## 1 Introduction  
The modern world adopts recent communication technologies to make a complete network coverage system and extended the intelligent object count which is correlated to the deployed system. One of the effective patterns named Internet of Things (IoT) is capable of increasing the volume of data generated from the Internet by connecting users and modern tools. Moreover, the IoT faces some issues, especially in location and time while connecting to humans and machines [1]. Moreover, IoT intends to achieve Quality of Service (QoS) such as energy, delay, throughput, etc. Bandwidth is one of the significant sources in the IoT platform, and appropriate management is essential to accomplish maximum QoS. Furthermore, the requirement for multimedia facilities in IoT platform is enhanced intensely. It finds useful in smart grids, smart homes, etc. [2,3]. Various services in IoT have distinct characteristics and QoS mechanisms. The fundamental strategy of IoT is to correlate one another with the help of the internet. In future, numerous applications might be developed and one among them is modern city. The exploitation of IoT has been increased rapidly and some of them are food, textiles, organization, transportation, academics, entertainment, and so forth. Due to the applicability of various communication models, IoT is applied in healthcare, army services, organizational fields, mobile communications, etc. The quantity of data balanced by IoT has not been presented due to insufficient storage space, request rate, and concentration of a tool. As persistent communication is provided, data management and its flow get varied by means of time. Numerous data and allocation services are emanated for IoT users by simple requests and responses via end-to-end servers as well as gateways. In IoT model, low power and lossy network (LLN) comprised of numerous nodes with robust limitation of storage space, computation ability capacity, and energy. Based on this approach, nodes from IoT systems have diverse hardware abilities and application domains. The application of IoT is depicted in Figure 1, where the nodes are connected to the Internet. Unlike, alternate nodes from similar systems, because of the hardware constraints, a direct Internet connection is not possible and necessities the application of Internet-connected nodes to apply the physical services. Form the content of low-power IoT systems, the routing protocols are classified as 2 types on the basis of route developing strategies namely, Proactive and Reactive. Initially, proactive protocols simulate the deployment of routes between nodes with no advanced data message transmission. In general, route creation is accelerated by gateway nodes by gathering data from alternate system (Multipoint-to-Point (MP2P) traffic). For this purpose, this kind of protocol has been applied extensively by periodical data collecting models. The significant module of proactive routing is IPv6 Routing Protocol (RPL). Unlike, reactive protocols make routes if a node decides to forward a message to the receiver. Therefore, this kind of protocol is induced for a non-periodical traffic domain, in which a node transfers a message to some other destination (Point-to-Point (P2P) traffic). The recently established and related reactive protocol for LLN is referred to be LOADng [4]. In the meantime, the increased IoT objectives have reasonable cybersecurity problems due to the presence of attacks. In recent times, the deployment of Deep Learning (DL) and hardware system provides a way to predict the traffic state, data offloading, and cyber-attacks in the edge system. Adopting DL methods are highly essential and significant in several applications where MEC offers the decisive advantage for traffic  
Figure 1: The Application of IoT. prediction and intrusion detection as huge data has been produced by IoT devices activates deep models to understand the workflow when compared with shallow methods.