自适应逃逸粒子群算法的网络节点覆盖优化仿真

研究无线传感器节点优化选择。传统LEACH分簇算法中,节点选择的随机性很大,没有很好地参考节点中的多个属性,通信的簇头分布也无规律,算法把能量消耗分摊到所有的节点上,一旦选择边沿节点作为簇头,一些节点必须经过长距离的路由转发才能到达簇头,造成通信效率较低。为了避免上述缺陷,提出了一种基于自适应逃逸粒子群算法的网络节点覆盖优化方法。建立自适应逃逸粒子群算法的数学模型,准确描述网络节点覆盖问题。利用自适应逃逸粒子群方法,计算无线传感网络节点最优位置,从而实现网络节点覆盖优化。实验结果表明,这种算法能够实现网络节点覆盖优化处理,从而提高无线传感网络数据传递的效率。     

基于改进LEACH的多簇头分簇路由算法

为了降低无线传感器网络(WSN)的能耗,延长网络的生存周期,提出一种多簇头双工作模式的分簇路由算法.算法对低功耗自适应集簇分层(LEACH)协议作了以下改进:采用多簇头双工作模式来分担单簇头的负荷,以解决单簇头因能耗较大而过早消亡的问题;选举簇头时充分考虑节点位置和节点剩余能量,并应用粒子群优化(PSO)算法优化簇头的选举,以均衡网络内各节点的能耗;建立簇与簇之间的数据传输路由,以减少簇间通信的能耗.仿真结果表明,算法有效降低了网络的能耗,延长了网络的生存周期.     

改进的自适应物联网技术节能优化控制仿真

研究异构传感网节能优化拓扑控制优化问题.在异构传感器网络中,每个传感器节点普遍存在初始能量异构,节点在无线通信过程中通信链路异构等异构现象.为了延长网络的生存期,提出一种自适应优化异构无线传感器网络拓扑结构控制算法.算法主要难点技术问题在于对参数E的选择控制问题.该算法基于传输数据跳数和相邻传感器之间通信距离,依据相似三角形几何原理,结合具体应用场景对传感器节点的分簇、成簇等操作进行自适应优化控制.仿真实验表明,改进的算法可以高效控制给定数据采集监测区域所有节点的网络拓扑同时极大地延长了异构传感网的生命周期.     

无线传感器网络功率路由算法研究

无线传感器网络的应用越来越广泛,主要是军事、工业、医疗等方面。然而无线传感器网络具有节点能量、存储和计算能力非常有限等特点,文中在传统的LEACH（低功耗自适应集簇分层型）协议的基础上进行了改进,并提出了一种新的无线传感器网络路由算法LEACH-N。新算法沿用了LEACH协议簇的结构,在簇头选择方法上基于传统DCHS算法的簇头选择算法,簇内成员与簇头之间直接通信,簇头与基站之间轮流进行单跳和多跳通信;多跳通信则是采用一种改进的MTE（最小传输能量）路由算法。通过NS2仿真软件对LEACH-N协议与LEACH协议分别进行了性能测试。实验结果表明,相比LEACH协议,LEACH—N协议系统能耗低、网络生命周期长,且具有更好的规模可扩展性。     

基于TinyOS的无线传感器网络LEACH算法的改进

LEACH协议是无线传感器网络中最流行的分簇路由协议之一.针对LEACH算法簇分布不均匀以及网络能耗不均衡等问题提出了一种高效节能多跳路由算法.在簇建立阶段,新算法根据网络模型计算出最优簇头间距值,调整节点通信半径以控制簇的大小,形成合理网络拓扑结构;在数据传输阶段,簇头与基站之间采用多跳的通信方式,降低了节点能耗.在TinyOS操作系统下,使用nesC语言设计实现了LEACH-EEMH算法.基于TOSSIM平台的仿真结果表明,新算法较LEACH算法在均衡网络能耗、延长网络寿命方面具有显著优势.     

