基于改进蚁群算法的高能效WSN协作传输策略*

针对传统传感器网络分簇不均匀,数据传输能耗相对较高的问题,提出了I-CoopACO(Cooperative transmission scheme based improved Ant Colony Optimal algorithm)算法.该算法在协作LEACH (Low Energy Adaptive Clustering Hierar-chy)的技术基础上,改进了成簇过程,使得分簇规模更加均匀;在稳定传输阶段,利用节点剩余能量和传输功耗构建启发因子,通过改进的蚁群算法搜索下一跳中继节点获得最优节点,使得传输功耗更低,能耗更均衡.仿真结果表明,在随机分布的感知网络中,I-CoopACO算法减少了传输能耗,均衡了网络负载,延长了网络工作寿命,比协作LEACH算法延长了64.93％的工作寿命.     

基于最优簇头数和三段路由的改进型LEACH算法

LEACH协议可延长无线传感器网络的使用寿命,提高信息传输量.但是研究发现基站距离网络区域愈远,LEACH协议的效果愈差,网络价值愈小.故本文提出了一种基于最优簇头数和三段路由的改进型LEACH算法,以克服基站位置对网络寿命和信息传输量的影响.该算法依据不同WSN的传感器节点数目,预先计算出理论上最优的簇头数目,残余能量最高的簇头将被选举为唯一的高层簇头,形成节点—簇头—高层簇头—基站的三段数据路由.实验结果表明,与LEACH协议相比,当传输距离小于距离阈值时,该算法有效提升了节点能耗的均衡性,推迟首节点死亡时间,从而提高信息传输量;当距离超过阈值后,网络寿命和信息传输量显著提高,算法优势更为明显.     

基于BWAS的无线传感器网络静态分簇路由算法

为提高路径搜索效率,避免动态分簇较多的能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)的无线传感器网络静态分簇路由算法.BWAS是对蚁群算法的改进,在路径搜寻过程中评价出最优最差蚂蚁,引入奖惩机制,加快了路径搜索速度.通过无线传感器网络静态分簇、簇内动态选举簇头,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,能减少路径寻优能量消耗,实现均衡能量管理,延长网络寿命,且具有较强的鲁棒性.通过与基于BWAS的动态分簇和基于蚁群算法的动态分簇路由的仿真实验相比较,证实了本算法的有效性.     

基于簇结构超宽带无线传感器网络的传输功率优化选择算法

在簇结构超宽带无线传感器网络中,为了在可接受的实现成本范围内解决节能与功率控制问题,提出了一种传输功率优化选择算法,同时满足了预设的数据传输速度和最大限度的节能要求.然后详细地分析了该算法实现的可行性、复杂度和实现成本.仿真结果表明,本算法得出的最优传输功率大大小于传感器节点可获取的最大功率,并可以根据网络背景噪声和最大传输速度的变化而动态调整;该算法可以较为显著地延长传感器节点寿命;另外,仿真结果还表明本算法的实现成本是合理的且可接受的.     

蚁群-遗传算法的无线传感器网络路径优化

为了提高无线传感器网络路径优化效率,快速找到最优路径,提出基于蚁群-遗传算法的传感器路径优化方法.利用遗传算法的快速全局搜索能力和蚁群算法的正反馈机制,实现了两种算法的融合.仿真结果表明,蚁群-遗传算法在时间和性能上都优于单独的蚁群算法和遗传算法,能快速找到无线传感器网络最优路径,有效延长了网络的生命周期.     

WSN中动态自适应蚁群路由算法

路由问题是无线传感器网络的核心问题,采用动态自适应调整信息素的蚁群算法,克服了基本蚁群算法的搜索时间过长、易于陷于局部最优解等缺点;同时对无线传感器网络存在的节点能量、传输延时、网络生命周期等问题有明显的改善与提高;最后通过实验仿真证实了该算法的有效性.     

无线传感器网络动态分簇路由BWAS的算法研究

为加快无线传感器网络(WSN)路径搜索速度,减少了路径寻优能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)算法的无线传感器网络动态分簇路由算法。该算法是基于WSN动态分簇能量管理模式,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,以多跳接力方式将数据发送至汇聚节点。BWAS算法在路径搜寻过程中评价出最优-最差蚂蚁,引入奖惩机制,加强搜寻过程的指导性。结合动态分簇能量管理,避免网络连续过度使用某个节点,均衡了网络节点能量消耗。通过与基于蚁群算法(ACS)路由算法仿真比较,本算法减缓了网络节点的能量消耗,延长了网络寿命,在相同时间里具有较少的死亡节点,具有较强的鲁棒性。     

一种近似秩排序的无线传感器网络分簇路由算法

为了延长无线传感器网络(WSN)的生存期,能量有效的路由算法至关重要。以分配网络中的业务负载为目标的传感器节点聚类是解决无线传感器网络能量均衡的有效方法。文中为无线传感器网络提出一种基于近似秩排序(ARO)的分层和基于距离的组合聚类方法,并使用多跳数据传输。仿真结果表明,ARO-WSN在能耗和网络生存期方面优于经典的LEACH算法、LEACH-C算法和K-means聚类算法,能有效地延长网络的生存期。     

