Latest advancements made in the processing abilities of smart devices have resulted in the designing of Intelligent Internet of Things (IoT) environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assisted environment since it contains visual sensors that examine the surroundings from a number of overlapping views by capturing the images incessantly. Since IoT devices generate a massive quantity of digital media, it is therefore required to save the media, especially images, in a secure way. In order to achieve security, encryption techniques as well as compression techniques are employed to reduce the amount of digital data, being communicated over the network. Encryption Then Compression (ETC) techniques pave a way for secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presents a new ETC technique to accomplish image security in IoT environment. The proposed model involves three major processes namely, IoT-based image acquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA) abbreviated as ST-NMWOA. The optimal key generation of signcryption technique takes place with the help of NMWOA. Besides, the presented model also uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed model. The obtained values infer that the presented model is superior in terms of both compression efficiency and data secrecy in resource-limited IoT environment.

The emergence of new technologies in Internet of Things (IoT) without considering security concerns may expose the users to threats and make them vulnerable. The incorporation of IoT with security approaches is a challenging process. The security aspects of IoT have received significant attention among the researchers in recent years. In addition, the transmission of confidential data over wireless networks is an ineffective process. At the same time, the use of digital media, captured by IoT devices, is also increasing. In general, the transmission of private information (like images, videos, etc.) through social media and networks is non-advisable due to improper security features and fear of data loss. It is essential to ensure data security at the time of sending data through unauthorized channels. Therefore, image encryption techniques have become essential to accomplish secure image communication in IoT environment. Most of the issues arise when transmitting the data through currently available networks [

Security risks can be resolved under the application of encryption model, in which the private data is masked with legal properties. Compression and Encryption methodologies are extremely interconnected with each other and interact at most of the times. At the initial stage, compression is applied to limit the redundant data. Data encryption, on the other hand, provides a brief security when the repetition is low in image data. Therefore, both compression and encryption are carried out together to accomplish precise, supreme, robust, and maximum confidentiality. Various methods have been presented by combining compression and encryption methodologies namely, [

In literature [

In the study conducted earlier [

A technique employed in [

Different types of compression-encryption methods have been employed by developers to offer better security and effective data exchange operations. These methods are categorized under three levels as described in [

The current research paper presents a new Encryption Then Compression (ETC) technique for securing colored images in IoT environment. The proposed model operates on two main stages such as encryption and compression. The presented model involves optimal signcryption technique with Whale Optimization Algorithm (NMWOA) abbreviated as ST-NMWOA. The optimal key generation of signcryption technique takes place with the help of NMWOA. Furthermore, the presented model makes use of Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm-based compression technique. A comprehensive simulation analysis was conducted to validate the superiority of the presented model.

In short, the key contributions of current paper are as follows.

A new ETC technique is proposed in this study using metaheuristics to accomplish secure communication in IoT environment.

An effective encryption technique is designed using ST-NMWOA technique in which NWOA technique is implemented for optimal key generation process.

A compression technique is presented using DFT and MM techniques

Both security and compression performance of the proposed model was validated against benchmark images under several aspects.

Rest of the sections in the study is as follows. Section 2 discusses the proposed ETC technique developed for IoT environment. Then, Section 3 offers a detailed note on performance validation of the ETC model. Finally, Section 4 highlights the concluding remarks of ETC technique and derives possible future enhancements of the approach.

The overall process, involved in the proposed model, is defined here. An input color image is initially encrypted with the help of optimal signcryption technique. Next, the encrypted image is compressed by DFT-MM model. Then, the compressed image is transmitted to the receiver side. Finally, decompression and decryption processes are executed on the compressed image to reconstruct the original image without losing its quality.

To achieve image compression, a public key cryptographic technique called ‘Signcryption’ is applied as it concurrently fulfills the components of digital signature and open key encryption at a minimal cost. The features of signcryption are as follows; confidentiality, unforgeability, integrity, and non-repudiation. In the presented encryption model, there involves three processes namely, key generation, signcryption, and unsigncryption process [

Signcryption denotes the primitive of a public key that establishes two important cryptographic gadgets to ensure privacy, honesty, and non-repudiation. At the same time, it is implemented to obtain both digital signature as well as encryption. In light of these, it obtains a private as well as public key for both sender as well as the receiver. In order to enhance medical image security, the presented method employs single private keys by optimization procedure.

D-Value, selected arbitrarily [

Transmitter key pair ((

Receiver key pair ((

For optimal selection of keys, NMWOA model is applied. In general, WOA is a metaheuristic approach developed in literature [

Here,

Initially, hunting is invoked by surrounding the prey [

where

where

Humpback whales use a model in which the prey is assigned with a value and a shrinking bubble net is developed spirally. The basic principle is developed numerically by reducing the measure of

where

Likewise, the spiral updating location is depicted herewith.

where

In case of optimization, the maximum opportunity is applied to shrink the encircling values whereas the remaining ones are deployed in the development of spiral-shaped path as depicted in the mathematical equation given below.

This stage is referred to as exploration stage. In this module, vector

where

A newly-presented direct search model

It is applied at different points and are estimated as best (

Here, a reflection point

Likewise, the expansion point

where γ implies the coefficient of expansion. While the objective function values for

When the expression

where β denotes a contraction coefficient. When

If

It is the final task and is determined based on the function given below.

where δ denotes the shrink coefficient.

