**1. Introduction**

In the field of cutting-edge remote media communication, the Internet of Things (IoT) is quickly establishing itself as a new paradigm. In the Internet of Things, people, data, processes, and things are connected to make network connections that are more relevant and useful than ever before. With the rapid advancement of IoT, it is exposed to numerous risks and challenges, such as handling huge amounts of data, processing energy efficiently, responding to security threats, and encrypting/ decrypting huge amounts of data. The concept refers to a system of interlinked computing items, such as RFID tags, sensors, actuators, and cell phones; digital machines; and people, allowing the sharing of data over a network without the need

for human-to-human interactions. In an IoT world, massive amounts of raw data will be continuously collected, requiring real-time sensor data streams as well as techniques for converting this raw data into useful information. Furthermore, data privacy and security will be a serious concern. A cryptographic algorithm designed for a device with incredibly low resources will have different design criteria than one commonly used. Modern cryptography has evolved from this very specific area into lightweight cryptography. The low energy requirement of these algorithms makes them resistant to physical attacks.

The storage space requirements of multimedia applications are more challenging due to the size of multimedia data, the need for real-time processing, transmission delay, and security protection. Many new applications have emerged in the Internet of Things (IoT) and cloud computing fields, where multiple devices and servers perform thousands of operations at the same time. Multimedia applications require real-time processing, resulting in a critical role for encryption and decryption speed. A variety of technological fields, including smart cities and homes, have benefited from the Internet of Multimedia Things (IoMT). Most multimedia contents require large storage discs to be uploaded and streamed to different devices. As a result, video data or media content can be thoroughly analyzed if an issue occurs. IoT devices are lowpowered and small in size, so they have been needed a cloud platform or third-party storage device to store, operate, and process information collected.

A majority of encryption is related to encrypting and decrypting text messages or documents, but images are also a prime bearer of crucial information, therefore, they have been needed to be encrypted. The encryption process involves modifying the pixels of an image so they had no longer representative of the original image. Once the receiver receives the encrypted image, it must be decrypted to reconstruct the image. Having encrypted images ensures that even if an interceptor gets access to a picture during transmission they had incomprehensible to them. Another practical use of encrypted images is for the security of biometric data. Fingerprint and retina scans involving biometric identification have become increasingly common, so these data must be securely shared and stored. When data is encrypted, it can be unintelligible to the intruder even if it is accessed maliciously.

IoT security has been emphasized by many organizations and research agencies. Open Web Application Security Project has identified privacy issues, inadequate authentication/authorization, lack of transport encryption, and poor physical layer security as the main causes of cyber-attacks on IoT. Identifying a device, validating its identity, authorizing it, establishing keys and managing them, as well as establishing trust and reputation are the five features in IoT security. Cryptographic primitives can help accomplish all of these objectives, including authentication, access control, non-repudiation, confidentiality, integrity, and availability. **Figure 1** shows the Thrust area in IoT security.

This chapter is organized in the following manner: in Section 2, I have presented a literature review. In Section 3, I have discussed the Advanced Encryption Standard (AES) and have described its detailed architecture framework. Section 4 presents the compared the current algorithms and approaches. Section 5 presents the results and discussion, while I have discussed the conclusions and provided further suggestions.

## **2. Literature review**

Several systems and approaches have been proposed to address the challenges and restrictions involved with the encrypted transmission of big multimedia data.

*A Survey of Lightweight Image Encryption for IoT DOI: http://dx.doi.org/10.5772/intechopen.104431*

**Figure 1.** *The thrust area in IoT security [1].*

Moreover, the security of multimedia data needs to be researched further. This section presents studies that have been previously conducted in different categories.

In their study, Shadi Aljawarneh, Muneer Bani Yassein & We'am Adel Talafha [2], the encryption of big multimedia data, developed and designed a multithreaded encryption algorithm system. An advanced encryption standard (AES), genetic algorithms, and the Feistel Encryption Scheme (FEES) are used in this system. The system was evaluated concerning computational run time and throughput for the encryption and decryption process to analyze the performance of the system on actual medical data and benchmarked against the RC6, MARS, 3-DES, DES, and Blowfish algorithms. They have been also implemented the encryption system with a multithreaded programming approach to improve efficiency and performance. Finally, they have been tested their system against the sequential version to evaluate its resource efficiency. Comparing our system to other available encryption algorithms, their results showed that our system took the least amount of time to run and delivered a higher throughput. Furthermore, they have been also able to achieve a 75% improvement in computation run time and a 4-fold increase in throughput versus their sequentially structured version. Based on the security objectives, our algorithm performed better than existing algorithms in achieving the Avalanche Effect, and they could therefore include it in any encryption/decryption process of large, plain, multimedia data.

Haidar Raad Shakir [3], a new method that combines the Haar wavelet transformation with the Advanced Encryption Standard (AES), as well as pixel shuffling based on chaotic logistic maps. This method calculates the Haar wavelet transform from the original image and uses the fields of the approximation coefficient (LL) and detail confidences (LH, HL, and HH) to derive the different frequency domains of the image. Using the AES algorithm, the approximation part (LL) is encrypted, and the Haar wavelet transformation inverse is then applied. In addition to the chaotic logistic map, a shuffled image is used to impede malicious image reconstruction attempts to strengthen the encryption. Several representative methods from the literature were examined and compared to the method. According to the test results, it achieved better levels of encryption and less image degradation across a variety of images.

