## 2 Literature Review
This has also improved the message slots allowance, eliminating any unnecessary communications within the cluster. The load balancing algorithm used for the clusters is considered as an improvement to the existing LEACH clustering algorithm [11] (a technique based on selecting new CHSN each round within the cluster based on their energy levels). This implementation categorises the SNs based on their energy level, location and neighbouring SNs. This information is obtained from the accumulated database (located in the cloud and updated by CHSNs each round) by a fuzzy logic-based controller (a model that has the ability to recognise, represent, and utilise data that is vague). The actual SN data collected by its CHSN include the SN's identifier, location, remaining energy level, average distance to neighbours and distance to the GW. Therefore, their proposed controller will, for each of the clusters in the network, generate a queue of all the potential CHSNs in descending order from the most capable to the least. Based on that, a new CHSN will be selected, and in its turn, it will broadcast a join request to the rest of the SNs in its cluster. Finally, and based on the available clusters and their sizes, this controller will create a scheduling table for message transmission for all SNs in every cluster. This schedule is sent to all SNs and is designed to control the SNs random transmission, by syncing messages of any cluster's SNs at specific time slots. Therefore, the CHSN and its associated SNs will have less wake-up time than before. The results of their test scenarios claim an improvement in the SNs' battery lifetime of up to 200% when compared to other conventional clustering techniques (LEACH-PSO [12] and LEACH). This work has inspired us that clustering techniques are the way to go in our DXN. We believe that clustering coupled with an AI engine analysis would be a powerful implementation to reduce the unnecessary communication traffic and preserve the power consumption of SNs. Another study of clustered networks was conducted and claimed that 60-80% of the total power consumption of the whole network is consumed by the CHSN nodes themselves [13]. This next work solves this issue. It is interesting to us because it uses two wireless connectivity technologies (LoRa and Zigbee) to cluster SNs before reaching the GW. This is mainly to increase the range of the IoT network coverage to a larger network as well as making its management scalable (and so to reduce CHSN power wasting). LoRa SNs are used to connect tail-end clusters of ZigBee SNs network to transmit their data to a remote GW using the long-range capability of LoRa. Furthermore, the authors deployed an AI model to help analyse the deployment to propose a better network control for the active/sleep periods of the LoRa SNs in the network. This AI engine predicts the fluctuation in the collected data to identify the periods when each SN needs to be active or sleep. Their proposed AI model was trained on a dataset generated by collecting data from various similar IoT networks. Using this dataset in an Interrupted Poisson Process (IPP) (a discrete probability distribution that calculates the probability of occurrence of a number of given events in a fixed period of time) will help in describing the traffic requirement for each SN (i.e., searching for the optimal transmission threshold and transmission rate). This information is indexed at the GW and then sent to each corresponding SN. Therefore, the GW will be able to adapt to any changes, for instance, if any SN's packet generation rate decreases, then based on the IPP outputs, the GW needs to apply a smaller redundancy threshold to that SN to reduce the power consumption. The GW also holds records of the active periods for each SN, so to be analysed by a shallow (a single layer) Recurrent Neural Network (RNN) to predict the trend of that SN's activity. This way, the GW will be able to adjust the sleeping/active strategy for each SN over time regardless of any sudden changes.