Adaptive Optimal Capacity Perception and Control for Wireless Multi-Hop Networks

In wireless multimedia communications, it is extremely difficult to derive general end-to-end capacity results because of decentralized packet scheduling and the interference between communicating nodes. In this paper, we present a state-based channel capacity perception scheme to provide statistical Quality-of-Service (QoS) guarantees under a medium or high traffic load for IEEE 802.11 wireless multi-hop networks. The proposed scheme first perceives the state of the wireless link from the MAC retransmission information and extends this information to calculate the wireless channel capacity, particularly under a saturated traffic load, on the basis of the interference among flows and the link state in the wireless multi-hop networks. Finally, the adaptive optimal control algorithm allocates a network resource and forwards the data packet by taking into consideration the channel capacity deployments in multi-terminal or multi-hop mesh networks. Extensive computer simulations demonstrate that the proposed scheme can achieve better performance in terms of packet delivery ratio and network throughput compared to the existing capacity prediction schemes.

基于分类平均跳距的无线传感器网络节点CADV-Hop定位方法

针对原型DV-Hop定位算法中平均跳距的计算产生较大定位误差的问题,提出了一种分类平均跳距的CADV-Hop算法。首先,揭示了无线传感网络中不同跳数路径的单跳距离分布不一致的现象,并分析了这种差异出现的原因与规律。其次,按照跳数对信标间路径分类再计算各类路径平均跳距的分类平均跳距。最后,在分类平均跳距所提供未知节点与最近信标节点之间更精确距离估计的基础上,结合加权最小二乘法最终实现节点坐标解算。仿真实验表明,CADV-Hop算法在不增加算法复杂度以及额外硬件的情况下有效地降低了定位误差,随着信标节点数量增加,CADV-Hop算法比原型DV-Hop算法和两种改进的DV-Hop算法具有更高的定位精度。

基于多应用场景的改进DV-Hop定位模型

针对距离矢量跳(DV-Hop)定位模型定位精度低、优化策略场景依赖性强的问题,提出一种基于函数分析和模拟定参的改进DV-Hop模型——函数修正距离矢量跳(FuncDV-Hop)定位模型。首先,分析DV-Hop模型的平均跳距、距离估计和最小二乘法中的误差原因,引入待定系数优化、阶跃函数分段实验、带等效点的权重函数策略和极大似然估计修正;其次,考虑多应用场景,用控制变量法,分别将总节点数、信标节点比例、通信半径、信标节点数和待测节点数作为变量,设计对照实验;最后,进行仿真定参和整合优化测试两阶段实验,最终的改进策略较原DV-Hop模型的定位精度提高了23.70%~75.76%,平均优化率57.23%。实验结果表明,FuncDV-Hop模型的优化率最高达到了50.73%,与基于遗传算法和神经动力学改进的DV-Hop模型相比,FuncDV-Hop模型的优化率提升了0.55%~18.77%。所提模型不引入其他参量,不增加无线传感器网络(WSN)的协议开销,且有效提高定位精度。

Load balancing strategy of heterogeneous wireless networks based on multi-hop routing algorithm of ad hoc network

Because of different system capacities of base station(BS)or access point(AP)and ununiformity oftraffic distribution in different cells,quantities of new call users may be blocked in overloaded cell incommunication hot spots.Whereas in some neighboring under-loaded cells,bandwidth may be superflu-ous because there are only few users to request services.In order to raise resource utilization of the wholeheterogeneous networks,several novel load balancing strategies are proposed,which combine the call ad-mission control policy and multi-hop routing protocol of ad-hoc network for load balancing.These loadbalancing strategies firstly make a decision whether to admit a new call or not by considering some param-eters like load index and route cost,etc.,and then transfer the denied users into neighboring under-load-ed cell with suiplus channel according to optimum multi-hop routing algorithm.Simulation results showthat the proposed load balancing strategies can distribute traffics to the whole heterogeneous wireless net-works,improve the load balance index efficiently,and avoid the call block phenomenon almost absolutely.

