how to simulate a wireless sensor network with 500 homogeneous nodes in an area of 100 m X 100 m with a base station at fixed position (0,150) with unlimited energy residues and communication power ? and how to form clusters in this given network?

each node has initial energy = 2 joule

nodes send data packets of 500 bytes to cluster heads and cluster head sent data to base station if it can otherwise to cluster heads in the between itself and base station.

radio dissipates 50 nJ/bit to run the transmitter an receiver circuitry (Eelec) and 100 pJ/bit/m^2 for transmitter amplifier(Eamp).

energy that a node dissipates for the radio transmission of a message of K bits over a distance of d meters is

Etx = Eelec * k + Eamp * k*d^2

energy that a node dissipates for the radio reception of a message of K bits over a distance of d meters is

Etx = Eelec * k

All the network nodes, which are assumed to be located within the sensor field, are dynamically grouped into clusters. One of the nodes within every cluster is elected to be the cluster head of this cluster.

In order to elect a cluster head, the routing information and the energy spent in the network are formulated as a linear system, the solution of which is computed using the Gaussian elimination algorithm. Therefore, cluster heads are elected as the nodes that minimize the total energy consumption in the cluster.

1.The BS creates a Time Division Multiple Access (TDMA) schedule and requests the nodes to advertise themselves, a process similar to that of other protocols.

2. Each node broadcasts a message to advertise its energy level and location to its neighbors. Based on this exchanged information, each node sets up a neighbor information table that records the energy level and the positions of its neighbors and sends this table along with its corresponding information to its neighbors. This step is repeated until the information of all the nodes in the network is sent to the BS, allowing the BS to have a global knowledge of the network. At this step, all the nodes are cluster head candidates, and each node has a unique ID that is also included in the exchanged table.

3. As soon as the node advertisement is completed, the BS runs the Gaussian elimination algorithm and computes the number of rounds at which every node can be a cluster head, trying to maximize the network lifetime. In the first step of the cluster head selection, the BS chooses the nodes closest to itself to be the high level cluster heads. Moreover, some of the nodes from which the BS has not received any direct advertisement message are considered to be low level cluster heads. The overall number of nodes, which are assigned to be cluster heads, is 5% of the total number of the nodes in the network, as this can be helpful in achieving good performance in a homogeneous network with various parameter settings .

4. The BS broadcasts the unique IDs of the newly selected cluster heads, and their cluster members and the nodes use this information to form and enter a cluster. Therefore, each node has the knowledge of the number of times that it can be a cluster head and the number of times that it cannot. The BS runs the Gaussian elimination algorithm and computes the appropriate number of rounds that the nodes can be cluster heads and sends this information to the nodes.

5. The lower level cluster heads do not transmit directly to the BS. They use the upper level cluster heads as intermediate repeaters of their data to the BS.

6. Each cluster head creates a TDMA schedule and broadcasts this schedule to the nodes in its cluster, in order to inform each node of the timeslot that it can transmit. Moreover, the radio component of each node is allowed to be turned off at all time periods, except during its transmission time. 7. Then, the data transmission starts. The nodes, based on the allocated transmission time, send the data concerning the sensed events to their cluster head. The transmission power of every node is adjusted to the minimum necessary to reach its next hop neighbor.

8. Every lower level cluster head aggregates the data and then transmits the compressed data to the upper lever cluster heads until the data reaches the base station. A round of data transmission has been completed, and the protocol continues from step 4 for the next round.

9. In case that there is a change in the network topology, due to either a change in a node position or in the total dissipation of a node residual energy, the BS uses again the Gaussian elimination algorithm to determine the appropriate cluster head election

10. The execution of the protocol is terminated as soon as all the nodes in the network run out of energy.

[Avinash Chaudhary]

