Editing Emerging Research Directions (section)

===7.1.2 Core Challenges in Scheduling===
====Dynamic, Real-time Environments====
The main challenges in task and resource scheduling are primarily the heterogeneous and constrained edge resources that these types of systems work with, the dynamic workloads and real-time requirements that they are deployed in, and privacy and security concerns. On the first point, not only are the devices heterogenous and geographically distributed, the data volume and data attributes are also heterogenous. While challenging, resource/task scheduling can reduce the difficult effects of this aspect of edge computing.

In terms of the dynamic workloads and real-time requirements, the main complication stems from user mobility with their devices. Smartphones, connected vehicles (CVs), and other mobile devices are often moving through the world connecting to different nodes over various protocols. Per [2], strategies that determine offloading and cache decisions can be incorrect very quickly after deciding, or its users may move out of range, or into range of a separate setup. 

====Security and Privacy====
For security and privacy, the main challenges are categorized as such: system-level, service-level, and data-level. System-level discusses the overall reliability of the edge system, whether by intrusion or malfunction. Service-level discusses user authentication/authorization and validation of offload nodes. Lastly, data-level discusses the trustworthiness and protection of the data as it passes between IoT devices and edge nodes, as the data can contain sensitive information.

====Resource Allocation====
A large portion of research on this topic is also done on resource allocation and computation offloading. Resource allocation focuses on efficiently distributing computing, communication, and storage resources to support offloaded tasks. Some studies consider single-resource allocation (e.g., just bandwidth or CPU cycles), while many optimize joint allocation of multiple resources (e.g., computing and communication). More comprehensive approaches also include caching strategies to reduce latency. Allocation decisions aim to balance energy use, service quality, and operational cost, often using advanced techniques like optimization algorithms or machine learning to dynamically adapt to changing workloads [2].

Computation offloading in edge computing determines whether and how much of a task should be processed locally or offloaded to another node (edge or cloud). Offloading can occur as both vertical- or horizontal-offloading – that is, device-to-edge, edge-to-cloud, cloud-to-edge, edge-to-edge, or even device-to-device – and helps optimize factors like latency, energy consumption, and system cost. Offloading can be binary (all or nothing) or partial (a portion of the task is offloaded), and decisions depend on resource availability, task size, and QoS requirements [3].