基于交叉干扰的多优先级网络线性激励价控

讨论了多优先级网络系统交叉干扰的线性激励价控问题.针对多用户多优先级系统的通信量价控管理的数学模型,利用对策论中的激励Stackelberg策略的概念,建立了基于交叉干扰的线性激励价格策略.通过对用户行为的激励,调整网络系统通信量的分配,使系统达到一个理想且稳定的状态,且用户和网络管理者都达到最佳盈余.同时讨论了激励参数的确定方法,给出了一般的激励参数矩阵.通过数值例子验证了该激励价控策略的有效性.     

化工过程多变量报警阈值优化方法

化工过程变量通常具有较强的相关性,从多变量的角度研究过程参数报警阈值的优化具有实际意义。论文针对实际生产过程中存在着大量无效报警问题,结合多变量报警相关性分析,提出了一类多变量报警阈值优化设计的新方法。首先基于过程参数的历史数据,应用基于皮尔逊相关系数法的凝聚层次聚类方法对报警进行聚类分组,然后采用基于差异驱动原理的赋权方法对报警进行优先级排序,最后遵循报警优先级高优先处理的原则,结合漏报警及误报警概率建立过程报警阈值优化的目标函数,并采用数值优化的方法进行求解,得到最优的报警阈值。对于TE过程进行了实验,结果验证了所提方法的优势。     

多优先级业务流多通道并行传输

针对已有的多业务流多通道并行传输不支持业务流之间优先级的问题,将业务的优先级映射到多个通道,建立了具有优先级保证的多业务流多通道数据传输模型。基于排队论知识将模型抽象成多维Markov链,并使用两阶段的PH分布将多维Markov链近似成一维Markov链,采用矩阵分析方法对模型进行定量分析,推导出系统的平均队长和平均等待时间。通过数值分析与简单多业务流单通道、多优先级业务流单通道、简单多业务流多通道这三种传输模型进行了比较。结果表明,不管业务流达到率如何变化,多优先级业务流多通道并行传输模型中的高优先级数据包均能够获得较高的处理能力,说明了该模型能够支持多通道并行传输中业务流之间的优先控制。     

基于模糊多目标的高超声速飞行器再入轨迹设计

针对多约束条件下的高超声速飞行器再入轨迹的优化问题,考虑多个具有不同重要性等级的优化指标,提出基于模糊多目标的轨迹设计方法.首先,利用直接配点法,将最优控制问题转化为带优先级的非线性多目标规划问题;然后,基于模糊满意优化的思想,根据更重要目标具有更高满意度的原则,将优先级表示为满意度序,并设计两步式优化模型.通过调节参数,能获得同时满足优化和重要性等级要求的最优轨迹.仿真结果表明了所提出方法的有效性.     

多特征参数的任务模糊调度算法

针对任务具有特征参数多和特征参数不确定性的特点,提出了一种基于模糊理论的任务调度算法。利用模糊集合来描述任务的不确定性特征;使用多层模糊综合评判和最大隶属度原理来综合考虑任务的多个特征参数并确定任务的优先级;采用动态构建多层评判模型的调度策略来减小任务优先级评判的失效率。仿真表明,该算法提高了任务调度的成功率,降低了任务截止期的错失率和任务优先级评判的失效率。该方法可应用于优先等级有限的实时系统任务动态调度中。     

多总线多处理机系统总线仲裁方案的分析与比较

本文分别提出多总线多处理机系统采用轮流优先级和循环优先级仲裁的分析模型。轮流优先级仲裁方案采用概率分析,循环优先级仲裁方案采用变更状态和参数分析。分析模型被用来对这二种不同仲裁方案进行性能分析和比较。某些结果表明循环优先级仲裁的总线访问延迟最小。     

基于改进生物激励神经网络算法的多移动机器人协同变电站巡检作业

针对大型变电站巡检作业效率低的问题,利用改进的生物激励神经网络算法和优先级启发式算法,结合基于变切线长的无障碍物区域分割法,提出一种多移动机器人协同全区域覆盖巡检以及多任务点协同巡检的方法.首先,分析生物激励神经网络算法的不足,如规划的路径曲折、转角大等问题,并提出一种改进方法,利用改进的算法和Hungarian算法实现对多任务点的巡检;然后,设计一种变切线法将电站区域分解成若干不含障碍物的子区域,各移动机器人分别在子区域内利用优先级启发式算法选择路径,利用改进的生物激励神经网络算法跳出死区,从而完成多机器人的协同全区域巡检任务;最后,通过仿真实验表明,改进的神经网络算法相比于原始算法与A*算法在路径长度和转向次数等方面具有明显的优化作用,仿真实验验证了所提出多机器人协同巡检方案的可行性.     

