A Graph Coloring Based TDMA Scheduling Algorithm for Wireless Sensor Networks

Wireless sensor networks should provide with valuable service, which is called service-oriented requirement. To meet this need, a novel distributed graph coloring based time division multiple access scheduling algorithm (GCSA), considering real-time performance for clustering-based sensor network, is proposed in this paper, to determine the smallest length of conflict-free assignment of timeslots for intra-cluster transmissions. GCSA involves two phases. In coloring phase, networks are modeled using graph theory, and a distributed vertex coloring algorithm, which is a distance-2 coloring algorithm and can get colors near to $(\updelta +1)$ , is proposed to assign a color to each node in the network. Then, in scheduling phase, each independent set is mapped to a unique timeslot according to the set’s priority which is obtained by considering network structure. The experimental results indicate that GCSA can significantly decrease intra-cluster delay and increase intra-cluster throughput, which satisfies real-time performance as well as communication reliability.     

基于蚁群算法的无线传感器网络数据聚合路由算法

传感器网络为减少冗余数据的传输耗能。降低延迟,需要在路由过程中采用数据聚合技术。文中采用定向传输方式,在消息路由机制基础上提出了一种基于蚁群算法的数据聚合路由算法。该算法主要思想在于将节点能耗、传输距离与聚合收益3方面作为启发因子,通过一组称为“蚂蚁”的人工代理寻找到达汇聚节点的最优路径。该算法利用蚁群算法的正反馈效应来达到数据汇集的目的,不需要网络节点维护全局信息,因此是一种实现数据聚合在能量与时延上折中的分布式路由算法。理论分析和仿真结果说明了新算法的有效性。     

传感器网络中一种基于估计代价的数据聚合树生成算法

无线传感器网络是一种全新的技术,能够广泛应用于恶劣环境和军事领域.传感器网络在数据收集中,为减少冗余数据的传输耗能,降低延迟,需要采用数据聚合技术.本文采用定向传输方式,在消息路由机制基础上提出了一种基于估计代价的数据聚合树生成算法.该算法主要思想在于将节点能耗、传输距离与聚合收益三方面作为估计代价,优化聚合路径,实现数据聚合在能量与时延上的折中.     

面向异构无线传感器网络的节点调度算法

针对传感器网络中的节点冗余问题,提出了一种冗余判别方法来关闭冗余节点,以达到延长网络生命周期的目的.首先按邻居节点的不同位置对节点进行分类,研究了每组邻居节点的网络覆盖率与工作节点数k之间的约束关系,在此基础上,按不同的冗余法则对节点进行判断,关闭冗余节点.理论分析和实验结果表明,提出的算法能关闭网络中的冗余节点从而有效地延长网络的生存时间.     

监测无线传感器网络休眠调度算法

休眠调度设计是无线传感器网络一种重要的通信节能方法。针对监测典型应用,为了实现长时间的监测应用要求,充分利用冗余部署提供的能量资源,提出了一种能量相关的分布式自适应休眠调度算法。算法利用极大独立集构建思想,结合节点层次级别、实时的能量消耗、连通度等信息动态选择连通支配节点集作为网络骨干,使得网络活跃节点数量最小化。仿真试验分析表明,算法能够有效地利用冗余节点提供的能量资源,扩展了网络的生命周期。     

一种无线传感器网络中目标跟踪的自适应节点调度算法

在无线传感器网络目标跟踪的过程中进行节点调度,可以综合考虑跟踪误差和能量消耗,延长传感器网络的使用寿命。为了综合考虑节点调度的短期和长远损失,该文将问题建模为部分可观测马尔科夫决策过程(POMDP)以得到更优的调度策略,并提出一种近似求解算法C-QMDP。该算法利用马尔科夫链蒙特卡洛方法(MCMC)推导连续状态空间的置信状态的转移,并计算瞬时代价。使用状态离散化方法,基于马尔科夫决策过程(MDP)值迭代求解未来代价的近似值。仿真结果表明,相比现有POMDP近似算法,该文算法既可以降低跟踪过程中的累积损失,又可以将大量运算进行离线计算,减小了在线决策时的计算量。     

基于容忍覆盖区域的无线传感器网络节点调度算法

节点调度机制是解决无线传感器网络节点能量受限问题的重要方法.传统的位置信息无关的节点调度方案以节点的感知区域覆盖为调度目标,导致处于边界区域的节点由于没有太多机会进入休眠状态而先死亡,进而引起死亡节点向监测中心扩散现象,我们称这种现象为"不均等休眠"问题.针对该问题,从理论上对节点覆盖模型进行分析,提出容忍覆盖区域的概...     