Next to image encryption process, the encrypted images are compressed with the help of DFT-MM model. In general, an actual image is sub-classified as non-overlapping blocks sized at M × N pixels that begins from top left corner of an image. Then, DFT is employed at every M × N block autonomously to illustrate the image in a frequency domain and this scenario provides real as well as imaginary units. Matrix Minimization approach is employed for all the components whereas zeros are eliminated by this approach [

In order to acquire maximum details, uniform quantization (Qr and Qi) is performed in a heuristic fashion. Thus, two matrices (Qr and Qi) are presented in a block which shows both real as well as imaginary portions correspondingly. Based on the real portion, low coefficient measures (such as Discrete Cosine (DC) values) are detached and are secured with novel matrix named ‘Low Frequency Coefficients (LFC-Matrix)’. This gets replaced with zero value in the quantized matrix. It is significant to know that the DC measures can be identified in real portions where significant information as well as features of images are involved. Followed by, the attained LFC-Matrix size is composed of minimum DC measures in comparison with High Frequency Coefficients (HFC-Matrix) and is illustrated in the form of bytes. Hence, the model is named after Matrix-Minimization and is employed in current study. This mechanism is employed to reduce the size of HFC matrices by contracting three coefficients to corresponding measures that monitors the actual values in decompression state.

Here, contraction is carried out on all three subsequent coefficients by applying Random-Weight-Values. A value is multiplied by different random values (Ki) and a summation is determined. The value, thus attained, is assumed to be a contracted value of input measures. It is apparent to point out that during decompression phase, search technology is essential to identify three actual values which are applied in the identification of the contracted value. Hence, minimum as well as maximum values of m × n block are recorded. The strategy used in this method is to reduce the grade of measures that are essential to win back the actual three values, to enlarge the contracted value and improve the efficiency of searching method. Therefore, it is feasible that the complicated images might exhibit maximum arrays during the degradation of compression process. For this purpose, the developers have recommended the application of alternate model in which DFT is applied while at the same time the search regions (search region is composed of two measures like [MIN, MAX]) are mitigated. This kind of bounding intends to make searching process, a fast, simple and a reliable one.

Once Matrix-Minimization approach is employed, the HFC-Matrix developed for real as well as imaginary portions are investigated. This analysis is viable to measure the probability in zero values in comparison with alternate measures of the matrix. Hence, the isolation of zero from non-zero values eliminates the repeated data and maximizes the efficacy of arithmetic coding compression. Moreover, a novel array named ‘Zero Matrix’ is developed and a zero value is appended in non-zero value. The actual HFC-Matrix is indexed by applying integer which implies the overall count of zeros from two non-zero measures. Next, the zero values present in zero-matrix mimics the original non-zero measures in series of actual matrices. At last, two matrices are applied in compression process with the application of coding model called arithmetic coding. It is clear that the newly developed approach can also be employed for LFC-Matrix with low-frequency coefficient measures of real part. Therefore, value-matrix as well as zero-matrix are assumed to be headers and are utilized in decompression tasks to reproduce actual HFC as well as LFC matrices.

Decompression method is defined as a counter compression task in which the act of compression is performed in inverse order. Decompression procedures are initialized by decoding LFC-matrix, value-matrix, and zero-matrix under the application of arithmetic decoding. However, the reconstruction of unified array is executed based on value and zero matrices, while HFC Matrix undergoes reformation. A new framework termed Sequential Search Algorithm is presented based on frequent processing of three pointers to generate three measures with contracted measures and guidance of MIN and MAX values preserved during compression. Therefore, MIN and MAX values are represented in minimum space search values and are applied in the restoration of original HFC for real and imaginary portions. At last, inverse quantization as well as DFT are employed for every portion to regenerate the compressed digital images.

This section details about the experimental validation of the presented model against benchmark color images and some sample test images as shown in

Test images | DFT-MMA | LZW | LZMA |
---|---|---|---|

Lena image | 58.46 | 40.76 | 42.87 |

Mandrill image | 49.61 | 37.98 | 39.15 |

DR image | 42.19 | 30.16 | 32.89 |

Test images | ST-NMWOA | ST-AEHO | Chaotic map | Rubik’s cube |
---|---|---|---|---|

Lena image | 60.21 | 59.52 | 54.92 | 52.91 |

Mandrill image | 59.32 | 56.22 | 51.02 | 50.54 |

DR image | 61.06 | 58.54 | 53.78 | 49.87 |

From the above discussed results of the analyses, the effective performance of the proposed model is understood compared to other techniques. The obtained values establish that the presented model is superior in terms of both compression efficiency and data security in resource-limited IoT environment. In addition, the presented model has proficiently generated both encrypted and decrypted images for the applied input image. From the decrypted image, it is apparent that the proposed ST-NMWOA model has effectively reconstructed the image without losing the quality of image. The comparison study results demonstrate that the proposed model outperformed the compression performance of LZW and LZMA models. Besides, the proposed model shows enhanced encryption results over ST-AEHO, Chaotic Map, and Rubik’s Cube models.

The current research paper presented an efficient ETC technique to secure the color images. The input color image is initially encrypted with the help of optimal signcryption technique. Next, the encrypted image is compressed by DFT-MM model. Then, the compressed image is transmitted to the receiver end. Finally, decompression and decryption processes are executed on the compressed image to reconstruct the original image without losing the quality. A comprehensive simulation analysis was carried out to validate the superiority of the presented model and a maximum PSNR of 61.06 dB was achieved. The obtained values infer that the presented model is superior in terms of both compression efficiency as well as data secrecy. In future, advanced lightweight compression techniques can be developed to increase the compression performance of DFT-MM model.