无线传感器网络多信道协议的调度机制

针对现有无线传感器网络多信道MAC协议没有区分数据业务优先级的问题,提出了一种基于区分服务的多信道协议（DP-McMAC）。该协议采用优先级调度策略和区分服务机制,对不同数据业务设置不同的退避参数和竞争窗口,并对提出的协议进行数学建模及延迟分析。仿真结果表明,该协议在高优先级数据的传输时延和吞吐量等性能方面比现有的协议均有所提高,并且保证了高优先级数据传输的实时性要求。     

基于多参数的μC/OS-Ⅱ任务优先级和调度方法

在μC/OS-Ⅱ进行实时任务调度时，可以使用单一的调度算法分配任务优先级。优先级判定标准的片面性、“错过率”较高的截止期，影响了μC/OS-Ⅱ的实时调度性能。该文提出了多参数任务优先级分配策略和μC/OS-Ⅱ任务的调度方法，实验证明，该方法截止期的平均错过率为60.1%，有效地改善了μC/OS-Ⅱ的实时调度性能。     

直觉模糊POWA算子及其在多准则决策中的应用

为了解决具有优先级的直觉模糊多准则决策问题,定义了直觉模糊优先有序加权平均(IFPOWA)算子.基于优先关系.利用直觉模糊值修正得分函数给出其关联权重向量的计算方法,分析并证明了IFPOWA算子的性质;提出了基于IFPOWA算了的具有优先级的直觉模糊多准则决策方法.最后,利用实例对方法的有效性进行了分析.     

基于多优先级的网络通信量的价控问题

在多优先级网络中，为了满足各个用户的发送要求，解决网络拥塞并且使用户和网络整体获益，讨论了多优先级网络中通信量的价控问题。使用Nasll平衡和Stackelberg策略，以价格控制为桥梁，调节用户在各优先级的发送量。在假设流量是不同优先级发送信息量函数的情况下，讨论了用户的盈余情况。不仅保证得到较满意的盈余值，而且能使网络更加平衡稳定地运行。改进了对已有的价控问题的研究，从而也可以更加合理、有效地利用网络。仿真结果说明了此方法的适用性。     

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.     

理想状态下网络非线性交叉干扰激励价控策略

针对多用户多优先级网络系统的管理问题，利用对策论中的Nash平衡和激励Stackelberg策略等相关概念，提出了理想状态下的激励价控策略设计．在系统的动态平衡状态下，利用信息量的瞬时变化率及用户与平衡点的偏离，给出了非线性交叉干扰的多激励价控策略，加强了用户与网络管理者的合作性，激励和引导非合作用户选取对系统整体有益的服务请求，以提高网络资源的利用率．     

群智感知中基于时空关联性的用户激励机制

为在群智感知系统中实现有效的用户激励,提出基于显性与隐性时空关联的两种用户激励算法。将显性时空关联的用户激励问题转化为集合覆盖问题并利用贪心算法对其进行求解,同时结合显性时空关联算法和马尔科夫模型求解隐性时空关联的用户激励问题。在仿真数据和真实数据集上的实验结果表明,与传统最小化花费算法、最大化覆盖算法和最小化花费覆盖数比值算法相比,显性时空关联算法和隐性时空关联算法有效解决了感知任务完成率低且花费高的问题,能在实现用户激励的情况下最大化社会收益。     

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。     

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.     

Efficient bandwidth utilization in LambdaGrids using pricing incentives

The ability to reserve network bandwidth is a critical factor for the success of high-performance grid applications. Reservation of lightpaths in dynamically switched optical networks facilitates guaranteed bandwidth. However, reservation of bandwidth can often lead to bandwidth fragmentation which significantly reduces system utilization and increases the blocking probability of the network. An interesting approach to mitigating this problem is to induce quasi-flexibility in the user requests. A smart scheduling strategy can then exploit this quasi-flexibility and optimize bandwidth utilization. However, there has to be an incentive for flexibility from the user’s perspective as well. In this paper, we explore how the network service provider (NSP) can influence user flexibility by dynamically engineering pricing incentives. Ultimately, user flexibility will lead to efficient network utilization, reduce the price for the users, and increase the revenue for the NSP.     

Smart pricing scheme: A multi-layered scoring rule application

Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA–EP–GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers’ payment.