一种基于非均匀分簇的混合无线传感网数据收集方法

提出了一种针对混合无线传感网的数据收集协议。将网络划分为非均匀高度的网格,并利用主次簇头分别构建针对矢量和标量信息的数据收集路径。实验结果表明,与MTP,CDFUD等分层和分簇的数据收集方法相比,本算法具备较好的能耗均衡性。     

基于模糊逻辑推理的WSNs非均匀分簇算法

为了进一步降低无线传感网络WSNs(Wireless Sensor Networks)能耗,拓延网络寿命,提出了基于模糊逻辑推理的WSNs非均匀分簇算法,记为DUCF.DUCF算法充分考虑了节点剩余能量、节点度以及离基站距离.根据经验制定模糊规则,通过模糊推理系统得到节点当选为簇头的几率和簇尺寸.DUCF算法形成非均匀簇,进而平衡簇头间的能量消耗.仿真结果表明,DUCF算法在网络寿命、能量消耗方面的性能优于LEACH、CHEF和EAUCF算法.     

基于可预测移动汇聚节点的无线传感网分簇算法研究

在汇聚节点移动可预测情况下,提出一种无线传感网分簇算法。该算法将subsink节点引入到HEED分簇算法中,以较快感知移动路径变化,快速形成分簇拓扑;采用sink节点注册机制,实现汇聚节点移动过程中的信息交互。实例分析表明,该算法能快速形成合理网络拓扑,延长无线传感网的生存期。     

基于非均匀分簇和信息熵的无线传感网络路由算法

针对无线传感网络分簇算法中能量分布不均衡导致的“热区”问题,提出一种基于非均匀分簇和信息熵的路由算法。在簇头选举和竞争半径计算过程中综合考虑节点能量、节点密度和节点距基站距离,均衡簇头能耗以延长生存时间。采用簇间单跳多跳混合通信的路由规则,减少簇间通信能耗。对节点信息熵进行数据融合,引入融合权重系数减小数据融合的不确定性,提高数据融合效率。仿真结果表明,与LEACH、EEUC和EBUCA相比,该算法能够有效均衡网络能耗,延长网络生命周期。     

基于模糊逻辑的无线传感网络分簇路由协议

能量消耗是无线传感器网络中首要考虑的问题. 为了均衡每个节点的能耗, 延长网络节点的生存寿命, 本文提出了一种基于模糊逻辑方法的FLCHE分簇路由协议. 该协议充分考虑了节点的剩余能量, 能量消耗速率以及节点的分布密度. 通过MATLAB实验仿真表明, 相比于经典的LEACH分簇路由协议, FLCHE协议更加均衡了网络节点的能耗, 有效地延长了网络的生命周期, 总体性能优于LEACH算法.     

一种能量高效的无线传感网分簇路由算法

针对无线传感网节点能耗不均造成的生命周期短的问题,提出一种能量高效的无线传感网分簇路由算法（NUC&GDF）。该算法从三个方面对无线传感网的路由进行优化;a）改进簇首选举机制,选择合适的簇首;b）改进簇首节点成簇半径规则,形成合理的簇规模大小;c）在簇首与基站的稳定数据传输过程中,引入改进的梯度下降法强化学习来计算权值最小的自适应无线簇间路由。实验结果分析表明,提出的算法性能比LEACH算法、LEACH-C算法以及DEBUC算法更优;在网络规模为100 m×100 m时,网络生命周期分别提高约50.3%、21.5%、16.4%,能更有效地延长网络生命周期。     

PTTC:无线传感网络分簇算法

分簇是延长无线传感网络寿命的有效技术。为此,提出了基于Prim的树簇拓扑的无线传感网络分簇PTTC算法。PTTC算法首先推导最优的簇数,再计算节点被选为簇头的平均概率。然后,结合节点的剩余能量以及被选为簇头的频率数选择簇头,最后利用Prim算法建立树,节点依据树传输数据,进而提高能量利用率,扩延网络寿命。仿真结果表明,提出的PTTC算法平衡了节点间的能量消耗,有效地延长了网络寿命。     

基于改进型蚁群算法的无线传感路由协议

针对无线传感网络中节点能量有限的特点,将蚁群算法应用于无线传感路由协议.通过蚁群算法构造一条链,选择其中能量最大节点为簇头,信息通过相邻节点传送.最后用VC 进行仿真,结果显示该算法对PEGASISpegasis有较大的改进,延长了节点生命周期,并且节点之间能量消耗非常均匀.     