无线传感器网络中协作波束形成的能量有效性分析

分析了无线传感器网络中端到端误码率给定情况下协作波束形成的能量效率,给出了不同路径损耗因子和传输距离下的最优协作发射节点个数.首先,综合考虑发射能耗和电路能耗,给出了接近实际情况的系统能耗模型,并推导出系统能耗与误码率之间的近似闭式关系.然后,基于该近似模型,给出了不同路径损耗因子和传输距离下使系统能耗最小的优化协作发射节点个数.理论分析和仿真结果表明:在系统调制方式和误码率给定的情况下,存在着一个临界距离使协作波束形成比非协作传输和协作空时编码都更节能;而且在不同路径损耗因子和传输距离下,存在不同的最优协作发射节点个数使系统能耗最小.     

一种基于协同演化算法的无线传感器网络拓扑设计方法

在无线传感器网络（WSN）中，传感器节点及其中心节点相距一定距离，以确保在相关区域上的完全覆盖。然而，传感器节点之间的通信距离与能源消耗成正比，最终的传输距离受到限制。为了克服这个问题，在每个集群的头节点之间建立基于集群的布局和消息路由算法，以确保WSN实现良好的覆盖，平衡各节点工作负载和流量负载，并延长整个网络寿命。在本文中，我们使用遗传算法（NEA）来解决复杂的多目标WSN布局和信号路由问题。实验结果表明，NEA是解决问题的有效途径。     

Avoiding Energy Holes to Maximize Network Lifetime in Gradient Sinking Sensor Networks

Unbalanced energy consumption is an inherent problem in gradient sinking sensor networks. This uneven energy dissipation can lead to the existence of energy holes, and significantly reduce network lifetime. In this paper, our study is based on corona-based network division. We investigate the unbalanced energy consumption among nodes both within the same coronas and within different coronas. We take advantage of mixed-routing strategy to achieve balanced energy consumption among nodes both within the same coronas and within different coronas. Besides, once the mixed-routing strategy can not balance energy consumption in local areas, the strategy of nonuniform energy distribution is used in these areas. Finally, we give algorithms to compute the optimal data distribution ratio and the initial energy of nodes. Simulation results show that our strategy can reduce the energy consumption, avoid energy holes and prolong the network lifetime dramatically.     

多簇无线传感网的优化生存时间近邻功率控制算法

针对非均匀分布的无线传感网的生存时间问题,提出多簇无线传感网的优化生存时间近邻功率控制(NPCAOL_MC)算法。该算法采用K-means算法确定网络的簇个数和对应每个簇的节点,利用近邻算法评估每个簇的节点密度,确定簇的最优通信距离。结合Friss自由空间模型计算当前簇的最优发送功率。Sink节点广播通知其他节点,如果是同一簇内的节点相互通信,则采用簇最优功率发送数据,否则采用默认最大发送功率发送数据。仿真结果表明,利用NPCAOL_MC算法可以分析整个网络节点的位置信息,采用簇最优发送功率发送数据,从而提高生存时间,并使能耗经济有效。在密度分布不均的无线传感网中,NPCAOL_MC比采用固定发送功率的Ratio_w算法更优。     

无线传感器网络中基于概率的能量平衡算法

各传感器节点的能耗不平衡严重地影响了无线传感器网络的生命周期。该文提出了基于传输概率的能量平衡算法。首先把圆形区域网络模型划分成若干圆环,每一圆环中的传感器节点以混合传输的方式传输数据。其次,为使每个传感器节点能耗均衡,提出了一种混合传输概率求解算法,获得一组传输概率决定节点传输数据的方式,从而更好地平衡网络能耗。然后对圆环宽度进行了分析和优化。仿真结果证明这些算法可以有效地降低网络能耗,延长网络生命周期。     

Energy Efficient Non-uniform Clustering Division Scheme in Wireless Sensor Networks

Wireless sensor networks can be used to monitor the interested region by multi-hop communication. Since sensor nodes are equipped with energy-limited batteries, energy conservation in such networks is of paramount importance in order to prolong the network lifetime. In this paper, considering the constrained radio range of node, we propose an energy efficient clustering division scheme from the viewpoint of energy consumption. The difference between our scheme and previous schemes is that ours is a non-uniform clustering hierarchy. With the algorithm that is proposed by this paper, we can divide the cluster into multiple non-uniform concentric rings and obtain the optimal thickness of each ring. Motivated by the derived results, every sensor node can adjust its radio range for transmission. Our extensive simulation results indicate that the proposed non-uniform clustering division scheme outperforms the conventional uniform clustering division schemes in terms of energy consumption and lifetime. The future research that should be explored is also discussed finally.