基于动态分簇路由优化和分布式粒子滤波的传感器网络目标跟踪方法

针对无线传感器网络中节点通信能力及能量有限的情况,该文提出基于动态分簇路由优化和分布式粒子滤波的传感器网络目标跟踪方法。该方法以动态分簇的方式将监测区域内随机部署的传感器节点划分为若干个簇,并对簇内成员节点与簇首节点之间、簇首节点与基站之间的通信路由进行优化,确保网络能耗的均衡分布,在此基础上,被激活的簇内成员节点并行地执行分布式粒子滤波算法实现目标跟踪。仿真结果表明,该方法能有效地降低传感器网络中节点的总能耗,能在实现跟踪的同时保证目标跟踪的精度。     

基于JAYA算法的紫外光通信无人机编队路由优化

紫外光通信由于其灵活性高、安全性好和全天候工作等优点,被认为是应急通信用无人机编队(UAV)的有潜力通信解决方案。为了提升紫外光通信无人机编队的有效作业时间,该文基于低功耗自适应集簇分层(LEACH)算法,并结合JAYA智能优化算法提出一种新颖的路由优化算法(RJLEACH)。该方法被用来改善紫外光通信无人机编队的有效操作时间。应用该算法对不同结构的紫外光通信无人机编队路由优化,并与其它算法得到的结果进行了比较分析。结果表明,RJLEACH算法在簇首选举阶段降低了无人机节点间的剩余能量方差,并且通过搜索最优路由降低了簇间通信的能量消耗。最终使网络出现第1个死亡节点和出现1/2死亡节点的时间相比经典LEACH算法分别延长了31.8%和13.8%,同时明显提高了能量利用率,能够为灾区救援和应急通信等任务争取宝贵的时间。     

一种基于演化博弈的分簇协作路由算法

现有的分簇协作路由没有依据协作通信的特点选择簇头,也没能根据簇头节点的服务能力均衡簇成员负载,因而不能充分发挥协作通信能量高效的优势.本文提出了一种基于演化博弈的分簇协作路由算法CCREG.算法首先定义虚节点剩余能量作为簇头确立的指标,然后通过动态演化博弈为簇联盟问题建立模型.簇成员节点选择不同簇头结成联盟,可获得不同的收益.收益由簇头的能力、簇成员节点个数等因素决定.簇成员节点都可以根据自身得到的信息有限理性的选择簇结成联盟,直到网络中所有节点改变簇联盟都不能获得更高的收益.实验结果表明,与协作多输入多输出路由算法CMIMO相比,CCREG算法的网络生存周期在两个簇头情况下延长14%到70%,三个簇头情况下延长5%到80%.     

SAHRC: 一种基于分簇的无线传感器网络路由控制算法

设计特定应用场合的路由控制算法是无线传感器网络路由控制领域研究的热点之一。在深入研究经典网络路由算法(LEACH)的基础上,提出一种基于分簇的自适应混合型路由控制(SAHRC)算法。该算法针对大规模事件驱动型网络场景应用,采用网内节点启发机制解决了LEACH算法面对大规模网络缺乏自适应性,未考虑节点剩余能量,通信效率难以得到保障等问题。仿真结果表明,新的SAHRC算法比原有LEACH算法有更好的节能性和稳定性。     

基于遗传算法的无线传感器网络自适应数据融合路由算法

针对移动代理以能量有效的方式收集相关性数据的问题,该文提出了一种新的基于遗传算法的自适应数据融合路由算法。算法选择移动代理路由时,根据数据传输和融合能量开销及节能增益,对移动代理迁移到每个传感器节点是否进行数据融合做自适应选择,以在信息收集过程中提高网络能量效率。仿真结果表明自适应数据融合路由算法的能量效率优于完全数据融合路由算法和最邻近启发式算法。     

WSN中引入移动节点的路由协议设计与仿真

对于节点部署不均或者节点死亡而导致的监测盲区,可通过在WSN中引入移动节点来修复。提出一种修复策略,可较为及时、准确地修复监测盲区,同时考虑节点的能量均衡问题。在LEACH-M分簇路由算法的基础上,给出了一种按节点能量分配工作量的能量均衡分簇路由算法LEACH-M-G,并运用MATLAB仿真工具进行了仿真分析。仿真结果表明,所提出的监测盲区修复策略、以及LEACH-M-G路由能有效地修复监测盲区,均衡网络能量、延长网络生命周期。     