基于粒子群的无线传感网络节点休眠调度算法

针对传统节点休眠调度算法中覆盖率低、活跃节点数量多以及能量消耗不均匀的问题,基于可信信息覆盖模型,提出一种基于粒子群优化算法(Particle Swarm Optimization,PSO)的无线传感网络节点休眠调度算法.算法充分利用可信信息覆盖模型的优势构建最优的可信信息覆盖集合和簇头候选集合,从可信信息覆盖集合和簇...     

Data Aggregation in Wireless Sensor Networks Using Firefly Algorithm

Wireless Personal Communications - The challenging issue of data aggregation in wireless sensor networks (WSNs) is of high significance for reducing network overhead and traffic. The majority of...     

数据融合传输算法在无线传感器网络中的应用

针对簇.树型的数据传输精确性提出一种算法.该算法是节点通过从基站接收到的话路密钥Kb与节点出厂时就设置的密钥Ki对感知数据进行加密,数据加密后使用密钥公式传送数据,可以进一步增强数据的精确性和完整性.     

无线传感器网络的时间同步算法分析

当前移动通信领域对于无线传感器网络的研究正在逐渐加深,时间同步技术是WSN应用中的支撑技术,是无线网络传感器网络研究中的重中之重。然而传统的同步方法NTP和GPS因为尺寸、代价、能量问题、复杂度等因素并不适用于传感器网络,而且传感器网络自身的体积与能量受限等特点给时间同步计算也增加了一定的难度,为了使无线传感器网络的应用潜力得到最大限度的发挥,对时间同步技术的研究就显得尤为必要。文章从无线传感器网络时间同步和相关问题入手,重点介绍了影响时间同步的关键因素、无线传感网络节点时间校正的几项基本技术以及具体的计算方法。     

一种用于分组调度的遗传模拟退火算法

分组调度已成为高速IP路由器中的关键技术之一。文章基于目前高速路由交换技术所采用的主体结构,带有虚拟输出队列（Virtual-output—Queue,VOQ）的输入队列交换结构,提出了一种遗传模拟退火算法,并将该算法应用于分组调度问题的求解之中。通过遗传模拟退火算法和传统遗传算法的仿真结果可以看出,遗传模拟退火算法具有良好的鲁棒性和收敛性。     

传感器网络中基于路线的隐私保护数据聚集算法

针对现有隐私保护数据聚集算法依赖某种网络拓扑结构和加解密次数过多的问题,本文提出了一种基于同心圆路线的隐私保护数据聚集算法PCIDA （Privacy-preserving and Concentric-circle Itinerary-based Data Aggregation algorithm）.PCIDA沿着设计好的理想路线执行数据聚集,使得算法不依赖网络拓扑结构.PCIDA利用安全通道保证数据的隐私性,避免了数据聚集过程中的加解密运算.PCIDA沿着同心圆并行处理,使得算法数据处理延迟较小.理论分析和实验结果显示,PCIDA在较低通信量和能耗的情况下获得较高的数据隐私性和聚集精确度.     

SCT Based Adaptive Data Aggregation for Wireless Sensor Networks

Communication overhead is a major concern in wireless sensor networks because of inherent behavior of resource constrained sensors. To degrade the communication overhead, a technique called data aggregation is employed. The data aggregation results are used to make crucial decisions. Certain applications apply approximate data aggregation in order to reduce communication overhead and energy levels. Specifically, we propose a technique called semantic correlation tree, which divides a sensor network into ring-like structure. Each ring in sensor network is divided into sectors, and each sector consists of collection of sensor nodes. For each sector, there will be a sector head that is aggregator node, the aggregation will be performed at sector head and determines data association on each sector head to approximate data on sink node. We propose a doorway algorithm to approximate the sensor node readings in sector head instead of sending all sensed data. The main idea of doorway algorithm is to reduce the congestion and also the communication cost among sensor nodes and sector head. This novel approach will avoid congestion by controlling the size of the queue and marking packets. Specifically, we propose a local estimation model to generate a new sensor reading from historic data. The sensor node sends each one of its parameter to sector head, instead of raw data. The doorway algorithm is utilized to approximate data with minimum and maximum bound value. This novel approach, aggregate the data approximately and efficiently with limited energy. The results demonstrate accuracy and efficiency improvement in data aggregation.     

