## 2 Literature Review
Therefore, CHSNs will only be active at peak times. The paper quoted that, by using this model, LoRa SNs are saving one half of their energy compared to staying active all the time. This is an interesting approach that showed how AI-based network traffic analysis can impact battery power consumption which is very important for IoT devices. Therefore, this inspired us to include SNs' activity in relation to the environment where each SN is placed in our clustering technique design. A level of intelligence can be implemented onboard SNs that will lead to balancing the load on the supporting GW (in terms of the number of SNs connecting/transmitting data through a GW at any one point in time). In LoRa, the duty cycle function inside SNs allows a GW to accommodate thousands of SNs at the same time. Moreover, this capability of connecting lots of SNs to a GW can be achieved either randomly or fixed. However, when using different types of SNs (including different types of sensors or transmitting at different intervals), it can create an imbalance in the load at the supporting GW level (traffic issue rather than SN numbers). A Game Theory (GT)-based intelligent engine was proposed to be implemented onboard the SNs to help each SN to select suitable GW among multiple available GWs for relaying its messages [14]. This approach uses a reputation model (rating GWs based on the transmission rate and time). This is based on feedback from SNs to evaluate the connectivity between the SN and the network server for the used GW. This approach will determine how successful a GW is in relaying data between the connected SNs and the network host server. By using Bayesian Game (BG) strategy (a strategy where participating players (the SNs) do not have information about other participating players, but they depend on their beliefs to make a decision to maximise their rewards), SNs will estimate the time required for transmitting their uplinks with the used power transmission. Then, based on how good the GW performs against this time estimation, SNs will give feedback to the GW that will be reconsidered in the reputation model. The network server will keep/update a record of all the GWs' reputations, and whenever it receives an uplink from any SN, it will reply to it with a reward. High rewards are given for SNs that use GWs with low reputation, and low rewards are given for SNs that use GWs with high reputation, so to balance the load among GWs to transmit their data through it. The goal of each SN is to collect as many rewards as possible while maintaining a good connection to the server. This will maintain a load balance across all the available GWs. This approach was compared with both random and fixed SNs allocation in terms of Packet Delivery Ratio (PDR) and Packet Delivery Delay (PDD). The results claim that the reputation scoring model has achieved best with a PDR of 98% and a PDD of 15%. We believe that it is useful to include a load balancing model in our DXN solution to prevent the load imbalance at any bridging SN available within the range of more than one blocked SN. The following section describes the compound features of DXN that we have adopted to ensure it delivers performance enhancement most suited to IoT networks, focusing on LoRaWAN deployments.