基于改进麻雀搜索算法的3DDV-Hop定位算法

针对经典3DDV-Hop算法定位精度不高的问题,提出一种基于改进麻雀搜索算法优化的改进的3DDV-Hop算法;算法首先通过多通信半径优化传感器节点之间跳数,并且在平均跳距计算过程中引入动态加权因子提高平均跳距计算精度,其次在麻雀搜索算法的基础上融合反向学习策略与萤火虫算法分别对麻雀搜索算法的种群与位置更新迭代进行优化,最后将未知节点坐标计算问题转化成改进后的麻雀搜索算法寻优问题,利用改进后的麻雀搜索算法替代最小二乘法计算未知节点坐标,进一步提高未知节点位置计算精度;经过MATLAB仿真验证,改进算法对比于经典3DDV-Hop算法和相关算法,定位精度得到有效提高。

Distributed link scheduling method with physical interference model in wireless multi-hop networks

To further increase the throughput of wireless multi-hop networks,a distributed scheduling method is proposed,which takes physical interference model into account.It is assumed that nodes in the network can perform physical carrier sensing,and the carrier sensing range can be set to different values.In the traditional carrier sensing mechanism,the carrier sensing range is computed under the protocol interference model,which is not accurate.Here the optimal carrier sensing range with physical interference model is achieved.Each sending node implements the distributed approach in three phases at each time slot,and all the concurrent transmissions are interference free.Good performance can be achieved under this scheduling approach.The approximation ratio of the distributed method to the optimal one is also proved.

基于螺旋更新策略蜜獾优化的DV-Hop定位算法

节点定位是无线传感器网络(Wireless Sensor Networks,WSNs)的关键技术之一。针对传统距离向量跳段(Distance Vector Hop,DV-Hop)算法定位误差偏大的问题,提出了一种改进蜜獾算法(Honey Badger Algorithm,HBA)与DV-Hop相结合的算法。首先,针对网络平均跳距估计不准的问题,依据锚节点比例对未知节点平均跳距进行分段修正。其次,采用改进型的HBA替换最小二乘法估算未知节点的位置,进一步降低计算误差。初始化蜜獾个体时引入Sobol序列,增加初始种群的多样性;为了加强HBA的局部搜索能力,引入了螺旋更新策略。最后,采用镜像策略规避估算位置越界的情况。结果表明,所提算法相较于传统DV-Hop算法、平均跳距修正的DV-Hop算法和基于粒子群优化(Particle Swarm Optimization,POS)的DV-Hop算法具有更高的定位精度和稳健性。

A Multi-Hop Dynamic Path-Selection (MHDP) Algorithm for the Augmented Lifetime of Wireless Sensor Networks

In Wireless Sensor Networks (WSN), the lifetime of sensors is the crucial issue. Numerous schemes are proposed to augment the life time of sensors based on the wide range of parameters. In majority of the cases, the center of attraction will be the nodes’ lifetime enhancement and routing. In the scenario of cluster based WSN, multi-hop mode of communication reduces the communication cast by increasing average delay and also increases the routing overhead. In this proposed scheme, two ideas are introduced to overcome the delay and routing overhead. To achieve the higher degree in the lifetime of the nodes, the residual energy (remaining energy) of the nodes for multi-hop node choice is taken into consideration first. Then the modification in the routing protocol is evolved (Multi-Hop Dynamic Path-Selection Algorithm—MHDP). A dynamic path updating is initiated in frequent interval based on nodes residual energy to avoid the data loss due to path extrication and also to avoid the early dying of nodes due to elevation of data forwarding. The proposed method improves network’s lifetime significantly. The diminution in the average delay and increment in the lifetime of network are also accomplished. The MHDP offers 50% delay lesser than clustering. The average residual energy is 20% higher than clustering and 10% higher than multi-hop clustering. The proposed method improves network lifetime by 40% than clustering and 30% than multi-hop clustering which is considerably much better than the preceding methods.

