一种能耗均衡的WSNs动态数据汇聚方法

无线传感器网络一般采用分簇路由协议实现数据的汇聚,这类协议要求Sink节点的位置固定,并通过节点间多跳接力传输,将数据汇聚到Sink节点。由于网络中不同节点承担中继的负载不同,这会导致某些负载过重的节点能量提早耗尽,从而形成网络空洞。虽然某些路由协议在网络能耗均衡方面做了一定的措施,但仍无法较好的解决该问题。为此提出一种能耗均衡的动态数据汇聚方法,该方法的汇聚节点(Sink)为可移动节点,为平衡网络能耗,利用网络节点的能量为Sink节点确定若干个数据汇聚位置。并结合TSP算法规划Sink节点的最佳移动路径,通过对该算法进行大量的仿真,并与现有的一些方案进行比较,验证了该算法在各种性能指标上的有效性。     

基于单向多汇聚节点的WSN分层路由协议

针对分层无线传感器网络的簇首节点容易成为网络瓶颈制约网络性能的不足,提出一种低能耗的路由协议。采用多汇聚（Sink）节点模式来构建网络,利用RSSI（接收信号强度指示）测出各传感器节点与各Sink节点之间的距离,并依据距离的远近为Sink节点划分作用域。通过传感器节点单向与所属Sink节点通信来降低簇首节点的负担。仿真实验结果表明提出的路由协议能有效克服簇首节点网络瓶颈问题,从而降低网络能耗,提高网络生存时间,对于无线传感器网络应用于大范围数据收集的网络具有重要的价值。     

无线传感器网络中一种基于移动Sink的数据收集算法

针对移动无线传感器网络设计一种不依赖于节点地理位置的基于移动汇聚节点(Sink)的数据收集算法(Mobile Sink-based Data Gathering,MSDG)。该算法解决了无线传感器网络中多跳路由通信时出现能量空洞的"热点"问题。Sink沿途以最近的固定节点作为根节点动态构建路由树。簇内移动节点感知的数据经簇头进行数据融合计算,然后将融合后的数据沿路由树反向逐跳转发给Sink。仿真结果表明,MSDG在节点的平均能耗和网络生存时间等方面的性能远超过LEACH、ACE-L等数据收集协议。     

基于移动 Ｓｉｎｋ 的带状无线传感器网络数据汇聚方法研究

无线传感器网络内部能耗不均衡容易导致节点提早死亡,为此大量的能耗均衡路由协议被提出,然而这类方法主要针对环状无线传感器网络。带状无线传感器网络呈长条形分布,传统的能耗均衡路由协议无法发挥节能和能耗均衡的作用。本文提出一种基于移动Sink的带状WSN数据汇聚方法,首先基于节点能耗模型计算簇内节点的平均能耗,然后利用节点平均能耗求取Sink节点的最佳移动速度,最后簇首节点根据它与Sink的距离动态调整数据传输范围,从而实现对带状传感器网络的数据汇聚。实验结果表明该方法具有较好的节能和能耗均衡性,能够有效延长网络的生存周期。     

一种新的基于LEACH的WSN分簇协议

LEACH是针对无线传感器网络设计的低功耗自适应分簇聚类路由算法,与一般的平面多跳路由算法相比,LEACH算法可以将网络生命周期延长15%。但是,靠近汇聚节点的簇头节点由于转发大量数据而导致自身能量消耗过快且节点易失效,从而造成网络分割,形成＂热区＂的问题,提出了一种新颖的基于分区能耗均衡的多跳非均匀分簇算法（CEUC）。改进后的算法采用固定分簇的方式;形成的簇是不均匀簇,即靠近Sink节点的簇的半径较大,而远离Sink节点的簇的半径较小;簇首选择的依据是节点的剩余能量。仿真实验结果表明,该路由协议有效地平衡了无线传感器网络的节点能耗,延长了网络的存活时间。     

基于蚁群的AdHoc网络分簇路由算法

无线AdHoc网络是一个多跳、临时性的对等移动自治系统,它由一组带有无线收发装置的移动节点组成。而路由协议是AdHoc网络体系结构中不可或缺的重要组成部分,因此路由协议的研究成为当前AdHoc网络研究的重点。针对AdHoc网络节点能量有限的特性,提出了一种基于分簇及蚁群的组合路由算法（CRBAC）。给出了分簇策略下的簇内簇间路由机制,簇内采用按需路由策略,将改进的蚁群算法应用到簇内路由机制中,通过扩散信息素选择能量高的邻节点均衡网络节点能量,而簇间采用尽可能简单的表驱动路由策略。仿真结果表明,该算法是合理的,不仅有效地减少了端到端时延,而且提高了网络的生存时间。     

