[WSN] Research on node energy consumption of WSN routing protocol (shortest path) based on ant colony algorithm (Matlab code implementation)…

?About the author: A Matlab simulation developer who loves scientific research. He cultivates his mind and improves his technology simultaneously. For cooperation on MATLAB projects, please send a private message.

Personal homepage: Matlab Research Studio

Personal credo: Investigate things to gain knowledge.

For more complete Matlab code and simulation customization content, click

Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system

Signal processing Image processing Path planning Cellular automaton Drone

Content introduction

In Wireless Sensor Network (WSN), energy consumption is an important issue because sensor nodes are usually powered by limited batteries. In order to extend the life of the network, researchers have been looking for effective routing protocols to reduce the energy consumption of nodes. This article will introduce a method based on the ant colony algorithm to solve the energy consumption problem in WSN routing protocols.

Ant Colony Algorithm is a heuristic algorithm inspired by the behavior of ants when searching for food. Ants release pheromones to guide other ants to find the shortest path. Similarly, the ant colony algorithm solves optimization problems by simulating the behavior of ants. In WSN, we can think of sensor nodes as ants, and the communication paths between nodes as pheromones. By simulating the behavior of ants, we can find routing paths that minimize energy consumption.

When using the ant colony algorithm to solve the energy consumption problem of WSN routing protocols, it is first necessary to define an appropriate objective function. The objective function can be to minimize the energy consumption of the entire network, or to minimize the energy consumption of a single node. Depending on the specific situation, we can choose different objective functions.

Next, we need to define the behavior rules of the ants. During the search process, ants choose paths based on the concentration of pheromone. To simulate this behavior, we can use some heuristic rules to guide the ants in choosing a path. For example, we can make ants prefer paths with lower energy costs.

During each round of search, the ants will choose the next path based on pheromone concentration and heuristic rules. When an ant reaches its destination, it releases a pheromone, and the concentration of the pheromone is updated based on the energy consumption of the path. Through multiple rounds of iteration, the pheromone concentration will gradually stabilize, eventually forming a routing path that can minimize energy consumption.

The ant colony algorithm has certain advantages in solving the energy consumption problem of WSN routing protocols. First, it can optimize globally and find the optimal solution for the entire network. Secondly, the ant colony algorithm is a distributed algorithm that does not require global information. Each node only needs local information to achieve optimization. In addition, the ant colony algorithm is adaptive and can adapt to changes in network topology.

However, the ant colony algorithm also has some challenges and limitations. First, the search process of the ant colony algorithm can be slow, especially when the network size is large. Secondly, the performance of the ant colony algorithm is highly dependent on the selection and adjustment of parameters. Improper parameter selection may cause the algorithm to fall into a local optimal solution.

In summary, the research on energy consumption of WSN routing protocols based on ant colony algorithm is an area worth exploring. By simulating the behavior of ants, the ant colony algorithm is able to find routing paths that minimize energy consumption, thus extending the life of the entire network. However, the performance of the ant colony algorithm still needs further research and improvement to improve search efficiency and solve parameter selection problems.

It is hoped that this article can provide some inspiration and reference for the research on energy consumption of WSN routing protocols and promote the development and innovation in this field.

Part of the code

function [pile, intermediate_piles] = resolvePeaks(pile, peak_pos, nbr_pos)
%resolvePeaks - Resolve all peaks in a pile
%
% Syntax: [pile, intermediate_piles] = resolvePeaks(pile, peak_pos)
%
%Inputs:
% pile - Matrix of shape (pile width, pile width,
% no. of history time steps), with integer values from 0 to 4
% peak_pos - Vector containing positions of all peaks
%
% Outputs:
% pile - Matrix of shape (pile width, pile width), with integer values
% from 0 to 4, with peaks in initial pile resolved (might now contain
% peaks resulting from resolving the initial peaks)
% intermediate_piles - Matrix of shape (pile width, pile width, no. of
% intermediate time steps), with integer values from 0 to 4, containing
% all intermediate steps taken in resolving the peaks
%
%Example:
% [pile, intermediate_piles] = resolvePeaks([4 1;3 2], 1)
%
% Other m-files required: none
% Subfunctions: none
% MAT-files required: none
%
% See also: scanPileForPeaks
%
% Author: Florian Roscheck
% Website: http://github.com/flrs/visual_sandpile
% January 2017; Last revision: 27-January-2017

