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:  1) THE IMPACT OF NbTrans 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Low-Power Wide Area Networks (LPWANs) are continuously gaining momentum as fundamental enablers of the Internet of Things (IoT) paradigm. These networks provide longrange coverage to end nodes, exploiting license-free frequency bands. The focus of this work is on one of the most prominent LPWAN technologies: LoRa™. We implemented a new ns-3 module to study the performance of a LoRa-based IoT network in a typical urban scenario. Simulation results show that a LoRa network can scale well, achieving packet success rates above 95% in presence of a number of end devices in the order of 104. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  As LoRaWAN networks are actively being deployed in the field, it is important to comprehend the limitations of this low power wide area network technology. Previous work has raised questions in terms of the scalability and capacity of LoRaWAN networks as the number of end devices grows to hundreds or thousands per gateway. Some works have modeled LoRaWAN networks as pure ALOHA networks, which fails to capture important characteristics such as the capture effect and the effects of interference. Other works provide a more comprehensive model by relying on empirical and stochastic techniques. This paper uses a different approach where a LoRa error model is constructed from extensive complex baseband bit error rate simulations and used as an interference model. The error model is combined with the LoRaWAN MAC protocol in an ns-3 module that enables to study multichannel, multispreading factor, multigateway, bi-directional LoRaWAN networks with thousands of end devices. Using the LoRaWAN ns-3 module, a scalability analysis of LoRaWAN shows the detrimental impact downstream traffic has on the delivery ratio of confirmed upstream traffic. The analysis shows that increasing gateway density can ameliorate but not eliminate this effect, as stringent duty cycle requirements for gateways continue to limit downstream opportunities. 
5. Abstract of Cited Paper (BIB005):  Long Range Wide Area Networks (LoRaWAN) is a proprietary network technology devised for the long range connectivity of massive number of low power network devices. This work gives an overview of the key aspects of LoRaWAN technology and presents results that we achieved via extensive evaluation of Class A LoRaWAN devices in different network settings using the state-of-the-art network simulator (NS-3). At first, we focus on a single device and its mobility. We further undertook evaluations in an extended network scenario with a changing number of devices and traffic intensity. In particular, we evaluate the packet delivery ratio (PDR), uplink (UL) throughput, and sub-band utilization for the confirmed and unconfirmed UL transmissions in different environments. Our results give new insights for future efforts to optimize the LoRaWAN performance for different large scale Internet of Things $(\mathrm {I}\mathrm {o}\mathrm {T})$ applications with low power end devices. 
6. Abstract of Cited Paper (BIB006):  LoRaWAN is an emerging low-power wide-area network (LPWAN) technology, which is gaining momentum thanks to its flexibility and ease of deployment. Conversely to other LPWAN solutions, LoRaWAN indeed permits the configuration of several network parameters that affect different network performance indexes, such as energy efficiency, fairness, and capacity, in principle making it possible to adapt the network behavior to the specific requirements of the application scenario. Unfortunately, the complex and sometimes elusive interactions among the different network components make it rather difficult to predict the actual effect of a certain parameters setting, so that flexibility can turn into a stumbling block if not deeply understood. In this article, we shed light on such complex interactions for a single-gateway (GW) system by analyzing the effect of some built-in features and configurations, including the GW’s limitations in terms of duty cycle and the number of parallel reception paths, the number of allowed retransmissions for confirmed traffic, and the preconfigured data rate used in downlink transmissions. The simulation-based analysis reveals various tradeoffs and highlights some inefficiencies in the design of the LoRaWAN standard. Furthermore, we show how significant performance gains can be obtained by wisely setting the system parameters, possibly in combination with some novel network management policies (e.g., enabling selective prioritization of downlink transmissions at the GW).

