- We selected LEACH (Low-energy adaptive clustering hierarchy) scheme for the minimization of energy consumption.
- We proposed a GWO optimization algorithm to minimize the LEACH protocol objective function as written in equation 3.
- Initialized the random position of the grey wolves in GWO algorithm with limits equal to cluster head formation.
- The cluster head selection is not efficient or optimal in LEACH, so we used a Metaheuristic algorithm called Grey Wolf Optimization (GWO) to select cluster head.
- The energy consumption of the sensor node is minimized by GWO optimized LEACH environment.
LEACH improves energy efficiency and increases the lifetime of the network. The clusters are formed in the LEACH scheme in which distributed sensor nodes are placed in a group. The sensor node has maximum residual energy selected as the cluster head (CH). All the cluster nodes transmit data to CH, and then CH is forwarded through the other CHs or directly to the base station. The cluster head selection procedure is not optimal in the LEACH scheme. So we proposed GWO optimization algorithm for the optimal selection of CH in the LEACH protocol.
LEACH is a MAC protocol implemented with clustering and a simple routing protocol in WSN. The key function of LEACH is to minimize the energy consumption necessary to form clusters to increase the lifetime of WSN. Most of the sensor nodes transmit to the cluster head in the LEACH protocol. The cluster head compresses the received sensor nodes’ data and forwarded it to the sink node (Base Station). The remaining nodes of the clusters communicate with the cluster head in the TDMA (Time Division Multiplexing Access) manner as per CH generated scheduled .
The working of LEACH is divided into two phases. The first phase is the setup phase in which clusters are formed in WSN. The second phase is known as a steady phase in which information or data is transferred to the sink. The cluster head selection phase and clustering phase are the key parts of the LEACH protocol. Each sensor node is having threshold energy as per the formulation
In equation 1, p is cluster head percentage in all nodes, r is the round and G is the set of non-selected cluster head nodes.
The energy model is followed by LEACH protocol with the two-channel model; free space (d2) for single hope path and multipath fading (d4) for the multihop path. So the energy consumption of l bit packets over distance d is estimated as
Where efs = free space energy loss, emp = multipath fading loss, d= distance between source and destination node, d0= crossover distance =square root of (efs/emp). The energy variable depends on the node distance, so via optimizing node distance, we can minimize the energy consumption at every sensor node in WSN.
GWO optimized LEACH
An objective function is necessary for the optimization algorithm. It must satisfy the condition and constraints of the design network. As per equation 2 the energy depends on the distance of sensor nodes, so the objective function shows the relationship between energy and distance variable. Equation 3 shows the fitness function of the LEACH protocol
Here a1 and a2 are the tradeoff factors, and their fixed value is considered as 0.4. The objective function shows the energy consumption with respect to the node distance.
The MATLAB code for the objective function on LEACH optimization is written below. This is a generalized function and can be used with any optimization technique. For full details, you can download the product.
%%This function is called in each iteration and defines the objective to %% minimize for cluster head selection %% function objval=objectiveFcn(nodeTable,x,nodes,clusterno,nodepos,sinknode) clusterheadpos=nodepos(x,:); sink=sinknode; % sink node %% calculate the distance of each clusterhead from each node for ii=1:clusterno % every CH to each node dist(ii,:) = sqrt((clusterheadpos(ii,1).*ones(100,1)-nodepos(:,1)).^2+... (clusterheadpos(ii,2).*ones(100,1)-nodepos(:,2)).^2); % base Station to every CH CHdistance(ii)=pdist([sink;clusterheadpos(ii,:)],'euclidean'); end for ii=1:nodes [D(ii),idx(ii)]=min(dist(:,ii)); end %% energy residual after each cluster head to sink node communication CHenergy=CHtoSinkEnergy(nodeTable,CHdistance,nodes,clusterno,x); %% energy residual after each cluster head to sensor node communication for ii=1:clusterno clusternodeID=nodeTable.nodeID(idx==ii); nodedistance=D(idx==ii); Clusternodes=numel(nodedistance); if Clusternodes~=0 [NodeEnergy,~,~,~]=nodetoCHEnergy(nodeTable,nodedistance,Clusternodes,clusternodeID); nodeTable.energy(clusternodeID)= NodeEnergy; end end %% objval calculation alpha1=0.4; alpha2=0.4; for ii=1:clusterno nodedistance=D(idx==ii); FirstTerm(ii)=alpha1*(sum(nodedistance)/numel(find(idx==ii))); secondTerm(ii)=alpha2*(sum(nodeTable.energy(idx==ii))/CHenergy(ii)); thirdTerm(ii)=(1-alpha1-alpha2)*(1/numel(find(idx==ii))); addTerms(ii)=FirstTerm(ii)+secondTerm(ii)+thirdTerm(ii); end objval=mean(addTerms); end
We proposed a GWO optimization algorithm to minimize the LEACH protocol objective function as written in equation 3. The GWO is a Meta-heuristic optimization algorithm developed in 2010.
