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:  1) ENERGY-AWARE ROUTING 
1. Abstract of Cited Paper (BIB001):  The Internet of Things (IoT) enables environmental monitoring by collecting data from sensing devices, including cameras and microphones. The popularity of smartphones enables mobile users to communicate and collect data from their surrounding sensing devices. The mobile devices can obtain useful environmental data from nearby sensors through short-range communication such as Bluetooth. Nevertheless, the limited contact time and the wireless capacity constrain the amount of data to be collected. With the increasing amount of multimedia big data such as videos and pictures from cameras, it is crucial for mobile users to collect prioritized data that can maximize their data utility. In this paper, we propose a distributed algorithm to provide information-centric ubiquitous data collection of multimedia big data by mobile users in the IoT. The algorithm can handle transmissions of multimedia big data recorded by the surrounding cameras and sensors, and prioritize the transmissions of the most important and relevant data. The mobile users construct data collection trees adaptively according to their dynamic moving speeds and the value of information carried by the multimedia and sensor data. The distributed algorithm can support smooth data collection and coordination of multiple mobile users. We provide both numerical analysis and extensive simulations to evaluate the information value, energy efficiency and scalability of our solution. The results showed that our distributed algorithm can improve the value of information up to 50% and reduce energy consumption to half compared with existing approach. Our algorithm also scales perfectly well with increasing number of mobile users and dynamic moving speeds. 
2. Abstract of Cited Paper (BIB002):  Energy harvesting (EH) is considered the key enabling technology for mass deployment of Internet-of-Things (IoT) devices. Efficient EH techniques eliminate the needs for frequent energy source replacement, thus offering a near perpetual network operating environment. Advances in the EH techniques have shifted the design paradigm of routing protocols for energy-harvesting wireless sensor network based IoT applications from “energy-aware” to “energy-harvesting-aware” . This paper aims to design an energy-harvesting-aware routing protocol for heterogeneous IoT networks in the presence of ambient energy sources. We propose a new routing algorithm energy-harvesting-aware routing algorithm (EHARA), which is further enhanced by integrating a new parameter called “energy back-off” . Combining with different energy harvesting techniques, the proposed algorithm improves the lifetime of the nodes and the network’s quality-of-service (QoS) under variable traffic load and energy availability conditions. This work also investigates the system performance metrics for different energy harvesting conditions. Performance results demonstrate that the proposed EHARA significantly improves energy efficiency while satisfying the QoS requirements of distributed IoT networks in comparison with existing routing protocols. 
3. Abstract of Cited Paper (BIB003):  New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in, e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy-based edge routing to handle sustained performance levels with efficient energy consumption. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel “offload decision-making” algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a “Sustainable Policy-based Intelligence-Driven Edge Routing” algorithm that uses machine learning within Mobile Ad hoc Networks. This algorithm is energy aware and improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for throughput performance sustainability, while also enabling flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed in the GENI Cloud infrastructure, and recreate disaster scenes of tornado damages within simulations. Our empirical results show how MEC can provide flexibility to users who desire energy conservation over low latency or vice versa in the visual data processing with a facial recognition application. In addition, our simulation results show that our routing approach outperforms existing solutions under diverse user preferences, node mobility, and severe node failure conditions.

Literature Review:
Xu et al. BIB002 proposed EQRoute, an energy-aware, and QoI-aware routing algorithm. The EQRoute is a distributed algorithm, designed for ubiquitous multimedia transmission based on the information value of data and capacity of the mobile user concerning it's moving speed. In the proposed model, a mobile user constructs the data collection tree, based on its capacity and moving speed. The route is selected based on information gain, which is calculated in the context of communication cost and information value. The aim of the proposed work to maximize information gain and reduce energy consumption. Energy harvesting has shifted the design paradigm from energy-aware to Energy-Harvesting-Aware (EHA). Nguyen et al. BIB003 devised the EHA Routing Algorithm (EHARA) based on the energy-backoff process and energy prediction process that defines cost metrics to select the best route. A hybrid energy harvesting sources are considered in the proposed work, i.e., solar panels, moving vehiclebased, and RF-based. The energy prediction process utilizes a Kalman filter approach that considers previous time step and current statistics to determine an improved estimation of current energy arrivals from different sources. The energy backoff process is proposed to extend the network lifetime by putting the nodes with minimum energy to sleep that are unable to perform operations until the energy level is recovered. The node with the highest energy level and minimum cost link based on Dijkstra's shortest path is selected to route the data. Carbon Dioxide (CO 2 ) emission is one of the vital factors affecting the network or node lifetime in green communication. Alvi et al. BIB001 proposed green-RPL routing for M-IoT to route the multimedia data by selecting the parent node operating on green energy that consumes minimum energy and delays to the sink. The objective of the green-RPL is to minimize carbon footprint subject to path delay, path energy, residual energy, and idle time. Trinh et al. in BIB004 presented SPIDER, a sustainable policy-based intelligence-driven edge routing algorithm based on MEC to detect geographical obstacles using DL to aid the routing engine to efficiently offload the data and route the priority data in an emergency for energy conservation. The proposed work presents a facial recognition use case in a disaster scenario.