Participants Recruitment for Coverage Maximization by Mobility Predicting in Mobile Crowd Sensing

Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.

An Incentive Mechanism for Mobile Crowd Sensing in Vehicular Ad Hoc Networks

In the mobile crowdsensing of vehicular ad hoc networks (VANETs), in order to improve the amount of data collection, an effective method to attract a large number of vehicles is needed. Therefore, the incentive mechanism plays a dominant role in the mobile crowdsensing of vehicular ad hoc networks. In addition, the behavior of providing malicious data by vehicles as data collectors will have a huge negative impact on the whole collection process. Therefore, participants need to be encouraged to provide data honestly to obtain more available data. In order to increase data collection and improve the availability of collected data, this paper proposes an incentive mechanism for mobile crowdsensing in vehicular ad hoc networks named V-IMCS. Specifically, the Stackelberg game model, Lloyd’s clustering algorithm and reputation management mechanism are used to balance the competitive relationship between participants and process the data according to the priority order, so as to improve the amount of data collection and encourage participants to honestly provide data to obtain more available data. In addition, the effectiveness of the proposed mechanism is verified by a series of simulations. The simulation results show that the amount of available data is significantly higher than the existing incentive mechanism while improving the amount of data collection.

Hybrid Two-Phase Task Allocation for Mobile Crowd Sensing

As a result of the popularity of mobile devices,Mobile Crowd Sensing (MCS) has attracted a lot of attention. Task allocation is a significant problem in MCS. Most previous studies mainly focused on stationary spatial tasks while neglecting the changes of tasks and workers. In this paper,the proposed hybrid two-phase task allocation algorithm considers heterogeneous tasks and diverse workers.For heterogeneous tasks,there are different start times and deadlines. In each round,the tasks are divided into urgent and non-urgent tasks. The diverse workers are classified into opportunistic and participatory workers.The former complete tasks on their way,so they only receive a fixed payment as employment compensation,while the latter commute a certain distance that a distance fee is paid to complete the tasks in each round as needed apart from basic employment compensation. The task allocation stage is divided into multiple rounds consisting of the opportunistic worker phase and the participatory worker phase. At the start of each round,the hiring of opportunistic workers is considered because they cost less to complete each task. The Poisson distribution is used to predict the location that the workers are going to visit,and greedily choose the ones with high utility. For participatory workers,the urgent tasks are clustered by employing hierarchical clustering after selecting the tasks from the uncompleted task set.After completing the above steps,the tasks are assigned to participatory workers by extending the Kuhn-Munkres (KM) algorithm.The rest of the uncompleted tasks are non-urgent tasks which are added to the task set for the next round.Experiments are conducted based on a real dataset,Brightkite,and three typical baseline methods are selected for comparison. Experimental results show that the proposed algorithm has better performance in terms of total cost as well as efficiency under the constraint that all tasks are completed.

基于HM与LWR算法的电子设备MCS推荐优化

为了提高移动群智感知(Mobile Crowd Sensing, MCS)中数据质量,设计了一种基于混合模型(Hybrid Model, HM)与列表级排序(List-Wise Ranking, LWR)相结合的推荐方法HM-LWR。研究结果表明:确定最优参数指标为学习速率μ为0.01,迭代100次,α取值0.5。采用HM-LWR算法模型能够较精确预测得到参与者的任务偏好情况,分配MCS任务时可以有效提升准确性与运算效率。该研究有助于提高电子设备移动群感知能力,在智慧城市领域具有很好的推广价值。

属性感知的MCS任务分配与隐私保护协同机制

针对移动群智感知网络中感知的任务分配以及保护感知用户的隐私信息问题,提出了一种基于用户属性感知的任务分配与隐私保护协同机制。首先,根据感知用户固有属性以及历史任务参与记录,挖掘用户对任务的不同倾向、意愿和访问等来量化出用户的静态属性和社会属性;然后,将用户属性作为输入,使用BP神经网络对用户服务能力进行分析,实现任务与用户的优化分配;最后,感知用户生成假名参与感知任务,结合环签名对用户属性生成随机数进行属性加密,确保感知用户在隐私安全的前提下,提升平台感知数据的准确程度。仿真实验结果表明,文中所提出的策略能够有效地选择出感知用户,验证了用户上传数据的可用性,保护了用户的身份安全。

Mobile crowd sensing task optimal allocation： a mobility pattern matching perspective