An adaptive‐aware energy and queue improvement of AOMDV

Because the node energy and network resources in the wireless sensor network (WSN) are very finite, it is necessary to distribute data traffic reasonably and achieve network load balancing. Ad hoc on‐demand multipath distance vector (AOMDV) is a widely used routing protocol in WSN, but it has some deficiencies: establishes the route by only using hop counts as the routing criterion without considering other factors such as energy consumption and network load; forwards route request in fixed delay resulting in building the nonoptimal path; and cannot update the path status after built paths. For the deficiency of AOMDV, this paper proposes a multipath routing protocol adaptive energy and queue AOMDV (AEQAOMDV) based on adaptively sensing node residual energy and buffer queue length. When sending a routing request, the forwarding delay of the routing request is adaptively adjusted by both the residual energy and the queue length of the intermediate node; when establishing routes, a fitness is defined as a routing criterion according to the link energy and the queue load, predicting the available duration of the node based on the energy consumption rate and adjusting the weight of the routing criterion by the available duration of the node; after the routes are established, the path information status are updated via periodically broadcasting Hello that carries the path information with the minimum fitness, making the source node update the path information periodically. By using NS‐2, simulations demonstrate that compared with AOMDV, AEQAOMDV has obvious improvements in increasing packet delivery ratio, reducing network routing overhead, reducing route discovery frequency, and decreasing the network delay. And AEQAOMDV is more suitable for WSN.     

无线自组传感器网络中基于能量的超宽带多路径路由算法

The Energy based Ultra-Wideband Multipath Routing (EUMR) algorithm for Ad hoc sensor network is proposed. It utilizes the function of UWB positioning to reduce the network communication delay and route overhead. Furthermore, the algorithm considers energy consumption, the residual energy and node hops of communication paths to make energy consumption more balanced and extend the network lifetime. Then routing which is stable, energy-saving and low-delay is realized. Simulation results show that the algorithm has better performance on saving energy, route overhead, stability and extending network lifetime.     

Research on trust sensing based secure routing mechanism for wireless sensor network

Aiming at the serious impact of the typical network attacks caused by the limited energy and the poor deployment environment of wireless sensor network (WSN) on data transmission,a trust sensing based secure routing mechanism (TSSRM) with the lightweight characteristics and the ability to resist many common attacks simultaneously was proposed.Based on the analysis of the characteristics of network attack,the trust degree calculation model was constructed by combining node’s behavior with energy,at the same time the security route selection algorithm was also optimized by taking trust degree and QoS metrics into account.Performance analysis and simulation results show that TSSRM can improve the security and effectiveness of WSN.     

A Novel Energy Efficient and Lifetime Maximization Routing Protocol in Wireless Sensor Networks

The energy consumption is a key design criterion for the routing protocols in wireless sensor networks (WSN). Some of the conventional single path routing schemes may not be optimal to maximize the network lifetime and connectivity. Thus, multipath routing schemes is an optimal alternative to extend the lifetime of WSN. Multipath routing schemes distribute the traffic across multiple paths instead of routing all the traffic along a single path. In this paper, we propose a multipath Energy-Efficient data Routing Protocol for wireless sensor networks (EERP). The latter keeps a set of good paths and chooses one based on the node state and the cost function of this path. In EERP, each node has a number of neighbours through which it can route packets to the base station. A node bases its routing decision on two metrics: state and cost function. It searches its Neighbours Information Table for all its neighbours concerned with minimum cost function. Simulation results show that our EERP protocol minimizes and balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime.     