海量文献数据无线传感DV-Hop定位算法设计

现有的数据无线传感定位算法无法获取锚节点与待定位节点之间的最小跳数,导致定位偏差较大,且可应用的网络环境单一。针对海量文献数据,设计新的无线传感DV-Hop定位算法。利用文献数据位置信息表获取锚节点至待定位文献数据未知节点的最小跳数,利用所确定的最小跳数确定无线传感器网络中每跳的平均距离,选取极大似然估计法利用每跳的平均距离确定待定位未知节点的定位结果。通过修正锚节点间跳数、修正未知节点跳数以及修正未知节点平均跳距三部分优化DV-Hop定位算法,提升海量文献数据定位精度。实验结果表明,修正系数为2.05时,文献数据定位精度较高;定位精度伴随锚节点覆盖率的提升有所降低;不同网络场景时,所研究算法的定位精度均高于0.94。

Analysis of Hops Length in Wireless Sensor Networks

Wireless sensor networks are provided with a limited source of power. The lifetime of such networks is an overwhelming matter in most network applications. This lifetime depends strongly on how efficiently such energy is distributed over the nodes especially during transmitting and receiving data. Each node may route messages to destination nodes either through short hops or long hops. Optimizing the length of these hops may save energy, and therefore extend the lifetime of WSNs. In this paper, we propose a theorem to optimize the hop’s length so to make WSN power consumption minimal. The theorem establishes a simple condition on hop’s length range. Computer simulation when performing such condition on Mica2 sensors and Mica2dot sensors reveals good performance regarding WSNs energy consumption.

基于蜣螂算法优化的DV-Hop定位算法

针对无线传感器网络中传统DV-Hop(distance vector-hop)算法定位误差大的问题,提出了一种基于蜣螂算法优化的DV-Hop定位算法。首先使用双通信半径的方式细化节点间跳数,并使用最小均方误差准则计算锚节点的平均跳距,将改进后的平均跳距的平均值当做每个未知节点的平均跳距,最后引入权重因子优化适应度函数,使用蜣螂优化算法代替三边测量法进行坐标计算。仿真结果表明,所提算法比经典DV-Hop算法平均定位误差提升了55.69%、59.61%和67.59%,误差方差提升了52.41%、45.58%和36.87%,具有良好的定位精度和较好的稳定性。

Multihop Deterministic Energy Efficient Routing Protocol for Wireless Sensor Networks MDR

The inception of Wireless Sensor Networks (WSN) has brought convenience into many lives with uninterrupted wireless network. The nodes that transmit data consist of heterogeneous and battery equipped sensor nodes (SNs) that are deployed randomly for network surveillance. To manage the random deployment of nodes, clustering algorithms are used with efficient routing protocols. This results in aggregation and dropping of redundant data packets that enables flawless data transmission from cluster nodes to Base Station (BS) via Cluster Heads (CHs). In this paper, a dynamic and multi-hop clustering and routing protocol for thorough behavior analysis is proposed, taking distance and energy into consideration. This forms a smooth routing path from the cluster nodes, CHs, Sub-CHs to the BS. On comparing proposed process with the existing system, experimental analysis shows a significant enhancement in the performance of network lifetime, with improved data aggregation, throughput, as the protocol showing deterministic behavior while traversing the network for data transmission, we name this protocol as Multi-hop Deterministic energy efficient Routing protocol (MDR).