With the proliferation of sensor-equipped portable mobile devices, Mobile CrowdSensing （MCS） using smart devices provides unprecedented opportunities for collecting enormous surrounding data. In MCS applications, a crucial issue is how to recruit appropriate participants from a pool of available users to accomplish released tasks, satisfying both resource efficiency and sensing quality. In order to meet these two optimization goals simultaneously, in this paper, we present a novel MCS task allocation framework by aligning existing task sequence with users＇ moving regularity as much as possible. Based on the process of mobility repetitive pattern discovery, the original task allocation problem is converted into a pattern matching issue, and the involved optimization goals are transformed into pattern matching length and support degree indicators. To determine a trade-off between these two competitive metrics, we propose greedy- based optimal assignment scheme search approaches, namely MLP, MDP, IU1 and IU2 algorithm, with respect to matching length-preferred, support degree-preferred and integrated utility, respectively. Comprehensive experiments on real- world open data set and synthetic data set clearly validate the effectiveness of our proposed framework on MCS task optimal allocation.

A Survey on Task and Participant Matching in Mobile Crowd Sensing

Mobile crowd sensing is an innovative paradigm which leverages the crowd, i.e., a large group of people with their mobile devices, to sense various information in the physical world. With the help of sensed information, many tasks can be fulfilled in an efficient manner, such as environment monitoring, traffic prediction, and indoor localization. Task and participant matching is an important issue in mobile crowd sensing, because it determines the quality and efficiency of a mobile crowd sensing task. Hence, numerous matching strategies have been proposed in recent research work. This survey aims to provide an up-to-date view on this topic. We propose a research framework for the matching problem in this paper, including participant model, task model, and solution design. The participant model is made up of three kinds of participant characters, i.e., attributes, requirements, and supplements. The task models are separated according to application backgrounds and objective functions. Offline and online solutions in recent literatures are both discussed. Some open issues are introduced, including matching strategy for heterogeneous tasks, context-aware matching, online strategy, and leveraging historical data to finish new tasks.

CrowdTracker:一种基于移动群智感知的目标跟踪方法

面向目标跟踪问题提出一种基于移动群智感知的解决方案CrowdTracker.不同于基于视频监控的目标跟踪方法,通过基于群智的多人协作拍照方式实现对移动目标的轨迹预测和跟踪,其优化目标为在保证准确实时地对目标进行跟踪的同时尽可能地减少用户激励的成本(假设激励与完成任务的参与者人数和参与者完成任务所移动的距离成正比).为实现该目标,提出了目标移动性预测的方法MPRE和任务分配的方法T-centric,P-centric.T-centric是以任务为中心的参与者选择方法,而P-centric是以人为中心的任务选择方法.MPRE通过分析大量的车辆历史轨迹建立城市里车辆的移动模型以预测目标下一步的位置.在预测的区域内通过T-centric或P-centric方法进行跟踪任务分配.通过一个大规模的真实数据集对移动性预测方法MPRE和2种任务分配算法进行实验评估,实验结果表明:CrowdTracker能有效地在实现目标实时跟踪的同时降低激励成本.

移动群智感知中基于网格混淆的位置隐私保护

针对现有移动群智感知(mobile crowd sensing,MCS)面临的隐私泄露问题,引入一种混合式区块链架构来构建系统,实现MCS的去中心化,并通过私有区块链保护工人隐私记录。采用无证书签密实现用户数据传输过程中的机密性和完整性,保证用户信息的安全性。为了降低位置隐私暴露的风险,提出一种基于网格混淆的位置隐私保护方案(location privacy protection scheme based on grid obfuscation,LPPSGO)。该方案通过H3索引系统划分多精度六边形网格,实现工人位置的空间隐匿,工人可以根据个性化隐私需求扰动自身位置,无须担心真实位置的暴露。仿真实验结果表明,LPPSGO能有效提高MCS系统的任务分配成功率,减少时间开销,与其他位置保护方案相比,安全性更强,覆盖性能更好。

基于综合评分的移动群智感知隐私激励机制

移动群智感知系统(MCS)能否高效地运行,很大程度上取决于是否有大量任务参与者参与到感知任务中。然而在现实中,用户的感知成本增加以及用户的隐私泄露等原因,导致用户的参与积极性不高,因此需要一种有效的手段,用于在保证用户隐私安全的同时,还能促进用户积极地参与到任务中。针对上述问题,结合本地化差分隐私保护技术,提出了一种基于综合评分的双边拍卖隐私激励机制(Privacy Incentive Mechanism of Bilateral Auction with Comprehensive Scoring, BCS),这种激励机制包括拍卖机制、数据扰动和聚合机制以及奖励和惩罚机制3个部分。拍卖机制综合考虑了各种因素对用户完成感知任务的影响,在一定程度上提高了任务的匹配程度;数据扰动和聚合机制在隐私保护和数据精度之间做出权衡,在保证数据质量的同时做到了对用户隐私的良好保护;奖励和惩罚机制奖励诚信度和活跃度高的用户,激励用户积极参与感知任务。实验结果表明,BCS可以在提高平台收益和任务匹配率的同时保证感知数据的质量。

