ShodhKarya (Research)

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In this post, we will design an another mobile trajectory called Hilbert. clc clear all close all %% Deployment of the nodes and anchor nodes N=100;   % Total Number of  Sensors A=10;    % Total Number of Anchors X=100;   % Area length Y=100;   % Area width R=10;     % Transmission radius (m) [s_x,s_y]=create_distribution(N,X,Y); %Static sensors random distribution plot(s_x,s_y,'bo'); % plot the sensor distribution hold on [a_x,a_y]=Hilbert(R,s_x,s_y) plot(a_x,a_y,'mo'); % plot the anchor distribution grid on hold on %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Create Distribution Function (Create a new file and paste the below code and saved the file as create_distribution.m) function[s_x,s_y]=create_distribution(N,X,Y)      s_x = rand(1,N)*X;   % Sensor random distribution of X axis      s_y = rand(1,N)*Y;    % Sensor random distribution of Y axis %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function[anchor_x, anchor_y]=Hilbert(r,node_x,node_y)  %% Mobility Model 

In this post, we will see an another mobile beacon trajectories called Spiral. clc clear all close all %% Deployment of the nodes and anchor nodes N=100;   % Total Number of  Sensors A=10;    % Total Number of Anchors X=100;   % Area length Y=100;   % Area width R=10;     % Transmission radius (m) [s_x,s_y]=create_distribution(N,X,Y); %Static sensors random distribution plot(s_x,s_y,'bo'); % plot the sensor distribution hold on [a_x,a_y]=Spiral(R,s_x,s_y) plot(a_x,a_y,'mo'); % plot the anchor distribution grid on hold on %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Create Distribution Function (Create a new file and paste the below code and saved the file as create_distribution.m) function[s_x,s_y]=create_distribution(N,X,Y)      s_x = rand(1,N)*X;   % Sensor random distribution of X axis      s_y = rand(1,N)*Y;   % Sensor random distribution of Y axis %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function[anchor_x, anchor_y]=Spiral(r,

In this post, we will design the circular trajectory of the mobile beacon. clc clear all close all %% Deployment of the nodes and anchor nodes N=100;   % Total Number of  Sensors A=10;    % Total Number of Anchors X=100;   % Area length Y=100;   % Area width R=10;     % Transmission radius (m) [s_x,s_y]=create_distribution(N,X,Y); %Static sensors random distribution plot(s_x,s_y,'bo'); % plot the sensor distribution hold on [a_x,a_y]=Circle(R,s_x,s_y) %Circular mobile trajectory plot(a_x,a_y,'mo'); % plot the anchor distribution grid on hold on %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Create Distribution Function (Create a new file and paste the below code and saved the file as create_distribution.m) function[s_x,s_y]=create_distribution(N,X,Y)      s_x = rand(1,N)*X;  % Sensor random distribution of X axis      s_y = rand(1,N)*Y;   % Sensor random distribution of Y axis %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Mobility Mod

In the previous post, we have seen various deployment scenarios of a WSNs. In this post, we will see the different trajectories of a mobile beacon and their related parameters. The trajectories of a mobile beacon has a grater importance in WSNs. In a sensor networks, we deploy sensors along with a mobile anchor. This mobile anchor traverse the sensing area and assist the sensors to create a better network.  Simple scenario of  mobile anchor and static sensor In a wireless sensor network, we normally see different types of nodes with different responsibility. Sensor node primarily sense the environmental events, anchor node assist the sensors to find there location as well as become as a coordinator node or cluster head, and lastly a gateway node that transfer the data to a network or a base station. All these responsibility a mobile anchor can perform very efficiently. Hence, an efficient mobility or path planning is necessary to increase the network lifetime and communicate

In this post, we will write a code for Static Sensors and Mobile Anchor deployment scenario. Initially, we will use the simplest mobility model called Random Way-point Mobility Model (RWP). This mobility model generates the random destinations for a mobile anchor. Once, a mobile anchor reached to a destination, it will again moves straight toward the other random destination. This code is the simple one to explain the RWP mobility model.  Deployment Scenario (Static Sensors and Mobile Anchor deployment) clc clear all close all %% Deployment of the nodes and anchor nodes N=100;   % Total Number of  Sensors MA=1;    % Total Number of Mobile Anchors X=100;   % Area length Y=100;   % Area width R=20;     % Transmission radius (m) [s_x,s_y, points]=create_distribution(N,X,Y); ma_x=points(:,1); ma_y=points(:,2); plot(s_x,s_y,'bo');    % plot the sensor distribution hold on plot(ma_x,ma_y,'*m:'); % plot the mobile anchor followed