You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Multimedia Internet of Things: A Comprehensive Survey 
Chapter Title:  C. MULTIMEDIA IoT IN INDUSTRIAL APPLICATIONS 1) SMART INDUSTRY 
1. Abstract of Cited Paper (BIB001):  With the increasing importance of information processing in industries arises the need for efficient data logging systems that are compatible with existing measuring devices such as LCD and LED meters. This paper presents the framework for an Internet of Things (IoT) device as an automated industrial meter reader that uploads the collected numeral data to a cloud storage for centralized data processing. The implementation of the device is done using Raspberry Pi as the platform. The device follows a four-step process-Image Acquisition using Raspberry Pi camera module, Optical Character Recognition using feature extraction technique, Internet Upload Mechanism using Google Forms and Online Data Processing using Google Spreadsheet. The performance of the device and techniques for debugging are also discussed. 
2. Abstract of Cited Paper (BIB002):  To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method. 
3. Abstract of Cited Paper (BIB003):  The industrial sites such as oil-gas producing facilities, process plants are complex systems consisting of expensive and production critical equipment or machines such as motors, drives etc. Plant owners employ systematic condition monitoring (CM) of equipment to avoid unplanned production downtime and to reduce operational expenses. Condition monitoring system employs combination of sensors and uses captured data in condition assessment algorithms to identify faults. In a typical plant deployment, each machine such as motor would be accompanied by an individual condition monitoring unit. CM management system integrated with controller in plant control room, gathers condition monitoring data from the various equipment sub-systems and generates automated alerts upon failure detection. Upon receiving the alarms, plant personnel locates a particular machine from site area and conducts the maintenance work for the same.

Literature Review:
IIoT is the main component of the smart industry, enhancing product manufacturing, and optimizing the industrial process. Multimedia characteristics further enhance the outcome of the industrial process, as shown in Fig. 7a . The steel industry is known as the backbone of any nation. The steel industry modernizes civilization by playing a lead role in technology development. Steel manufacturing is performed at high temperature. Therefore track identification is challenging under such circumstances. Automatic depth-based vision feature extraction and track identification in steel billet to maintain the quality of steel products in a smart industry by M-IoT is proposed in BIB003 . Online images are taken once steel billets are manufactured. Features are extracted using Local Binary Pattern (LBP) and stored in a database with identification codes. The framework for 3D surface inspection of steel billets is presented in  . 3D scanning is performed using a camera by continuously capturing multiple images, and green-line lasers are utilized to extract the depth information of steel billets to inspect the object from various angles in the smart industry. Condition Monitoring (CM) system based on M-IoT is defined in BIB004 . It detects defective machines to prevent production outage and reduce the operational cost in the oil-gas petroleum industry. Thermal and gas turbine power plants are the biggest assets of the nation. Flame color video recording and images are analyzed to optimize air to fuel ratio that ensures combustion quality. Sujatha et al.  proposed Fishers Linear Discriminant (FLD) analysis technique for dimension reduction and classification. An Artificial Neural Network (ANN) based on Back-Propagation Algorithm (BPA) and Ant Colony Optimization (ACO) for feature extraction from flame images and videos are utilized to maintain the combustion quality. An automatic industrial meter reading device based on online Optical Character Recognition (OCR) is designed in BIB002 . After extracting readings of various industrial meters from images, the device logged the readings to the cloud. At the cloud, data is processed and made centrally available to authorized users on PC, smartphones, and laptops. The proposed smart device improves time utilization by reducing human intervention. Solar-powered grid estimation employing sky camera based on Energy Harvesting Wireless Sensor Networks (EHWSN) is presented in BIB001 . Authors in this article maximize the transmission quality of sky camera images and minimize the energy consumption of multimedia data transmission by determining the best forward relay path.