基于改进灰狼优化算法的DV-Hop传感器定位算法

定位技术一直以来是无线传感器网络领域的研究热点。传统距离向量(Distance Vector-Hop,DV-Hop)定位算法的定位误差较大,针对该问题,在传统DV-Hop算法的基础上,提出了一种基于改进灰狼优化算法的DV-Hop传感器定位算法(Levy Grey Wolf Optimization Distance Vector-Hop,LGWODV-Hop)。第1阶段,采用锚节点双通信半径计算跳数;第2阶段,将锚节点的估计距离与真实距离的差值作为误差因子来修正全网络中的平均跳距;第3阶段,用结合Levy飞行策略的灰狼优化算法代替最小二乘法求解未知节点坐标。仿真结果表明,在一定的条件下,所提出的算法平均定位误差相较于传统DV-Hop算法、DEDV-Hop算法和PSODV-Hop算法分别降低了56.09%,22.87%和3.02%。

基于新息的改进EKF-DV-Hop定位算法研究

为提高扩展卡尔曼DV-Hop(EKF-DV-Hop)定位算法的准确性,降低其定位误差,提出了一种基于新息的改进自适应EKF-DV-Hop定位算法(Adaptive Extended Kalman Filter DV-Hop,AEKF-DV-Hop)。首先定义误差因子,通过除去对计算信标节点跳距误差大的节点进而增加跳距准确性,并应用最小二乘法估算未知节点的坐标;然后将估算的坐标和信标节点间的欧氏距离作为EKF的观测量,进一步优化未知节点坐标;最后使用自适应算法在线更新状态噪声矩阵和观测噪声矩阵。仿真对比得到,AEKF-DV-Hop对环境具有更好的适应性,定位精度有较明显的提升,增强了环境适应性。

Hop distance fairness for wireless mesh network based on queue management

Wireless mesh network(WMN) is a new multi-hop network for broadband accessing to internet.However,there exists a server unfairness problem based on different hop distances in WMN.To solve this problem,the unfairness issue was analyzed in test-bed experiment and NS2 simulation.A dynamic queue management scheme E-QMMN was proposed,which allocates the queue buffer according to the hop distance of every flow.The experimental results show that the proposed scheme can not only increase the hop distance fairness of the legacy scheme at most 50%,but also reduce the average round trip time at least 29% in congested WMN environments.

THROUGHPUT ANALYSIS OF TWO-HOP WIRELESS NETWORKS WITH NETWORK CODING

A Two-hop Wireless Network (TWN) is the basic topology structure that provides network coding opportunity for improving throughput. Network coding on a homogeneous TWN, in which all the data flows have the same packet size and all the links have the same transmission rate, has been extensively investigated. In this paper, network coding on more practical heterogeneous TWNs, featured by various packet sizes and transmission rates, is studied. Based on the Markov model, the throughput of the proposed network coding scheme, together with the throughput gain, is derived, which matches the simulation results very well. Numerical analyses indicate that, encoding the packets with close size and close transmission rate and enlarging buffer size at the relay node help in improving the throughput gain.

基于改进NSGA-Ⅲ算法的DV-Hop三维定位算法

在三维空间节点定位算法中,DV-Hop算法凭借其原理简单、成本低的优势得到了大量学者的广泛关注。针对DV-Hop算法中因计算误差导致的定位精度较低的问题,研究NSGA-Ⅲ算法的交叉和变异并进行自适应改进,使得子代个体的进化更加均匀,提高找到全局最优解的概率,减少陷入局部的解空间。最后结合蚁群算法提升了该算法在复杂网络环境中的定位精度。仿真结果表明,与其他算法相比,文章提出的基于改进NSGA-Ⅲ和蚁群算法的DV-Hop三维定位算法具有较好的定位精度。

基于自适应差分粒子群优化的DV-Hop定位算法

针对常规DV-Hop算法存在定位精确度偏低,难以满足高精度定位应用的需求等问题,提出一种基于自适应差分粒子群优化的DV-Hop定位算法。该算法首先利用节点间RSSI值实现跳数计算的精确化,然后引入跳距可信度提高平均跳距的计算精度,最后利用自适应差分粒子群优化算法来估计未知节点的位置。实验结果表明,在不同节点数量、通信半径以及锚节点数三种情况下,改进后算法的节点定位精确度相比于传统DV-hop定位算法分别提高了45.3%、42.9%和39.7%。