用户的隐私安全，达到了98%的匹配精确度。     

Survey-based exploration of attitudes to participatory sensing tasks in location-based gaming communities

An increasing number of participatory sensing applications have been developed in recent years. However, most of them are still in the early adoption phase and count only few users as compared to the billions of devices that could be leveraged. On the other side, existing location-based games, such as geocaching or Ingress, gain in popularity and attract up to millions of users worldwide. Since the players of location-based games are already exploring their environment, one approach could be to especially address these communities in order to increase the user base of participatory sensing applications. To this end, we conduct a preliminary questionnaire-based study involving 337 participants to investigate the possible attitudes of such players towards participatory sensing applications. In particular, we analyze the potential interests of our participants in sensing tasks based on their demographics, played games, and sensing modalities. Our results show that our participants would prefer contributing to sensing tasks when integrated in geocaching. Moreover, a point-based reward system would not significantly motivate them and could even have negative consequences.     

具有多优先级多服务网络的激励价格控制

运用Stackelberg对策中的激励原理,研究具有多优先级的多服务网络系统的价控问题。提出了具有管理者的用户两优先级的激励模型,给出了确定激励参数的方法。将此方法扩展到多优先级的情形,推导出互联激励参数矩阵。通过数值例子说明了激励价格策略的有效性。     

共享单车维护任务完成概率防策略机制设计

共享单车作为一种绿色低碳的出行方式,给人们的出行带来便利。然而,人们自由使用单车给共享单车的维护带来许多问题(例如需要将某个区域无序放置的单车送到某个指定位置),因此,共享单车平台可能需要雇佣用户去完成单车维护任务,同时平台需要给予用户合理的报酬以激励用户完成任务。当存在多个用户竞争时,用户可能谎报任务完成概率来获得更高的报酬,从而导致平台最终不能完成所有的维护任务。考虑用户在任务完成概率方面的策略行为,在满足一定任务完成概率的情况下,设计防策略性机制,实现完成维护任务完成成本最小化。该机制包括任务分发算法和用户定价算法,其中任务分发机制采用贪心算法思想进行设计,而用户定价算法则通过迈尔森定理来设计。理论证明该机制满足激励相容性和个体理性,接着进一步基于摩拜单车数据集来评估该机制的性能,主要包括任务完成成本、用户平均期望效用、用户期望效用概率密度等评价指标。通过与VCG机制相比较,该机制能够达到常数倍的近似比,任务完成成本更低,用户平均期望效用更高,并且能够防止用户在任务完成概率方面的策略行为。     

Pricing Internet services: proposed improvements

Flat-rate pricing appeals to Internet users and service providers because of its simplicity and predictability. However, congestion is the inevitable consequence of flat-rate pricing because Internet users who pay a fixed access fee have no incentive to limit their network usage. Future applications that require timely delivery of data will require mechanisms for allocating network resources that give consumers choices in services and prices while allowing service providers to recover their costs. We examine the proposed improvements in Internet pricing that are designed to increase its economic efficiency and support the deployment of new applications that require a better quality of service than the Internet currently offers     

Unlocking the Advantages of Dynamic Service Selection and Pricing

Bandwidth limitations, resource greedy applications verbose mark-up languages and an increasing number of voice and data users are straining the air interface of wireless networks. Hence, novel approaches and new algorithms to manage wireless bandwidth are needed. In addition, usage based pricing is becoming increasingly prevalent (pre-paid cell phones, calling cards, non-contract minutes, etc.). This paper unlocks the potential to improving the performance of overall system behavior by allowing users to change service level and/or service provider for a (small) price. The ability to dynamically re-negotiate service gives the user the power to control QoS while minimizing usage cost. On the other hand, the ability to change service level pricing dynamically allows the service providers to manage traffic better, improve resource usage and most importantly maximize their profit. This provides a surprising win-win situation for BOTH the service providers AND the users. In this paper we present easy to implement on-line algorithms to minimize the overall usage cost to individual mobile users. This on-line algorithm continuously receives pricing information and evaluates minimum QoS requirements. The algorithm then determines appropriate service level, chooses a service provider and sets a time for re-negotiation dynamically. Our algorithm can handle many practical issues such as capacity limitations, arbitrary price fluctuations and loss/gain of service providers due to mobility. Our results do not assume any specific technologies and can be applied to any environment that can employ dynamic pricing, including wired networks. In fact, dynamic pricing is becoming increasingly desirable since service provider and capacity changes are a growing by-product of mobility. Arriving and departing users at/from a cell tower (or wireless LAN) can effectively reduce or increase the available bandwidth in a cell (or LAN transmission area) and represents a natural opportunity for a pricing change.     

