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: A Survey on the Viability of Confirmed Traffic in a LoRaWAN 
Chapter Title:  2) CONFIRMED TRAFFIC RATIOS 
1. Abstract of Cited Paper (BIB001):  LoRaWAN is a promising low power long range wireless communications technology for the Internet of Things. An important feature of LoRaWAN gateways is related to so-called capture effect: under some conditions the gateway may correctly receive a frame even if it overlaps with other ones. In this paper, we develop a pioneering mathematical model of a LoRaWAN network which allows finding network capacity and transmission reliability taking into account the capture effect. 
2. Abstract of Cited Paper (BIB002):  In this paper we analyze and discuss the capacity limits of LoRaWAN technology for smart metering applications using a real LoRa network simulator including uplink and downlink. First, we present the modeling of the network simulator. Then results for a critical case in the center of Paris show that, with meters all located deep indoor, the capacity of a network covering a dense urban area of 17 km2 with 19 gateways distant from 1 km, considering only UL emissions, could be enough for an average targeted QoS of 98 % as soon as there is no other traffic in the considered band. But we also show that we could very quickly reach the limits when considering that meters will not be the only LoRa devices communicating and above all considering the important impact of DL emissions on capacity. 
3. Abstract of Cited Paper (BIB003):  The need for low power, long range and low cost connectivity to meet the requirements of IoT applications has led to the emergence of Low Power Wide Area (LPWA) networking technologies. The promise of these technologies to wirelessly connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out `what-if analyses' and pre-deployment studies are needed to understand the implications of choices that are made at design time. While there are several promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts. 
4. Abstract of Cited Paper (BIB004):  LoRaWAN is gaining momentum in the arena of IoT connectivity technologies thanks to the low cost, ease of deployment, and support for adaptable transmission rates and bidirectional communications. The research community has then been working to develop suitable analytical models that can be instrumental in the study of this technology. In this work we propose a mathematical model that makes it possible to accurately estimate the packet success probability of a LoRaWannetwork in presence of bi-directional traffic, i.e., both uplink (UL) and downlink (DL) transmissions, and that accounts for the most critical features of the LoRa chipset and the LoRaWanstandard. The proposed model, furthermore, makes it possible to study the effect of different parameters configurations, thus offering a valid tool to investigate possible improvements to the system configuration. The proposed model is first validated by comparison with some accurate simulation results and, then, its potential is exemplified by analyzing the system settings that yield the best network performance. 
5. Abstract of Cited Paper (BIB005):  Although LoRaWAN offers many advantages, one of its main limitation is the insufficient downlink capacity in large-scale scenarios. This makes reliable communication impractical as, in LoRaWAN, reliability is achieved through the acknowledgement of confirmed messages. The contribution of this paper is to provide a comprehensive description of the problems associated with the presence of downlink traffic and to propose solutions to enable the use of acknowledgments for low to medium downlink load. The proposed mechanisms build on a more adequate and flexible choice of the gateway for each downlink transmission by the network server. Thus, they are entirely compatible with existing LoRaWAN technologies and deployments. Our simulation results show that the proposed solutions enable the successful realization of a higher percentage of LoRaWAN confirmed messages. Thus, the common practice of strongly discouraging the use of confirmed messages could be reconsidered to allow applications that require reliable communication.

Literature Review:
A mathematical model was presented in BIB005 and used to study the performance of single gateway networks with 100 % confirmed traffic. Their validation with the ns-3 simulations (  , BIB001 ) showed that their model is slightly optimistic when predicting the PDR but remains useful. Their further comparison with BIB002 highlighted that DC restrictions are a key parameter for models to include, as without it the achievable performance will seem much higher. One of the assumptions made by BIB005 is that the probability of receiving an ACK sent in the second receive window is 1, the reasoning for this is that the frequency channel chosen for window 2 transmissions are only used for downlink and thus no collisions can occur. This is true for networks in remote areas in which the only traffic on channel 869.525 MHz is the current network. In urban areas, other LoRaWANs will likely be present and this assumption can no longer be made. A MATLAB simulator, presented in BIB003 , was used to compare scenarios in which nodes require no ACKs, all nodes require an ACK, 25 % require an ACK and 33 % of nodes require an ACK. This simulator only utilised the first receive window when calculating the viability of downlink responses and networks consisted of either 1000 or 2000 end devices. The simulator considered the Packet Error Rate (PER) per node and an average QoS value calculated as the network's total PDR. For the 1000 node network, ACKs did not significantly decrease the average QoS, but requiring an ACK for each uplink increased the percentage of nodes with a PER of between 10 % and 50 % from 1.2 % to 23.9 %. In a 2000 node network, the decrease in QoS and negative impact on packet delivery was much more pronounced. The percentage of nodes with a PER between 10 % and 50 % increase from 6.9 % for no ACKs to 24.5 % when 25 % of traffic require an ACK and when all traffic required an ACK it increased to 70 %. To investigate bidirectional traffic the authors of BIB004 created LoRaWANSim, a closed source LoRaWAN simulator based on LoRaSim. Several downlink configurations were tested namely 5 % downlink application layer data (no ACKs), 5 % of uplink traffic require ACKs and finally the combination of the previous two options. As with most evaluations, some simplifications were made. The first was that each device was set to use the maximum data rate allowing it to reach the gateway (similar to the ADR scheme but not dynamic). Their investigation found that the leading cause of retransmissions was the DC restrictions imposed on gateways BIB004 . Even when only 5 % of transmissions required an ACK and there was an additional 5 % downlink data, the DC restrictions caused the success of downlink transmissions to drop below 80 % once a network reaches 1000 devices. Any downlink data transmissions aggravate the problem as they cause the gateway to reach its limitations earlier, increasing the number of ACKs it can not transmit. The network's goodput (application layer throughput) also drops significantly as the percentage of traffic requiring an ACK increases BIB004 . When 100 % of the traffic required acknowledgement, the goodput drops to 15 % of the level achievable by a network with no acknowledgements. The impact is minimal for low volumes of requests (≤ 5 % require ACKs) but increases to nearly 20 % when 20 % of the traffic requires confirmation. Beyond 20 % of traffic requiring ACKs, the goodput is increasingly negatively impacted. The impact of traffic ratios was also examined in  . In a network where the number of devices is fixed, the PDR was quickly reduced when the percentage of devices using confirmed traffic was increased. In the inverse case where the percentage of devices remains fixed, increasing the number of devices did not significantly decrease the PDR. To better understand why the PDR is so negatively affected, a scenario in which all devices send confirmed traffic, up to 8 transmissions are allowed and each device sends a packet every 30 minutes was examined closely. It was found that channel impairments had a negligible impact on the PDR and that there is a threshold where interference no longer dominates and the unavailability of receiving paths become the leading cause. It is noted that the addition of more gateways will help to alleviate this problem, but this must be done to optimise the SF allocations within the network. Uniformly spreading gateways will allow more nodes to use the higher data rates, decreasing transmission times and collision probabilities. A simulator was fed with recorded real traffic traces collected from up to 4 Semtech PicoCell gateways in BIB006 . When the ratio is below 20 %, the packet loss remains below 10 % and is mainly caused by uplink transmissions being missed due to the half-duplex nature of the gateways. Above this point, DC restrictions become the leading cause of packet loss. In the case of 100 % confirmed traffic, the network loses 86 % of packets, with 76 % of this as a result of their ACKs being unable to be transmitted.