Backup path set selection in ad hoc wireless network using link expiration time

Topological changes in mobile ad hoc networks frequently render routing paths unusable. Such recurrent path failures have detrimental effects on quality of service. A suitable technique for eliminating this problem is to use multiple backup paths between the source and the destination in the network. Most of the proposed on-demand routing protocols however, build and rely on single route for each data session. Whenever there is a link disconnection on the active route, the routing protocol must perform a path recovery process. This paper proposes an effective and efficient protocol for backup and disjoint path set in an ad hoc wireless network. This protocol converges into a highly reliable path set very fast with no message exchange overhead. The paths selection according to this algorithm is beneficial for mobile ad hoc networks, since it produces a set of backup paths with much higher reliability. Simulations are conducted to evaluate the performance of our algorithm in terms of route numbers in the path set and its reliability. In order to acquire link reliability estimates, we use link expiration time (LET) between each two nodes.In another experiment, we save the LET of entire links in the ad hoc network during a specific time period, then use them as a data base for predicting the probability of proper operation of links.Links reliability obtains from LET. Prediction is done by using a multi-layer perceptron (MLP) network which is trained with error back-propagation error algorithm. Experimental results show that the MLP net can be a good choice to predict the reliability of the links between the mobile nodes with more accuracy.     

Optimized performance data transmission in Mobile IP networks

In a Mobile IP network (MIPN), nodes move. When a node moves, it may go away from other nodes and this decreases available bandwidth and data rate and increases the propagation delay of links. Therefore, nodes’ movement can decrease data delivery and handoff latency; these will reduce network efficiency. Suppose that an MIPN uses an optimal routing algorithm and transmits data from a source node to a destination node optimally. Nodes’ movement can violate the optimality of the data transmission and this will waste bandwidth and network resources. In this paper we present a new parametric optimal unicast multichannel routing algorithm that computes a domain for a mobile node and this domain will hold the optimality of data transmission and prevent network efficiency failure. Our new method determines an optimal domain for each mobile node and does not allow nodes to exit from that optimal domain. Simulation results show that our new method increases data rate and network efficiency.     

Physical Path Planning Using a Pervasive Embedded Network

We evaluate a technique that uses an embedded network deployed pervasively throughout an environment to aid robots in navigation. The embedded nodes do not know their absolute or relative positions and the mobile robots do not perform localization or mapping. Yet, the mobile robot is able to navigate through complex environments effectively. First, we present an algorithm for physical path planning and its implementation on the Gnats, a novel embedded network platform. Next, we investigate the quality of the computed paths. We present quantitative results collected from a real-world embedded network of 60 nodes. Experimentally, we find that, on average, the path computed by the network is only 24% longer than the optimal path. Finally, we show that the paths computed by the network are useful for a simple mobile robot. Results from a network of 156 nodes in a static environment and a network of 60 nodes in a dynamic environment are presented.     

基于MLP和SPSO的机器人行为选择与运动控制方法

针对传统行为选择机制(ASM)不能很好地做出控制决策的问题,提出一种基于多层感知(MLP)前馈神经网络的ASM,并将其应用到移动机器人目标跟踪中。首先,根据具体应用场景预定义多个机器人行为。然后,根据机器人配备的图像和红外传感器获得的目标位置和障碍物信息,通过MLP神经网络从预定义行为中选择出所需执行的行为。另外,为了构造最优的MLP模型,采用一种简化粒子群算法(SPSO)来优化网络权值参数。机器人目标跟踪仿真的结果表明,提出的ASM能够准确选择出合适的行为,实现了控制机器人跟踪目标移动且能够避开各种障碍物。     

无线传感器网络中基于移动锚节点的APIT的改进定位算法

针对APIT定位算法定位误差大,覆盖率低等缺点,提出了一种基于移动锚节点的改进的定位算法.在网络中引入移动锚节点,通过移动覆盖算法尽量使节点均匀分布,并提出了一种基于异构传感器网络的最佳节点数量的计算方法,另外引入了RSSI量化模型对APIT算法进行修正,解决了用APIT算法不能进行定位的问题.仿真结果表明,其与传统方...     