边缘环境下基于移动群智感知计算卸载的数据汇聚

当前“云-端”式移动群智感知(mobile crowd sensing,MCS)系统面临负载过重的问题,导致数据汇聚过程中时延和能耗显著增加,从而降低了数据汇聚的效率。针对该问题,提出了一种基于AP-DQN的“云-边-端”MCS计算卸载算法。首先,考虑时延和能耗的均衡优化建立效用函数,以最大化系统效用作为优化目标。其次,优化P-DQN算法,提出一种联合资源分配的计算卸载算法AP-DQN,结合MCS优势,将空闲用户作为卸载设备之一。最后,使用该方法求解问题。实验结果显示,与已有算法相比,该方法能有效提高数据汇聚效率,并具有很好的稳定性。

基于能量效益的无人机辅助移动群智感知系统任务分配

与传统移动群智感知系统的感知工人相比,无人机具有能够执行危险任务、易控制、不需要额外的激励成本等优点,因此无人机更适合作为任务执行的主体。然而无人机的能量有限,且感知任务的完成时间有限制,因此如何综合考虑以上2个因素设计一种高效的任务分配方法是一个关键问题。由此提出一种基于能量效益的无人机辅助移动群智感知系统任务分配方法,在能量效益最大化的同时提高系统任务完成率。该方法首先在无人机获得的回报和消耗的能量基础上定义了能量效益计算方式,用于评价任务分配方案的优劣;然后,设计了一种改进的模拟退火遗传算法以获得能量效益最大化的任务分配方案。经实验证明,与其他基准算法相比,所提出方法在任务平均能耗、系统任务完成率、系统能量效益有更好的表现。

基于选择的移动群智感知反向拍卖激励机制

由于移动群智感知中工人和平台的自私性,社会福利和数据质量往往难以达到平衡,损害了基于移动群智感知的服务可用性和准确性。为了解决这一问题,本文提出一种基于选择的反向拍卖激励机制,分别研究反向拍卖中以工人为中心的任务选择和以平台为中心的工人选择,在最大限度提高社会福利的同时优化数据质量。首先,根据工人和任务的位置等属性评估工人与任务之间的匹配度,提出基于动态规划的任务选择算法,在工人资源约束下为每个工人选择具有最佳回报的任务进行投标;其次,使用多臂赌博机建模反向拍卖赢家选择过程,并设计了一种基于置信度上界的赢家选择算法,以确定最佳中标工人集;最后,实验验证本文所提出的激励机制能够同时有效提升社会福利和数据质量。

去中心化移动群智感知系统中可靠和高效的数据交易

移动群智感知(mobile crowd sensing,MCS)系统提供了充分利用人群智慧的机会,具有部署成本低、空间覆盖范围广等优点。由于中央服务器可能存在的故障或风险问题,该研究以分散的方式构建具有不可信参与者的高效MCS系统。基于分布式拍卖过程和区块链系统,提出一种高效实用的去中心化MCS系统。该方法通过一个中立、公开和可信的平台,实现了满足个人理性和保护个人隐私的最优社会利益。理论分析和数值实验均证实该方法的有效性。

移动群智感知中高效可验证的安全真值发现方法

针对移动群智感知中参与者数据的真值和隐私保护问题,提出了一种高效可验证的安全真值发现方法 EVSTD,通过安全迭代更新参与者权值和评估对象真值,从而得到对象的真实数据。EVSTD中,参与者利用本地随机数和协商随机数对敏感数据进行双掩码数据扰动,使得EVSTD不仅能够保证敏感数据的隐私性,且解决了参与者因延迟发送感知数据而导致的敏感数据泄露问题。同时, EVSTD利用秘密共享协议解决了参与者掉线或失效的问题,且通过动态选择L邻居节点策略让参与者只与其关联邻居进行通信从而大大降低了参与者的计算和通信开销。此外,参与者通过计算敏感数据的同态哈希值以用于数据的验证并上传给服务器,服务器对敏感数据进行聚合和对验证信息进行乘积,并将计算结果发送给参与者,参与者再对聚合结果和证明信息进行验证,验证通过则说明聚合结果正确,进一步保证了真值发现结果的可信性,防止服务器对参与者的敏感数据进行篡改,保证了聚合结果的真实性。实验结果显示所提方法在保证数据隐私的同时获得真实可靠的数据信息,且能够有效的防止服务器篡改数据和共谋攻击。

