Deployment Scenario (Static Sensor-Mobile Anchor)

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 path
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,points]=create_distribution(N,X,Y)

     s_x = rand(1,N)*X;  % sensor random distribution on X axis
     s_y = rand(1,N)*Y;   % sensor random distribution on Y axis

%%%%%%%%%%%% Mobile anchor RWP mobility model%%%%%%%%%%%%%%

    d = 10;         % distance of generating the random points (interval)

    n = 50;         % Number of points to generate

    points = [rand(1, 2) .* [X Y]]; % points hold all the generated random points.

    d2 = d ^ 2;

% Keep adding points until we have n points.

while (size(points, 1) < n)

       point = rand(1, 2) .* [X Y];     % Randomly generate a new point

       % Calculate squared distances to all other points

       dist2 = sum((points - repmat(point, size(points, 1), 1)) .^ 2, 2);

       % Only add this point if it is far enough away from all others.

       if (all(dist2 > d2))
           points = [points; point];
       end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

These code you can get it from my google drive shareable link given below..




Comments

Post a Comment

Popular posts from this blog

Types of Algorithm and Its Complexity Analysis

Image
In this post, we will learn types of Algorithm and its time complexity analysis with examples  An algorithm is divided into two forms called iterative and recursive, as shown above. Firstly, we analyze the time complexity of the iterative algorithm and then recursive. Example 1:                           A()                           {                            int i;                            for(i=1 to n)   % loop run n times                            pf("deepa");                           } First, the above function is fall into the independent category. Hence, the running time of the loop will be n and it print the particular name n times. Therefore, the worst case time complexity of this algorithm will be O(n). Example 2:                          A()                         {                         int i;                         for(i=1, i^2<=n, i++)                         printf("deepa");                         } Similarly
Read more

First Step of Problem Solving Using Data Structure and Algorithm

Image
What is problem ? A state of difficulty that need to be resolve A problem exist and its solution needs to be  attain either correct or incorrect A problem gives an opportunity to improve the current state A problem is a difference between the  current state and goal state Problem Faced in daily life        Like making the decisions what should i wear today to look good? what career? what job? what shoes? what course? What happens when bad decisions are made ? Wastage of time and resource   Six steps to ensure  a Best decision in PROBLEM SOLVING Identify the problem first understand the problem Identify the different way to solve the problem (efficient) Select the best one solution among the various alternative solutions Identify the list of steps to solve a problem (instructions) Evaluate the solution of given problem (different set of inputs) Take the problem of and apply the six step of problem solving     A   Water Jug Pro
Read more

Asymptotic Notations

Image
 In computer science, any problem can be solved using the programming language. And a particular problem can have multiple solutions in terms of Algorithm. To find out which one is the best algorithm for a problem, we analyse it in terms of time and space.  To define the time and space complexity of an algorithm, we use the Asymptotic notation. Mostly three types of Asymptotic notation is widely preferred to represent the complexity of the algorithm. Big-O mathematically represented as O The X-axis of the above graph showing the size of the input (n) and Y axis showing the increase in  time (t) . In the graph,   f(n) function showing the increase of time as input   size   n is increasing for a particular algorithm. Beside, the another function called cg(n) showing the worst case of f(n) . Hence, in the above graph cg(n)  is above the f(n). If we bound the time complexity or f(n) of a given algorithm in terms of input n with another function called cg(n) for all
Read more