基于TinyOS的CC2420驱动组件的研究

首先介绍了nesC语言及专门面向传感器网络的TinyOS操作系统软件平台,然后阐述了网络节点的硬件模块功能特点及其接口电路,最后根据硬件抽象体系结构的原则,在TinyOS操作系统平台下,对CC2420无线收发模块驱动组件设计作了深入的研究,以组件的形式将其硬件功能封装成接口供上层软件调用,实现了对上层软件的硬件特征屏蔽...     

A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors

Ultra-wideband (UWB) has been widely recommended for significant commercial and military applications. However, the well-derived coherent structures for UWB signal detection are either computationally complex or hardware impractical in the presence of the intensive multipath propagations. In this article, based on the nonparametric Parzen window estimator and the probabilistic neural networks, we suggest a low-complexity and noncoherent UWB detector in the context of distributed wireless sensor networks (WSNs). A novel characteristic spectrum is firstly developed through a sequence of blind signal transforms. Then, from a pattern recognition perspective, four features are extracted from it to fully exploit the inherent property of UWB multipath signals. The established feature space is further mapped into a two-dimensional plane by feature combination in order to simplify algorithm complexity. Consequently, UWB signal detection is formulated to recognize the received patterns in this formed 2-D feature plane. With the excellent capability of fast convergence and parallel implementation, the Parzen Probabilistic Neural Network (PPNN) is introduced to estimate a posteriori probability of the developed patterns. Based on the underlying Bayesian rule of PPNN, the asymptotical optimal decision bound is finally determined in the feature plane. Numerical simulations also validate the advantages of our proposed algorithm.     

Angle-of-arrival localization based on antenna arrays for wireless sensor networks

Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices.A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed.     

A modeling approach on the TelosB WSN platform power consumption

Substantial characteristics of wireless sensor networks, such as autonomy and miniature size, are achieved at the expense of restricted energy resources. Optimal resource management is thus among the most important challenges in WSNs development and its success requires accurate and practical models based on detailed insight concerning the factors contributing to the overall power consumption of a WSN mote. To achieve such awareness, that will enable models development, appropriate measuring test-beds and methodologies are needed, facilitating reliable and accurate power consumption measurements of critical functionalities.To cover the need for energy models that precisely define the power consumption behavior of WSN hardware platforms, this paper contributes with a measuring methodology including three steps: the design and implementation of a measuring system for a wide range of power consumption thresholds, the identification, isolation and measurement of elementary functionalities of a WSN platform with respect to their contribution to the overall mote power consumption, and the extraction of valuable conclusions based on the respective measurements resulting in the composition of a practical, yet accurate power consumption model.     

Performance analysis of opportunistic broadcast for delay-tolerant wireless sensor networks

This paper investigates a class of mobile wireless sensor networks that are unconnected most of the times; we refer to them as delay-tolerant wireless sensor networks (DTWSN). These networks inherit their characteristics from both delay tolerant networks (DTN) and traditional wireless sensor networks. After introducing DTWSNs, three main problems in the design space of these networks are discussed: routing, data gathering, and neighbor discovery. A general protocol is proposed for DTWSNs based on opportunistic broadcasting in delay-tolerant networks with radio device on-off periods. Three performance measures are defined in the study: the energy for sending queries to ask for data from possible neighbors (querying energy), data transfer energy, and absorption time (delay). A simple yet accurate approximation for the data-transfer energy is proposed. An analytic model is provided to evaluate the querying energy per contact (epc). Simulation results for the data propagation delay show that the querying energy per contact measure obtained from the analytic model is proportional to the product of the querying energy and the delay. A practical rule of thumb for an optimal query interval in terms of delay and energy is derived from different parts of the study.     

无线传感器网络的一种可调节的拓扑控制算法

在无线传感器网络的拓扑控制问题中,保持节点能耗最低路径和低节点度之间存在一种平衡.最佳的平衡点与具体的应用和网络状态有关.文中提出一种新的拓扑控制算法,使所构造的拓扑能在这两个不一致的目标之间进行调节.该算法所构造的拓扑结构在一极能保持所有能耗最低路径,另一极能使平均节点度逼近理论最小值.仿真结果证实新算法在比已有方案更真实的能量消耗模型下可以保持所有能耗最低路径,同时也显示新算法对节点度有更大的调节范围.     

传感器网络中多近似连续范围查询的处理技术

无线传感器网络为数据库研究开辟了新的研究领域,高效利用节点的有限能量是当前研究的主要目标.如果发布到网络中多个近似连续范围查询不经优化处理而独立执行,会造成节点为不同查询重复发送相同感知数据,从而降低网络寿命.针对近似连续范围查询研究了多查询优化技术,设计了一种索引多维范围查询的多叉树结构rq-kd-tree,通过获取多查询的公共查询部分(查询相交区域)以及基于查询相似度合并相交区域上的多个查询、重写查询.最后,实验证明了所提的算法可以实现能量有效的多查询处理过程.     

基于RBF预估神经网络控制器的无线传感器网络拥塞算法

无线传感器网络中,汇聚节点是网络的瓶颈.由于传感器网络自身的特点,传统有线网络中的拥塞控制策略不再适用.已有的大多数拥塞控制策略和算法都没有充分考虑往返时延(RTT)对算法性能的影响.同时由于实际传感器网络运行中存在非线性、时间延迟和参数时变等干扰因素,若设计的控制器参数固定,不具有学习能力,则实际运行中收敛性差,收敛速度慢,无法达到控制队列长度的目标.针对以上问题,提出一种基于灰色预估神经网络控制队列的控制器,利用RBF神经网络的自学习能力解决网络实时变化时算法参数的在线整定问题,并利用灰色GM(1,1)预测器有效地解决了大时滞对网络性能的影响,最后通过仿真验证了这一算法的有效性.     

采用任意播方式的机会数据聚集算法

数据收集是无线传感网络研究的关键问题,是诸多无线传感网络应用的基础.降低数据聚集的延迟是数据聚集研究中的重点问题.现有的面向延迟的数据聚集算法,多是通过在树型网络结构上设计无冲突的节点调度算法,来降低数据聚集的延迟,没有考虑到无线网络数据易丢失的特性,不能达到期望的延迟效果.本文针对上述问题,提出一种采用任意播(anycast)方式的机会数据收集算法(OA算法).该算法利用机会传输(opportunistic transmission)的思想,用任意播方式传输数据,通过减少数据聚集中重传数据包的数目,来降低数据聚集的延迟.实验表明,与SPT(Shortest path tree)上的数据聚集延迟相比,该方法的发包数目减少了15%,延迟降低了10%.     

含时间参量的传感器网络修正感知模型

目标与传感器之间的距离对传感器感知能力具有重要影响,但现有研究仅考虑以距离作为参量建立量化传感器感知能力的模型,而忽略了时间因素的影响。为此,引入时间参量建立修正的传感器感知模型。实验结果表明,与已有的感知模型相比,该模型能更真实地模拟传感器对目标的感知行为,量化传感器对运动目标的感知能力。