【WSN】基于蚁群算法的WSN路由协议(最短路径)消耗节点能量研究（Matlab代码实现）

news2024/11/24 11:55:25

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📋📋📋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 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;
STenergy=inf; 
packet=0;
iterationcounter=1;
plotgraphs=1; %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

G=graph(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;
STenergy=inf;
packet=0;
iterationcounter=1;
plotgraphs=1; %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

G=graph(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)