惠州专业网站设计公司,网站 规划方案,网站开发要,如何查看网站开发源码#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码实现 1 概述
【WSN】基于蚁群算法的路由协议(最短路径)对节点能量的消耗研究是一个十分重要的课题。
在无蚁群算法(ACO)的情况下的无线传感器网络(WSN)中当使用相同的路由协议(最短路径)时节点能量会不断消耗最终导致节点死亡。这是因为传感器节点在进行数据传输时往往需要通过多个中继节点才能到达目的地这些中继节点的数据转发会消耗大量的能量。而在没有ACO算法的情况下网络中的节点并没有考虑到能量消耗的差异因此无法做出针对性的路由选择。
而在应用了ACO步骤的WSN网络中情况就不同了。同样是通过路由协议(最短路径)进行数据传输但ACO分析了正在使用的路径的能量消耗情况并根据能量消耗的评估结果来进行路由调整。这意味着在ACO算法的指导下节点能够根据路由路径上的能量变化情况做出相应的决策。比如如果某个路径的能量消耗较大ACO可以选择其他能量消耗相对较小的路径以减少节点的能量消耗。
通过引入ACO算法WSN网络中的节点能够更加智能地选择路由路径从而减少节点能量的消耗。这将延长整个网络的寿命并提高网络的稳定性和性能。此外ACO算法也可以根据网络的实际情况进行调整和优化以更好地适应不同的应用场景和节点能量消耗的变化。
总而言之通过研究基于蚁群算法的路由协议(最短路径)对节点能量的消耗我们可以深入理解WSN网络中能量问题的关键因素并为解决节点能量消耗过高的问题提供有效的方法。引入ACO算法可以使节点能够根据能量消耗情况智能地选择路由路径从而优化能量分配延长网络寿命并提高网络的可靠性和性能。
2 运行结果 持续运行中。 部分代码
%% Main configuration values for this simulationdataset.nodeNo 9; %Number of nodes
ACOnodeNo dataset.nodeNo;
dataset.nodePosition(1,:) [1 50 50]; %(Sender node fixed position)
dataset.nodePosition(2,:) [2 900 900]; %(Receiver node fixed position)
dataset.NeighborsNo 5;
dataset.range 500; %Tolerance distance to became neighbor of one node (Euclidean distance based)
dataset.atenuationFactor 1.8; %Atenuation factor in freespace - ranges from 1.8 to 4 due environment
dataset.minEnergy 80; % Mw - Miliwatts (70% energy)
dataset.maxEnergy 100; % Mw - Miliwatts (Full energy (100%) - 1 mAh charge capacity within 1 Volt energy)
dataset.energyconsumptionperCicle 0.85;
dataset.energyrecoveryperCicle 0.2;
dataset.minenergyfactor 0.18;
dataset.maxenergyfactor 0.2;
STenergyinf;
packet0;
iterationcounter1;
plotgraphs1; %Choose 1 for yes or 0 for no if you want to plot graphs or no (Better performance if no)
reprodutibily 0; %1 yes (always generate same random numbers) (0) for no reprodutibility (Different random numbers every code execution);% Node position sortition
if reprodutibily 0rng(shuffle);
elserng(default);
end
for a 3 : dataset.nodeNodataset.nodeId a; garbage.x randi([1 900]); %Xpos sortitiongarbage.y randi([1 900]); %Ypos sortitiondataset.nodePosition(a,:) [dataset.nodeId garbage.x garbage.y]; %NodeID, X and Y position into nodePosition tableend% Euclidean Distance calc from one node to all othersfor i 1 : dataset.nodeNofor j 1: dataset.nodeNogarbage.x1 dataset.nodePosition(i,2); garbage.x2 dataset.nodePosition(j,2); garbage.y1 dataset.nodePosition(i,3); garbage.y2 dataset.nodePosition(j,3);dataset.euclidiana(i,j) sqrt( (garbage.x1 - garbage.x2) ^2 (garbage.y1 - garbage.y2)^2 ); end
end% Edges matrix definition due range variable valuedataset.weights lt(dataset.euclidiana,dataset.range);% Graph constructionGgraph(dataset.weights,omitselfloops); %Graph creation based on adjacency matrix (Edges matrix) built above% Euclidean distance extraction for all existente end-to-end formed by
% distance tolerance (range variable value)
%% Main configuration values for this simulation
dataset.nodeNo 9; %Number of nodes ACOnodeNo dataset.nodeNo; dataset.nodePosition(1,:) [1 50 50]; %(Sender node fixed position) dataset.nodePosition(2,:) [2 900 900]; %(Receiver node fixed position) dataset.NeighborsNo 5; dataset.range 500; %Tolerance distance to became neighbor of one node (Euclidean distance based) dataset.atenuationFactor 1.8; %Atenuation factor in freespace - ranges from 1.8 to 4 due environment dataset.minEnergy 80; % Mw - Miliwatts (70% energy) dataset.maxEnergy 100; % Mw - Miliwatts (Full energy (100%) - 1 mAh charge capacity within 1 Volt energy) dataset.energyconsumptionperCicle 0.85; dataset.energyrecoveryperCicle 0.2; dataset.minenergyfactor 0.18; dataset.maxenergyfactor 0.2; STenergyinf; packet0; iterationcounter1; plotgraphs1; %Choose 1 for yes or 0 for no if you want to plot graphs or no (Better performance if no) reprodutibily 0; %1 yes (always generate same random numbers) (0) for no reprodutibility (Different random numbers every code execution); % Node position sortition if reprodutibily 0 rng(shuffle); else rng(default); end for a 3 : dataset.nodeNo dataset.nodeId a; garbage.x randi([1 900]); %Xpos sortition garbage.y randi([1 900]); %Ypos sortition dataset.nodePosition(a,:) [dataset.nodeId garbage.x garbage.y]; %NodeID, X and Y position into nodePosition table end
% Euclidean Distance calc from one node to all others
for i 1 : dataset.nodeNo for j 1: dataset.nodeNo garbage.x1 dataset.nodePosition(i,2); garbage.x2 dataset.nodePosition(j,2); garbage.y1 dataset.nodePosition(i,3); garbage.y2 dataset.nodePosition(j,3); dataset.euclidiana(i,j) sqrt( (garbage.x1 - garbage.x2) ^2 (garbage.y1 - garbage.y2)^2 ); end end
% Edges matrix definition due range variable value
dataset.weights lt(dataset.euclidiana,dataset.range);
% Graph construction
Ggraph(dataset.weights,omitselfloops); %Graph creation based on adjacency matrix (Edges matrix) built above
% Euclidean distance extraction for all existente end-to-end formed by % distance tolerance (range variable value)
3 参考文献 文章中一些内容引自网络会注明出处或引用为参考文献难免有未尽之处如有不妥请随时联系删除。 [1]廖明华,张华,谢建全.基于蚁群算法的WSN能量预测路由协议[J].计算机工程, 2012, 38(3):88-90.DOI:10.3969/j.issn.1000-3428.2012.03.030.
[1]米奕萍.基于改进型蚁群算法的WSN路由算法的研究[D].中北大学[2023-09-19].DOI:CNKI:CDMD:2.1012.336755.
4 Matlab代码实现
