Blockchain-Based MCS Detection Framework of Abnormal Spectrum Usage for Satellite Spectrum Sharing Scenario

In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.

Dynamic data-sharing based user recruitment in mobile crowdsensing

Mobile crowdsensing(MCS) has become an emerging paradigm to solve urban sensing problems by leveraging the ubiquitous sensing capabilities of the crowd. One critical issue in MCS is how to recruit users to fulfill more sensing tasks with budget restriction, while sharing data among tasks can be a credible way to improve the efficiency. The data-sharing based user recruitment problem under budget constraint in a realistic scenario is studied, where multiple tasks require homogeneous data but have various spatio-temporal execution ranges, meanwhile users suffer from uncertain future positions. The problem is formulated in a manner of probability by predicting user mobility, then a dynamic user recruitment algorithm is proposed to solve it. In the algorithm a greedy-adding-and-substitution(GAS) heuristic is repeatedly implemented by updating user mobility prediction in each time slot to gradually achieve the final solution. Extensive simulations are conducted using a real-world taxi trace dataset, and the results demonstrate that the approach can fulfill more tasks than existing methods.

A Blockchain Based Mobile Crowdsensing Market

Mobile crowdsensing(MCS)is an emerging pattern which means task initiators attract mobile users sensing with their own devices by some platforms.MCS could exploit idle resources in low cost,while it has lots of flaws,which impede its developments.First,isolations between different MCS systems leads to wastage of social resources.What’s more,current MCS always operate in a centralized way,which causes it vulnerable and unbelievable.Blockchain is a promising technology which could supply a credible and transparent environment.This paper construct a blockchain based MCS market and design smart contract for its operation.In our design,platform breaks isolation by blockchain,task initiators and mobile users manage their tasks by smart contract and bargain price with distributed algorithm.By this way,resource could be exploited better,and the market could be more fair.What’s more,the paper analyzes Walrasian Equilibrium(WE)in the market,and details how to deploy MCS in blockchain.Evalution results shows that Equilibrium could be found.

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

With the popularity of sensor-rich mobile devices,mobile crowdsensing(MCS)has emerged as an effective method for data collection and processing.However,MCS platform usually need workers’precise locations for optimal task execution and collect sensing data from workers,which raises severe concerns of privacy leakage.Trying to preserve workers’location and sensing data from the untrusted MCS platform,a differentially private data aggregation method based on worker partition and location obfuscation(DP-DAWL method)is proposed in the paper.DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size(the number of workers).Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform.In the stage of data aggregation,DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise(including both added noise of sensing data and the system noise in the sensing process).Through using optimal estimation of noisy aggregated sensing data,the platform can finally gain better utility of aggregated data while preserving workers’privacy.Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.

BPPF:Bilateral Privacy-Preserving Framework for Mobile Crowdsensing

With the emergence of mobile crowdsensing (MCS), merchants can use their mobiledevices to collect data that customers are interested in. Now there are many mobilecrowdsensing platforms in the market, such as Gigwalk, Uber and Checkpoint, which publishand select the right workers to complete the task of some specific locations (for example,taking photos to collect the price of goods in a shopping mall). In mobile crowdsensing, in orderto select the right workers, the platform needs the actual location information of workersand tasks, which poses a risk to the location privacy of workers and tasks. In this paper, westudy privacy protection in MCS. The main challenge is to assign the most suitable worker toa task without knowing the task and the actual location of the worker. We propose a bilateralprivacy protection framework based on matrix multiplication, which can protect the locationprivacy between the task and the worker, and keep their relative distance unchanged.

Secure Mobile Crowdsensing Based on Deep Learning

To improve the quality of multimedia services and stimulate secure sensing in Internet of Things applications, such as healthcare and traffic monitoring, mobile crowdsensing(MCS) systems must address security threats such as jamming, spoofing and faked sensing attacks during both sensing and information exchange processes in large-scale dynamic and heterogeneous networks. In this article, we investigate secure mobile crowdsensing and present ways to use deep learning(DL) methods, such as stacked autoencoder, deep neural networks, convolutional neural networks, and deep reinforcement learning, to improve approaches to MCS security, including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DLbased approaches compared to traditional security schemes and identify the challenges that must be addressed to implement these approaches in practical MCS systems.