基于均匀分簇的正三角模型节点轮换路由算法

针对节点随机分布的无线传感器网络能耗问题,提出一种在均匀分簇后采用正三角模型对簇内节点进行调度的低能耗路由算法。该算法首先计算网络内节点总能耗最小时的分簇数目,再由Sink节点选择相应数目的剩余能量最大、地理位置最优的节点为簇首,完成均匀分簇。簇内节点采用正三角模型和节点覆盖概率进行工作节点的选择。仿真结果表明,该路由算法可以均衡节点能耗,延长网络工作轮数,降低网络延迟,并体现出了更优的网络鲁棒性。     

基于模糊规则的WSNs分簇路由算法

考虑到无线传感网络WSNs(Wireless Sensor Networks)的电源能量有限问题,提出基于模糊规则算法的分簇-能效-路由算法FLECR(Fuzzy Logic-based Energy-efficient Clustering Routing)。FLECR路由引用"分布簇头选举"方式,并采用按需方式进行簇重构。同时,FLECR路由利用相对剩余能量、距离以及中心度三个变量,并结合模糊规则算法选举簇头。仿真结果表明,相比于低功耗自适应层次路由LEACH(Low Energy Adaptive Clustering Hierarchy),FLECR路由的网络生存时间得到大幅度提升。     

MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks

This study introduces a new clustering approach which is not only energy-efficient but also distribution-independent for wireless sensor networks (WSNs). Clustering is used as a means of efficient data gathering technique in terms of energy consumption. In clustered networks, each node transmits acquired data to a cluster-head which the nodes belong to. After a cluster-head collects all the data from all member nodes, it transmits the data to the base station (sink) either in a compressed or uncompressed manner. This data transmission occurs via other cluster-heads in a multi-hop network environment. As a result of this situation, cluster-heads close to the sink tend to die earlier because of the heavy inter-cluster relay. This problem is named as the hotspots problem. To solve this problem, some unequal clustering approaches have already been introduced in the literature. Unequal clustering techniques generate clusters in smaller sizes when approaching the sink in order to decrease intra-cluster relay. In addition to the hotspots problem, the energy hole problem may also occur because of the changes in the node deployment locations. Although a number of previous studies have focused on energy-efficiency in clustering, to the best of our knowledge, none considers both problems in uniformly and non-uniformly distributed networks. Therefore, we propose a multi-objective solution for these problems. In this study, we introduce a multi-objective fuzzy clustering algorithm (MOFCA) that addresses both hotspots and energy hole problems in stationary and evolving networks. Performance analysis and evaluations are done with popular clustering algorithms and obtained experimental results show that MOFCA outperforms the existing algorithms in the same set up in terms of efficiency metrics, which are First Node Dies (FND), Half of the Nodes Alive (HNA), and Total Remaining Energy (TRE) used for estimating the lifetime of the WSNs and efficiency of protocols.     

An agent-assisted QoS-based routing algorithm for wireless sensor networks

Existing routing algorithms are not effective in supporting the dynamic characteristics of wireless sensor networks (WSNs) and cannot ensure sufficient quality of service in WSN applications. This paper proposes a novel agent-assisted QoS-based routing algorithm for wireless sensor networks. In the proposed algorithm, the synthetic QoS of WSNs is chosen as the adaptive value of a Particle Swarm Optimization algorithm to improve the overall performance of network. Intelligent software agents are used to monitor changes in network topology, network communication flow, and each node's routing state. These agents can then participate in network routing and network maintenance. Experiment results show that the proposed algorithm can ensure better quality of service in wireless sensor networks compared with traditional algorithms.     

无线传感器网络中一种能量有效的分簇算法

传感器网络中簇头与基站的通信方式可分为两种:多跳和单跳.如果采用多跳方式,靠近基站的簇头因传送数据较多而导致较早死亡;而在单跳方式中,远离基站的簇头因传送数据能耗太高而很快死亡.针对上述问题,提出一种基于非均衡分簇的数据收集算法,使靠近基站的簇的大小小于远离基站的簇.仿真实验表明,非均衡分簇的数据收集算法能有效地延长网络的生命周期.     

