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:  H. SECURITY AND PRIVACY 
1. Abstract of Cited Paper (BIB001):  This context-aware middleware system facilitates diverse multimedia services in heterogeneous network environments by combining an adaptive service provisioning middleware framework with a context-aware multimedia middleware framework. 
2. Abstract of Cited Paper (BIB002):  In this paper, the design and development of an effective secured framework for transmission of heterogeneous wireless sensor network data related to Internet of Things is presented. The description about the integrated multimedia applications network design and the secured mechanisms for reliable data transmission of smart sensors data is illustrated. Mechanisms for multimedia aware traffic analysis and data security in the integrated framework are discussed with the help of simulation of multimedia networks. The experiment results of the proposed security strategy for wireless sensor scalar data and performance analysis of the multimedia wireless sensing traffic are demonstrated using a simulator. 
3. Abstract of Cited Paper (BIB003):  The Internet of Things’ (IoT’s) rapid growth is constrained by resource use and fears about privacy and security. A solution jointly addressing security, efficiency, privacy, and scalability is needed to support continued expansion. We propose a solution modeled on human use of context and cognition, leveraging cloud resources to facilitate IoT on constrained devices. We present an architecture applying process knowledge to provide security through abstraction and privacy through remote data fusion. We outline five architectural elements and consider the key concepts of the “data proxy” and the “cognitive layer.” The data proxy uses system models to digitally mirror objects with minimal input data, while the cognitive layer applies these models to monitor the system’s evolution and to simulate the impact of commands prior to execution. The data proxy allows a system’s sensors to be sampled to meet a specified quality of data target with minimal resource use. The efficiency improvement of this architecture is shown with an example vehicle tracking application. Finally, we consider future opportunities for this architecture to reduce technical, economic, and sentiment barriers to the adoption of the IoT. 
4. Abstract of Cited Paper (BIB004):  Mobile edge computing (MEC) is being introduced and leveraged in many domains, but few studies have addressed MEC for secure in-home therapy management. To this end, this paper presents an in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario. To the best of our knowledge, this non-invasive, MEC-based IoT therapy platform is first done by our group. This platform can provide a full-body joint range of motion data for physically challenged individuals in a decentralized manner. With MEC, the framework can provide therapy diagnostic and analytical data on demand to a large portion of humanity who are either born with disabilities or became disabled due to accidents, war-time injuries, or old age. For security, the framework uses blockchain–Tor-based distributed transactions to preserve the therapeutic data privacy, ownership, generation, storage, and sharing. Our initial test results from a complete implementation of the framework show that it can support a sufficiently large number of users without considerable increase in mean processing time. 
5. Abstract of Cited Paper (BIB005):  In this paper, we present a novel method, process, and system for calculating dyslexic symptoms, generating metric data for an individual user, community, or group in general. We present a mobile multimedia Internet of Things (IoT)-based environment that can capture multimodal smartphone or tab-based user interaction data during dyslexia testing and share it via a mobile edge network, which employs auto-grading algorithms to find dyslexia symptoms. In addition to algorithm-based auto-grading, the captured mobile multimedia payload is stored in a decentralized repository that can be shared with a medical practitioner for replay and further manual analysis purposes. Since the framework is language-independent and based on Blockchain and a decentralized big data repository, dyslexic patterns and a massive amount of captured multimedia IoT test data can be shared for further clinical research, statistical analysis, and quality assurance. Notwithstanding, our proposed Blockchain and off-chain-based decentralized and secure dyslexia data storage, management, and sharing framework will allow security, anonymity, and multimodal visualization of the captured test data for mobile users. This paper presents the detailed design, implementation, and test results, which demonstrate the strong potential for wider adoption of the dyslexia mobile health management globally. 
6. Abstract of Cited Paper (BIB006):  The interactive robot is expected to support emotion analysis and utilize the deep learning and machine learning to provide users with continuous emotional care. However, it is a great challenge to securely acquire sufficient data for emotion analysis such that the privacy of emotional data is adequately protected. To address the security issue, this paper proposes a security policy based on identity authentication and access control to ensure the security certificate through an interactive robot or edge devices while the access control of private data stored in the edge cloud is adequately protected. Specifically, this paper adopts a polynomial-based access control policy and designs a secure and effective access control scheme. At the same time, this paper puts forward an identity authentication mechanism in view of edge cloud systems, which can reduce the computational overhead and authentication delay in a collaborative authentication of multiple edge clouds. The effectiveness of the proposed access control policy and identity authentication mechanism is verified by an actual testbed platform. 
7. Abstract of Cited Paper (BIB007):  The Internet of Things (IoT) has attracted extensive attention in the information field. Its rapid development has promoted several monitoring application domains. However, the resource constraint of sensor nodes and the security of data transmission have emerged as significant issues. In this paper, an image communication system for IoT monitoring applications is exploited to solve the above-mentioned problems simultaneously. The proposed system can satisfy the requirements of sensor nodes for low computational complexity, low-energy consumption, and low storage overhead. We also present a new compressed sensing (CS) model, as well as the corresponding parallel reconstruction algorithm, which help to reduce the image encryption/decryption time. Based on chaotic systems, we integrate the quantization and diffusion operations into the system to further enhance the transmission security. The simulations are executed to demonstrate the feasibility and the effectiveness of the proposed method. Compared with the traditional CS, our numerical results indicate that the proposed model reduces $\text{413}\,{\text{ms}}$ computation time and $\text{3.13} \times {\text{10}^\text{6}}$ elements stored for large-scale images. Besides, we verify the flexibility and the diversity of choosing two submatrices for different-sized images. Experimental results also show the proposed system performs well in terms of security performance. Particularly the key space reaches ${2^{253}}$ ::: .

Literature Review:
The transmission of bandwidth-hungry and delay-sensitive multimedia applications over the scares spectrum requires protection from eavesdroppers and threats. Confidential industrial and health multimedia data needs P2P, MP2P, and P2MP encryption. Very partial research has been conducted on securing multimedia transmission over the IoT network BIB008 , BIB003 . There are several threats that compromise the user's data and privacy BIB004 . New approaches should be adopted to secure the network from any attack. Multimedia is content-based data BIB002 . However, content-aware secure transmission of multimedia data over M-IoT has not been studied. The authentic distribution of multimedia content is also required to verify legal copyrights. The miniature M-IoT devices are computational and energy-constrained. Thus, lightweight data cryptography is required for secure communication. AI offers to secure multimedia data at the cost of high power consumption due to complex algorithms, that are not feasible to implement on IoT devices BIB007 , BIB001 . Fog/Edge enables IoT devices to perform computationally better and faster. Fog/Edge offers to implement blockchain-based secure and transparent systems BIB005 , BIB006 . However, security issues related to multimedia data has not been explored in detail.