基于贪婪算法无线传感器网络中继节点布局的研究

为实现远距离的无线通信, 在网络中添加中继节点, 采用多跳路由传输数据。对于中继节点的布局问题, 依据线性结构使网络整体能量消耗最小的特征, 提出一种中继节点贪婪布局算法。该算法通过最近贪婪策略、中继节点通信容量、传感器节点数据转发跳数等约束方法限制中继节点的布局位置。理论分析和实验验证了该算法能够有效减少能量消耗, 延长网络寿命。     

WSN自适应负载均衡集簇分层路由协议

结合考虑传统无线传感器网络(wireless sensor networks,WSN)路由协议特点以及实际应用中节点的不对等性,提出了一种自适应负载均衡集簇分层路由协议——ALBCH.该协议在簇头选举时引入剩余能量等相关因子,将贪婪算法成链机制分别引入分层路由协议的簇内通信和簇头间通信,对贪婪算法成链机制进行了一些改进.仿真结果表明,ALBCH能更有效地均衡网络负载,具有更好的健壮性和更高的实时性能,同时解决了传统协议在处理异构网络时的局限性.     

基于蚁群算法的WSN多路径负载均衡路由

为使无线传感器网络节点能量消耗相对均衡,在定向扩散路由算法的基础上,结合蚁群算法,提出一种多路径负载均衡路由算法。该算法利用蚁群的自适应和动态寻优能力,在源节点和目的节点之间搜索建立多条传输路径,并将节点剩余能量引入启发因子,均衡节点能量消耗。同时,运用层次分析法,赋予每条路径一定的负载分配比例,使数据总能在链路性能较优的多路径中均衡传输,延长整个网络的生命期。仿真结果表明,与定向扩散路由算法相比,该算法能够均衡节点能耗,有效延长网络寿命。     

一种可分负载WSN的能耗均衡负载调度算法

无线传感器网络的节点电源能量有限,其能耗均衡问题已成为研究热点。针对星型可分负载无线传感器网络的负载调度过程,在同时感知、顺序返回信息的工作模型下提出了一种以能耗均衡为目标的负载调度算法(DLEBS)。该算法以降低网络能耗标准差为优化目标,在保证网络总体传输时间及传输顺序的情况下,可以得到相应的负载调度策略。仿真实验表明,算法得到的负载调度可以有效降低网络的能耗标准差。并且随给定时间的增加,算法得到的负载调度使得能耗标准差相应地减小。     

面向WSN数据汇集应用的动态负载均衡算法

针对WSN数据汇集应用中,由于负载分配不均衡,使得网络节点出现早死,缩短网络寿命问题,提出一种面向WSN数据汇集应用的动态负载均衡算法（DLB-DGA）。DLB-DGA算法采用压力传输和压力均衡的思想,通过压力计算模型和流量均衡计算模型,动态调整子节点转发给父节点的数据流量比例,使网络上游节点的负载逐渐趋于最大程度的均衡,延长网络寿命。仿真实验表明DLB-DGA算法可行。     

WSN中APIT节点定位改进算法研究

为了提高无线传感器网络中APIT定位算法的定位覆盖率,提出了Min-max方法与APIT相结合的定位算法。改进算法不需要额外添加硬件,且容易实现。仿真结果表明改进算法与APIT算法相比定位覆盖率有显著提高。     

能耗均衡的移动传感器节点派遣算法

在混合无线传感器网络中,移动传感器节点最耗能的操作是移动,如何减少移动传感器节点的移动距离同时能让其完成任务是一个富有挑战性的研究课题。本文提出了一个移动传感器节点的派遣算法,旨在均衡各个移动传感器节点的移动负载,并且能按优先级响应事件地点,适用于任意数量的移动传感器节点和事件地点的情况。当移动传感器节点数量大于事件地点数量时,将其转化为一个带权完全二分图上的最大匹配问题。当事件地点数量大于移动传感器节点的数量时,本文提出的算法先将事件地点聚类分簇,然后派遣移动传感器节点到各个簇中分别完成访问任务。为了减少传感器节点之间的消息传输量,本文在集中式算法的基础上又提出了一个分布式算法。仿真实验结果表明本文提出的分布式算法能有效降低传感器节点之间的消息传输量,算法能够使得整个混合无线传感器网络的生存寿命延长20%左右。     