A simple, least-time, and energy-efficient routing protocol with one-level data aggregation for wireless sensor networks

The area of wireless sensor networks (WSN) is currently attractive in the research community area due to its applications in diverse fields such as defense security, civilian applications and medical research. Routing is a serious issue in WSN due to the use of computationally-constrained and resource-constrained micro-sensors. These constraints prohibit the deployment of traditional routing protocols designed for other ad hoc wireless networks. Any routing protocol designed for use in WSN should be reliable, energy-efficient and should increase the lifetime of the network. We propose a simple, least-time, energy-efficient routing protocol with one-level data aggregation that ensures increased life time for the network. The proposed protocol was compared with popular ad hoc and sensor network routing protocols, viz., AODV ( [35] and [12]), DSR (Johnson et al., 2001), DSDV (Perkins and Bhagwat, 1994), DD (Intanagonwiwat et al., 2000) and MCF (Ye et al., 2001). It was observed that the proposed protocol outperformed them in throughput, latency, average energy consumption and average network lifetime. The proposed protocol uses absolute time and node energy as the criteria for routing, this ensures reliability and congestion avoidance.     

高数据融合的非均匀分簇无线传感器网络路由协议*

探讨了基于非均匀分簇的无线传感器网络路由协议,提出了一种高数据融合的非均匀分簇无线传感器网络路由协议。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。     

Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.     

Efficient distributed data scheduling algorithm for data aggregation in wireless sensor networks

With the rapid development of applications for wireless sensor networks, efficient data aggregation methods are becoming increasingly emphasized. Many researchers have studied the problem of reporting data with minimum energy cost when data is allowed to be aggregated many times. However, some aggregation functions used to aggregate multiple data into one packet are unrepeatable; that is, every data is aggregated only at most once. This problem motivated us to study reporting data with minimum energy cost subject to that a fixed number of data are allowed to be aggregated into one packet and every data is aggregated at most once. In this paper, we propose novel data aggregation and routing structures for reporting generated data. With the structures, we study the problem of scheduling data to nodes in the networks for data aggregation such that the energy cost of reporting data is minimized, termed MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING. In addition, we show that MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING is NP-complete. Furthermore, a distributed data scheduling algorithm is proposed accordingly. Simulations show that the proposed algorithm provides a good solution for MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING.     

FAMACROW: Fuzzy and ant colony optimization based combined mac,routing, and unequal clustering cross-layer protocol for wireless sensor networks

This paper presents Fuzzy and Ant Colony Optimization Based Combined MAC, Routing, and Unequal Clustering Cross-Layer Protocol for Wireless Sensor Networks (FAMACROW) consisting of several nodes that send sensed data to a Master Station. FAMACROW incorporates cluster head selection, clustering, and inter-cluster routing protocols. FAMACROW uses fuzzy logic with residual energy, number of neighboring nodes, and quality of communication link as input variables for cluster head selection. To avoid hot spots problem, FAMACROW uses an unequal clustering mechanism with clusters closer to MS having smaller sizes than those far from it. FAMACROW uses Ant Colony Optimization based technique for reliable and energy-efficient inter-cluster multi-hop routing from cluster heads to MS. The inter-cluster routing protocol decides relay node considering its: (i) distance from current cluster head and that from MS (for energy-efficient inter-cluster communication), (ii) residual energy (for energy distribution across the network), (iii) queue length (for congestion control), (iv) delivery likelihood (for reliable communication). A comparative analysis of FAMACROW with Unequal Cluster Based Routing [33], Unequal Layered Clustering Approach [43], Energy Aware Unequal Clustering using Fuzzy logic [37] and Improved Fuzzy Unequal Clustering [35] shows that FAMACROW is 41% more energy-efficient, has 75–88% more network lifetime and sends 82% more packets compared to Improved Fuzzy Unequal Clustering protocol.     

无线传感网络中的分簇融合决策方法

无线传感网络的簇划分和簇内节点访问顺序对数据融合决策能耗和耗时具有重要影响．对此,提出一种分簇融合方法,采用最大熵聚类法和蚁群算法实现分簇和节点访问顺序规划,在簇内由移动代理以渐近方式完成局部融合,中心服务节点通过二次融合得到最终结果．仿真实验以能耗×耗时为评价指标,分析了簇数目对数据融合效率和准确性的影响．验证了分簇融合决策方法能有效降低网络能耗和耗时．提高融合准确性和执行效率．     

模糊神经网络语音数据融合算法的研究

针对高嗓音环境中的语音识别问题，提出一种利用模糊神经网络进行语音数据融合的新算法。该算法按一定模糊规则对语音信号的特征参数进行模糊化，并通过神经网络对每个传感器语音信号的模糊特征参数进行分类和融合。仿真实验表明，该算法鲁棒性更强；与单传感器算法相比，语音识别率得到较大的提高。