Recently, the fundamental problem with Hybrid Mobile Adhoc Networks (HMANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy Consumption (EC) was high. This paper presents a novel Node Qualitybased Clustering Algorithm (NQCA) based on FuzzyGenetic for Cluster Head and Gateway Selection (FGCHGS). This algorithm combines NQCA with the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm divides the network into clusters based upon node priority, transmission range, and neighbour fidelity. In addition, the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC, packet loss rate (PLR), etc.
In general, MANETs consist of peertopeer, selfconfiguring, selfalleviating, wirelesslyconnected mobile nodes that share no infrastructure [
Even various algorithms had been proposed to obtain an optimum number of clusters, none have noticed that all the network factors are necessary to improve cluster performance [
In each cluster, nodes are categorized into one of the following types: CH, cluster member and gateway. For each cluster, CH acts as a local controller and has the responsibilities like routing, channel assignment, scheduling and transmitting intercluster traffic from each cluster member. Nodes other than CH are cluster members. Cluster members act as normal nodes and do not participate in routing or intercluster communication. Cluster gateway is a boundary node that involves the minimum one adjacent belonging to various clusters and tends to transmit the routing data from one cluster to the other [
In the steady load balancing gateway election algorithm [
The main contribution of the work is to achieve efficient load balancing in the gateways of the hybrid mobile adhoc network to overcome the packet loss and delay caused in the network during the selection of overload and gateways.
Here, a novel NQCA is presented with a gateway selection algorithm based on the IWCA. The MANET is initially split into different clusters in this algorithm. For each cluster, its CH is selected based on the node priority, transmission range and node neighborhood fidelity. In addition, the quality of clustering is also estimated by using additional parameters like node degree, environmental distance (Dist), clustering Stability Factor (SF), EC, Residual Battery Energy (RBE) and weight of the node including DL, PLR, NRO and BLI. Moreover, these parameter converts the values into fuzzy values using the FL system. The system calculates the combined weight value after acquiring the fuzzy sets. Then, GA is applied to optimize the weight value and select both optimal CH and gateway. If the convergence time of GA was high, error due to parameter tuning like changes in population and fitness function for GA is noted as high. As a result, the modified FGCHGS algorithm utilizes an effective optimization algorithm instead of GA. In addition, a new FF algorithm that reduces computation times and convergence times to select the optimal CH or gateway. Thus, the proposed algorithms can minimize the node EC and simplify the network maintenance.
The ClusteringBased Gateway Placement Algorithm (CBGPA) (2009) [
Papadaki & Friderikos proposed a compact representation of Uncapacitated and Capacitated joint Gateway Selection and Routing (U/CGSR) (2010) [
A proactive loadaware gateway decision (2010) [
Sahana et al. [
The performance of gateway choice protocols (2012) [
Multicriteria gateway choice and multipath routing protocol (2012) [
Gateway discovery algorithm was proposed (2012) [
Hussain et al. [
CH decision approach using FL (2014) [
Gateway selection optimization was proposed (2015) [
Zaman et al. [
Kumar & Ramamoorthy [
It is necessary to develop an energyefficient load balancing mechanism for wireless sensor networks (WSNs). Adil et al. [
In this part, the proposed FGCHGS & FFFCHGS algorithms are explained in detail. Initially, the NQCA algorithm is performed for network clustering based on the IWCA algorithm. The clustering is performed based on the two models such as node priority and range region aggregation models. Then, the cluster characteristics are measured based on the FL system by considering the various QoS parameters. After that, both CH and gateway are selected by using GA according to the fitness values of each node which are estimated as combined weight values using fuzzy sets of network metrics. Further, an improved FF algorithm is proposed instead of GA for both CH and gateway selection efficiently.
Group of nodes in the network  
Group of connections in the network  
Neighbour node  
The group of nodes that are communicated directly and situated in 

Mean distance between 

Communication region of the nodes  
Degree of node 

Range indicator  
Combined indicator  
Node Quality of the node 

Environmental distance of the nodes  
Relative dissemination of the node 

Data transfer Interval in node 

Stability factor of the node 

Packet loss ratio of the node 

Normalized Routing Overhead  Correlation between accurately received packets and total control packets  
Balancing Load Index  The degree of overload endured by each node 