基于负载均衡的WSN非均匀分簇算法

针对分簇的无线传感器网络(WSN)中负载不均衡问题,提出一种实现节点负载均衡的WSN非均匀分簇算法。引入非均匀簇机制计算出最优的网络分簇数量,通过调整节点的簇首归属来控制网络的分簇的大小,形成合理的网络拓扑结构。仿真实验结果证明,该算法能有效均衡网络的节点负载,降低节点能耗,延长网络的生存时间。     

WSN中基于端到端的贪婪故障定位算法

为了维持无线传感器网络的正常运行,所有的故障链路需要被精确定位。将该问题转换为基于端到端的数据引导,以减少主动监测次数为目的的最优监测序列的问题。提出了通过拓扑拆分得到故障子图,并通过子图的概率集进一步计算节省主动探测次数的基于节点监测多条链路的启发式贪婪算法NTHG(node testing using heuristic greedy)。仿真结果表明仅需要监测小部分的节点,就可以定位网络中所有的故障链路。与该问题最新的解决算法LTHG(link testing using heristic greedy)相比,新算法需要更少的监测次数和平均CPU耗时,从而很好地降低了网络能耗,缩短了故障定位耗时。     

中继网关的设计及其负载均衡的实现

分析了IP网和PSTN互通中的信令转换,提出了一种中继网关软件系统设计方案.该设计包括信令模块、信令分发模块和管理模块.从IP网到PSTN的数据或语音流量过多时,为了更好地解决一个E1口上最大30路呼叫的限制,中继网关还实现了负载均衡功能.     

Relay node placement in structurally damaged wireless sensor networks via triangular steiner tree approximation

Wireless sensor networks (WSNs) have many applications which operate in hostile environments. Due to the harsh surroundings, WSNs may suffer from a large scale damage that causes many nodes to fail simultaneously and the network to get partitioned into multiple disjoint segments. In such a case, restoring the network connectivity is very important in order to avoid negative effects on the applications. In this paper, we pursue the placement of the least number of relay nodes to re-establish a strongly connected network topology. The problem of finding the minimum count and the position of relay nodes is NP-hard and hence we pursue heuristics. We present a novel three-step algorithm called FeSTA which is based on steinerizing appropriate triangles. Each segment is represented by a terminal. Each subset of 3 terminals forms a triangle. Finding the optimal solution for a triangle (i.e. connecting 3 segments) is a relatively easier problem. In the first step, FeSTA finds the best triangles and form islands of segments by establishing intra-triangle connectivity. Then in the second, disjoint islands of segment are federated. In the final step, the steinerized edges are optimized. The performance of FeSTA is validated through simulation.     

Recovery from multiple simultaneous failures in wireless sensor networks using minimum Steiner tree

In some applications, wireless sensor networks (WSNs) operate in very harsh environments and nodes become subject to increased risk of damage. Sometimes a WSN suffers from the simultaneous failure of multiple sensors and gets partitioned into disjoint segments. Restoring network connectivity in such a case is crucial in order to avoid negative effects on the application. Given that WSNs often operate unattended in remote areas, the recovery should be autonomous. This paper promotes an effective strategy for restoring the connectivity among these segments by populating the least number of relay nodes. Finding the optimal count and position of relay nodes is NP-hard and heuristics are thus pursued. We propose a Distributed algorithm for Optimized Relay node placement using Minimum Steiner tree (DORMS). Since in autonomously operating WSNs it is infeasible to perform a network-wide analysis to diagnose where segments are located, DORMS moves relay nodes from each segment toward the center of the deployment area. As soon as those relays become in range of each other, the partitioned segments resume operation. DORMS further model such initial inter-segment topology as Steiner tree in order to minimize the count of required relays. Disengaged relays can return to their respective segments to resume their pre-failure duties. We analyze DORMS mathematically and explain the beneficial aspects of the resulting topology with respect to connectivity, and traffic balance. The performance of DORMS is validated through extensive simulation experiments.     

无线传感器网络通信空洞分析

本文简述无线传感器网络近代发展的情况。详细介绍了以地理位置信息为基础的贪婪路由算法。分析了现有的贪婪算法存在的不足,着重讨论了贪婪路由算法中通信空洞现象各种不同的解决方法。基于以上分析对地理位置信息在无线传感器网络路由协议的发展提出看法。     

A grid-based dynamic load balancing approach for data-centric storage in wireless sensor networks

In many data-centric storage techniques, each event corresponds to a hashing location by event type. However, most of them fail to deal with storage memory space due to high percentage of the load is assigned to a relatively small portion of the sensor nodes. Hence, these nodes may fail to deal with the storage of the sensor nodes effectively. To solve the problem, we propose a grid-based dynamic load balancing approach for data-centric storage in sensor networks that relies on two schemes: (1) a cover-up scheme to deal with a problem of a storage node whose memory space is depleted. This scheme can adjust the number of storage nodes dynamically; (2) the multi-threshold levels to achieve load balancing in each grid and all nodes get load balancing. Simulations have shown that our scheme can enhance the quality of data and avoid hotspot of the storage while there are a vast number of the events in a sensor network.     