面向异构效用的移动群智感知多目标任务分配

当前移动群智感知(MCS)任务分配往往只考虑工人或平台单方面的效用,并且效用的构成也不够全面。因此基于工人信誉指数和任务熟练指数,设计了工人和平台两方面的异构效用机制,并提出一种双种群竞争的多目标进化算法(DCMEA)来获得最优的工人和平台异构效用。该算法首先通过随机贪婪初始化种群,然后使用二元竞标赛算法将种群划分为胜者种群和败者种群,并针对每个种群采用不同的进化策略。最后,通过修复算子使进化过程中的无效个体满足约束条件。在真实场景的数据集上进行实验表明,与基线算法相比,DCMEA收敛速度更快,能够找到精度更优、稳定性更好的任务分配解集,同时在更为复杂的场景中依然能够保持其性能。

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。

基于区块链的移动群智感知数据处理研究综述

针对移动群智感知(MCS)数据处理的用户广泛参与性、采集设备灵活移动性与通信环境复杂性的特点,对基于区块链的移动群智感知数据处理进行评估.回顾移动群智感知与区块链的发展历程,总结移动群智感知数据处理面临的挑战与区块链技术的特点;设计基于区块链的移动群智感知体系结构(BMCA),实现数据去中心化管理、数据安全保障、数据质量精准评估与激励可信性增强;从隐私保护、数据质量评估、激励机制3个维度,对比分析现有的数据处理关键技术研究工作;探讨基于区块链的移动群智感知数据处理研究在资源消耗控制、数据精准分析、全周期与差异化隐私保护、融合模式应用等方面存在的问题及未来可能的发展方向.

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。

群智感知系统中面向高斯差分隐私的数据新鲜度性能分析

群智感知是基于众包思想,利用智能感知终端完成传感数据收集的一种数据获取模式,具有部署成本低、实现方式灵活、可扩展性强等优点。随着6G网络技术的日渐成熟,针对基于6G的群智感知系统中亟需解决的传感数据时效性与隐私安全问题,提出了一种基于高斯差分隐私的传感数据内容保护模型,利用信息年龄(Age of Information, AoI)指标对传感数据的新鲜度进行时效性分析,得到了不同队列模型、服务准则以及传输缓存的数据新鲜度性能表达式,突破了传感数据时效性分析与隐私安全提升研究相互独立的现状,为面向隐私保护的群智感知系统时效性性能评估及优化提供理论支撑。通过不同环境参数设置下的仿真实验,所提方案的正确性与有效性得到了验证。结果表明,在典型参数设置下,高斯机制的差分隐私保护效果与传感数据新鲜度性能呈负相关,即高时效性的传感数据隐私安全风险较高,反之亦然。

边云协同群智感知中隐私增强多任务分配机制

在移动群智感知中现有研究普遍基于边缘服务器或云服务器是可信的这一前提假设,无法在提高感知数据质量的同时有效保护参与者隐私。提出一种基于半可信执行环境的隐私增强多任务分配(PEMTA)机制,基于Hilbert曲线特性对任务进行位置聚类,将相邻边缘服务器结合Paillier加密体系的同态特性进行相互协作,根据参与者和任务的匹配度为每个任务挑选最佳参与者集合,完成感知任务且不泄露参与者隐私。设计贪心冲突排除算法,根据任务佣金对冲突任务进行等级划分,按照划分后的任务等级依次为冲突任务挑选最佳的替换参与者,解决了多任务分配产生的参与者匹配冲突问题。利用动态信誉值更新算法,通过量化参与者提交的感知数据与聚合后数据的偏差,动态更新参与者的信誉值,缓解了恶意攻击造成的数据质量损失。实验结果表明,PEMTA机制具有良好的抗恶意攻击性能,感知数据质量和任务完成率相比于同类多任务分配机制平均提升了18.14%和15.47%。

Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively,with each mobile user completing much simpler micro-tasks.This paper discusses the task assignment problem in mobile crowdsensing,which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals.The goal is to minimize aggregate sensing time for mobile users,which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality.This paper introduces a two-phase task assignment framework called location time-based algorithm(LTBA).LTBA is a framework that enhances task assignment in MCS,whereas assigning tasks requires overlapping time intervals between tasks and mobile users’tasks and the location of tasks and mobile users’paths.The process of assigning the nearest task to the mobile user’s current path depends on the ant colony optimization algorithm(ACO)and Euclidean distance.LTBA combines two algorithms:(1)greedy online allocation algorithm and(2)bio-inspired traveldistance-balance-based algorithm(B-DBA).The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user.B-DBA was location-based and worked on maximizing total task quality.The results demonstrate that the average task quality is 0.8158,0.7093,and 0.7733 for LTBA,B-DBA,and greedy,respectively.The sensing time was reduced to 644,1782,and 685 time units for LTBA,B-DBA,and greedy,respectively.Combining the algorithms improves task assignment in MCS for both total task quality and sensing time.The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time,and the greedy algorithm follows it then B-DBA.

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.

Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data

Out-door billboard advertising plays an important role in attracting potential customers.However,whether a customer can be attracted is influenced by many factors,such as the probability that he/she sees the billboard,the degree of his/her interest,and the detour distance for buying the product.Taking the above factors into account,we propose advertising strategies for selecting an effective set of billboards under the advertising budget to maximize commercial profit.By using the data collected by Mobile Crowdsensing(MCS),we extract potential customers’implicit information,such as their trajectories and preferences.We then study the billboard selection problem under two situations,where the advertiser may have only one or multiple products.When only one kind of product needs advertising,the billboard selection problem is formulated as the probabilistic set coverage problem.We propose two heuristic advertising strategies to greedily select advertising billboards,which achieves the expected maximum commercial profit with the lowest cost.When the advertiser has multiple products,we formulate the problem as searching for an optimal solution and adopt the simulated annealing algorithm to search for global optimum instead of local optimum.Extensive experiments based on three real-world data sets verify that our proposed advertising strategies can achieve the superior commercial profit compared with the state-of-the-art strategies.

考虑工人培养的移动群智感知任务分配机制

移动群智感知(MCS)通过大量感知工人的移动性和工人随身携带的感知设备来收集数据,是一种新的大规模数据感知范式。现有大量研究致力于解决移动群智感知中的任务分配问题,使感知数据质量得以提高,但忽略了缺乏优质工人的感知任务,导致任务完成质量降低。为了解决上述问题,对于缺乏优质工人的感知任务,该文关注将经验不足的工人培养为优质工人,并令其执行这些感知任务,实现工人的长期复用,提高感知数据质量和长期平台效用。具体来说,该文考虑了缺乏优质工人的感知任务所需的能力和工人的能力类型,并据此应用稳定匹配算法选择待培养工人,提出一种基于能力聚合和半马尔可夫预测的多阶段工人选择培养(MWSD)算法。结果表明,相比基于区块链的非确定团队协作(BNTC)算法,该文所提算法能够有效将缺乏优质工人的感知任务的数据质量提高24%,长期平台效用提高17%。

采用离散烟花算法的移动群智感知异构任务分配

建立移动群智感知异构任务分配问题的数学模型,该模型考虑参与者的心理与行为过程,并引入环境信息和参与者健康状况、信誉度和测量时间等因素,通过寻找最优任务分配方案,最小化任务完成的总成本,该总成本包括补偿成本、数据损失成本和距离成本3方面。为求解该模型,提出一种引入了预测信息的离散烟花算法。该算法采用整数编码方式,利用模型中的距离和匹配度2种启发信息设计烟花爆炸算子,提出了爆炸振幅的分组线性预测策略和变异算子的自适应竞争机制。实验结果表明,与已有算法相比,所提算法在移动群智感知异构任务分配问题上能够搜索到更优的分配方案。

基于区块链的边缘移动群智感知声誉更新方案

移动群智感知利用移动用户的智能终端设备以低成本获取大量感知数据,而恶意用户可能上传虚假数据以获取奖励。声誉管理是一种有效的解决办法,但是基于云服务器的移动群智感知系统存在高延迟、单点故障和隐私泄露问题。针对这些问题,结合区块链和边缘计算构建基于区块链的边缘移动群智感知系统,提出一种感知数据隐私保护的声誉更新方案,采用轻量级的隐私保护方法聚合感知数据,根据数据质量和历史任务表现更新声誉。该方案可有效抵抗恶意用户、降低时延,避免单点故障和保护数据隐私。仿真实验证明了所提方案的可行性和高效性,理论分析证明了系统的安全性。

群组协作的移动群智感知任务分配方法

时空覆盖类感知任务对参与者的时间与空间约束使得传统单参与者模式难以适用。为此,该文提出群组协作的移动群智感知任务分配方法,以群组模式替代传统单参与者模式。设计层次化群组协作的任务分配框架,提出偏好感知的社交群组生成方法,引入社交关系生成社交群组,提高任务完成率。提出效用优化的任务群组匹配方法,采用网络流理论进行群组-任务匹配,保证平台效用最大化。仿真结果表明所提方法在任务完成率与平台效用方面均有较大提升。

移动群智感知中基于联邦学习的参与者选择机制

移动群智感知的发展使得一些任务收集的数据量过大,需要在不接收参与者原始数据的情况下评估数据质量并进行参与者选择。针对这一问题,提出一种基于联邦学习的移动群智感知参与者选择机制。考虑参与者智能终端资源水平、所处交互状态构建参与者智能终端资源评价机制,提出基于线性回归和长短期记忆网络的智能终端资源预测模型。通过预训练测试模型,评估参与者提供的数据质量,结合历史任务完成情况建立参与者信誉评价模型,实现对参与者的动态评价选择。仿真实验结果表明,所提的参与者选择机制在任务完成质量、能量消耗、通信轮数及任务完成时间等多方面体现出较好的性能。