压缩感知的能量异构WSN分簇路由协议

分簇路由协议对用于环境监测的无线传感器网络具有较好的节能性,数据压缩可以减少节点通信的数据量,但增加了分簇层次结构簇头的能耗和汇聚节点算法的复杂度,而由高能力节点担任簇头可以实现能量均衡并改善网络性能。针对无线传感器网络能量异构普遍存在的特点,提出了一种基于压缩感知的能量异构分簇路由协议（CSCH算法）。该算法根据异构节点能量确定多极簇头选举的概率,将簇内节点的信息集中在簇头上,而簇头对所采集的数据进行稀疏、压缩,以减少向汇聚节点传输数据的节点数和通信量,汇聚节点利用重构算法可从来自簇头的少量数据中恢复出信号源。同时设计了一种基于正态分布的权值系数,以优化在数据量过少情况下压缩感知算法的信号重构性能。仿真实验结果表明,该协议不仅能充分利用能量异构资源,均衡网络能耗,延长整个网络生命周期,而且能精确恢复信号源。     

一种基于LEACH协议改进的簇间多跳路由协议

为了均衡无线传感器网络的能量消耗,延长网络的生存时间,在研究几种基于均匀分簇和非均匀分簇的路由协议基础上,提出一种基于LEACH协议改进的簇间多跳路由协议。该协议引入能量因子和距离因子修正了LEACH协议的阈值函数。在簇间通信过程,簇头节点与Sink节点之间采用多跳通信方式,簇头与簇头之间形成一条通向Sink节点的优化路径。实验结果表明,相比于LEACH协议和EEUC协议,本文提出的新协议能够有效的均衡网络的能量消耗,延长无线传感器网络的寿命。     

一种新的支持移动Sink的多媒体传感器网络路由协议

提出了一种新的支持移动Sink的多媒体传感器网络路由协议,该协议利用锚节点作为转发节点与移动Sink进行通信,避免多媒体传感器节点与Sink直接进行远距离通信,在多媒体传感器节点中采用改进的基于地理信息的路由协议建立路由路径.仿真实验表明:该协议不仅能支持移动Sink,而且能够有效降低节点能耗,延长网络寿命,提高数据传...     

一种基于能量和时延的动态分簇算法

在无线传感器网络中,传感器节点的能量由电池提供,有时难以更换。因此,降低能耗是目前无线传感器网络设计中一个很重要的技术问题。通过对层次型路由协议的研究,提出了一种基于能量和时延的动态分簇算法,该算法通过动态地确定每一轮数据收集时无线传感器网络中的簇头数目,从而在满足不超过网络最大延迟时间的基础上,使网络能耗达到最小,最大延迟时间由Sink节点确定。通过仿真实验与传统的LEACH和PEGASIS协议进行比较,结果表明,该算法有效地减少了网络能耗,同时显著降低了传输时延。     

Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedingly flexible manner during the second stage. Extensive simulation has been done and results are evaluated to point out the strength of the proposed work, while dynamically varying the specific requirements of IoT applications. We observe that the work proposed is path-breaking the prevailing methods, where a specific routing protocol is employed throughout the period of time. It’s clearly shown that the proposed, Low-cost Context-Aware Protocol Switching (LCAPS) scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.     

Energy efficient and QoS based routing protocol for wireless sensor networks

The increasing demand for real-time applications in Wireless Sensor Networks (WSNs) has made the Quality of Service (QoS) based communication protocols an interesting and hot research topic. Satisfying Quality of Service (QoS) requirements (e.g. bandwidth and delay constraints) for the different QoS based applications of WSNs raises significant challenges. More precisely, the networking protocols need to cope up with energy constraints, while providing precise QoS guarantee. Therefore, enabling QoS applications in sensor networks requires energy and QoS awareness in different layers of the protocol stack. In many of these applications (such as multimedia applications, or real-time and mission critical applications), the network traffic is mixed of delay sensitive and delay tolerant traffic. Hence, QoS routing becomes an important issue. In this paper, we propose an Energy Efficient and QoS aware multipath routing protocol (abbreviated shortly as EQSR) that maximizes the network lifetime through balancing energy consumption across multiple nodes, uses the concept of service differentiation to allow delay sensitive traffic to reach the sink node within an acceptable delay, reduces the end to end delay through spreading out the traffic across multiple paths, and increases the throughput through introducing data redundancy. EQSR uses the residual energy, node available buffer size, and Signal-to-Noise Ratio (SNR) to predict the best next hop through the paths construction phase. Based on the concept of service differentiation, EQSR protocol employs a queuing model to handle both real-time and non-real-time traffic.     

Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Wireless sensor networks with fixed sink node often suffer from hot spots problem since sensor nodes close to the sink usually have more traffic burden to forward during transmission process. Utilizing mobile sink has been shown as an effective technique to enhance the network performance such as energy efficiency, network lifetime, and latency, etc. In this paper, we propose a particle swarm optimization based clustering algorithm with mobile sink for wireless sensor network. In this algorithm, the virtual clustering technique is performed during routing process which makes use of the particle swarm optimization algorithm. The residual energy and position of the nodes are the primary parameters to select cluster head. The control strategy for mobile sink to collect data from cluster head is well designed. Extensive simulation results show that the energy consumption is much reduced, the network lifetime is prolonged, and the transmission delay is reduced in our proposed routing algorithm than some other popular routing algorithms.     

