## 5 Open Research Issues  
The motivation of this section is to introduce research open challenges and opportunities in the security and privacy issues related to the EC paradigm. The centralized computational approach in data centers and hyperscale clouds is robust against security threats as it \"hides\" the user's data behind layers of security defenses, both virtually and physically. However, EC faces many security challenges, and here we present some of the open research challenges as well as the scope for further work [96]. * **Heterogeneous EC architecture**: The users in a traditional cloud computing approach are masked from the hardware in place and how software/application performance depends on hardware resources. EC introduces complexity and a need for multi-layered security schemes because of an assortment of standards and protocols [280]. It introduces the need for unique data propagation management schemes among the heterogeneous edge devices [121]. Data privacy is achievable through encryption techniques, but EC architecture makes the existing encryption schemes too cumbersome for the limited processing resources [281]. Furthermore, research needs to focus on ML algorithms explicitly for IoT forensics, matching the distinct features of diverse IoT devices. The potential of emerging technologies such as blockchain needs to be explored in securing digital networks [282; 283]. * **Dynamic resources allocation**: Contrarily to cloud computing, the resources in the EC network are rather limited; thus, static allocation techniques cannot achieve optimal resource utilization. The dynamic allocation of computing and storage resources in a distributed EC network remains a bigger challenge. The resource allocation strategy in EC is important for ensuring efficient and effective use of resources and maintaining the quality of service (QoS) for applications that demand real-time data processing and low-latency response. The task of partitioning in EC poses the challenge of optimal partitioning and faces challenges in dynamic resource allocation without the computational or storage capacity or location of edge nodes. Several obstacles exist in the deployment and optimization of resource scheduling in EC and cloud collaboration using deep reinforcement learning (DRL). A significant challenge remains in terms of higher computational cost and extended convergence times of DRL algorithms [284]. * **Data abstraction**: The edge node needs a certain amount of training data to carry out analysis tasks. Data abstraction carries out data preprocessing techniques like noise cancellation, data classification, and data computation. Heterogeneous devices use different data formats, and data security algorithms cannot be fed with a complete set of raw data, but it should only abstract the relevant part. Storage is a limiting  
Figure 21: Xilinx _VitisTM_ AI integrated development environment. factor while selecting the size of raw data and prediction accuracy. The selection of an optimal data abstraction technique is not easy because of the heterogeneity of devices, different data formats, and different corresponding operations. Thus, a unified architecture or interface standard for the EC-based IoT applications that supports migration between diverse embedded operating systems is needed for the abstraction of IoT and edge devices [96]. * **Secured EC nodes**: Devices in an EC network need a foolproof access control and an end-to-end threat protection mechanism. Edge security refers to device security, network security, data security, and application-level security, focused mainly on the protection and privacy of user data. Mitigation strategies include first the risk definition, uncompromised device functionality, multiuser edge node security, and minimal service levels at user nodes. The development of authentication mechanisms for specific edge nodes and the privacy module is needed to maintain the trustworthiness of edge data centers [285].