Want to complete my code according to result any one help me ??? Want to result according to the graph picture

clear; %Field Dimensions - x and y maximum (in meters)/ Tarife Size mohite shabake(m) xm=100; ym=100; p=1; %x and y Coordinates of the Sink / sink.x=0.5*xm; sink.y=0.5*ym; %Number of Nodes in the field / Tedade Node haye shabake n=100; number_of_ch=0.1*n; %Optimal Election Probability of a node to become cluster head/ Ehtemale Entekhab Node be onvane Cluster Head %p=0.1; %Energy Model (all values in Joules)/ Energy ha bar hasbe Joule %Initial Energy / Energy Avaliye Eo=0.5; %maximum number of rounds %rmax=input('enter the number of iterations you want to run : '); rmax=3000;

figure(1); for i=1:1:n S(i).xd=rand(1,1)*xm; XR(i)=S(i).xd; S(i).yd=rand(1,1)*ym; YR(i)=S(i).yd; S(i).G=0;

    S(i).E=Eo;

    % Calculation of distance from sink node to normal node %
     D(i)=sqrt(((S(i).xd-50)^2)+ ((S(i).yd-50)^2)); 

     R(i)=(D(i)+Eo*150)*3;

    %initially there are no cluster heads only nodes/ Dar ebteda hich Cluster Head i mojud nist
    S(i).type='N';
    S(i).e=Eo

    %temp_rnd0=i;
    %Random Election of Normal Nodes/ Entekhabe Tasadofi Node ha 
        plot(S(i).xd,S(i).yd,'o',...
            'markerFace','b');
        hold on;

end S(n+1).xd=sink.x; S(n+1).yd=sink.y; plot(sink.x,sink.y,'o', ... 'markerSize',15,'markerFace','r'); hold on;

%Eelec=Etx=Erx ETX=50*0.000000001; ERX=50*0.000000001; %Transmit Amplifier types Efs=10*0.000000000001; Emp=0.0013*0.000000000001; %Data Aggregation Energy EDA=5*0.000000001;

%Computation of do do=sqrt(Efs/Emp); S3=S; totalclust=10; %First Iteration %counter for CHs LcountCHs=0; %counter for CHs per round LrcountCHs=0; Lcluster=1;

%LcountCHs; LrcountCHs=LrcountCHs+LcountCHs; Lflag_first_dead=0;

figure(3) for r=1:rmax r hold off;

clust = randperm(n,totalclust); for i=1:totalclust

% clust=randi(n,1); S3(clust(i)).type='C'; cluster(i)=clust(i); end

 for i=1:totalclust
     plot(S3(cluster(i)).xd,S3(cluster(i)).yd,'*-g');
     hold on;
 end
  plot(S3(n+1).xd,S3(n+1).yd,'o', 'MarkerSize', 12, 'MarkerFaceColor', 'r');  
  hold on;

 for i=1:totalclust
     clster_count(i)=0;
 end
 for i=1:n
%      if(S3(i).type=='N')
%          min_dis=sqrt( (S3(i).xd-S3(n+1).xd)^2 + (S3(i).yd-S3(n+1).yd)^2 );
         min_dis=999999;
         k=n+1;
         k1=totalclust+1;
        for j=1:totalclust
             min_dis2=sqrt( (S3(i).xd-S3(cluster(j)).xd)^2 + (S3(i).yd-S3(cluster(j)).yd)^2 );
             if(min_dis2<min_dis)
                 min_dis=min_dis2;

                 k=cluster(j);
                 k1=j;
             end

        end
%        node_number(k1,i)=cluster(j); 
      S3(i).clustnum= k1;
      S3(i).clustnode= k;
      line([S3(i).xd, S3(k).xd], [S3(i).yd, S3(k).yd], 'Color', 'b', 'LineWidth', 0.5);
       hold on;
      clster_count(k1)=clster_count(k1)+1;
%             line([S3(i).xd, S3(k).xd], [S3(i).yd, S3(k).yd], 'Color', 'b', 'LineWidth', 0.5);
%        hold on;

