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THE PROPOSED CLUSTERING ALGORITHM_

The working process of the QOKHC algorithm is shown in Figure 11.2. As depicted in the figure, the IoT sensor network undergoes node deployment in a random manner. Once the nodes are deployed, the nodes are initialized and information exchange takes place. Then, the node executes the QOBL algorithm and selects the CHs in an appropriate way. After the CHs are properly chosen, the nearby nodes join the CHs and construct clusters. Finally, data transmission from CMs to CHs takes place and reaches the BS via intercluster communication.

KH is a new optimized method that is used to solve difficult problems. Krill swarms hunt for food and communicate through members of the swarm type the fundamental of these techniques. Three stages are executed that are frequent in these KH methods; an optimal solution is continued by the explored ways. It involves three tasks in that the location of a krill has been set:

  • • Attempt inclined by other krill
  • • Foraging performance
  • • Physical diffusion
Block diagram of the QOKHC algorithm

FIGURE 11.2 Block diagram of the QOKHC algorithm.

The KH considered the Lagrangian method, as illustrated in equation (11.1):

where Nt is the motion developed with other krill, F; is the exploring motion, and D, is the physical sharing; i = l,2,...,NP and NP is the size of the population. In the initial motion, the aim, local, and repulsive result are defined in its motion way af. For krill i, equation (11.2) is indicated by

where Nmax signifies the maximal tempted speed, (On is the inertia weight, and M signifies the final motion. In second motion, the place of the food and past experience are denied. For the /th krill, it is determined in equations (11.3) and (11.4):

where Vj is the seeking speed, (Oj represents the inertia weight to the second motion, and u is the final motion; f}]ooa is the food attractive, and Д' is the result of the optimal fitness of the ith krill so far. The third motion is an arbitrary procedure in which there are two parts: a highest diffusion speed and an arbitrary directing vector. Besides, equation (11.5) can be expressed by

where Dmax signifies the highest speed flow and 8 refers to an arbitrary vector. Using three processes, the position of a krill from t to t + Af can be signified, as given in equation (11.6):

where At is attained from equation (11.7):

where NV indicates the entire count of variables, LBf and UBj are likewise the lower and upper bounds of the jth variables, and C, is the constant number 0.5. The progress of typical KH is influenced by another krill to suitable count of generations or until stopping condition is met, foraging and physical diffusion maintain for occurring. The flowchart of KH algorithm is depicted in Figure 11.3.

Flowchart of KH algorithm

FIGURE 11.3 Flowchart of KH algorithm.

An OBL method is applied for accelerating the convergence and enhances the quality of solutions with regard to the present solutions and opposite solutions synchronously. At the beginning of the probability theory, the arbitrary solution is 50% superior to the opposite solutions and vice versa. So, the higher solution among the two inverse solutions is selected as the candidate solution that is improved explores the efficiency of evolutionary techniques. The OBL model has been efficiently executed to a different type of issues. In OBL, the models of opposite number as well as opposite point are determined in the following: Opposite number: When x is an arbitrary number in the explore region [a, b], its opposite number is illustrated as

Opposite point: When P(xl,x2,...,xi>...,xa) is a point in d-dimensional space where x, e [я,, bi . its opposite point OP(xf,x%.....x?,...,xd) can be determined as follows:

But it might be pointed out that the OBL has few enhancement methods, in that QOBL has been implemented by several researchers and verified to be efficient compared to OBL. Furthermore, the quasi-opposite number and the quasi-opposite point are determined as follows.

Quasi-opposite number: A quasi-opposite number xi° of an arbitrary number x in the explore region [я, b] is illustrated as follows:

Quasi-opposite point: A quasi-opposite point QOP(x^0,xf ,...,x'f<’,...,x‘f')in d-dimensional space is computed as follows:

The QOBL is implemented not only in the initialize method but also in the evolutionary method of CS technique to update the population. In this chapter, the result is created by mutation method utilizing a quasi-opposite solution.

The communication situations of WSN take place in two ways: intercluster communication and intracluster communication. A broadcast in both varieties can be with a single hop to the BS or with multiple hops, initially to the CH and thereafter from CH to BS. Improving the intracluster communication and choosing a suitable cluster illustrative of every node in every round is the purpose of clustering. The data from many member nodes is combined at the CH and thereafter send to the BS. It can reduce the energy utilized. But a problem with these techniques is that the CH is constantly a suitable node that regularly loses its energy in the procedure. Thus, a node has to be allocated as the CH in all rounds. These decisions of choosing a fixed node are accepted by the KH. The fresh CH in a definite round is selected depending upon the energy taken by the node and distance from the member nodes that are not CH.

It mainly comprises four stages involving the clustering protocol function: (i) selecting the CH, (ii) constructing of clusters, (iii) collection of information, and (iv) communicating information. A set-up phase and the steady-state phase are the two phases. At first, in a single set-up stage, the sensor transmits the location and the remaining energy data to the BS. Then, the BS evaluates the average energy depending upon this information. In some provided round, a CH is chosen depending upon the maximum average energy in that specific round. So, the capable node is chosen as the CH to that round. A BS afterwards executes the technique to define the К count of fittest CHs. It diminishes the cost function, as given in equations (11.12)—(11.14):

where fx represents the highest of average Euclidean distance of nodes to their connected CHs and cp k is the count of nodes suitable to cluster Ck of krills. The function f2 is determined as the ratio of entire first energy of each node njti = 1,2, in the network to the entire present energy of the CH candidates in the present round. P is a user-specified constant utilized for weight giving to all of the sub-objectives.

The purpose of the FF has been separated for minimizing the intracluster distance among the nodes as well as CHs simultaneously. It can be quantified by /,;/2 quantifies the energy efficiency in the network that is quantified. Based on the classification of the cost function above, a lesser value of f and f2 implies that the cluster contains an optimal count of nodes and has requisite energy to carry out the functions compared to a CH. i.

i. Allocate S krills to maintain К arbitrarily assigned to CHs among the fixed CH candidates

ii. Initialize krills using QOBL concept

iii. Compute the cost function of all krills For all nodes, и, = 1,2, ....,1V.

Calculate distance d(n;, jVHp k among node n and every CHpk.

Allocate node nf to CHp k, where Estimation of the cost function:

iv. Determine the optimal to all krills and determine the optimal positioned krills.

v. Update the positions separately in the explore space utilizing equations (11.16) and (11.17):

vi. Go to steps 2-4 until highest count of iteration is reached.

 
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