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排序方式: 共有2545条查询结果,搜索用时 15 毫秒
41.
无线传感器网络自组织问题越来越受到人们的关注,控制方法也大量涌现,但在对自组织性能的评价上,现在大多仍停留在定性分析阶段。本文针对无线传感器网络自组织中连接和覆盖两个重要指标,利用Delaunay三角剖分评价节点实体和他们的关系以及结点之间的信息传递和融合;利用Voronoi图进行评价节点覆盖的区域;同时,对整个自组织过程,引用自组织度的概念对其分布效果进行定量分析。仿真结果表明,我们提出的性能分析方法能够很好地评价无线传感器网络自组织算法的优劣。 相似文献
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李春叶 《数字社区&智能家居》2009,(15)
目前,无线传感器网络在智能环境检测,灾难控制,战场侦察,安全监视方面取得了日益广泛的应用,引起人们日益关注,在分析无线传感器网络能量消耗特征的基础上,基于Markov模型提出了无线传感器网络节点能量消耗模型,改进了无线传感器网络多路径路由协议。仿真结果表明,与传统的多路径路由机制相比,能够有效地降低无线传感器网络节点能量消耗,提高网络生存时间。 相似文献
44.
以节能为主要目标,基于最小跳路由的思想提出一种基于网络拓扑优化的WSN最小跳路由算法——MH-TO算法。该算法采用折半匹配的功率调整策略对网络拓扑进行优化,并引入“塔模型”实现节点的最小跳信息的学习,使得信息包路由时沿着最小跳的路径向sink节点传送。理论分析和仿真实验结果表明,与基于最小跳数场的自组织路由算法相比,该算法能够降低能量消耗并均衡能量负载,从而显著延长网络的生存期。 相似文献
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Allon Rai Sangita Ale Syed S. Rizvi Aasia Riasat 《通讯和计算机》2009,6(10):37-43,53
With the tremendous applications of the wireless sensor network, self-localization has become one of the challenging subject matter that has gained attention of many researchers in the field of wireless sensor network. Localization is the process of assigning or computing the location of the sensor nodes in a sensor network. As the sensor nodes are deployed randomly, we do not have any knowledge about their location in advance. As a result, this becomes very important that they localize themselves as manual deployment of sensor node is not feasible. Also, in WSN the main problem is the power as the sensor nodes have very limited power source. This paper provides a novel solution for localizing the sensor nodes using controlled power of the beacon nodes such that we will have longer life of the beacon nodes which plays a vital role in the process of localization as it is the only special nodes that has the information about its location when they are deployed such that the remaining ordinary nodes can localize themselves in accordance with these beacon node. We develop a novel model that first finds the distance of the sensor nodes then it finds the location of the unknown sensor nodes in power efficient manner. Our simulation results show the effectiveness of the proposed methodology in terms of controlled and reduced power. 相似文献
47.
洪玲 《计算机与数字工程》2009,37(7):50-52
无线传感器网络是由大量低成本的传感器节点构成的自组织网络。因为工作环境和成本因素,传感器节点通常不会更换电池,能量十分有限。节能是传感器网络中媒体访问控制(MAC)协议设计的首要问题,节点睡眠调度机制是节能的一个有效手段。文章介绍和分析了S-MAC,T-MAC,D-MAC中的睡眠调度机制的特点,并对未来研究方向提出了展望。 相似文献
48.
无线传感器网络中的隐私保护研究 总被引:2,自引:1,他引:1
随着无线传感器网络的广泛应用,安全问题发生变化,通信安全成为重要的一部分,隐私保护日渐重要。首先分析了无线传感器网络的通信安全特点、通信安全的需求、面临的保密性威胁及攻击模型。最后,基于对无线传感器网络隐私保护问题的分析和评述,指出了今后该领域的研究方向。 相似文献
49.
Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series. 相似文献
50.
The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy. 相似文献