Wireless Sensor Network Path Optimization Using Sensor Node Coverage Area Calculation Approach

The proposed work is based on the path optimization approach for wireless sensor network (WSN). Path optimization is achieved by using the NSG 2.1 Tool, TCL Script file and NS2 simulator to improve the quality of service (QoS). Path optimization approach finds best suitable path between sensor nodes of WSN. The routing approach is not only the solution to improve the quality but also improves the WSN performance. The node cardinally is taken under consideration using the ad-hoc on demand distance vector routing protocol mechanism. Ad hoc approach emphasize on sensor nodes coverage area performance along with simulation time. NSG 2.1 Tool calculates the sensor node packet data delivery speed which can facilitate inter-node communication successfully. An experimental result verified that the proposed design is the best possible method which can escape from slow network response while covering maximum sensor nodes. It achieves coverage support in sensor node deployment. The result outcomes show best path for transferring packet from one sensor node to another node. The coverage area of sensor node gives the percentage of average coverage ratio of each node with respect to the simulation time.

     

Improved ant colony-based multi-constrained QoS energy-saving routing and throughput optimization in wireless Ad-hoc networks简

In order to establish a route supporting multi-constrained quality of service（QoS）, increase network throughput and reduce network energy consumption, an improved ant colony-based multi-constrained QoS energy-saving routing algorithm（IAMQER） is proposed. The ant colony algorithm, as one of the available heuristic algorithms, is used to find the optimal route from source node to destination node. The proposed IAMQER algorithm, which is based on the analysis of local node information such as node queue length, node forwarding number of data packets and node residual energy, balances the relationship between the network throughput and the energy consumption, thus improving the performance of network in multi-constrained QoS routing. Simulation results show that this IAMQER algorithm can find the QoS route that reduce average energy consumption and improves network packet delivery ratio under the end-to-end delay and packet loss ratio constraints.     

Energy Efficient Clustering Scheme (EECS) for Wireless Sensor Network with Mobile Sink

The participants in the Wireless Sensor Network (WSN) are highly resource constraint in nature. The clustering approach in the WSN supports a large-scale monitoring with ease to the user. The node near the sink depletes the energy, forming energy holes in the network. The mobility of the sink creates a major challenge in reliable and energy efficient data communication towards the sink. Hence, a new energy efficient routing protocol is needed to serve the use of networks with a mobile sink. The primary objective of the proposed work is to enhance the lifetime of the network and to increase the packet delivered to mobile sink in the network. The residual energy of the node, distance, and the data overhead are taken into account for selection of cluster head in this proposed Energy Efficient Clustering Scheme (EECS). The waiting time of the mobile sink is estimated. Based on the mobility model, the role of the sensor node is realized as finite state machine and the state transition is realized through Markov model. The proposed EECS algorithm is also been compared with Modified-Low Energy Adaptive Clustering Hierarchy (MOD-LEACH) and Gateway-based Energy-Aware multi-hop Routing protocol algorithms (M-GEAR). The proposed EECS algorithm outperforms the MOD-LEACH algorithm by 1.78 times in terms of lifetime and 1.103 times in terms of throughput. The EECS algorithm promotes unequal clustering by avoiding the energy hole and the HOT SPOT issues.     

A distributed,multi‐hop,adaptive, tree‐based energy‐balanced routing approach

To accomplish the primary objective of data sensing and collection of wireless sensor networks (WSN), the design of an energy efficient routing algorithm is very important. However, the energy constrained sensing nodes along with the intrinsic properties of the (WSN) environment makes the routing a challenging task. To overcome this routing dilemma, an improved distributed, multi‐hop, adaptive, tree‐based energy‐balanced (DMATEB) routing scheme is proposed in this paper. In this scheme, a relay node is selected in view of minimum distance and high energy from a current sensing node. Further, the parent node is chosen among the selected relay nodes on the basis of high residual energy and less power consumption with due consideration of its associated child nodes. As each sensing node itself selects its parent among the available alternatives, the proposed scheme offers a distributive and adaptive approach. Moreover, the proposed system does not overload any selected parent of a particular branch as it starts acting as a child whenever its energy lowers among the other available relay nodes. This leads to uniform energy utilization of nodes that offers a better energy balance mechanism and improves the network lifespan by 20% to 30% as compared with its predecessors.     

Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.