Security is a vital parameter to conserve energy in wireless sensor networks (WSN). Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission. But the available routing techniques do not involve security in the design of routing techniques. This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack detection, Moreover, a trust based dingo optimizer (TBDO) algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN. Besides, the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN. For demonstrating the enhanced outcomes of the SADO-RRS technique, a wide range of simulations was carried out and the outcomes demonstrated the enhanced outcomes of the SADO-RRS technique.
In recent times, WSN has extended the application range from early deployment for battlefield intelligence surveillance to fields like meteorological weather forecasting, emergency response support, factory automation security application, and so on. WSN consists of inexpensive and small sensors without a current architecture [
Even though conventional security approaches like authentication and cryptography could offer security at certain level, they alone could not handle compromised node attacks [
Rathee et al. [
Haseeb et al. [
This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack detection, Moreover, a trust based dingo optimizer (TBDO) algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN. Besides, the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN. In order to demonstrate the enhanced outcomes of the SADO-RRS technique, a wide range of simulations was carried out.
In this study, a novel SADO-RRS technique has been developed for reliable routing in WSN. The proposed SADO-RRS technique determines the presence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based LDA for attack detection. Furthermore, a TBDO algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN.
Primarily, the LDA model is applied for the detection of intrusions in the network. The discriminant analysis concentrates on the connection amongst several independent variables and definite dependence variables by creating multiple independent variables. This kind of multivariate investigation defines the extents of the sum of composite variables discriminate amongst more than two existing sets of subjects and also could develop the classifier model to predict the group membership of novel observation. During this case, a linear discriminant function (LDF) which passed with the means of 2 groups (centroids) are utilized for discriminating subjects amongst 2 centroids. If there are further centroids, the amount of centroid minus one purpose has been required for classifying an observation amongst them. In order to all centroids, LDA considers as the explanatory variable that is usually distributed with equivalent covariance matrices. To all cases, the evaluated co-efficient to independent variables are multiplied by case’s score on that variables.
The LDF has been demonstrated as:
The rule by that the discriminant co-efficient (or weights) were chosen is that maximizing the distance amongst 2 centroid means (centroids)
The dingoes are sufficient able for finding the place of prey. Afterward trace the place, the pack after that alpha surrounds the prey. For modeling dingo’s social hierarchy, it can be considered that the present optimum agent’s method is an objective or purpose prey that is same as the optimum as the quest region is not recognized a priori [
The place of neighborhood dingoes is signified utilizing a 2D place vector. Based on the place of prey
Conversely, during the searching space based on the model, agent does not usually have computation of place of the prey (optimally). Scheming the dingoes hunting plan mathematical, it is considered that each pack member containing alpha, beta, and others are optimum skill on the potential place of prey. The alpha dingo continuously commands the hunting. But, at times beta and other dingo’s can also be participating from hunting. Therefore, it is assumed the 1st two optimum values attained so far. According to place of optimum searching agents, other dingoes are also required for updating their place. Due to the discussion,
For calculating the intensity of all the dingoes, subsequent formulas are being utilized:
The place upgrade from the
When there is no place upgrade, it refers the dingo done the hunting by attacking the prey. In order to mathematical formulate the approach, the value of
The presented surrounding technique does certainly reveal exploration to any extent; but, for accentuating exploration, DOX needs further operation. The DOX supports their quest agent from changing its place dependent upon the locating of,
The dingoes hunt the prey frequently based on the pack’s place. It is continuously travel forwarded to hunt and strike predators. So,
It is optimum to search and avoidance of neighboring optimal. According to a dingo’s place, it can be arbitrarily agreed on prey value and create it essential for meeting dingo rigidly/beyond. Purposely, it is utilized
The presented approach develops a FF utilizing 3 input parameters such as trust level, distance to neighbors, and energy to optimum route selective.
Objective 1: Minimizing
Objective 2: Maximizing
Objective 3: Minimizing
In the proposed technique, it could be vital to decrease the linear group of main functions. So, the potential energy function of presented approach was implemented as:
This section inspects the performance validation of the SADO-RRS technique with existing techniques. The results are inspected interms of distinct aspects.
Methods | Precision | Recall | Accuracy |
---|---|---|---|
SVM | 0.6758 | 0.7111 | 0.6950 |
SOM | 0.7432 | 0.7732 | 0.7550 |
ANN-IDS | 0.7948 | 0.8313 | 0.8120 |
DMN-NB | 0.8021 | 0.8283 | 0.8150 |
GANs | 0.8531 | 0.8830 | 0.8650 |
DELM | 0.8964 | 0.9310 | 0.9123 |
SADO-RRS | 0.9313 | 0.9546 | 0.9437 |
Network lifetime (Rounds) | ||||
---|---|---|---|---|
Compromised nodes (%) | DEBR | EENC | QEBSR | SADO-RRS |
5 | 793 | 878 | 927 | 961 |
10 | 749 | 858 | 912 | 958 |
20 | 697 | 810 | 880 | 932 |
30 | 615 | 766 | 868 | 919 |
Network lifetime (Rounds) | ||||
---|---|---|---|---|
No. of nodes | DEBR | EENC | QEBSR | SADO-RRS |
100 | 693 | 827 | 888 | 1040 |
200 | 1100 | 1228 | 1386 | 1599 |
400 | 1976 | 2098 | 2360 | 2439 |
Packet delivery ratio | ||||
---|---|---|---|---|
Compromised nodes (%) | DEBR | EENC | QEBSR | SADO-RRS |
5 | 0.9363 | 0.9617 | 0.9649 | 0.9670 |
10 | 0.9418 | 0.9599 | 0.9607 | 0.9653 |
20 | 0.9457 | 0.9560 | 0.9580 | 0.9634 |
30 | 0.9369 | 0.9418 | 0.9480 | 0.9572 |
A detailed PLR examination of the SADO-RRS technique with recent models in
Packet loss rate | ||||
---|---|---|---|---|
Compromised nodes (%) | DEBR | EENC | QEBSR | SADO-RRS |
5 | 0.0637 | 0.0383 | 0.0351 | 0.0330 |
10 | 0.0582 | 0.0401 | 0.0393 | 0.0347 |
20 | 0.0543 | 0.0440 | 0.0420 | 0.0366 |
30 | 0.0631 | 0.0582 | 0.0520 | 0.0428 |
In this study, a novel SADO-RRS technique has been developed for reliable routing in WSN. The proposed SADO-RRS technique determines the presence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based LDA for attack detection. The presented approach develops a FF utilizing 3 input parameters such as trust level, distance to neighbors, and energy to optimum route selective. In the proposed technique, it could be vital to decrease the linear group of main functions. The experimental result analysis of the SADO-RRS technique highlighted the enhanced outcomes. In future, data aggregation approaches can be integrated into the SADO-RRS technique for enhanced performance in WSN.