%------------- BEGIN CODE --------------
%% initialize
pile_width = size(pile,1);
prealloc_size = round(pile_width^1.3); % preallocate empty array depending
                                       % on pile side length, it is
                                       % expected that no. of piles
                                       % increases exponentially with pile
                                       % side length, exponent 1.3 is
                                       % arbitrary and a tradeoff between
                                       % unneccessarily slow initialization
                                       % and unneccessary overhead when
                                       % expanding the array later in the
                                       % code
intermediate_piles = zeros(pile_width,pile_width,prealloc_size);
intermediate_pile_ct = 1;

peak_pattern = [0 1 0;1 -4 1;0 1 0;]; % pattern for resolving peaks,
                                      % characteristic of Abelian sandpile

pile_frame = zeros(pile_width + 2); % construct frame around pile to catch
                                  % falling off the grid
pile_frame(2:end-1,2:end-1) = pile; % insert pile into frame

%% process peaks
% resolve peaks one by one
for peak = 1:numel(peak_pos)
    % fast ind2sub (see http://tipstrickshowtos.blogspot.com/2011/09/fast-r
    % eplacement-for-ind2sub.html, checked on 2017-01-26)
    peakY = rem(peak_pos(peak)-1, pile_width) + 1;
    peakX = (peak_pos(peak)-peakY)/pile_width + 1;%Find the coordinates of the avalanche point
    
    % resolve peaks
    z = zeros(3);
    index = peak_pos(peak);
    nbr = nbr_pos(index);
    z(5) = -1;
    z(nbr) = 1;
    peak_pattern = z;
    pile_frame(peakY:peakY + 2, peakX:peakX + 2) = ...
        pile_frame(peakY:peakY + 2, peakX:peakX + 2) + z;
    
    % extract new pile from frame
    pile = pile_frame(2:end-1, 2:end-1);
    
    % expand intermediate pile array when it has reached its size limit
    if intermediate_pile_ct>size(intermediate_piles, 3)
        intermediate_piles = ...
            cat(3, intermediate_piles, ...
            zeros(pile_width, pile_width, prealloc_size));
    end
    
    % append new pile to intermediate pile array
    intermediate_piles(:, :, intermediate_pile_ct) = pile;
    
    intermediate_pile_ct = intermediate_pile_ct + 1;
end

if intermediate_pile_ct>1
    % eliminate unused, preallocated entries from intermediate pile array
    intermediate_piles = ...
        intermediate_piles(:, :, 1:intermediate_pile_ct-1);
else
    % no piles resolved
    intermediate_piles = [];
end

end

Run results

References

[1] Mi Yiping. Research on WSN routing algorithm based on improved ant colony algorithm[D]. North China University[2023-09-20].DOI:CNKI:CDMD:2.1012.336755.

[2] Li Hao, Dai Tianhong, Gao Lina. Research on WSN routing protocol based on improved ant colony algorithm [J]. Control Engineering, 2017, 24(11):5.DOI:10.14107/j.cnki.kzgc.140839.

[3] Liao Minghua, Zhang Hua, Xie Jianquan. WSN energy prediction routing protocol based on ant colony algorithm [J]. Computer Engineering, 2012, 38(3):88-90.DOI:10.3969/j.issn.1000-3428.2012. 03.030.

Some theories are quoted from online literature. If there is any infringement, please contact the blogger to delete it

Follow me to receive massive matlab e-books and mathematical modeling materials

Complete private message code and data acquisition and paper simulation simulation

1 Improvements and applications of various intelligent optimization algorithms

Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization

2 Machine learning and deep learning

Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis

2. Image processing

Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing

3 Path planning

Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization

4 UAV application

UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories

5 Wireless sensor positioning and layout

Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization

6 Signal processing

Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization