# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 2 Edge Computing Overview
### Challenges  
Even though there are many applications of edge systems, as highlighted above, these also bring several challenges in effectively integrating and using such innovative solutions. Naming challenges are closely associated with the heterogeneity and dynamic topology of edge networks. There is a lack of a standardized naming system, which complicates the efficient management of the dynamic and mobile characteristics of edge devices and applications [16; 19]. At the same time, configuring and managing multiple virtual functions and dynamic network conditions across dispersed locations represents a complex problem. As the network grows, the systems must be capable of dynamically adjusting to network conditions without manual intervention [4; 6]. Service discovery in distributed edge computing systems is challenging due to the increasing number of mobile devices requiring simultaneous and uninterrupted services. The automatic discovery of edge computing nodes to required resources in a heterogeneous system is a complex task [2; 17]. Computing resources and the diversity of heterogeneous edge devices can lead to inconsistent results across different IoT clusters when performing coordination tasks. To address this, a robust coordination mechanism that includes both hardware and middleware adaptations is mandatory [3; 9]. Managing a diverse set of devices and applications requires robust systems that can handle varied data formats, communication protocols, and security standards. Ensuring seamless interoperability between different types of devices and maintaining consistent performance across various networks can be complex [2]. Despite the advantages of edge computing in tackling latency issues, it introduces important security challenges due to its distributed data processing and distinctive characteristics. Security threats in edge computing span various perspectives, including personal, attribute-based, and compliance-related issues [2; 16]. The diversity of service providers, devices, and applications increases the overall complexity, which results in a need for attention to the interaction between them. Another important characteristic that must be considered is the current limitations of IoT devices, which in a congested state must use more computational resources to achieve the task. In edge resource management, motivating factors are aligned with those of smart systems, necessitating effective allocation, sharing, and pricing strategies. To take advantage of the distributed nature of the resources, complex AI models can be partitioned into smaller subtasks and effectively offloaded between nodes to enable collaborative work [3; 4]. Privacy is a critical issue in edge computing environments due to the inherent properties of edge computing, such as the processing of data at the edge and the storage of privacy-sensitive information. Efficient tools to protect data privacy and security at the edge are lacking, and the dynamic environment of edge networks adds to their vulnerability [14; 19]. The success of edge computing technology is heavily dependent on consumer acceptance, which is closely tied to trust. Developing consumer trust models that incorporate security and privacy requirements is important for the adoption of edge computing systems [2]. Effective scheduling strategies must consider factors such as the real-time demands of applications, varying input priorities, and the dynamic nature of edge environments. Innovative approaches are required for task prioritization, load balancing, and resource allocation to ensure optimal performance while minimizing latency and maximizing resource utilization [1]. Edge computing needs to be easily reconfigurable and information-aware to handle many kinds of packet traffic and time-varying radio channels. Pre-execution of data analytics at the edge is a necessity to prevent bottlenecks that come from the massive volume of data collected by edge devices at the network level [18].