FIMI: A Constant Frugal Incentive Mechanism for Time Window Coverage in Mobile Crowdsensing

Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. An incentive mechanism is important for a mobile crowdsensing system to stimulate participants and to achieve good service quality. In this paper, we explore truthful incentive mechanisms that focus on minimizing the total payment for a novel scenario, where the platform needs the complete sensing data in a requested time window (RTW). We model this scenario as a reverse auction and design FIMI, a constant frugal incentive mechanism for time window coverage. FIMI consists of two phases, the candidate selection phase and the winner selection phase. In the candidate selection phase, it selects two most competitive disjoint feasible user sets. Afterwards, in the winner selection phase, it finds all the interchangeable user sets through a graph-theoretic approach. For every pair of such user sets, FIMI chooses one of them by the weighted cost. Further, we extend FIMI to the scenario where the RTW needs to be covered more than once. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve the properties of RTW feasibility (or RTW multi-coverage), computation efficiency, individual rationality, truthfulness, and constant frugality.     

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。     

移动P2P文件共享系统中基于用户偏好的激励机制

提出一种基于用户偏好的激励机制,鼓励用户通过提供共享文件和转发操作为系统做出贡献。IMBPC结合了虚拟价格机制和预期文件传输延时,基于用户对价格和延时的偏好度来选择最合适的节点进行文件下载。通过设置模拟实同仅通过虚拟价格机制来激励用户做出贡献的策略进行比较,显示应用IMBPC策略的系统,节点的贡献积极性明显增加。     

混合群智感知中服务节点优化选择机制

移动群智感知应用依赖于以人为主导的移动用户参与,用户的移动规律和用户所携带感知设备的剩余资源等都会制约其参与感知服务的能力,从而影响系统的感知质量。现有研究工作对服务节点的选取操作比较单一,因此有必要设计合理的节点优化选择机制,选择到达并覆盖目标区域的最优服务节点集,从而保证对目标区域的感知质量。针对服务节点的优化选取展开研究,基于人的移动特性,定义节点服务度量标准,并结合遗传算法设计服务节点优化选取算法,从而提出一种新的服务节点优化选择机制。仿真实验表明,该机制可以有效选取最优服务节点集,达到提高混合群智网络感知服务质量的目的。     

Encouraging user participation in a course recommender system: An impact on user behavior

User participation emerged as a critical issue for collaborative and social recommender systems as well as for a range of other systems based on the power of user community. A range of mechanisms to encourage user participation in social systems has been proposed over the last few years; however, the impact of these mechanisms on users behavior in recommender systems has not been studied sufficiently. This paper investigates the impact of encouraging user participation in the context of CourseAgent, a community-based course recommender system. The recommendation power of CourseAgent is based on course ratings provided by a community of students. To increase the number of course ratings, CourseAgent applies an incentive mechanism which turns user feedback into a self-beneficial activity. In this paper, we describe the design and implementation of our course recommendation system and its incentive mechanism. We also report a dual impact of this mechanism on user behavior discovered in two user studies.     

参与式感知系统中用户累积行为信誉计算

参与式感知系统中,由于感知数据质量可能受参与者影响,提出了基于用户累积行为的信誉计算模型以帮助选择可信赖用户.针对感知环境中用户群体的广泛性及核心用户的不确定性,该模型采用OPTICS聚类算法定义用户场景并划分行为数据集,建立用户累积行为信誉计算模型,同时引入时间戳标记信息抛弃部分旧行为以更新用户信誉.实验表明,该信誉模型能够结合新旧行为较好地计算并调整用户信誉,在感知环境用户信誉评价中具有良好的应用前景.     