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.     

WSN in cyber physical systems: Enhanced energy management routing approach using software agents

Recently, the cyber physical system has emerged as a promising direction to enrich the interactions between physical and virtual worlds. Meanwhile, a lot of research is dedicated to wireless sensor networks as an integral part of cyber physical systems. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensors to monitor physical or environmental conditions. These autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. Shrinking size and increasing deployment density of wireless sensor nodes implies the smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization to increase the WSN lifetime. The network lifetime is defined as the time duration until the first sensor node in a network fails due to battery depletion. One solution for enhancing the lifetime of WSN is to utilize mobile agents. In this paper, we propose an agent-based approach that performs data processing and data aggregation decisions locally i.e., at nodes rather than bringing data back to a central processor (sink). Our proposed approach increases the network lifetime by generating an optimal routing path for mobile agents to transverse the network. The proposed approach consists of two phases. In the first phase, Dijkstra’s algorithm is used to generate a complete graph to connect all source nodes in a WSN. In the second phase, a genetic algorithm is used to generate the best-approximated route for mobile agents in a radio harsh environment to route the sensory data to the base-station. To demonstrate the feasibility of our approach, a formal analysis and experimental results are presented.     

An enhanced real-time routing protocol with load distribution for mobile wireless sensor networks

Mobile wireless sensor network (MWSN) is a wireless ad hoc network that consists of a very large number of tiny sensor nodes communicating with each other in which sensor nodes are either equipped with motors for active mobility or attached to mobile objects for passive mobility. A real-time routing protocol for MWSN is an exciting area of research because messages in the network are delivered according to their end-to-end deadlines (packet lifetime) while sensor nodes are mobile. This paper proposes an enhanced real-time with load distribution (ERTLD) routing protocol for MWSN which is based on our previous routing protocol RTLD. ERTLD utilized corona mechanism and optimal forwarding metrics to forward the data packet in MWSN. It computes the optimal forwarding node based on RSSI, remaining battery level of sensor nodes and packet delay over one-hop. ERTLD ensures high packet delivery ratio and experiences minimum end-to-end delay in WSN and MWSN compared to baseline routing protocol. In this paper we consider a highly dynamic wireless sensor network system in which the sensor nodes and the base station (sink) are mobile. ERTLD has been successfully studied and verified through simulation experiment.     

基于蜂窝结构的改进混合无线传感器网络覆盖优化算法

基于蜂窝结构的混合无线传感器网络（HWSN）覆盖优化算法HWSNBCS存在移动节点平均移动距离较大的问题,为此,提出一种改进的HWSN覆盖优化算法IHWSNBCS。寻找移动传感器节点初始位置与通过HWSNBCS算法得出的候选目标位置之间的最优匹配,将移动节点移动距离之和最小化问题转化为二分图最优匹配问题,利用带权二分图匹配算法KM寻找该匹配问题的最优解,从而得到移动节点最终的目标位置,并实现对HWSNBCS算法移动节点平均移动距离的进一步优化。实验结果表明,IHWSNBCS算法在取得与HWSNBCS算法相同网络覆盖率的前提下,移动节点的平均移动距离减少幅度达到38.87%～43.28%,单个移动节点的最大移动距离减少幅度达到22.65%～66.58%,降低了系统因重新部署移动传感器节点所产生的能耗以及单个传感器节点因能量耗尽而失效的概率,从而延长了网络生命周期,同时,IHWSNBCS的ΔCov-Dist性能指标为HWSNBCS算法的1.64～1.76倍,表明移动节点移动相同距离时IHWSNBCS算法的网络覆盖率提升更大。     

