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:  B. ZIGBEE STANDARD 
1. Abstract of Cited Paper (BIB001):  ABSTRACTWireless local area networks (WLANs) are widely deployed for internet-centric data applications. It is predicted that by 2018, about two-thirds of the world's internet traffic will be video, and more than half of the traffic will be offloaded to Wi-Fi networks. Consequently, WLANs need major improvements in both throughput and efficiency. New technologies continue to be introduced for WLAN applications for this purpose. The IEEE 802.11ac standard is the currently implemented amendment by the IEEE 802.11 standard working group that promises data rates at gigabits per second. The main features of the IEEE 802.11ac standard are adopting increased bandwidth and higher order modulation than the previous standard, and multiple-input multiple-output (MIMO) and multi-user MIMO transmission modes. These features are designed to improve the user experience. In addition to technologies that enhance the efficiency of the WLAN, the IEEE 802.11ax standard is also investigating and evaluating advanced wireless t... 
2. Abstract of Cited Paper (BIB002):  The IEEE 802.11ax high-efficiency wireless local area network (HEW) is promising as a foundation for evolving the fifth-generation (5G) radio access network on unlicensed bands (5G-U). 5G-U is a continued effort toward rich ubiquitous communication infrastructures, promising faster and reliable services for the end user. HEW is likely to provide four times higher network efficiency even in highly dense network deployments. However, the current wireless local area network (WLAN) itself faces huge challenge of efficient radio access due to its contention-based nature. WLAN uses a carrier sense multiple access with collision avoidance (CSMA/CA) procedure in medium access control (MAC) protocols, which is based on a binary exponential backoff (BEB) mechanism. Blind increase and decrease of the contention window in BEB limits the performance of WLAN to a limited number of contenders, thus affecting end-user quality of experience. In this paper, we identify future use cases of HEW proposed for 5G-U networks. We use a self-scrutinized channel observation-based scaled backoff (COSB) mechanism to handle the high-density contention challenges. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is formulated to analyze the performance efficiency of the proposed solution. The analytical and simulation results show that the proposed mechanism can improve user experience in 5G-U networks. 
3. Abstract of Cited Paper (BIB003):  Carrier sense multiple access with collision avoidance(CSMA/CA) in current IEEE 802.11 wireless local area networks (WLANs) uses a uniformly distributed binary exponential backoff (BEB) mechanism that is mainly based upon exponential increases of the contention window (CW) to avoid repeated collisions. After each consecutive collision, the CW is doubled until it reaches the maximum value. Under dense conditions, however, the blind selection of the CW greatly reduces throughput, whereas under sparse conditions with smaller number of contending stations, the blind exponential increase of the CW for collision avoidance causes unnecessarily long delays. Therefore, it fails to achieve high efficiency in both dense and sparse environments with the current BEB mechanism. In this paper, we propose a cognitive backoff (CB) mechanism that adaptively determines the CW to provide efficient collision avoidance with high throughput and low delay under both dense and sparse conditions. In the proposed CB mechanism, the measured conditional collision probability and the number of backoff stages determine the CW. A performance analysis with an event-driven simulator, NS3, reveals that the proposed CB can achieve higher throughput and lower delay than the BEB, without much implementation complexity while preserving fairness. 
4. Abstract of Cited Paper (BIB004):  The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best channel resources with the assistance of self-scrutinized wireless channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of channel observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ::: ${i}$ ::: QRA) mechanism is proposed for MAC layer channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ ::: QRA mechanism is evaluated in diverse WLANs with throughput, channel access delay, and fairness as performance metrics. 