基于演化博弈的领先用户知识共享行为激励机制

针对用户创新社区中未考虑企业激励机制对领先用户知识共享行为影响的问题,提出一种基于演化博弈的领先用户知识共享行为激励机制。首先,将企业和领先用户作为博弈主体,分别构建企业未采取激励措施和企业采取激励措施条件下的演化博弈模型;其次,分别对两个模型进行局部稳定性分析,以探讨系统的动态演化过程与演化稳定策略;最后,通过计算机模拟仿真,对比两种条件下领先用户知识共享行为的演化结果,分析领先用户知识共享行为的影响因素及最佳激励策略。实验结果表明,企业采取激励措施可以有效促进领先用户的知识共享行为,并且将激励分配系数控制在一定范围内时系统将达到最佳的稳定状态;最佳激励分配系数大小由知识共享成本、知识搜索成本及额外成本共同决定;知识共享成本、知识搜索成本以及激励分配系数会显著影响领先用户知识共享行为的水平。     

移动群智感知中支持隐私保护的动态激励机制

针对移动群智感知中高质量感知数据与参与用户隐私之间的矛盾,提出一种支持隐私保护的动态激励机制。首先,采用轻量级隐私保护方法,利用安全加密哈希函数为竞标用户生成不少于256位的可变地址序列,并结合随机数对候选用户节点的效用报价进行隐匿和约束;其次,通过定义区域热度、时间热度、数据完整率和数据质量等多维参数,实现任务价值与用户效用报价的动态平衡;最后,依据用户提交的效用报价和任务预算,并利用逆向拍卖思想,完成对任务参与节点的最优选择和动态激励。在群智感知系统模拟平台上进行仿真实验,结果表明所提机制不仅增强了隐私保护度和数据精确度,同时提升了时间效率和激励效果。     

An incentive game based evolutionary model for crowd sensing networks

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers.     

考虑信誉更新的移动群智感知在线激励机制研究

激励更多用户参与感知任务并提供高质量数据是移动群智感知研究的热点问题之一。针对在线到达的激励机制场景中,参与用户提供数据的质量以及其信誉值没有得到足够重视等问题,本文提出用户在线参与感知任务的信誉评价方法并构建其信誉评价模型。综合考虑用户历史和现实的信誉记录,建立信誉更新算法模型,设计基于信誉更新的多阶段在线激励机制(Reputation-updated online mechanism,ROM)。仿真结果表明,该算法能够帮助平台获得更好的效用,提高收集数据的质量从而提高雇佣效率。     

优化的IP-DiffServ动态资源定价机制

在参考了美国国家基金会(NSF)的CAREER提出的IP-DiffServ的动态定价机制后,提出了一个以市场和计划为基础的优化动态定价机制.该机制以业务计划和资源规划为基础,从实现用户的最大性能价格比和ISP的最大利益出发实现了对业务类的定价,在计算用户可感觉到的利益时,考虑了负荷因素,从而可以引导业务量按照业务计划有序分布.仿真实验证明了它对NSF CAREER的业务类价值评估公式进行的改进是合理而有效的.     

More Requests,Less Cost: Uncertain Inter-Datacenter Traffic Transmission with Multi-Tier Pricing

With the multi-tier pricing scheme provided by most of the cloud service providers (CSPs), the cloud users typically select a high enough transmission service level to ensure the quality of service (QoS), due to the severe penalty of missing the transmission deadline. This leads to the so-called over-provisioning problem, which increases the transmission cost of the cloud user. Given the fact that cloud users may not be aware of their traffic demand before accessing the network, the over-provisioning problem becomes more serious. In this paper, we investigate how to reduce the transmission cost from the perspective of cloud users, especially when they are not aware of their traffic demand before the transmission deadline. The key idea is to split a long-term transmission request into several short ones. By selecting the most suitable transmission service level for each short-term request, a cost-efficient inter-datacenter transmission service level selection framework is obtained. We further formulate the transmission service level selection problem as a linear programming problem and resolve it in an on-line style with Lyapunov optimization. We evaluate the proposed approach with real traffic data. The experimental results show that our method can reduce the transmission cost by up to 65.04%.     

移动群智感知中的空间任务分配机制

在移动群智感知的空间任务分配问题中用户与任务的空间距离直接影响完成任务所需的成本,而现有的研究在这方面却考虑不足,因此以最小化感知成本为目标设计了移动群智感知中的空间任务分配机制。首先,以感知成本最小为目标,基于遗传算法和贪心算法设计了一种高效的任务分配方法;其次,针对用户感知质量的随机性,基于用户的历史感知情况和当前任务的执行情况设计了用户感知质量的更新机制。为验证所提机制的效果,通过仿真实验与两种基准的任务分配方法作比较。实验结果表明,所提机制在感知总成本和用户执行任务所移动的总距离等方面均有更好的效果,因此该空间任务分配机制具有很好的应用前景。