Hybrid multilayered perceptron networks

This paper introduces a modified multilayered perception network (MLP) called the Hybrid Multilayered Perceptron (HMLP) network to improve the performance of a MLP network. The convergence rate of the proposed network is further improved by proposing a modified version of the recursive prediction error algorithm as the training algorithm. The capability of the proposed network architecture trained using the modified recursive prediction error algorithm was demonstrated using simulated and real data sets. The results indicated that the proposed network provides a significant improvement over a standard MLP network. These additional linear input connections do not significantly increase the complexity of the MLP network since the connections are linear. In fact, by using the linear input connections, the number of hidden nodes required by the standard MLP network model can be reduced, which will also reduce computational load. The performance of the HMLP network was also compared with Radial Basis Function (RBF) and Hybrid Radial Basis Function (HRBF) networks. It was found that the proposed HMLP network was much more efficient than both RBF and HRBF networks.     

优化网络生命周期和最短化路径的WSN移动sink路径规划算法

为了缓解无线传感器网络（WSN）中传感器节点分布不均匀、传感器节点感知数据量不同而造成能耗不均衡、"热区"等问题,提出一种优化网络生命周期和最短化路径的WSN移动sink路径规划算法（MSPPA）。首先,通过监测区域网格化,在每个网格内分布若干个移动sink候选访问站点,sink在每个网格中选择一个站点停留收集网格中节点数据;然后,分析所有传感器节点的生命周期与sink站点选择的关系,建立权衡网络生命周期和sink移动路径的优化模型;最后,使用双链遗传算法规划移动sink遍历网格的顺序和选择每个网格中移动sink访问站点,得到移动sink节点遍历所有网格收集数据的路径。仿真结果显示,与已有的低功耗自适应分簇（LEACH）算法与基于移动sink节点与集合节点（RN）的优化LEACH分簇算法（MS-LEACH-RN）相比,MSPPA在网络生命周期方面提高了60%,且具有良好的能耗均衡性。实验结果表明,MSPPA能有效缓解能量不均衡、"热区"问题,延长网络生命周期。     

无线传感器网络中基于数据融合的移动代理曲线动态路由算法研究

和传统的C/S模型相比,移动代理模型在数据融合方面更适合无线传感器网络.在基于移动代理的数据融合算法中,移动代理访问传感节点的顺序以及总数对算法的效率、网络寿命等有着重大影响.为此提出了一种基于数据融合的移动代理曲线动态路由算法设计方案.通过构造特定数据结构的数据报文和数据表,给出了目标节点基本信息收集算法获取目标节点到处理节点的最优路径;将移动代理路由归结为一个优化问题,由静态路由算法求出移动代理迁移的静态最优路由节点序列,进而获得了移动代理基于曲线的动态路由算法.理论分析和模拟实验表明,随着传感器网络规模的增大和传感数据量的增加,和其它算法相比,该算法有更小的网络耗能和延时.     

A general insight into the effect of neuron structure on classification

This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.     

Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization

A mobile ad hoc network (MANET) is dynamic in nature and is composed of wirelessly connected nodes that perform hop-by-hop routing without the help of any fixed infrastructure. One of the important requirements of a MANET is the efficiency of energy, which increases the lifetime of the network. Several techniques have been proposed by researchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-efficient solution. Clustering involves the selection of cluster-heads (CHs) for each cluster and fewer CHs result in greater energy efficiency as these nodes drain more power than noncluster-heads. In the literature, several techniques are available for clustering by using optimization and evolutionary techniques that provide a single solution at a time. In this paper, we propose a multi-objective solution by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad hoc network as well as energy dissipation in nodes in order to provide an energy-efficient solution and reduce the network traffic. In the proposed solution, inter-cluster and intra-cluster traffic is managed by the cluster-heads. The proposed algorithm takes into consideration the degree of nodes, transmission power, and battery power consumption of the mobile nodes. The main advantage of this method is that it provides a set of solutions at a time. These solutions are achieved through optimal Pareto front. We compare the results of the proposed approach with two other well-known clustering techniques; WCA and CLPSO-based clustering by using different performance metrics. We perform extensive simulations to show that the proposed approach is an effective approach for clustering in mobile ad hoc networks environment and performs better than the other two approaches.     