Smell concentration 
The network is built by the nodes and the connections characterized via an undirected graph
In
Generally, clustering is a necessity for partitioning the nodes, thus it should satisfy the below criteria:
Each ordinary node has no less than one CH as adjacent and no two CH can be adjacent.
Each common node affiliates with the adjacent CH that has the minimum weight.
The proposed NQCA consists of two models such as follows:
Node Priority Aggregation Model
In NQCA, CH is selected by assigning the priorities to the nodes according to their degree. This model is built according to the following manner:
Here, the node types such as Strong Node (SN), Weak Node (WN) and Border Node (BN) are identified by computing the node type indicator
Region Aggregation Model
The ability of adjacent is measured by the node neighbourhood fidelity for conserving their region provided that a parent node. A parent node is any CH candidate and the adjacent can be located at a various distance (Dist) from their parent. Because of increasing distance (Dist), the parent’s neighbourhood fidelity can decrease and beyond nodes are expected to depart from the parent at any time. As a result, the parent strength is influenced so that its possible to be a CH is minimized. Hence, the communication range of a parent is divided into 3 virtual regions namely excellent, intermediary and endangered regions which are located within the circle with radius
Both excellent and intermediary regions include trusted adjacent for a definite time. In endangered regions, the adjacent nodes are taken into consideration as topologically untrusted nodes due to the assumption that they can escape from the partition before the trusted nodes. The range indicator
In
The QoC is measured by different parameters which help to improve the cluster characteristics and the selection of both CH and gateway. The main aim of QoC is to provide the capacity of a cluster for delivering the expected outcomes. The QoC is measured based on the IWCA. The different parameters are given in below:
a) Node Quality:
The node quality is calculated as the product of node degree and node combined indicator.
b) Environmental Distance
The environmental distance is measured instead of calculating the distance between parent and adjacent nodes
The total environmental distance from a parent
c) Clustering Stability Enhancement
For a given time, the cluster formation remains unchanged due to the node’s stability. As a result, the SF for each
In this NQCA, the adjacent nodes with the maximum
d) Load Balancing Clustering Scheme
Consider that each node is identical and create data at a similar rate. For load balancing, the number of nodes in a cluster and the transmit power needed per CH should be balanced. Therefore, the relative deviation of adjacent in a recent configuration is measured by relative dissemination degree as follows:
In
e) Energy Consumption & Remaining Battery Energy (RBE)
A high amount of energy is required for long distance transmission. Hence for each node
If
f) Delay
DL
g) Packet Loss Rate (PLR)
The PLR
h) Normalized Routing Overhead (NRO)
NRO is defined as the correlation between accurately received packets and total control packets in the networks. If
i) Balanced Load Index (BLI)
The BLI stands for the degree of overload endured by each
In
After that, these computed parameters are converted into fuzzy sets by using the Fuzzy Inference System (FIS) that contains fuzzy membership functions and linguistic terms. Here, the triangular fuzzy membership function is used and five linguistic terms are used, namely Very Low (L), Low (L), Medium (M), High (H) and Very High (VH). According to each parameter and their weights, the combined weight value is calculated as follows:
Based on these weight values for each node, the most optimal CH and gateway are selected. The lowest weight node in a particular cluster is decided as CH and the lowest weight node that acts as a border node in that cluster is elected as the gateway. The fuzzy system generates the number of rules according to these parameters and among the number of rules, a few rules are shown in
L  L  L  L  L  L  L  L  VL 
M  L  M  L  M  L  L  L  L 
H  M  L  M  H  H  M  H  H 
M  L  H  M  M  L  L  M  M 
H  H  H  H  H  H  H  H  VH 
L  H  M  H  L  M  M  L  M 
M  M  H  H  L  M  L  M  L 
In the GA algorithm, the obtained weighted values are optimized for selecting the CH and gateway. The objective function of the GA is to determine the optimum weight function for a MANET system. Based on the selected optimal values, the network performance improved by a better CH and gateway selection. Initially, the GA algorithm creates the population of chromosomes for all nodes. These parameters are calculated for individual chromosomes and input to the FIS, which outputs the weight values. Then, the obtained weight function is assigned as the fitness function for determining the best solution of the GA. Once the fitness function is computed, the new individuals are evaluated with the prior population. Each individual from both populations is sorted according to their fitness values. An individual with the minimum fitness function is assumed as superior to the individual with the maximum fitness value. When successive populations develop, the best performers are engaged. The new individuals in a generation are obtained based on the following two processes:
Crossover
Mutation
Crossover tends to generate new individuals in the current population by fusing two fittest individuals and reorganizing the previous individuals. The crossover function gets executed as a distributed operator. Alternately, mutations rarely occur to allow the specific child to acquire the features which are not inherited by the parent. By using the Gaussian mutation, a novel and enhanced population is analysed. Based on the proposed FGCHGS algorithm, both optimal CH and gateway are selected in HMANET for performance improvement by solving the inefficiencies in WCA.
1. 
2. Find the adjacent of 
3. Compute the parameters like EC, SF and RBE; 
4. Determine the relative dissemination degree; 
5. Calculate the DL, PLR, NRO and BLI; 
6. Convert the parameter values into fuzzy sets; 
7. Obtain the weight function 
8. Initialize the population of chromosomes and number of iterations 
9. 
10. Set fitness function=min 
11. Evaluate the fitness function value by using fuzzy sets; //Generate the new population 
12. Select two parent chromosomes from a population based on their better fitness values; 
13. Perform crossover and mutation processes; 
14. Obtain the new population and replace the previous chromosomes; 
15. Return the best solution in current population; 
16. Go to step 11; 
17. 
18. Choose the best fitness value nodes as CH and the best fitness value nodes located in the border as a gateway; 
19. 
To optimize, the weight values for CH and gateway selection, an FF optimization algorithm is proposed instead of GA. The main aim of FF is to obtain the optimal value of weight function for a MANET system and enhance the network efficiency by choosing the most optimum CH and gateway. FF is used to explore global optimization based on the food hunting behaviours of the FF swarm. Initially, the food source is identified by smelling all types of fragrances buoyant in the air and wings to the related positions. Once near the source, the food may be located. The osphresis foraging stage and the visual step are the two main steps. In the osphresis phase, a swarm of fruit flies explores food in a random manner around the swarm position. In the visual phase, the sharp vision is utilized for flying to the swarm’s best position.
Although the FF suffers from premature convergence levels and reduced efficiency because of the fixed number of iteration steps, i.e., the FF is dependent on the iteration step. Moreover, there is a problem with the FF exploring how to generate new solutions based on random details of the foregoing solutions. An improved FF algorithm introduces a lineardiminishing step to overcome these limitations. Specifically, it is difficult to discover the food position at the end of iteration while the iteration step is constant. Perhaps, a few individual fruit flies are far away from the food. To avoid local optimization traps and to improve precision, the fixed step is changed into a linear diminishing step. The steps in the proposed improved FF algorithm are:
Step 1 (Initialization phase): Assign the parameters of the first iteration and FF swarm location
Step 2: Perform the individual searches for food i.e., the minimum weight function
Here,
Step 3 (Path construction phase): As the food position i.e., the position of
The nearby position is to the origin position, smaller the density of the food. Then, the reciprocal of the Dist is defined as the smell concentration judgement value
Step 4 (Fitness function computation phase): The smell concentration
Step 5: The FF with the highest smell concentration,
In
Step 6 (Movement phase): After that, the current highest smell concentration rate
Step 7: As well, the iteration step is as:
In
Step 8:
Step 9: If the most iteration range is attained, then the best solution is obtained, if not go to Step 5, otherwise the circulation is terminated.
1. 
2. Find the adjacent of 
3. Compute the parameters like EC, SF and RBE; 
4. Determine the relative dissemination degree; 
5. Calculate the DL, PLR, NRO and BLI; 
6. Convert the parameter values into fuzzy sets; 
7. Obtain the weight function 
8. Initialize the population of FF swarm, iteration number 
9. Initialize the location of the population 
10. 
11. Perform the individual searches for food in random directions and Dist as 
12. Compute the Dist 
13. Calculate the smell concentration judgement value 
14. Set fitness function 
15. Evaluate the fitness function value by using fuzzy sets; 
16. 
17. 
18. Update 
19. Change step value 
20. Find the global best solution 
21. 
22. 
23. 
24. Select the best solution nodes (FF swarms) as CH and select the best solution value nodes located in the border as a gateway; 
25. 
The effectiveness of the FFFCHGS and FGCHGS algorithms is simulated through Network Simulator2 (NS2.34) and compared with the NQCA and FGCH algorithms based on the DL, PLR, NRO, BLI and EC.
Simulation parameters  Values 