A GWO optimized STATCOM TWO KUNDUR AREA SYSTEM MATLAB code is available at free-thesis.com
We developed a MATLAB script of the GWO algorithm to optimize the above mention objective function. GWO is inspired by the food searching behavior of grey wolves. The wolves are attacks in a group and the best position of any wolf can provide the best solution. We call the objective function in GWO script and get the optimal or best solution in terms of the consumption energy of sensor nodes. The position of the grey wolves updated the position of the cluster head of LEACH protocol.
The objective function written in equation 3 is minimized by the GWO algorithm. The position of wolves allocates the position of the cluster head in each cluster. The position of wolves has been updated the position of cluster heads are also changed.
Figure 1 Architecture of GWO optimized LEACH module @free-thesis.com
- We create a geographical area of 100*100 in the MATLAB 2018a. The sensor nodes are randomly placed in that area.
- Initialize the parameters of LEACH protocol like Initial Energy, Number of Nodes, rounds in LEACH, energy for transferring/receiving of each bit, transmit amplifier free space/multipath energy, aggregation energy, and the packet length. Consider 5% of the total nodes as clusters. So, among 100 nodes 5 clusters are formed using LEACH protocol.
- Initialized the random position of the grey wolves in GWO algorithm with limits equal to cluster head formation. Among the 100 nodes, 5 cluster heads are selected.
- Optimized the objective function value which is given in equation 2 and computed the best fit solution in terms of residual energy. Calculate the objective function value for each wolf in each iteration and save the output.
- Update the best position of wolves as per the GWO algorithm and estimate the best fitness value of the objective function in the next iteration.
- All the process is repeated until the final iteration is finished. We obtained the minimum value of the objective function in terms of energy and distance.
- So the efficient LEACH protocol is achieved by GWO algorithm. The residual energy is reduced in LEACH protocol via an optimal selection of cluster head.
Figure 2 WSN network generated for 100 nodes placed randomly
In this work, we minimize the energy consumption of sensors in the Wireless Sensor Network (WSN). The configuration of the WSN environment is done as per the LEACH (Low-energy adaptive clustering hierarchy) protocol due to efficient energy and network lifetime. In LEACH protocol the distributed sensor nodes are placed in a group or cluster. A sensor node of the cluster is selected as the cluster head which has maximum residual energy. The cluster head selection is not efficient or optimal in LEACH, so we used a Metaheuristic algorithm called Grey Wolf Optimization (GWO) to select cluster head. The energy consumption of the sensor node is minimized by GWO optimized LEACH environment.
Published Paper similar to this work
- Agrawal, Deepika, Muhammad Huzaif Wasim Qureshi, Pooja Pincha, Prateet Srivastava, Sourabh Agarwal, Vikram Tiwari, and Sudhakar Pandey. “GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs.” International Journal of Communication Systems 33, no. 8 (2020): e4344.
- Sharawi, Marwa, and Eid Emary. “Impact of grey wolf optimization on WSN cluster formation and lifetime expansion.” In 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), pp. 157-162. IEEE, 2017.
- Indrapandi, A., and S. Rizwana. “Energy Efficient Cluster based Data Aggregation using GWO Optimization with SPFA Technique for Wireless Sensor Networks.” Solid State Technology 63, no. 6 (2020): 24069-24083.
- Pratha, S. Jaya, V. Asanambigai, and S. R. Mugunthan. “Grey wolf optimization based energy efficiency management system for wireless sensor networks.” (2021).
- Jiang, Shuyu. “LEACH Protocol Analysis and Optimization of Wireless Sensor Networks Based on PSO and AC.” InÂ 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 246-250. IEEE, 2018.
- Beiranvand, Zahra, Ahmad Patooghy, and Mahdi Fazeli. “I-LEACH: An efficient routing algorithm to improve performance & to reduce energy consumption in Wireless Sensor Networks.” InÂ The 5th Conference on Information and Knowledge Technology, pp. 13-18. IEEE, 2013.
- Mann, Palvinder Singh, and Satvir Singh. “Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks.”Â Artificial Intelligence Review51, no. 3 (2019): 329-354.
- Hatta, N.M & Zain, Azlan & Sallehuddin, Roselina & Abd. Rahim, Shayfull Zamree & Yusoff, Yusliza. (2018). Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artificial Intelligence Review. 10.1007/s10462-018-9634-.