Yong Zhang [4], designed a C program that uses AES in cipher block chaining mode for image encryption. He presented an image cryptosystem that is compared with existing chaos-based image cryptosystems based on encryption/ decryption speed and security performance. In simulations, AES is shown to apply to image encryption, which argues against the commonly held perception that AES is not suited to image encryption. As a result of this paper, He recommends using AES-based image encryption as a benchmark for the speed of image encryption algorithms. And all other encryption algorithms whose speeds are lower should be discarded in practical communications.

Yong Zhang, Xueqian Li, Wengang Hou [5], According to their study, AES cannot cryptograph images in CBC mode. However, AES in CBC mode could be used to encrypt images. AES can be used to encrypt an image and generate an initial vector (IV). AES is secured by far, so the tested image cryptosystem is secure. Simulation results indicate the AES-based image cryptosystem is faster than some chaotic systems-based image cryptosystems. The tested system can thus be used as a reference for comparing other newly offered image cryptosystems. Cryptosystems for images that perform encryption and decryption slower than AES in the same computer need to be enhanced.

Sohel Rana. Saddam Hossain, Hasan Imam Shoun, Dr. Mohammod Abul Kashem [6], propose a lightweight cryptographic algorithm with 16.73% lower power usage than the existing cipher. Modern electronics and the internet will enable resourceconstrained devices to become daily necessities for everyone, so data security will be an important consideration. Those devices will be communicating with one another incessantly, so information must be protected at all times. The implementation shows promising performance making the algorithm an ideal candidate for resourceconstrained devices.

Charanjit Lal Chowdhary, Pushpam Virenbhai Patel, Krupal Jaysukhbhai Kathrotia, Muhammad Attique, Kumaresan Perumal, and Muhammad Fazal Ijaz [7], an analysis to decrypt and encrypt images using hybridization of Elliptic Curve Cryptography (ECC) and Hill Cipher (HC), ECC and AES (Advanced Encryption Standard), and ElGamal and Double Playfair Cipher (DPC). The measurements used in this analysis are (i) encode and decrypt times, (ii) entropy of the encrypted image, (iii) intensity loss of the decrypted image, (iv) Peak Signal to Noise Ratio (PSNR), (v) Number of Pixel Change Rate (NPCR), and (vi)Unified Average Changing Intensity (UACI). ECC and ElGamal cryptosystems offer asymmetric key cryptography, while HC, AES, and DPC provide symmetric key cryptography. Hybrid processes combine the speed and ease of implementation of symmetric algorithms with the security of asymmetric algorithms. According to the metric measurement with test cases, ECC and HC have a good overall solution for image encryption with smaller image sizes when using AES with ECC.

Bing Ji, LLijunWang an, d Qinghua Yang [8], an improved AES-ECC hybrid encryption system that has good flexibility and versatility and optimized ECC multiplication unit design according to the characteristics of wireless sensor networks. It was capable of generating and authenticating digital signatures at a faster rate. It also fully met wireless sensor networks' reliability, processing power, and power consumption requirements. AES encryption module is currently undergoing highperformance enhancements (increase throughput, decrease logic unit occupancy) and optimizations of ECC cryptographic module random point multiplications are currently being implemented. There are three properties of the scheme: (1) it provides better security with relatively low resource requirements, (2) it is straightforward to administer keys, and (3) it is resistant to some attacks and a digital signature can be generated and verified quickly and easily.

*A Survey of Lightweight Image Encryption for IoT DOI: http://dx.doi.org/10.5772/intechopen.104431*

Alireza Jolfaei(B), Xin-Wen Wu, and Vallipuram Muthukkumarasamy [9], method encrypts texture images via bit masking and permutation procedures using Salsa20/12 stream cipher as part of a novel texture encryption scheme that complements the existing methods for 3D object encryption. As a result, the method has very low overhead and meets the security requirements, and protects the 3D surface geometry from partial disclosure by keeping the texture patterns hidden. Compared to full encryption and selective encryption (using the 4 most significant bits), the scheme has a higher speed-security profile. The schemes are implemented and tested with 500 sample texture images. Comparing the experimental results with full/selective encryption by 128-bit AES, the scheme demonstrated better encryption performance.

M. Sankari P. Ranjana [10], To protect the image data in the mobile cloud through privacy-preserve lightweight image encryption (PLIE), they have been introduced a method that keeps metadata on mobile while maintaining user privacy. Mobile data is split, distributed, and scrambled (SDS) to maintain user privacy and store it in the cloud. As a result, the throughput increases, the encryption time is sped-up, and the complexity is minimized. Using the PLIE method implemented in Python language, the encryption time was approximately 50% shorter than that of AES. They have been measured the performance of the existing method (AES) versus the method (PLIE) using various parameters. Furthermore, they have been evaluate the security level by presenting some security attacks.