一种满足数据时新性的WSN运载路由算法

为满足无线传感器网络应用的数据时新性,综合节点传送数据和移动设备辅助传送数据2种方式的优点,提出一种能量高效的运载路由算法。通过计算移动设备的最优接收数据位置,规划移动设备的路径,降低传感器节点的能量消耗,由此提高网络性能。模拟结果表明,该算法能在保证数据时新性的前提下,较大地减少网络能耗。     

Research on an optimal selection method for sensor network node under high-speed mobile environment

The traditional selection methods for the selection of sensor network node under high-speed mobile environment is randomness, which cannot effectively use the multiple attributes of nodes, resulting in the irregular distribution of cluster head of nodes, and high energy consumption. An optimal selection method for sensor network node under the high-speed mobile environment based on EERNFS and Naive Bayesian Networks is proposed. By using EERNFS algorithm within each network intercept / sleep cycle, the sensor network node is made processing under high-speed mobile environment, to ensure the stable local connectivity and consistent collaboration intercepts, and reduces energy consumption. Naive Bayes algorithm is used to make optimization of general Bayesian classification method, and set the parameters. In the two-dimensional area, several sensor nodes are randomly placed, and the known two-dimensional area is divided. A certain node is selected arbitrarily in different regions to constitute the original set of Naive Bayes algorithm, and compute the training results and thresholds in the Bayesian system of node set at this moment. On the basis of the threshold, the optimal selection of sensor network node is achieved. Simulation results show that the proposed method not only has higher node coverage, but also the operating efficiency and energy consumption are better than the genetic method.     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

基于多元分层感知的移动物联网区域分割算法

为解决物联网区域分割引发的低质量数据传输、高频率网络拥塞等问题,提出一种基于多元分层感知机制的高效安全区域分割算法。根据节点间数据交换的紧密程度,将网络拓扑分割为网络层、传输层和最终汇聚层,设计多元分层感知模型,增强簇头节点的更新能力。结合机会路由连通特性,设计基于机会碰撞信息提取机制的区域分割子算法,借助拉格朗日模型进行特征挖掘,提升节点机会碰撞度并优化区域分割效果。基于簇内节点关联度,构建能量-路由双因子裁决机制,实现区域信息与簇头节点的数据交互并缓解数据拥塞。实验结果表明,与基于改进移动中继和楔形合并-能量空洞消除的区域分割算法相比,该算法具有更好的网络区域分割效果及更强的数据拥塞控制能力。     

基于PSO和共轭梯度法的MIP路由优化方案

针对移动IP网络中三角路由算法效率不高,导致移动网络性能难以达到最优的问题,提出了一种基于PSO和共轭梯度法的移动IP路由优化方案。首先利用粒子来取代网络节点中的路由选择表,将IP网络和粒子群算法联系起来,研究将粒子群算法用于求解移动IP路由选择当中的最短路径,针对粒子群算法早熟收敛和局部搜索能力不足的缺陷,引入局部搜索能力强的共轭梯度算法对其进行优化,从而有效提高找出移动IP最短路由的速度;仿真结果表明了该算法的有效性。     

基于移动医疗大数据平台下深度最优匹配算法的机会网络转发机制

通过分析在移动医疗大数据平台下,机会网络中节点传递信息方式的特点,遍历所有邻接节点,对两节点的数据进行比较,通过最优匹配方式,选择匹配结果最优的邻接节点作为下一跳的节点,从而找出一条使数据高效转发的路径。根据此过程,提出一种基于移动医疗大数据平台下深度最优匹配算法的机会网络转发机制,即DOM算法,用来匹配节点中的数据分组,从而选择一条数据高效转发的路径。通过与机会网络中的经典算法比较,表明DOM算法能够在数据传播的过程中减少冗余数据并且显著提高传输成功率。