Due to the recent proliferation of cyberattacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scalefree WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFSSA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFSSA is an enhanced version of the Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The proposed RHAFSSA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate. For experimentation, free scale networks are produced by the Barabási–Albert (BA) model, and realworld networks are employed for testing the outcome on both syntheticfree scale and realworld networks. The experimental results exhibited that the RHAFSSA model is superior to other models interms of diverse aspects.
Wireless Sensor Networks (WSN) has been applied for collecting data regarding the physical parameters in diverse application areas. As a promising technology it brings about a faster development in the area of advanced wireless telecommunication. The Internet of Things (IoT) has gained significant interest and offers advantageous to numerous realtime applications. Sensor interfacing devices are needed to detect different types of sensing data in the IoT environment. WSN plays a vital role in sensing the atmosphere and collecting the data. For sensing the ecological metrics, a massive number of sensors, like a sink and sensor nodes, have been deployed in wider regions. Such nodes tend to develop a multihop
The scalefree topology comes under the tedious network hypothesis that is comprised of extensive domains of realtime applications, like the global transportation system [
To produce the powerlaw distribution of node degrees in scalefree networks, it is projected with a model named BA, under the application of 2 procedures to attain a scalefree topology. Initially, Growth: new nodes consecutively combine with the network. A newly merged node links with a previous node with a possible degree. These procedures tend to provide a maximum node connection where it receives novel connections. Hence, it is named as the “Mathew Effect”. This criterion is effectively applied to produce the powerlaw distribution of node degrees that is evident from Fornasier et al. [
In scalefree networks, a lower count of nodes is comprised of higher degrees, where the networks are more vulnerable for MA. The whole network topology has been segmented rapidly. Hence, the major aim of this method is to boost the robustness of scalefree networks in WSN for MAs. Additionally, edge nodes are capable of solving the issues involved in this model. Several models have been presented and available in the literature. Peng et al. [
Louzada et al. [
Wang et al. [
Tnag et al. [
Kuang et al. [
To achieve robustness in the network and to improve the network security, this paper introduces a new Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFSSA) Algorithm over malicious attacks (MA) with no change in degree distribution. The proposed RHAFSSA is an enhanced version of the Improved Artificial Fish Swarm algorithm (IAFSA) and the SA model. The proposed RHAFSSA algorithm discards the IAFSA from unforeseen vibration and improves the convergence value. For experimentation, free scale networks are produced by the BA and the realworld networks are employed for testing the outcomes on syntheticfree scale and realworld networks. The experimental results exhibited that the RHAFSSA model is superior to the other models interms of diverse aspects. The remaining sections of the paper has been organized in the following order. Section 2 introduces the proposed RHAFSSA algorithm and the experimental details are provided in Section 3. At last, the paper is concluded in Section 4.
AFSA is said to be the current heuristic searching approach that has been evolved from the food hunting nature of fish. IAFSA is an extended version of improved exploration potential to identify the position with maximum food concentration. But, IAFSA is not assumed to be the ideal process as it provides the outcomes which have a minimum accuracy because of the unknown vibrations, in case of minimum visibility. Besides, SA is named as a global optimization model based on the annealing of solids. It is capable of finding global optimum under the application of stochastic searching methodologies. The main aim is to explore the global optimum, but it is vulnerable due to the sensitivity of the annealing schedule as well as the perturbation method. Therefore, SA is comprised of a better local stability and concatenated with alternate models such as AFSA to supply optimal simulation outcomes. In this approach, it is proposed with an alternative hybrid optimization technique that integrates the IAFSA and the SA to boost the searching efficiency and convergence of global optimum.
A network can be expressed as a graph
In developing the scalefree networks, the popular BA model has been employed. The BA initiates with a minimum clique of
where
AFSA is a current heuristic that has been developed by searching the global optimum. It is considered to be a randomized search as well as an optimizing model assisted by the strategies of the fish swarm. It offers closer optimized solutions of the objective function. AFSA, the metrics of search space undergoes the encoding as
Parameters  Definition 

