基于蚁群优化算法的无线传感器网络路由研究

无线传感器网络是一种能量、资源受限的网路系统,实现网络中节点能量使用均衡延长整个网络的生命周期是无线传感器网络路由设计的重要目标。本文在基本蚁群算法在无线传感器网络应用的基础之上提出了几点改进策略。将节点现有的能量水平作为计算转移概率的条件之一,使优秀路径上的节点在网络中存在的时间更长。将节点的位置信息作为计算转移概率的条件,通过将位置信息写入转移概率中,使节点在搜索路径时具有方向性。最后本文利用MATLAB工具对改进的策略进行了实验仿真,并将结果和原始的ACO算法进行比较分析,仿真结果显示改进策略在延长节点的生命周期,维持网络能量均衡方面比其他俩种算法具有一定的提升。     

基于自组织聚类和蚁群算法的无线传感器网络路由算法

根据无线传感网络能量受限的特点,提出一种低能耗路由算法SOC-IACO,算法由自组织聚类算法SOC和改进蚁群算法WAC组成.先通过SOC将节点分簇,选取簇头构造簇头数据链,再通过WAC构造簇内节点数据链.簇内数据沿节点数据链汇聚至簇头、簇头数据沿簇头数据链汇聚至总簇头,由总簇头发送数据至基站.实验表明,由于聚类过程中考虑了节点分布和簇负载均衡并采用双层链路由,SOC-IACO算法能大幅降低节点能耗提高网络寿命.     

无线传感器网络中覆盖控制理论与算法研究

分析了无线传感器网络中的覆盖控制理论及相关问题,在此基础上对无线传感器网络中的覆盖控制算法进行了研究。     

基于信号源能量模型的传感器网络追踪算法研究

比较了应用于传感器网络声源追踪定位的3种模型,其中信号源能量模型对数据处理简单,更利于节省能量。针对现有基于信号源能量模型的算法的不足,给出了一种全新的算法—梯度迭代算法,并通过仿真实验证明了其在追踪的精确度和速度方面具有明显的优势。     

基于LEACH协议与蚁群算法的无线传感器网络路由协议

无线传感器领域的所有关键技术中,路由协议一直是研究热点,采用合适的路由协议能够有效降低无线传感器网络节点能耗以延长网络寿命。LEACH作为经典的无线传感器网络路由协议,具有重要研究意义。传统的LEACH协议采用随机簇头选举方法与唯一的单跳路由策略,在算法运行过程中存在许多不必要的能源消耗,鉴于此,提出一种新的有效路由协议,即静态分簇多跳按需路由协议（OCMR）。它是一个按需路由协议,仅在协议运行首轮选定一定数量的簇头|在数据传输阶段,采用一种基于蚁群算法的单跳结合多跳的路由方法。Matlab仿真实验证明,新协议在节省网络能耗与延长网络寿命方面优于传统LEACH协议。     

基于遗传机制的蚁群算法求解连续优化问题

A new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the continuous optimization problem.Each component has a seed set.The seed in the set has the value of component,trail information and fitness.The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed.The genetic method is used to form new solutions from the solutions got by the ants.Best solutions are selected to update the seeds in the sets and trail information of the seeds.In updating the trail information,a diffusion function is used to achieve the diffuseness of trail information.The new algorithm is tested with 8 different benchmark functions.     

能量收集型无线传感器网络中高效的能量调度算法

为了通过控制能量调度来最大化数据吞吐量,研究了随机能量到达和电池容量有限情况下的能量采集无线传感器节点的离线调度问题。假设能量收集大小在一个传输时间段T内为恒定的,但不同阶段之间能量收集服从随机模型,提出一个离线能量调度策略。基于能量随机变化的特性,能量调度策略可根据能量收集的分布来改变,并且数据吞吐量可达到最优离线算法的99%。仿真结果进一步验证了所提离线算法的效率。     

蚁群优化算法的理论、改进及应用

蚁群优化算法是一种新型的模拟进化优化算法,为求解复杂的组合优化问题提供了一种新的思路。文章对蚁群优化算法理论及其收敛性进行了分析,并从选择策略、信息素更新、信息素浓度的变异等多方面对蚁群优化算法提出了改进,不仅使其跳出局部最优解的能力更强,而且能较快地收敛到全局最优解,在TSP问题上的应用表明改进算法具有良好的性能。     

基于类别覆盖集的改进蚁群算法研究

结合蚁群算法在解决分类问题方面的优势,以及中文网页内容特征值的离散性特点,提出一种改进的基于蚁群算法的网页分类方法。该算法通过携带类别信息的种群蚂蚁的爬行,在迭代过程中寻找一条最佳路径与之匹配,实现了Web页面的分类。最佳路径通过计算测试文档与每一类别的覆盖集合,进而比较最优覆盖集合得到。其中类别权重计算中引入了文字链接比和标签权值,进一步提高了分类精度。实验证明,引入类别覆盖集的蚁群分类算法能够取得更好的分类效果。     

