Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

Recent technological advancements such as Artificial Intelligence (AI), Edge Computing, 5G, Internet of Things (IoT), and big data analytics are incorporated in industries with innovation and cognitive skills. These cutting-edge technologies might be helpful for industries to rapidly escalate their manufacturing and delivery processes and customization of their goods [

On the other hand, avoiding or reducing manpower in industries will become a huge issue in the upcoming years, when Industry 4.0 becomes fully operative. Further, it would also face opposition from politicians and labor unions to compromise on the advantages of Industry 4.0 to improve employment opportunities [

Owing to their adaptability, automation abilities, and less cost, drones have been extensively applied to meet civilian needs in the past few years [

The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The proposed CSODL-SUAVC technique consists of Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. Besides, the image classification process includes three components such as Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The proposed CSODL-SUAVC method was experimentally validated using benchmark datasets and the outcomes were assessed under distinct aspects.

The aim of the study conducted earlier [

Bhat et al. [

In literature [

In this article, a novel CSODL-SUAVC algorithm has been developed to accomplish secure UAV classification and communication in the Industry 4.0environment. The presented CSODL-SUAVC technique performs image steganography via ML-DWT, CSO-related optimal pixel selection, and signcryption base- encryption technique. At the same time, the image classification module encompasses SRCNN-based feature extraction, Adam optimizer, and softmax classifier.

To accomplish secure UAV communication, the proposed model deploys the CSO-OPS technique to select the optimal pixel points in a cover image. Then, the secret image, encrypted by the signcryption technique, is embedded onto the cover image.

RGB cover images are classified based on Low High (LH), High Low (HL), Low Low (LL), and High High (HH) frequency bands to find the location of a pixel. Here, 2D-DWT is the prominent spatial applied in the frequency domain conversion model [

In

The co-efficient in the lower level band

In

CSO algorithm is applied in this stage to select the optimal pixels of the images. The geometrical circle is an underlying closed curve that has a similar distance from the center to every point [

CSO seeks an optimal answer inside a random circle to widen the possibilities of the searching region. By utilizing the center of the circle as a target point, the circumference of the circle and the angle of contacting points of the tangent line reduce gradually, until it approaches the center ofthe circle. Owing to the probability that this circle gets stuck with the local solution, the angle, where the tangent line touches the point, is randomly changed. The

Step 1: Initialization: This is a crucial phase in CSO in which the overall set of the dimensions of the searching agent must be randomized equally, as demonstrated in Algorithm 1. The majority of the existing codes randomize the dimension unequally. This phenomenon occasionally makes the algorithm achieve a better outcome unexpectedly. Next, the searching agent is initialized between the (UB) and (LB) upper and lower limits of the searching region as given below.

In

Step 2: Upgrade the location of the searching agent; the location of the searching agent

In

In this expression, the random number is represented by rand that lies in the range of 0 and 1. Iter refers to the iteration count, Maxiter indicates the maximal iteration amount, and

Input LB and UB.

Do for every searching agent

Utilize

End Do

Algorithm 2 Pseudocode of CSO

Initializes the searching agent

Input the constant value,

Whereas Iter is lesser than Maxiter

Use

Do for each searching agent

Utilize

Utilize

Utilize

Utilize

Once the upgrade searching agent is out of the boundary, the set searching agent is equivalent to the boundary defining the fitness function

End Do

Estimate the

End While

Output

Fitness Function is used to evaluate the objective function. The primary intention is to design a steganography model that must maximize PSNR and minimize the error rate (MSE) and is achieved using the following equation.

Both maximized and minimized values can be acquired by leveraging the CSO system.

The proposed model enables an encryption approach to encrypt secret images. Signcryption is a public key cryptosystem that provides sufficient privacy to private images, by producing digital signatures and following the encryption process. The parameter, utilized in the Signcryption technique, is denoted by standard ‘cp’ while ‘xs’ denotes the private key of the sender, ‘S’ denotes the sender, ‘ys’ denotes the sender and receiver public key, and the public key of the receiver is denoted by ‘yr’. While ‘yr’ is fed as input in the form of ‘binfo’ to the Signcryption system. The variable ‘binfo’ is fundamental to secure the Signcryption process and is composed of strings that exclusively recognize the receiver and the sender or the hash value of the public key. The steps that are used to signcrypt the private images are discussed below.

Step 1: Choose any value for ‘x’ in the range of 1 to

Step 2: The hash function is evaluated to receive the public key and ‘N’ with

Step 3: Then, it is segregated into two 64-bit strings such as K1 and K2 (key pairs).

Step 4: The message ‘m’ is encrypted, through the sender, using a public key encryption system ‘E’ in which key K1 is used to achieve the cipher text ‘c’; here,

Step 5: K2 is utilized in a one-way keyed hash ‘KH’ to retrieve the hash of messages. Here, ‘r’ represents the hash value of 128 bits for the message

Step 6: Next, the value of ‘s’ is calculated based on the ‘x’ value and the private key, ‘xa’ while a large prime value Ln and ‘r’ are used in s = x/(r + xa) mod Ln

Step 7: c, s, and r values are transferred to the receivers at once via signcryptext ‘C’ to complete the secured communication.