Population size of AF (No. of variables).  
The recent position of 

Fitness value (food concentration)  
Distance among the 

Visual  Visual distance of the AF 
Searching visual area of AF 

Crowd factor 

No. of neighbours inside the visual area of AF  
Trynumber  Number of iteration that AF attempts to implement prey behaviour 
Rand 
Generating arbitrary values among 0 and 1 
Step  The moving step length (higher swim distance of 
It is considered that the recent position of
where
It is pointed out that, the higher number of inspecting iterations by
Fish often collects the process to hold a colony and eliminates the potential issues. When the current position of
If a single fish identifies the food by using the moving process of the fish swarm, AFs have the movement of the following fish to obtain the optimal foodconcentration position. Assume that the recent location of
Generally, the behaviors of
The AFSA and the
It is evident that, when the best AF present in a population has been chosen to implement the leaping behavior, immediate vibration occurs in the successive iteration task. Finally, it gradually decreases the converging speed. For increasing the convergence speed, optimized AFs could not be selected for implementing the leaping behavior.
Describes the number of clusters
Load the population of AF,
where
(a) Based on the
Implement IAFSA with the application of
(b) Go to Step 4 if the result meets the termination condition, else,
Improve
(a) Identify the optimal individual
(b) Determine the MF matrix
(c) Upgrade
(d) Terminate the iteration when the result meets the termination condition, else, improve
The initial SA model has been presented by Metropolis et al. It has been evolved from the annealing of solids. Annealing is defined as the task of melting the solids to high temperatures by a lower cooling pattern by reducing the environmental temperature of the ecology. Here, the temperature has to be provisioned in a reliable state for a given time interval which is enough to attain the thermal equilibrium. For thermal equilibrium, it is comprised of several configurations linked with the diverse energy levels. The likelihood of a solid is to approve the modification from a recent configuration to the novel function of variations in the energy levels between 2 states. At the initial stage, a trial configuration has been attained by a randomly produced perturbation of a recent configuration. It has been considered that the E_{c} and the E_{t} imply the corresponding energy levels of the present trial configurations. When E_{c} > E_{t}, the trial configuration is approved to be a novel configuration. Besides, the E_{c}E_{t}, the trial configuration is named only when the positive probability is as expressed in
Also, it is referred to the ad Metropolis Law. From the recent temperature T cur, it has been followed ‘Markov Length’ number of times as well as minimizes the temperature based on the annealing schedule. On the other hand, Markov Length computes the count of the trial configurations at every particular temperature. Since the temperature T is 0, the possibility of accepting a trial configuration is 0. The thermal equilibrium can be attained only with an enhanced energy level.
SA has been incorporated with the AF prey behavior. The AFs arbitrary selection of position inside the visual region and moving forward towards the direction of maximum food concentration takes place. When there is a forwarding condition that could not be satisfied once the iterations are completed, the AFs move to a distance in a random direction. The random migration behavior enables the model to eliminate the complexity in the local minimum. But, the AF seeks global optimum, without the vibration. Hence, it has to be presented with a novel hybrid model that integrates the IAFSA with the SA. The AF accepts an arbitrary position with a probability exp((deltaJ)/T cur), else, the AF remains in its recent position, asAs the acceptance rule has been computed by the current temperature, in the primary iterations. AF accepts the random position and exists from the local optimum. On the other hand, the value of FF is enabled to become poor inside an assertive extent in the primary events. As the temperature reduces in all iterations, the search area seeks for a neighborhood of the global optimum, and accepting the hypothesized optimal AF minimizes the irregular vibration. Finally, the reliability and convergence speed of a model can be enhanced.
This section discusses the experimental analysis of the RHAFSSA algorithm. The proposed RHAFSSA algorithm has been simulated using MatLab 8.1 simulation. The performance of the RHAFSSA model has been validated under several aspects. The parameters used for simulation is shown in
Parameter  Value 

Simulator  MatLab 8.1 
Node Count  250 
Protocol Used  EROSE and SPRT (L&H) 
Simulation Area  1000 × 1000 
Total Time  100 (secs) 
Number of Node  EROSE  SPRT (L&H)  RHAFSSA 

50  10  7  6 
100  14  10  8 
150  17  14  12 
200  23  16  13 
250  25  20  17 
Number of Node  EROSE  SPRT (L&H)  RHAFSSA 

50  65.44  73.44  74.23 
100  72.55  78.65  79.50 
150  79.22  83.67  84.20 
200  84.52  89.33  90.00 
250  91.07  93.23  94.10 
A detailed PDPR analysis is made between the RHAFSSA algorithm and the existing methods as shown in
Number of Node  EROSE  SPRT (L&H)  RHAFSSA 

50  29  22  20 
100  23  17  14 
150  18  11  10 
200  16  09  08 
250  11  06  04 
Number of Node  EROSE  SPRT (L&H)  RHAFSSA 

50  34  42  44 
100  44  56  57 
150  52  65  67 
200  66  74  76 
250  73  82  85 
An extensive communication overhead analysis takes place between the RHAFSSA algorithm and the compared methods under a varying numbers of nodes as given in
Number of Node  EROSE  SPRT (L&H)  RHAFSSA 

50  12246  10348  10300 
100  16571  12079  11900 
150  18655  15896  15560 
200  20247  16383  15980 
250  22567  18458  17500 
At last, it is depicted that an effective performance is exhibited by the RHAFSSA algorithm by attaining a minimal communication overhead under all the applied scenarios. By looking into the abovementioned tables and figures, it is ensured that the RHAFSSA algorithm has offered superior performance over the existing methods interms of delay, PDR, PDPR, residual energy and communication overhead.
This paper has presented an effective RHAFSSA Algorithm over the MA for the free scale networks with no change in degree distribution. The proposed RHAFSSA is a hybridization of the IAFSA and the SA algorithms. The presented RHAFSSA system discards the IAFSA from the unforeseen vibration and speeds up the convergence rate. For experimentation, free scale networks are produced by the BA model as well as the realtime networks are employed for testing the outcome on syntheticfree scale and realworld networks. The experimental results exhibited that, the RHAFSSA model is superior to other models interms of diverse aspects such as delay, PDR, PDPR, residual energy, and communication overhead. In the future, the performance of the proposed model can be enhanced by the use of deep learning models.