Design of fault tolerant wireless sensor networks satisfying survivability and lifetime requirements

Sensor networks are deployed to accomplish certain specific missions over a period of time. It is essential that the network continues to operate, even if some of its nodes fail. It is also important that the network is able to support the mission for a minimum specified period of time. Hence, the design of a sensor network should not only provide some guarantees that all data from the sensor nodes are gathered at the base station, even in the presence of some faults, but should also allow the network to remain functional for a specified duration. This paper considers a two-tier, hierarchical sensor network architecture, where some relay nodes, provisioned with higher power and other capabilities, are used as cluster heads. Given a distribution of sensor nodes in a sensor network, finding the locations to place a minimum number of relay nodes such that, each sensor node is covered by at least one relay node, is known to be a computationally difficult problem. In addition, for successful and reliable data communication, the relay nodes network needs to be connected, as well as resilient to node failures. In this paper, a novel integrated Integer Linear Program (ILP) formulation is proposed, which, unlike existing techniques, not only finds a suitable placement strategy for the relay nodes, but also assigns the sensor nodes to the clusters and determines a load-balanced routing scheme. Therefore, in addition to the desired levels of fault tolerance for both the sensor nodes and the relay nodes, the proposed approach also meets specified performance guarantees with respect to network lifetime by limiting the maximum energy consumption of the relay nodes.     

基于传感器网络能量均衡分簇算法仿真研究

研究传感网络能量均衡分簇延长网络寿命问题。针对LEACH算法中簇首分布不均及簇首与基站一跳通信能耗大的问题,造成网络簇头的不均匀分布带来的能耗不均衡问题以及簇头的瓶颈等,为了解决上述问题,提出了一种基于能量高效的无线传感器网络分簇路由算法。算法首先在簇头选择过程中利用节点的能量、邻节点数以及每一轮中簇头的个数等参数设置节点当选簇头的优先度,使簇头均匀地分布在网络中;在簇的组建过程中利用能量参数设置簇的重建条件,达到减小簇的重建频率的目的。     

多入口集群负载均衡问题研究

分析了常用的负载均衡算法在多个调度器上同时运行时的效果,提出多入口集群的水平分割调度方案,该方案通过对各调度器设置阈值来实现。为度量轮循调度的接口负载并设置阈值,提出了轮循频率的概念,当来自不同接口的流量不一致时,需要对阈值进行调整以提高整个集群的吞吐量,为此,提出了阈值动态调整算法和一种分布式调整触发机制。该方案兼容现有的单调度器方案,有效地解决了集群环境中存在多个调度器时的负载均衡问题。     

A dynamic programming approach: Improving the performance of wireless networks

Traditional wireless networks focus on transparent data transmission where the data are processed at either the source or destination nodes. In contrast, the proposed approach aims at distributing data processing among the nodes in the network thus providing a higher processing capability than a single device. Moreover, energy consumption is balanced in the proposed scheme since the energy intensive processing will be distributed among the nodes. The performance of a wireless network is dependent on a number of factors including the available energy, energy–efficiency, data processing delay, transmission delay, routing decisions, security architecture etc. Typical existing distributed processing schemes have a fixed node or node type assigned to the processing at the design phase, for example a cluster head in wireless sensor networks aggregating the data. In contrast, the proposed approach aims to virtualize the processing, energy, and communication resources of the entire heterogeneous network and dynamically distribute processing steps along the communication path while optimizing performance. Moreover, the security of the communication is considered an important factor in the decision to either process or forward the data. Overall, the proposed scheme creates a wireless “computing cloud” where the processing tasks are dynamically assigned to the nodes using the Dynamic Programming (DP) methodology. The processing and transmission decisions are analytically derived from network models in order to optimize the utilization of the network resources including: available energy, processing capacity, security overhead, bandwidth etc. The proposed DP-based scheme is mathematically derived thus guaranteeing performance. Moreover, the scheme is verified through network simulations.     

Pareto-based evolutionary computational approach for wireless sensor placement

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks’ compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.