At last, the encrypted cover image is embedded as an optimal designated pixel point of the cover images. This guarantees the privacy of the stego images, due to the encryption process and the embedding of private images.

To perform UAV image classification, the CSODL-SUAVC model carries out three sub-processes namely, SRCNN-based feature extraction, Adam optimizer, and softmax classifier. There has been some research conducted on utilizing the DL technique for high image resolution [

At first, patch extraction and representation are formulated as given herewith.

In

Next, the nonlinear mapping is expressed as follows.

In

In

In

SM classifier is used to allocate the class labels to the input UAV images.

It multiplies every value obtained irrespective of its nature and converts it to an entire number that is continuously between

To improve the performance of the SRCNN algorithm, the Adam optimizer is employed. Adam is an optimized approach that is utilized for iteratively upgrading the network weight with the help of trained data, instead of the standard Stochastic Gradient Descent (SGD) process. This method is the most effective technique in overcoming difficult issues with a huge number of variables or data. It is effectual and economical in terms of memory. It performs a mix of Gradient Descent (GD) with momentum and Root Mean Square propagation techniques [

After all the iterations are over, it is instinctively altered to GD thereby remaining constant and impartial across the procedure, and is given the name, Adam. At this point, rather than the normal weighted parameters,

During every technique, this optimization is utilized due to its maximal efficacy and less memory utilization requirement.

In this section, the proposed CSODL-SUAVC approach was experimentally validated utilizing UCM [

Test samples | CSODL-SUAVC | AIUAV model | Cuckoo search algorithm | Grey wolf algorithm | ||||
---|---|---|---|---|---|---|---|---|

MSE | PSNR | MSE | PSNR | MSE | PSNR | MSE | PSNR | |

Sample 1 | 0.049 | 61.229 | 0.069 | 60.807 | 0.108 | 57.80 | 0.164 | 55.98 |

Sample 2 | 0.062 | 60.207 | 0.081 | 59.380 | 0.136 | 56.80 | 0.188 | 55.39 |

Sample 3 | 0.037 | 62.449 | 0.054 | 61.796 | 0.104 | 57.96 | 0.151 | 56.34 |

Sample 4 | 0.119 | 57.375 | 0.132 | 57.162 | 0.192 | 55.30 | 0.238 | 54.37 |

Sample 5 | 0.121 | 57.303 | 0.138 | 57.059 | 0.206 | 54.99 | 0.253 | 54.10 |

A comparative Peak Signal to Noise Ratio (PSNR) study was conducted on the CSODL-SUAVC model and other existing models and the results are shown in

A comparative CC analysis was conducted between the CSODL-SUAVC approach and other existing methodologies and the results are illustrated in

Test samples | CSODL-SUAVC | AIUAV model | Cuckoo search algorithm | Grey wolf algorithm |
---|---|---|---|---|

Sample 1 | 99.95 | 99.68 | 99.46 | 99.29 |

Sample 2 | 99.93 | 99.73 | 99.59 | 99.45 |

Sample 3 | 99.94 | 99.80 | 99.58 | 99.32 |

Sample 4 | 99.95 | 99.72 | 99.45 | 99.18 |

Sample 5 | 99.95 | 99.79 | 99.61 | 99.51 |

Test Samples | CSODL-SUAVC | AIUAV Model | Cuckoo Search Algorithm | Grey Wolf Algorithm |
---|---|---|---|---|

Sample 1 | 1.115 | 1.735 | 2.145 | 2.465 |

Sample 2 | 1.231 | 1.731 | 2.191 | 2.581 |

Sample 3 | 1.448 | 1.878 | 2.178 | 2.578 |

Sample 4 | 1.218 | 1.798 | 2.258 | 2.668 |

Sample 5 | 1.271 | 1.621 | 2.001 | 2.321 |

Methods | Precision | Recall | F1-Score | F2-Score |
---|---|---|---|---|

CSODL-SUAVC | 95.66 | 94.43 | 95.12 | 95.68 |

BO-SqueezzeNet | 94.59 | 93.14 | 93.43 | 94.42 |

VGGNet | 92.73 | 92.73 | 91.90 | 92.86 |

GoogleNet | 91.58 | 91.68 | 91.09 | 91.57 |

ResNetv2 | 89.69 | 91.12 | 89.98 | 90.67 |

CTF-CNN | 87.77 | 90.03 | 88.58 | 90.16 |

MobileNetv2 | 85.88 | 88.20 | 86.76 | 88.69 |

ResNet | 84.05 | 86.23 | 85.85 | 87.79 |

Besides, concerning

In this study, a new CSODL-SUAVC model has been developed to accomplish secure UAV communication and classification in Industry 4.0 environment. The presented CSODL-SUAVC technique performs image steganography via ML-DWT, CSO-based optimal pixel selection, and signcryption-based encryption technique. At the same time, the image classification module encompasses SRCNN-based feature extraction, Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance on UAV communication. The proposed CSODL-SUAVC method was experimentally validated using benchmark datasets and the outcomes were measured under distinct aspects. The simulation outcomes infer the better efficiency of the proposed CSODL-SUAVC model over recent approaches. Thus, the presented CSODL-SUAVC model can be applied to enable secure communication and classification in a UAV environment. In the future, hybrid DL methodologies can be applied to improve the classification performance of the proposed CSODL-SUAVC model.