Distributed Algorithm for En Route Aggregation Decision in Wireless Sensor Networks

In sensor networks, enroute aggregation decision regarding where and when aggregation shall be performed along the routes has been explicitly or implicitly studied extensively. However, existing solutions have omitted one key dimension in the optimization space, namely, the aggregation cost. In this paper, focusing on optimizing over both transmission and aggregation costs, we develop an online algorithm capable of dynamically adjusting the route structure when sensor nodes join or leave the network. Furthermore, by only performing such reconstructions locally and maximally preserving existing routing structure, we show that the online algorithm can be readily implemented in real networks in a distributed manner requiring only localized information. Analytically and experimentally, we show that the online algorithm promises extremely small performance deviation from the offline version, which has already been shown to outperform other routing schemes with static aggregation decision.     

A Power-Efficient Distributed TDMA Scheduling Algorithm with Distance-Measurement for Wireless Sensor Networks

This paper describes a power-efficient distributed TDMA slot scheduling algorithm which the slot allocation priority is controlled by distance measurement information in details. In our former proposed scheme, L-DRAND+, an extension of Lamport’s bakery algorithm for prioritized slot allocation based on the distance measurement information between nodes and a packet-based transmission power control had been applied. In this paper, we propose its enhanced scheme with a weighted rule control and state machines refinements of L-DRAND+, named L-DRAND++. This aims at the achievement of media access control methods which can construct a local wireless network practically by limiting the scope, and eliminate the redundant power consumption in the network. The proposed scheme can be shown as a possible replacement of DRAND algorithm for Z-MAC scheme in a distance-measurement-oriented manner. In addition, to evaluate the ordered node sequence determined by the algorithm, node sequence metric is proposed. By using the metric, we can evaluate protocol behaviors according to the environmental situation around the node.     

一种基于多权值优化的无线传感网分簇算法的研究

在无线传感网(WSN)中,网络的拓扑结构影响传感器节点的负载平衡,关系网络的容量与生存周期,而分簇结构是一种有效的拓扑控制方式。该文着眼于无线传感网络的拓扑结构,提出基于多权值的分簇算法MWBC(Multi-WeightBasedClustering),在初期通过节点间的信息交互,获得较多的局部网络信息,如:节点的度、当前能量值、发射功率、链路质量、相对位置等,在此基础上根据不同的网络应用背景作出不同的分簇决策,并预设簇的最大规模以利于接入协议的资源管理与分配。仿真结果表明,与具有代表性的分簇算法LEACH与HEED相比,在分簇的合理性上有较大的优势。     

无线传感器网络中基于数据融合树的压缩感知算法

针对无线传感器网络能量有限等特点,将路由策略考虑到投影矩阵的设计中,该文提出了基于数据融合树的压缩感知算法(Compressed Sensing algorithm based on Data Fusion Tree,CS-DFT)。该算法采用稀疏投影矩阵最小化通信消耗,在生成数据融合树的同时减小投影矩阵与稀疏基之间的相关度以保证数据的重构质量。仿真结果表明,该文提出的算法不仅在重构质量和能量消耗之间做到了很好的平衡,同时对于不同稀疏基下的数据也有较高的适应性。     

Energy-Efficient Scheduling for Wireless Sensor Networks

We consider the problem of minimizing the energy needed for data fusion in a sensor network by varying the transmission times assigned to different sensor nodes. The optimal scheduling protocol is derived, based on which we develop a low-complexity inverse-log scheduling (ILS) algorithm that achieves near-optimal energy efficiency. To eliminate the communication overhead required by centralized scheduling protocols, we further derive a distributed inverse-log protocol that is applicable to networks with a large number of nodes. Focusing on large-scale networks with high total data rates, we analyze the energy consumption of the ILS. Our analysis reveals how its energy gain over traditional time-division multiple access depends on the channel and the data-length variations among different nodes.