延迟容忍移动传感器网络中基于最优距离的可靠节能传输策略

在延迟容忍移动传感器网络（Delay Tolerant Mobile Senor Networks，DTMSN）的数据传输过程中容易出现数据丢失及节点因能量耗尽而导致网络生存时间减少的问题.针对上述问题本文提出了基于最优距离的可靠节能传输策略，该传输策略利用距离与能量的关系解决了DTMSN中的三个重要问题：可靠性、能源效率、网络生存时间.首先，在链路质量保证下，引入了"可靠节能距离"和"可靠能量平衡距离"两个概念.此外，通过分析节点间距离、节点运动方向及节点当前剩余能量来确定节点的综合效用值，最后根据综合效用值的大小来路由消息.实验结果表明，该策略显著提高了消息的投递率及可靠性保证下传感器节点的能量利用率，延长了网络的生存时间.     

Multi-factor and Distributed Clustering Routing Protocol in Wireless Sensor Networks

One of important issues in wireless sensor networks is how to effectively use the limited node energy to prolong the lifetime of the networks. Clustering is a promising approach in wireless sensor networks, which can increase the network lifetime and scalability. However, in existing clustering algorithms, too heavy burden of cluster heads may lead to rapid death of the sensor nodes. The location of function nodes and the number of the neighbor nodes are also not carefully considered during clustering. In this paper, a multi-factor and distributed clustering routing protocol MFDCRP based on communication nodes is proposed by combining cluster-based routing protocol and multi-hop transmission. Communication nodes are introduced to relay the multi-hop transmission and elect cluster heads in order to ease the overload of cluster heads. The protocol optimizes the election of cluster nodes by combining various factors such as the residual energy of nodes, the distance between cluster heads and the base station, and the number of the neighbor nodes. The local optimal path construction algorithm for multi-hop transmission is also improved. Simulation results show that MFDCRP can effectively save the energy of sensor nodes, balance the network energy distribution, and greatly prolong the network lifetime, compared with the existing protocols.     

Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.     

Multipath energy balancing for clustered wireless sensor networks

In wireless sensor networks, sensors at different locations in the field use different energy levels to propagate sensing data back to the sink or base station. This causes unbalanced energy usage among sensors and also lowers the network lifetime. Currently there are several techniques to mitigate this problem, such as deploying multiple sinks, adding more sensors on heavy traffic areas, or managing the size of clusters depending on the distance from sensor to sink. In this paper, we propose a distributed algorithm and protocol called Multipath Energy Balancing (MEB) to mitigate unbalanced energy usage in clustered wireless sensor networks using multi-path and multi-hop, with a transmission power control approach. The network field is divided into regions, where the ratio of inter-region transmission traffic from all cluster head sensors in one region to other cluster head sensors in the two regions in front can be pre-computed and pre-programmed into the sensors to ease sensor deployment. To further prolong network lifetime, we also present a simple heuristic algorithm to procrastinate cluster formation and routing. Simulation results show that MEB can balance energy much better than Energy-efficient Clustering (EC) and Balancing Energy Consumption (BEC) solutions. It also has a longer network lifetime than EC and BEC protocols, especially when the required cluster size is small. Procrastinating cluster formation and routing also can further improve the network lifetime.

     

Load balancing techniques for lifetime maximizing in wireless sensor networks

Energy consumption has been the focus of many studies on Wireless Sensor Networks (WSN). It is well recognized that energy is a strictly limited resource in WSNs. This limitation constrains the operation of the sensor nodes and somehow compromises the long term network performance as well as network activities. Indeed, the purpose of all application scenarios is to have sensor nodes deployed, unattended, for several months or years.This paper presents the lifetime maximization problem in “many-to-one” and “mostly-off” wireless sensor networks. In such network pattern, all sensor nodes generate and send packets to a single sink via multi-hop transmissions. We noticed, in our previous experimental studies, that since the entire sensor data has to be forwarded to a base station via multi-hop routing, the traffic pattern is highly non-uniform, putting a high burden on the sensor nodes close to the base station.In this paper, we propose some strategies that balance the energy consumption of these nodes and ensure maximum network lifetime by balancing the traffic load as equally as possible. First, we formalize the network lifetime maximization problem then we derive an optimal load balancing solution. Subsequently, we propose a heuristic to approximate the optimal solution and we compare both optimal and heuristic solutions with most common strategies such as shortest-path and equiproportional routing. We conclude that through the results of this work, combining load balancing with transmission power control outperforms the traditional routing schemes in terms of network lifetime maximization.     

Prolonging the lifetime of wireless sensor networks by utilizing feedback control

A new algorithm aiming to prolong the lifetime of wireless sensor networks (WSNs) is proposed to balance energy depletion. Using a feedback control combined with a discrete nonlinear programming method to adjust the transmission radii of sensor nodes located in different locations, makes network load redistribution possible and balances energy consumption, further prolongs the lifetime of the entire network. A data distribution model which specific to WSNs with sensor nodes that can adjust transmission radii is proposed to analyze the load spread of the network. This model contributes to predicting and analyzing energy consumption balance effectively. Compared with two other algorithms, dynamic transmission range adjustment and SP, respectively, the experimental results show that the proposed algorithm can lengthen the lifetime of WSNs by up to 22.7 and 27.2 %.