Analyzing multi-hop routing feasibility for sensor data harvesting using mobile sinks

This paper presents a modeling framework for characterizing the feasibility and impacts of multi-hop packet routing in sensor networks with mobile sinks. Data collection in sensor networks using mobile sinks has recently been investigated to improve energy performance at the cost of collection delay. Although the data collection can be accomplished with varying degrees of multi-hop routing, for a given data generation rate, as the extent of multi-hop routing increases, the round traversal time of the sink decreases. At the same time, the interference experienced by the mobile sink-to-sensor links and the consequent upload time go up. This paper characterizes these competing effects and develops a methodology for determining the extent of multi-hop routing that is feasible for given network and application parameters such as sensor data generation rate, wireless link capacity between sensors and mobile sink, the speed of the mobile sink and node density.     

Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply, constrained memory capacity, processing capability, and available bandwidth. The main problem in event gathering in wireless sensor networks is the formation of energy-holes or hot spots near the sink. Due to the restricted communication range and high network density, events forwarding in sensor networks is very challenging, and require multi-hop data forwarding. Improving network lifetime and network reliability are the main factors to consider in the research associated with WSN. In static wireless sensor networks, sensors nodes close to the sink node run out of energy much faster than nodes in other parts of the monitored area. The nodes near the sink are more likely to use up their energy because they have to forward all the traffic generated by the nodes farther away to the sink. The uneven energy consumption results in network partitioning and limit the network lifetime. To this end, we propose an on-demand and multipath routing algorithm that utilizes the behavior of real termites on hill building termed Termite-hill which support sink mobility. The main objective of our proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The performance of our proposed algorithm was tested on static, dynamic and mobile sink scenarios with varying speed, and compared with other state-of-the-art routing algorithms in WSN. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that our proposed routing algorithm was able to balance the network traffic load, and prolong the network lifetime.     

一种基于DPSO的无线传感器网络QoS路由算法

针对无线传感器网络中不同业务对服务质量(QoS)的不同需求,提出一种基于离散粒子群优化(DPSO)的无线传感器网络QoS路由(DPSO-QR)算法。算法将路由建立过程抽象为多目标优化过程,以节点间通信的传播损耗、时延、带宽、丢包率为优化目标,利用DPSO算法实现多目标优化,为拥有不同QoS需求的网络业务提供满足其特有需求的优化路由。仿真实验表明:与SAR,EQR算法相比,DPSO-QR算法降低了网络平均端到端时延,减小了丢包率,延长了网络寿命。     

VD-PSO: An efficient mobile sink routing algorithm in wireless sensor networks

Efficient energy consumption is crucial for energy constrained networks such as Wireless Sensor Networks (WSN). Using a mobile sink to collect the data of the nodes is a good method to balance the energy level of the nodes and prolong the lifetime of the whole network. For the mobile sink, an efficient path planning can make the mobile sink visit significantly more nodes during a limited period and shorten the latency of information gathering. Considering the communication range of the nodes, we can deduce this routing problem as a special case of traveling salesman problem with neighborhoods (TSPN), which is a NP-hard problem [1]. In this paper, we propose a novel routing design algorithm based on Variable Dimension Particle Swarm Optimization (VD-PSO). In this algorithm, every feasible path solution of TSPN is expressed as a particle. Each dimension of the particle is the coordinates of a rendezvous point (RP, the point where the mobile sink stays to gather data). The dimensionality of the particle is equal to the number of the rendezvous points in the path. Using the evolutionary method of the particles, we can derive the optimal path of the mobile sink. Simulation results show that the proposed algorithm has fast convergence speed, and the result is quite approximate to the optimal solution.     

WSN数据收集中移动sink的路径规划和蔟头节点选取问题的综合研究

针对较大规模的无线传感器网络通过多跳传输进行数据收集而引起的能量空洞问题,本文提出了一种基于移动sink的簇头节点数据收集算法（MSRDG）,该算法基于图论原理,在满足时延性的条件下,综合考虑了普通节点到簇头节点路由和移动sink遍历路经选取的问题,构建了一条通过的簇头节点尽可能多的移动轨迹。通过NS-2仿真软件对算法的性能进行评估,结果显示出该算法能减少数据的多跳传输,降低无线传感器网络节点的能量消耗,延长网络寿命。     

一种基于遗传算法的多约束QoS多播路由优化算法

随着Internet、移动网络和高性能网络的不断发展,在网络和不确定参数下具有多约束QoS多播路由优化技术已成为网络及分布式系统领域的一个重要研究课题,这也是下一代Internet和高性能网络的难题。它吸引了许多爱好者。论文研讨了具有多约束QoS多播路由问题,其中主要包含延迟、延迟抖动、带宽、分组丢失率等QoS约束,文中描述了一种适应于研究QoS多播路由的网络模型。论文在网络环境及不确定参数下,提出了一种在网络规模、可行性方面优化Internet、移动网络和其他高性能网络的基于遗传算法的多约束QoS多播路由优化算法(MQROGA)。MQROGA在网络环境及不确定参数下能够优化网络资源,如带宽、延迟、分组丢失率等得到一个最优解和次优解。仿真实验结果表明,MQROGA为QoS多播路由提供了一种新的有效途径。