共查询到19条相似文献,搜索用时 468 毫秒
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部分可观察马尔可夫决策过程(partially observable Markov decision processes,简称POMDPs)是动态不确定环境下序贯决策的理想模型,但是现有离线算法陷入信念状态“维数灾”和“历史灾”问题,而现有在线算法无法同时满足低误差与高实时性的要求,造成理想的POMDPs模型无法在实际工程中得到应用.对此,提出一种基于点的POMDPs在线值迭代算法(point-based online value iteration,简称PBOVI).该算法在给定的可达信念状态点上进行更新操作,避免对整个信念状态空间单纯体进行求解,加速问题求解;采用分支界限裁剪方法对信念状态与或树进行在线裁剪;提出信念状态结点重用思想,重用上一时刻已求解出的信念状态点,避免重复计算.实验结果表明,该算法具有较低误差率、较快收敛性,满足系统实时性的要求. 相似文献
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通过分析目标跟踪无线传感器网络监测精度、节点能量消耗与簇成员唤醒/休眠之间的内在联系,针对网络节点能量有限、密集部署节点监测数据存在冗余、传感器节点的自身位置估计误差和目标监测估计误差等问题,引入部分可观察Markov决策过程(POMDP)理论,提出一种基于目标跟踪准确度和节点能量消耗加权回报率的动态簇成员调度模型;针对动态簇成员调度算法复杂度偏高的问题,采用基于信念点的值迭代在线策略求解算法,实现传感器簇成员节点协作策略的动态生成和在线调整。仿真结果表明:该算法能够提高目标跟踪准确性,降低节点能量消耗,延长网络生存时间。 相似文献
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针对数字化车间中无线传感器网络(WSNs)对数据采集频率高,能量消耗快,提出了基于网格和虚拟力导向的蚁群优化(Grid-VFACO)高能效WSNs路由算法。该算法根据最优簇首数将数据采集区划分成网格,在网格中采用基于候选者的机制选择簇首,实现簇首均匀分布。在簇首形成的上层网络中,利用节点间的虚拟吸引力作为蚁群算法中转移概率规则启发因子,寻找最优数据转发路径。仿真实验结果表明:该算法能够有效减少网络能耗,保证数字化车间WSNs长时间稳定地工作。 相似文献
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研究无线传感器网络路由优化问题,由于无线传感器节点的能量受到限制,通信过程能量损耗,影响网络的性能。传统粒子群算法难以获得最优网络路由方案。为延长网络生存时间,结合粒子群的快速性和混沌的遍历性优点,提出了一种混沌粒子群(CPSO)的无线网络路由优化方法。通过粒子群算法的自组织、动态寻优能力,并通过混沌机制对粒子群进行混沌扰动,增加多样性,加快最优路由优化速度,使网络最优路由和能量消耗间尽量平衡。仿真结果表明,相对于传统优化算法,CPSO提高了无线传感器网络路由优化速度,减少网络能量消耗,有效延长了网络生存时间,为提高整个网络通信效率提供了参考。 相似文献
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针对部分可观察马尔可夫决策过程(POMDPs)的信念状态空间是一个双指数规模问题,提出一种基于 Monte Carlo 粒子滤波的 POMDPs 在线算法.首先,分别采用粒子滤波和粒子映射更新和扩展信念状态,建立可达信念状态与或树;然后,采用分支界限裁剪方法对信念状态与或树进行裁剪,降低求解规模.实验结果表明,所提出算法具有较低的误差率和较快的收敛性,能够满足系统实时性的要求. 相似文献
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罗剑 《数字社区&智能家居》2021,(7)
节点能耗是决定无线传感器网络(WSNs)生存期的重要参数,设计良好的网络通信协议可以很大程度上减少和平衡能量消耗。网络协议设计簇头和簇间路由的计算过程是多项式时间无法解答的NP问题,该文讨论了5种自然元启发算法,既4种群体智能算法和遗传算法应用于WSNs能耗优化的关键技术,给出了不同网络能量结构模型的簇间单跳和多跳场景的设计建议,旨在为搭建大规模WSNs网络提供参考和借鉴。 相似文献
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This paper focuses on sensor scheduling and information quantization issues for target tracking in wireless sensor networks (WSNs). To reduce the energy consumption of WSNs, it is essential and effective to select the next tasking sensor and quantize the WSNs data. In existing works, sensor scheduling’ goals include maximizing tracking accuracy and minimizing energy cost. In this paper, the integration of sensor scheduling and quantization technology is used to balance the tradeoff between tracking accuracy and energy consumption. The main characteristic of the proposed schemes includes a novel filtering process of scheduling scheme, and a compressed quantized algorithm for extended Kalman filter (EKF). To make the algorithms more efficient, the proposed platform employs a method of decreasing the threshold of sampling intervals to reduce the execution time of all operations. A real tracking system platform for testing the novel sensor scheduling and the quantization scheme is developed. Energy consumption and tracking accuracy of the platform under different schemes are compared finally. 相似文献
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Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks 总被引:1,自引:1,他引:0
Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the
existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are
scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals,
ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient
adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling
intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled
in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance
mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended
Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model
to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the
proposed approach can save energy cost significantly without degrading the tracking accuracy. 相似文献
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在无线传感器网络(WSN)的分簇路由算法中,节点间能耗不均容易引发 “能量空洞”现象,影响整个网络的性能。针对这个问题,提出了一种基于博弈论能耗均衡的非均匀分簇路由(GBUC)算法。该算法在分簇阶段,采用非均匀分簇结构,簇的半径由簇头到汇聚节点的距离和剩余能量共同决定,通过调节簇头在簇内通信的能耗和转发数据的能耗来达到能耗的均衡;在簇间通信阶段,通过建立一个以节点剩余能量和链路可靠度为效益函数的博弈模型,利用其纳什均衡的解来寻找联合能耗均衡、链路可靠性的最优传输路径,从而提高网络性能。仿真结果表明:与能量高效的非均匀分簇(EEUC)算法和非均匀分簇节能路由(UCEER)算法相比,GBUC算法在均衡节点能耗、延长网络生命周期等性能方面有显著的提高。 相似文献
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Understanding optimal data gathering in the energy and latency domains of a wireless sensor network 总被引:3,自引:0,他引:3
The problem of optimal data gathering in wireless sensor networks (WSNs) is addressed by means of optimization techniques. The goal of this work is to lay the foundations to develop algorithms and techniques that minimize the data gathering latency and at the same time balance the energy consumption among the nodes, so as to maximize the network lifetime. Following an incremental-complexity approach, several mathematical programming problems are proposed with focus on different network performance metrics. First, the static routing problem is formulated for large and dense WSNs. Optimal data-gathering trees are analyzed and the effects of several sensor capabilities and constraints are discussed, e.g., radio power constraints, energy consumption model, and data aggregation functionalities. Then, dynamic re-routing and scheduling are considered. An accurate network model is proposed that captures the tradeoff between the data gathering latency and the energy consumption, by modeling the interactions among the routing, medium access control and physical layers.For each problem, extensive simulation results are provided. The proposed models provide a deeper insight into the problem of timely and energy efficient data gathering. Useful guidelines for the design of efficient WSNs are derived and discussed. 相似文献
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Zhixin Liu Yazhou Yuan Xinping Guan Xinbin Li 《IEEE/CAA Journal of Automatica Sinica》2015,2(3):267-273
Wireless sensor networks (WSNs) are energyconstrained, so energy saving is one of the most important issues in typical applications. The clustered WSN topology is considered in this paper. To achieve the balance of energy consumption and utility of network resources, we explicitly model and factor the effect of power and rate. A novel joint optimization model is proposed with the protection for cluster head. By the mean of a choice of two appropriate sub-utility functions, the distributed iterative algorithm is obtained. The convergence of the proposed iterative algorithm is proved analytically. We consider general dual decomposition method to realize variable separation and distributed computation, which is practical in large-scale sensor networks. Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission, and validate the performance in terms of prolonging of network lifetime and improvement of throughput. 相似文献