# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 4 Literature Review
### IoT Security Applications Based on Image Processing  
Images were used as a means of encrypting and protecting other relevant data. While data is transferred, it is stored on a medium that is not considered to be secure. However, images have been used in such a way that they should have secured data while it was sent until it arrived safely at its destination. Reviewed are several attempts of proposed methods in which digital image processing techniques have been utilized to help secure IoTSs. The overall architecture for IoTSs is illustrated in Figure 4.  
Biometrics-based encryption is recommended as an alternative to the commonly employed strategies for protecting data exchanged between IoTSs and parties. In [48], it is stated that the proposed method is stronger against vulnerabilities than other strategies, such as password-based authentication, owing to the unique qualities the biometrics-based techniques have. The proposed method is backed up by a case study that uses pertinent biometrics to accomplish facial recognition. The face image is captured by an IoT-connected device such as a smartphone. IoT platform will be used to send the captured image to the intended destination across a communication network. End-to-end encryption with the aid of image processing techniques may be accomplished. Image processing techniques have been employed in order to execute a security scheme for an IoT-based home management system [29]. The primary purpose of the proposed system is to conduct a security scheme for the home through an IoT platform by capturing an image at the site the camera-embedded system is put at. Image initialization and pre-processing, analysis, and image matching stored in a database are performed. Figure 4: IoTSs security lifecycle: conception and systems. To encrypt images, Cellular Automata (CA) was utilized in conjunction with image processing. An 8-bit string series will be generated from pixel intensity values (image elements). These images contain critical information since the camera initially takes an image that will be encrypted at the perception layer. As a result, at this level, they are also encrypted. Secondly, such images will be forwarded to network layers. Once they have been decoded, they can be read. This security strategy has been implemented on sensitive images to provide a secure route between the network and perception levels. The network layer is where the fog nodes are placed. Fog nodes are in charge at this point of sending any images they receive to the Cloud for further processing if that becomes necessary [49]. Since so much data is being delivered in the form of images over the Cloud via IoTSs, image encryption has become more popular in recent years. Therefore, image-based encryption is yet to be developed to serve better [50]. In order for there to be a safe and reliable public connection between the source of the image capture and the fog node, the image must first be encrypted before it can be sent. The combination of pixel intensities with CA-generated values is an intriguing encryption technique. Security of IoT devices has leveraged image processing by categorizing distributed denial of service (DDoS) malware assaults. Gray-scale images may be initially categorized according to the families. DDoS malware assaults may also be identified, which is the second benefit of monitoring. An additional security scheme may be accomplished with the aid of image processing techniques [51]. For security considerations, the face recognition process is essential. This has been used in a wide range of IoTSs. Face recognition may be utilized with smart homes for numerous functions, such as control and security. Other applications and IoTS are specialized to conduct a face recognition process for the purpose of crowd surveillance in specified places and zones such as airports [52]. The research presented in [11] is an example of another IoT security application that makes use of image processing.