Literature Review:
To determine why confirmed traffic impacts network performance so severely the authors of BIB001 build a MATLAB based simulator. They found that as the ratio between confirmed and unconfirmed messages increases, the throughput for both types of traffic decrease. Increasing the NbTrans value (number of transmissions per uplink) was effective in increasing the packet delivery for confirmed traffic, but came at the expense of unconfirmed traffic's delivery success. Additionally, higher numbers are not necessarily the best as the maximum number of 15 performed the worst as this causes too many collisions. Simulations determined that a value of nine performed best for confirmed traffic. These high numbers may not be usable in public networks as resources must be shared fairly and the cost to unconfirmed traffic might not be acceptable. When NbTrans was examined in BIB002 , it was found that this number should depend on network size. Simulations using LoRaWANSim found that only a small improvement is seen in small networks. For a 5000 node network, switching from transmitting once to using 5 attempts improved the number of successfully acknowledged frames from 40 % to ≈ 75 %. This increase does however come at a cost, energy consumption rose by nearly 10 % due to the extra energy required when allowing for up to 5 retransmissions attempts. The evaluation presented in  , also concluded that a dynamic mechanism is needed and that is should be based on the traffic load. Their ns-3 simulator, which added downlink support to their original work ( BIB003 ) was first compared with the analytical model presented in BIB004 . Unlike the model, the simulator accounts for SF pseudo-orthogonality and the DC restrictions imposed on transmissions. For small networks (500 devices), increasing the number of transmission attempts was effective in improving the PDR as the DC restrictions are not yet exceeded. Increasing the number of transmission attempts from 2 to 8 allowed a PDR of 95 % to be maintained as the ratio of confirmed traffic is increased from 10 % to 70 %. In large networks (2000 devices), increasing this number no longer allowed a significantly larger percentage of traffic to be confirmed as it also increased the probability of collisions. In large networks, it was found that receiver saturation starts to become the leading cause of packet loss and thus increasing the number of transmissions is no longer beneficial. The simulator developed in BIB005 was also used in the LoRaWAN evaluation presented in BIB006 . Their evaluation focused on the impact of the number of transmissions per packet and the number of available channels. Only two options for the maximum number of transmission attempts are considered (one or eight) and only two for the number of channels available (one or seven). The comparison evaluated 100 % unconfirmed against 100 % confirmed traffic. In a network with seven available channels and under maximum load, unconfirmed traffic's PDR slowly reduces from 100 % to 80 % as the network scales from 1 to 150 devices. In contrast, confirmed traffic's PDR has an aggressive exponential decay and reaches 40 % at 20 devices. This is very aggressive, however, it should be noted that this decay did however not start from 100 % for a single device network, the results of their simulations showed that only a PDR of 80 % was achievable. Increasing the number of retransmissions enables a high PDR of nearly 100 % for unconfirmed traffic, but has no impact on the performance of confirmed traffic. When a reduced load is considered through a traffic intensity of 10 % the network's performance differs from above. In small one channel networks (less than 40 nodes) confirmed traffic which allowed up to 8 transmissions performed best. Over this 40 device threshold, however, unconfirmed traffic with only 1 transmission remains the best performer. As only one channel is available, increasing the number of retransmissions for unconfirmed traffic drastically reduces the PDR due to the increased collisions it causes. In sevenchannel networks, unconfirmed traffic maintained a near 100 % PDR for both maximum transmission options with a less than 5 % difference between them. Confirmed traffic with up to 8 transmissions outperformed only 1 transmission significantly and also maintained this a near 100 % PDR up to 60 devices, after which it declines sharply. The presented results show that there are cases in which confirmed traffic outperforms unconfirmed traffic, but only when the number of channels is limited, the number of devices is low and in networks with moderate loads. Increasing the number of retransmissions can be a method of increasing the PDR for confirmed traffic but does not guarantee performance similar to that of unconfirmed traffic. As part of investigating the impact of several parameters, the authors of BIB007 examined NbTrans. They found that increasing this parameter does improve performance for confirmed traffic, but that the increases have diminishing returns. Adjusting this parameter hurt unconfirmed traffic's performance and thus choosing the lowest possible value that meets confirmed traffic's performance requirement is best.