% end end for j=1:totalclust total_node(j)=0; incre(j)=1; end for i=1:n % if (S3(i).type=='N') for j=1:totalclust if (S3(i).clustnum==j) total_node(j)=total_node(j)+1; node_no(j,incre(j))=i; res_energy(j,incre(j))=S3(i).E; incre(j)=incre(j)+1; end end % end end

     for i=1:totalclust
         for j=1:total_node(i)
             dist_bs(i,j)=sqrt( (S3(node_no(i,j)).xd-S3(n+1).xd)^2 + (S3(node_no(i,j)).yd-S3(n+1).yd)^2 );
         end
         j=j+1;
         dist_bs(i,j)=sqrt( (S3(cluster(i)).xd-S3(n+1).xd)^2 + (S3(cluster(i)).yd-S3(n+1).yd)^2 );
     end
      for i=1:totalclust
         for j=1:total_node(i)
             max_dist(i,j)=0;
             total_dist(i,j)=0;
            for k=1:total_node(i)
                 max_dis2=sqrt( (S3(node_no(i,j)).xd-S3(node_no(i,k)).xd)^2 + (S3(node_no(i,j)).yd-S3(node_no(i,k)).yd)^2 );
             total_dist(i,j)=total_dist(i,j)+ max_dis2;
              if(max_dis2>max_dist(i,j))
                 max_dist(i,j)=max_dis2;
              end
            end
         end
      end
        for i=1:totalclust
         for j=1:total_node(i)
               avg_dist(i,j)=total_dist(i,j)/total_node(i);
                distance1=sqrt((S3(node_no(i,j)).xd-S3(n+1).xd)^2 + (S3(node_no(i,j)).yd-S3(n+1).yd)^2 );
                if(distance1>do)
                min_energy(i,j)=4000* total_node(i) * ERX+ 4000* Emp* ETX * distance1*distance1*distance1*distance1;
                else
                     min_energy(i,j)=4000* total_node(i) * ERX+ 4000* Emp* ETX *distance1*distance1;
                end
         end
        end
       for i=1:totalclust
         for j=1:total_node(i)
             delay(i,j)=res_energy(i,j);
         end
       end
     alpha=0.33;
     beta=0.33;
     gama=0.33;
     for i=1:totalclust
         for j=1:total_node(i)
             fitness(i,j)=delay(i,j);
         end
     end
      [B I]=sort(fitness,2,'descend');

      for i=1:totalclust
     selected_ch(i)=node_no(i,I(i,total_node(i)));
     if (selected_ch(i)==0)
         selected_ch(i)=node_no(i,I(i,total_node(i))-1);
     end
     if (selected_ch(i)==0)
         selected_ch(i)=node_no(i,I(i,total_node(i))-2);
     end
      end

     for i=1:n
         S3(i).type='N';
     end

     for j=1:n
         for i=1:totalclust
         if (j==selected_ch(i))
         S3(i).type='C';
         end
         end
     end

     % Same for all algorithms
     for i=1:n
         if (S3(i).type=='N')
             for j=1:totalclust
                 if (S3(i).clustnum==j)
              min_dis=sqrt( (S3(i).xd-S3(selected_ch(j)).xd)^2 + (S3(i).yd-S3(selected_ch(j)).yd)^2 );
              if (min_dis>do)
                S3(i).E=S3(i).E - ( ETX *(4000) + Emp*4000*( min_dis * min_dis * min_dis * min_dis)); 
            end
            if (min_dis<=do)
                S3(i).E=S3(i).E - ( ETX *(4000) + Efs*4000*( min_dis * min_dis)); 
            end
                 end
             end
         else
            Ldistance=sqrt( (S3(i).xd-S3(n+1).xd)^2 + (S3(i).yd-S3(n+1).yd)^2 );
              if (Ldistance>do)
                S3(i).E=S3(i).E- ( (ETX+EDA)*(4000) + Emp*4000*( Ldistance*Ldistance*Ldistance*Ldistance )); 

            %S2(i).E=S2(i).E- ( (ETX+EDA)*(4000) + Emp*4000*( Ldistance*Ldistance*Ldistance*Ldistance )); 
            end
            if (Ldistance<=do)
                S3(i).E=S3(i).E - ( (ETX+EDA)*(4000)  + Efs*4000*( Ldistance * Ldistance )); 
            %S2(i).E=S2(i).E- ( (ETX+EDA)*(4000)  + Efs*4000*( Ldistance * Ldistance )); 
            end
         end
     end

