## 2 Literature Review
This proposed method claims that with the relay SN sensing the two channels every 500 milliseconds, then the battery capacity for 5 years of operation will be 2.88 Amp.hour (A.h). Furthermore, for an end SN that transmit every 30 min, the battery capacity will be 0.16 A.h, which can be reduced to 0.08 A.h in case it transmits every 6 h. Although the method is based on using relay SNs with regular resources just as a typical LoRa SN, it is still fully dependent on these dedicated relay SN devices. That means that these relay SNs need to be strategically deployed in order to be useful. Therefore, it is less suitable for dynamic SN deployments, or the environment of the application changes the SN positions. Furthermore, this method suggests dedicating two channels for the handshake, and once these channels are used, they will be unavailable to use again for a certain amount of time, due to the duty cycle restrictions. Therefore, in the case of more than two end SNs available, they will be unable to use these channels. However, this work has inspired us to focus on enhancing regular SNs that can be adjusted as needed, on the go, to perform the bridging instead of a dedicated relay SN. The first inspiration for DXN came from an AI-based project to enhance the security of IoT devices against malware attacks [8]. This is because SNs are very vulnerable to all forms of breach as, by design, they are simple with minimum resources and so include minimum protection in pursuit of maximum battery life. This paper proposes a scheme that continuously analyses the information received from the IoT network SNs. The AI engine tries to detect anomalies in the SN's code behaviour in comparison with its neighbouring SNs, in case the SN's code has been tampered with by a hacker. This AI engine is based on a CNN model and is located at the host server in the cloud. This scheme claims to work irrespective of the connectivity technology/protocol of the IoT network. This scheme is achieved by first inserting debugging code, which runs on the assembly language code, in each of the SNs in the network. This debugging code helps to identify the code flow of the received messages from these SNs. Once the messages are received at the server, the process will extract features from the received code of every SN, including features such as its memory used, network type, system calls, etc., into a CSV file. The process then converts these extracted SN features into vectors represented as unique images (similar to creating QR images that have embedded information to be interpreted by the reader). The process concludes by AI analysis by the ZFNet, a CNN based model [9], to determine if an attack has been carried out. Therefore, once the scheme detects a suspicious code from any SN, it will prevent the data from being used by the application, and so prevent an attack from taking place. This scheme is dynamic in terms of processing and analysing the data received from the SNs continuously in real time. Thus, it claims to achieve a malware detection accuracy of 99.28%. In addition, the use of an AI engine helped to identify not only the attacks that have been trained with but also identifying new evolving attacks. This has inspired us to think about implementing an AI engine based in the cloud to conduct a continuous live analysis of the IoT network messages for our solution. In pursuit of reducing battery energy spent by SNs communicating localised messages about whatever they sensing to the GW of an IoT network, the following article has implemented a smart clustering algorithm that balances the connectivity load of the SNs [10]. This will keep the various SNs in the network alive longer as they proved in their testing. The authors have proposed a dynamic Cluster Head Sensor Node (CHSN) allocation based on the cluster network activity and traffic based on a set criterion. This means that no one CHSN will drain down its energy handling the communication for its cluster SNs.