PRPS:用于移动群智感知的隐私保护和信誉感知的参与者选择方案

作为一种新兴的感知范式,移动群智感知(mobile crowd sensing,MCS)包括一组移动用户,这些用户利用他们的传感设备有效地执行和发送数据贡献。然而,隐私和信誉机制(可靠性评估)的集成是构建安全可靠的MCS应用程序的关键。首先,即使参与者提供敏感的个人数据,也能确保他们的隐私得到保护。其次,由于有偏见或不准确的贡献可能会降低系统质量,信誉机制允许服务器监控参与者的行为和可靠性,服务器必须对参与者进行验证。将信誉机制与隐私相结合具有挑战性和矛盾性。信誉机制衡量参与者在整个感知活动期间的行为,而隐私旨在保护参与者的身份。因此,提出了一种针对MCS的新型隐私保护和信誉感知的参与者选择(privacy-preserving and reputation-aware participant selection,PRPS)方案。PRPS方案将隐私与信誉机制相结合,使用假名和隐身技术来分别保护参与者身份和信誉值隐私,并保护位置和数据隐私。通过仿真模拟和性能评估,分别比较了PRPS方案、隐私保护和效用感知的参与者选择(privacy-preserving and utility-aware participant selection,PUPS)方案及效用感知的参与者选择(utilityaware participant selection,UPS)方案,证明了PRPS方案的精度、有效性和可扩展性,并证明了隐私和信誉机制对数据贡献的影响。再次,评估了PRPS方案的结果。最后,估计了PRPS方案在评估参与者可靠性和行为方面的效率和准确性。

云边端协同移动群智感知中基于两阶段资源均衡的任务分配方法

在集中式移动群智感知中,云平台负责任务分发和数据收集等全部过程.当面对各种实时应用场景的需求时,云平台将面临数据计算、存储以及通信压力.针对上述问题,本文结合云边端体系结构,设计了两阶段的任务分配策略,分析参与者资源状态对任务的影响,构建了资源评估模型.以最小化任务平均响应时间为优化目标,以资源均衡为约束,提出了基于两阶段资源均衡的任务分配方法.该方法首先提出基于资源占用率的边缘服务器资源均衡任务分配方法,为边缘服务器选择任务集,再提出基于改进蚁群算法的参与者资源均衡任务分配方法,通过边缘服务器和参与者之间的协作实现资源均衡.仿真实验结果表明,本文所提算法同其他算法相比具有良好的性能.

基于移动群智感知计算的智慧景区监管系统研究与设计

针对景区监管存在的成本高、实时性差等问题,设计一种基于移动群智感知计算的景区智能监管系统。首先,该系统利用众包思想和群智感知理论采集游客手机的用户生成内容(UGC)数据,包括照片、视频、评论等;然后,通过深度学习技术进行视觉分析和文本分析,基于BP神经网络进行决策分析,从而实现监管事件的智能发现和智能决策。所设计系统不仅节省了景区铺设物联网设备的成本,而且能够实现更深入、广泛和智能的景区监管,也可为智慧景区建设提供一种新的解决方案。

参与式感知:以人为中心的智能感知与计算

越来越多的无线智能移动设备集成了大量不同种类的传感器模块,与此同时无线移动网络也迅速普及.这些因素推动了参与式感知概念的提出与发展.参与式感知又被称为城市感知、以用户为中心的感知、群智感知.参与式感知可以解决传统无线传感网络难以大规模部署以及部署成本高昂的问题.参与式感知系统通过利用移动智能设备中的传感器、社交网络以及个人设备使用行为对参与者周围的物理环境、社会环境或者个人状态进行自主式采集、传输和分析,并做出智能化决策.这对未来实现智慧城市、普适计算以及物联网等重大概念具有重要的意义.首先介绍了参与式感知相关概念、理论以及相应的应用原型系统;然后介绍了参与式感知相关研究前沿热点,包括参与式感知的原型设计、感知数据处理、激励机制、隐私保护、恶意攻击以及不同移动网络的问题等;最后给出研究参与式感知的一般方法.

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。