     % Same for all algorithms
    for i=1:n
         if(S3(i).E>0)
         Eres(r,i)=S3(i).E;
         else
             Eres(r,i)=0;
             S3(i).E=0;
         end
    end

      deadnode=0;
     %figure(4);
     %Same for all algoriths.
     for i=1:n
         dead(i)=0;
        if(S3(i).E<=0)
            dead(i)=dead(i)+1;
            deadnode=deadnode+1;
            %S3(i).type='D'
            %plot(S(i).xd,p,'o',...
             %   'markerFace','b');

            % hold on;
            % p=p+1;
        end
     end
     d(r)=deadnode;

end

 % no of nodes remaining after each rounds
 for r=1:rmax
     figure(4);
     plot(p,n-d(r),'o',...
      'markerSize',5,'markerFace','g');
     hold on;
     p=p+1;
 end

[Avinash Chaudhary]

The code is this want to result soon......

clear; %Field Dimensions - x and y maximum (in meters)/ Tarife Size mohite shabake(m) xm=100; ym=100; p=1; %x and y Coordinates of the Sink / sink.x=0.5*xm; sink.y=0.5*ym; %Number of Nodes in the field / Tedade Node haye shabake n=100; number_of_ch=0.1*n; %Optimal Election Probability of a node to become cluster head/ Ehtemale Entekhab Node be onvane Cluster Head %p=0.1; %Energy Model (all values in Joules)/ Energy ha bar hasbe Joule %Initial Energy / Energy Avaliye Eo=0.5; %maximum number of rounds %rmax=input('enter the number of iterations you want to run : '); rmax=3000;

figure(1); for i=1:1:n S(i).xd=rand(1,1)*xm; XR(i)=S(i).xd; S(i).yd=rand(1,1)*ym; YR(i)=S(i).yd; S(i).G=0;

    S(i).E=Eo;

    % Calculation of distance from sink node to normal node %
     D(i)=sqrt(((S(i).xd-50)^2)+ ((S(i).yd-50)^2)); 

     R(i)=(D(i)+Eo*150)*3;

    %initially there are no cluster heads only nodes/ Dar ebteda hich Cluster Head i mojud nist
    S(i).type='N';
    S(i).e=Eo

    %temp_rnd0=i;
    %Random Election of Normal Nodes/ Entekhabe Tasadofi Node ha 
        plot(S(i).xd,S(i).yd,'o',...
            'markerFace','b');
        hold on;

end S(n+1).xd=sink.x; S(n+1).yd=sink.y; plot(sink.x,sink.y,'o', ... 'markerSize',15,'markerFace','r'); hold on;

%Eelec=Etx=Erx ETX=50*0.000000001; ERX=50*0.000000001; %Transmit Amplifier types Efs=10*0.000000000001; Emp=0.0013*0.000000000001; %Data Aggregation Energy EDA=5*0.000000001;

%Computation of do do=sqrt(Efs/Emp); S3=S; totalclust=10; %First Iteration %counter for CHs LcountCHs=0; %counter for CHs per round LrcountCHs=0; Lcluster=1;

%LcountCHs; LrcountCHs=LrcountCHs+LcountCHs; Lflag_first_dead=0;

figure(3) for r=1:rmax r hold off;

clust = randperm(n,totalclust); for i=1:totalclust

% clust=randi(n,1); S3(clust(i)).type='C'; cluster(i)=clust(i); end

 for i=1:totalclust
     plot(S3(cluster(i)).xd,S3(cluster(i)).yd,'*-g');
     hold on;
 end
  plot(S3(n+1).xd,S3(n+1).yd,'o', 'MarkerSize', 12, 'MarkerFaceColor', 'r');  
  hold on;

 for i=1:totalclust
     clster_count(i)=0;
 end
 for i=1:n
%      if(S3(i).type=='N')
%          min_dis=sqrt( (S3(i).xd-S3(n+1).xd)^2 + (S3(i).yd-S3(n+1).yd)^2 );
         min_dis=999999;
         k=n+1;
         k1=totalclust+1;
        for j=1:totalclust
             min_dis2=sqrt( (S3(i).xd-S3(cluster(j)).xd)^2 + (S3(i).yd-S3(cluster(j)).yd)^2 );
             if(min_dis2<min_dis)
                 min_dis=min_dis2;

                 k=cluster(j);
                 k1=j;
             end

        end
%        node_number(k1,i)=cluster(j); 
      S3(i).clustnum= k1;
      S3(i).clustnode= k;
      line([S3(i).xd, S3(k).xd], [S3(i).yd, S3(k).yd], 'Color', 'b', 'LineWidth', 0.5);
       hold on;
      clster_count(k1)=clster_count(k1)+1;
%             line([S3(i).xd, S3(k).xd], [S3(i).yd, S3(k).yd], 'Color', 'b', 'LineWidth', 0.5);
%        hold on;

% end end for j=1:totalclust total_node(j)=0; incre(j)=1; end for i=1:n % if (S3(i).type=='N') for j=1:totalclust if (S3(i).clustnum==j) total_node(j)=total_node(j)+1; node_no(j,incre(j))=i; res_energy(j,incre(j))=S3(i).E; incre(j)=incre(j)+1; end end % end end

     for i=1:totalclust
         for j=1:total_node(i)
             dist_bs(i,j)=sqrt( (S3(node_no(i,j)).xd-S3(n+1).xd)^2 + (S3(node_no(i,j)).yd-S3(n+1).yd)^2 );
         end
         j=j+1;
         dist_bs(i,j)=sqrt( (S3(cluster(i)).xd-S3(n+1).xd)^2 + (S3(cluster(i)).yd-S3(n+1).yd)^2 );
     end
      for i=1:totalclust
         for j=1:total_node(i)
             max_dist(i,j)=0;
             total_dist(i,j)=0;
            for k=1:total_node(i)
                 max_dis2=sqrt( (S3(node_no(i,j)).xd-S3(node_no(i,k)).xd)^2 + (S3(node_no(i,j)).yd-S3(node_no(i,k)).yd)^2 );
             total_dist(i,j)=total_dist(i,j)+ max_dis2;
              if(max_dis2>max_dist(i,j))
                 max_dist(i,j)=max_dis2;
              end
            end
         end
      end
        for i=1:totalclust
         for j=1:total_node(i)
               avg_dist(i,j)=total_dist(i,j)/total_node(i);
                distance1=sqrt((S3(node_no(i,j)).xd-S3(n+1).xd)^2 + (S3(node_no(i,j)).yd-S3(n+1).yd)^2 );
                if(distance1>do)
                min_energy(i,j)=4000* total_node(i) * ERX+ 4000* Emp* ETX * distance1*distance1*distance1*distance1;
                else
                     min_energy(i,j)=4000* total_node(i) * ERX+ 4000* Emp* ETX *distance1*distance1;
                end
         end
        end
       for i=1:totalclust
         for j=1:total_node(i)
             delay(i,j)=res_energy(i,j);
         end
       end
     alpha=0.33;
     beta=0.33;
     gama=0.33;
     for i=1:totalclust
         for j=1:total_node(i)
             fitness(i,j)=delay(i,j);
         end
     end
      [B I]=sort(fitness,2,'descend');

      for i=1:totalclust
     selected_ch(i)=node_no(i,I(i,total_node(i)));
     if (selected_ch(i)==0)
         selected_ch(i)=node_no(i,I(i,total_node(i))-1);
     end
     if (selected_ch(i)==0)
         selected_ch(i)=node_no(i,I(i,total_node(i))-2);
     end
      end

     for i=1:n
         S3(i).type='N';
     end

     for j=1:n
         for i=1:totalclust
         if (j==selected_ch(i))
         S3(i).type='C';
         end
         end
     end