5. Abstract of Cited Paper (BIB005):  One of the key applications for the Internet of Things (IoT) is the eHealth service that targets sustaining patient health information in digital environments, such as the Internet cloud with the help of advanced communication technologies. In eHealth systems, wireless networks, such as wireless local area networks (WLAN), wireless body sensor networks (WBSN), and wireless medical sensor networks (WMSNs), are prominent technologies for early diagnosis and effective cures. The next generation of these wireless networks for IoT-based eHealth services is expected to confront densely deployed sensor environments and radically new applications. To satisfy the diverse requirements of such dense IoT-based eHealth systems, WLANs will have to face the challenge of assisting medium access control (MAC) layer channel access in intelligent adaptive learning and decision-making. Machine learning (ML) offers services as a promising machine intelligence tool for wireless-enabled IoT devices. It is anticipated that upcoming IoT-based eHealth systems will independently access the most desired channel resources with the assistance of sophisticated wireless channel condition inference. Therefore, in this study, we briefly review the fundamental models of ML and discuss their employment in the persuasive applications of IoT-based systems. Furthermore, we propose Q-learning (QL) that is one of the reinforcement learning (RL) paradigms as the future ML paradigm for MAC layer channel access in next-generation dense WLANs for IoT-based eHealth systems. Our goal is to contribute to refining the motivation, problem formulation, and methodology of powerful ML algorithms for MAC layer channel access in the framework of future dense WLANs. This paper also presents a case study of next-generation WLAN IEEE 802.11ax that utilizes the QL algorithm for intelligent MAC layer channel access. The proposed QL-based algorithm optimizes the performance of WLAN, especially for densely deployed devices environment. 
6. Abstract of Cited Paper (BIB006):  Quality of service (QoS) implementation in a wireless local area network (WLAN) enables the prediction of network performance and utilization of effective bandwidth for multimedia applications. In QoS-supported WLAN, enhanced distributed channel access (EDCA) adjusts back-off parameters to implement priority-based channel access at the medium access control (MAC) layer. Although conventional QoS-supported EDCA in WLANs can provide a certain degree of QoS guarantee, the performance of best effort data (low-priority) traffic is sacrificed owing to the blind use of a binary exponential back-off (BEB) mechanism for collision avoidance among WLAN stations (STAs). In EDCA, the BEB mechanism exponentially increases the contention window (CW[AC]) for any specific priority access category (AC) when collision occurs and resets it to its initial size after successful data transmission. This increase and reset of CW[AC] is performed regardless of the network density inference, i.e., a scarce WLAN does not require an unnecessary exponential increase in CW[AC]. Similarly, a dense WLAN causes more collisions if CW[AC] is reset to its initial minimum size. Machine-learning algorithms can scrutinize an STA’s experience for WLAN inference. Therefore, in this study, we propose a machine-learning-enabled EDCA (MEDCA) mechanism for QoS-supported MAC layer channel access in dense WLANs. This mechanism utilizes a Q-learning algorithm, which is one of the prevailing models of machine learning, to infer the network density and adjust its back-off CW[AC] accordingly. Simulation results show that MEDCA performs better as compared to the conventional EDCA mechanism in QoS-supported dense WLANs.

Literature Review:
ZigBee is another standard that is used in the M-IoT scenario. It is particularly utilized in the consumer electronics sphere for home automation. However, the ZigBee and IEEE 802. BIB001 The standards in the IEEE 802.11 family are also known as Wi-Fi. These standards provide PHY and MAC protocols for M-IoT devices. The key characteristics of this standard can be seen in Table 9 . From the day first, WLANs have been facing the challenge of efficient MAC layer resource allocation while accessing the channel BIB002 . CSMA/CA is one of the popular collision avoidance mechanisms used by the MAC layer. In CSMA/CA, a randomized backoff mechanism is performed by the devices before accessing the channel resources. However, a blind exponential increase and reset of contention values in this backoff mechanism induce performance degradation. Ali et al. proposed solutions in BIB003 - BIB004 to tackle the blindness of currently implemented CSMA/CA. Their proposed channel observation-based mechanisms adaptively increase and decrease the contention parameters. Besides, ML-based techniques have been playing a vital role in the performance optimization of MAC layer resource allocation mechanisms BIB005 . Such ML-enabled techniques are also utilized for other low-power energy-constrained networks of IoT applications, such as smart healthcare systems BIB006 . In the context of M-IoT, an ML-enabled distributed channel access (MEDCA) mechanism is proposed in BIB007 . The proposed MEDCA mechanism utilizes Q learning algorithm to optimize the performance of channel access in multimediabased WLANS (that is IEEE 802.11e).