基于改进蚁群算法的物流配送优化研究

物流配送是物流管理的重要环节,物流配送关系到物流活动的正常开展,因此,物流配送优化成为学术界和企业界的研究热点。而传统蚁群算法由于存在着缺点,所以采用一种改进蚁群算法以改善传统算法的不足。基于改进蚁群算法,构建物流配送模型,求解最优解。通过算例,求得最优解,实现目标函数最小化。结论表明：改进蚁群算法是求解物流配送优化问题的有效方法。     

Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics

To find the optimal routing is always an important topic in wireless sensor networks （WSNs）. Considering a WSN where the nodes have limited energy, we propose a novel Energy^＊Delay model based on ant algorithms （＂E＆D ANTS＂ for short） to minimize the time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. Our goal is not only to maximize the lifetime of the network but also to provide real-time data transmission services. However, because of the tradeoff of energy and delay in wireless network systems, the reinforcement learning （RL） algorithm is introduced to train the model. In this survey, the paradigm of E＆D ANTS is explicated and compared to other ant-based routing algorithms like AntNet and AntChain about the issues of routing information, routing overhead and adaptation. Simulation results show that our method performs about seven times better than AntNet and also outperforms AntChain by more than 150% in terms of energy cost and delay per round.     

User-preferences based service selection model in large-scale services with various QoS properties

With the rapid growth of service scale, there are many services with the same functional properties but different non-functional properties on the Internet. There have been some global optimizing service selection algorithms for service selection. However, most of those approaches cannot fully reflect users’ preferences or are not fully suitable for large-scale services selection. In this paper, an ant colony optimization (ACO) algorithm for the model of global optimizing service selection with various quality of srevice (QoS) properties is employed, and a user-preference based large-scale service selection algorithm is proposed. This algorithm aims at optimizing user-preferred QoS properties and selecting services that meet all user-defined QoS thresholds. Experiment results prove that this algorithm is very efficient in this regard.     

基于蚁群优化的多水平图划分算法

Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration (VLSI) design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed withits guidance function. During the refinement phase, the vertex gain is exploited as ant’s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute (ANSI) C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks.     

Power-efficient topologies for wireless sensor networks with fixed communication range

The expectations for sensor networks are growing. The performance of wireless sensor networks （WSNs） is greatly influenced by their network topology. In this paper, we consider four patterned topologies that best support connectivity among these deployed sensor nodes in two-tiered WSNs. The theoretical and simulation results show that the triangle-based topology has smaller cell number, shorter maximum hop length, less total energy consumption, and better performance than other topologies. The analysis carried out in this paper could provide the guidelines for network deployment and protocol design in the future applications.     

Real-time localization estimator of mobile node in wireless sensor networks based on extended Kalman filter

Localization of the sensor nodes is a key supporting technology in wireless sensor networks (WSNs). In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter (KF) is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter (EKF) is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation (MLE) and traditional KF algorithm.     

蚁群优化算法在物流配送车辆路径问题中的应用研究

车辆路径问题(VRP)是一类物流配送领域具有广泛应用的组合优化问题,属于NP难题。一种改进的蚁群优化算法可以用于求解VRP。实验结果表明,采用蚁群优化算法能有效求解VRP问题。     

WAPN: a distributed wormhole attack detection approach for wireless sensor networks

As the applications of wireless sensor networks （WSNs） diversify, providing secure communication is emerging as a critical requirement. In this paper, we investigate the detection of wormhole attack, a serious security issue for WSNs. Wormhole attack is difficult to detect and prevent, as it can work without compromising sensor nodes or breaching the encryption key. We present a wormhole attack detection approach based on the probability distribution of the neighboring-node-number, WAPN, which helps the sensor nodes to judge distributively whether a wormhole attack is taking place and whether they are in the influencing area of the attack. WAPN can be easily implemented in resource-constrained WSNs without any additional requirements such as node localization, tight synchronization, or directional antennas. WAPN uses the neighboring-node-number as the judging criterion, since a wormhole usually results in a significant increase of the neighboring-node-number due to the extra attacking link. Firstly, we model the distribution of the neighboring-node-number in the form of a Bernoulli distribution. Then the model is simplified to meet the sensor nodes＇ constraints in computing and memory capacity. Finally, we propose a simple method to obtain the threshold number, which is used to detect the existence of a wormhole, Simulation results show that WAPN is effective under the conditions of different network topologies and wormhole parameters.     

一种基于精英策略的蚁群优化QoS单播路由算法

指出传统蚁群算法在解决QoS单播路由选择问题时,受到时延和带宽的约束,为降低路由费用,容易出现陷入局部最优且收敛速度慢的现象。针对上述问题,提出一种基于精英策略的蚁群优化QoS单播路由算法,该算法利用蚁群算法原理,并引入精英策略。通过仿真模拟一个20节点的计算机网络QoS单播路由选择实例,并与传统蚁群优化(ACO)算法进行对比,仿真结果表明该算法是有效的。     

基于蚁群算法的仿生模式识别神经网络构造方法

仿生模式识别神经网络（BPRNN）同传统BP、RBF神经网络相比具有更好的模式识别能力;训练样本库变更后网络的重新训练时间更小,但该网络构造过程中样本覆盖几何体参数的选择对网络识别率和复杂度有很大影响.本文通过引入蚁群算法来构造并优化网络参数,实验证明该算法法能较好的平衡网络性能和复杂度.