     % Same for all algorithms
     for i=1:n
         if (S3(i).type=='N')
             for j=1:totalclust
                 if (S3(i).clustnum==j)
              min_dis=sqrt( (S3(i).xd-S3(selected_ch(j)).xd)^2 + (S3(i).yd-S3(selected_ch(j)).yd)^2 );
              if (min_dis>do)
                S3(i).E=S3(i).E - ( ETX *(4000) + Emp*4000*( min_dis * min_dis * min_dis * min_dis)); 
            end
            if (min_dis<=do)
                S3(i).E=S3(i).E - ( ETX *(4000) + Efs*4000*( min_dis * min_dis)); 
            end
                 end
             end
         else
            Ldistance=sqrt( (S3(i).xd-S3(n+1).xd)^2 + (S3(i).yd-S3(n+1).yd)^2 );
              if (Ldistance>do)
                S3(i).E=S3(i).E- ( (ETX+EDA)*(4000) + Emp*4000*( Ldistance*Ldistance*Ldistance*Ldistance )); 

            %S2(i).E=S2(i).E- ( (ETX+EDA)*(4000) + Emp*4000*( Ldistance*Ldistance*Ldistance*Ldistance )); 
            end
            if (Ldistance<=do)
                S3(i).E=S3(i).E - ( (ETX+EDA)*(4000)  + Efs*4000*( Ldistance * Ldistance )); 
            %S2(i).E=S2(i).E- ( (ETX+EDA)*(4000)  + Efs*4000*( Ldistance * Ldistance )); 
            end
         end
     end

     % Same for all algorithms
    for i=1:n
         if(S3(i).E>0)
         Eres(r,i)=S3(i).E;
         else
             Eres(r,i)=0;
             S3(i).E=0;
         end
    end

      deadnode=0;
     %figure(4);
     %Same for all algoriths.
     for i=1:n
         dead(i)=0;
        if(S3(i).E<=0)
            dead(i)=dead(i)+1;
            deadnode=deadnode+1;
            %S3(i).type='D'
            %plot(S(i).xd,p,'o',...
             %   'markerFace','b');

            % hold on;
            % p=p+1;
        end
     end
     d(r)=deadnode;

end

 % no of nodes remaining after each rounds
 for r=1:rmax
     figure(4);
     plot(p,n-d(r),'o',...
      'markerSize',5,'markerFace','g');
     hold on;
     p=p+1;
 end

[Avinash Chaudhary]

Please help me want a result soon

[sakshi bhatia]

Hey Avinash! Did you get your query resolved?

Answer 1

[Walter Roberson]

http://www.mathworks.com/matlabcentral/answers/8026-best-way-s-to-master-matlab

http://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer

[Aanchal Bura]

SIR I NEED MATLAB CODE TO SIMULATE A WIRELESS SENSOR NETWORK WITH 100 METER BY 100 METER ARE AND 100 IMMOBILE NODES TO BE SPREAD IN THIS AREA WITH A BASE STATION AT (0,150)

[Walter Roberson]

You need the code. We do not need the code. Your options at this time are:

1. Do a lot of Google searching trying to find someone who has already implemented that particular Honours Project who is willing to give the code to you. Probability of success in obtaining code: very very low. Probability of being expelled for academic plagiarism: fairly low, but only because you probably would not be handing in anything
2. Hire a consultant to write your Honours Project for you. Probability of success in obtaining code: moderately good (a lot of consultants turn down this kind of work.) Probability of being expelled for academic plagiarism: moderately high
3. Use one of the services designed to write projects for students. Probability of success in obtaining code: high. Probability of being expelled for academic plagiarism: quite high
4. Hire a tutor to guide you through in how to program in MATLAB and how to construct an effective project of this size. Probability of success in obtaining code: difficult to say as we do not know your skills, other than we know that you are not good at following instructions, but likely a fair bit higher than working on your own on this. Probability of being expelled for academic plagiarism: low (unless you happen to be enrolled at certain high ranking universities in India or China, as some of those consider it to be cheating to talk to anyone other than the professor about an assignment.)
5. Write the code yourself without assistance. Probability of success in obtaining code: difficult to say as we do not know your skills, other than that we know that you are not very good at asking questions. Probability of being expelled for academic plagiarism: zero

Answer 2

[sara R.]

see LEACH.m file available in internet have the same idea of your project .