Simulation Tool  NS2.34 
Simulation Area  1400 × 1400 sqm 
Network Topology  Flatgrid 
Physical Type  Phy/WirelessPhy 
MAC Layer  IEEE802.11 
No. of Nodes  200 
No. of Gateways  20 
Node Velocity  0–50 m/s 
Mobility Model  Random Way Point 
Simulation Period  600 s 
Transmission Range  250 m 
Packet Size  512 bytes 
Traffic Type  Constant Bit Rate (CBR) 
Compared to the existing load balancing techniques, the proposed work gives performance improvement of 0.9 to 0.999 with the improved efficiency.
In this article, an FFFCHGS algorithm is proposed for improving the gateway selection in HMANET. In this algorithm, initially, the network is split into different clusters. In every cluster, their CH is selected based on the IWCA which utilizes different metrics such as node fidelity, transmission range, etc. Then, each cluster quality is measured by using the FL system which considers the different parameters such as DL, load balancing, EC, RBE, etc. In addition, the gateway is also selected based on these parameters which are converted into fuzzy sets and the combined weight value is computed. Then, the computed combined weight values are optimized by using GA. Further, an improved FF algorithm is performed instead of GA for CH and gateway selection with increased accuracy and convergence speed. Thus, both CH and gateway selection are optimized and the effectiveness of the gateway selection is enhanced. At last, the experimental outcomes proved that the FFFCHGS algorithm achieves higher efficiency concerning minimized DL, PLR, NRO, EC, etc. But the limitation of the work includes further enhancement in the convergence precision as the result of low convergence.
Machine learning based load balancing with the proposed optimized NQCA for further enhancement in the efficiency in the future work.
The authors with a deep sense of gratitude would thank the supervisor for his guidance and constant support rendered during this research.