## 2 Related Work
It also improves network performance by caching frequently requested data in readily accessible locations. This leads to quicker data retrieval and extends the network's overall lifespan. [PERSON] and [PERSON] [21] proposed a caching method for IoT networks that keeps updated data while lowering caching costs. Their approach includes a freshness mechanism that regularly checks cached data, replacing outdated information. This strategy's careful handling of data freshness keeps information relevant and accurate in constantly changing IoT environments. It ensures that data remain up to date despite frequent updates. The caching process selects nodes directly connected to consumers along the data path to store data. This setup shortens the time needed to retrieve data by reducing the number of hops. This approach speeds up response times and boosts network efficiency. Combining data freshness checks with smart cache placement allows this approach to tackle major IoT challenges. This makes it a useful solution for real-time, data-driven applications in networks with limited resources. [PERSON] and [PERSON] [22] proposed the Enhanced Edge-Linked Caching (EELC) algorithm and Policy Optimization (PPO) to optimize caching in IoT networks. This proposed method dynamically adapts to current network conditions and prioritizes popular content to make the system more efficient. The EELC scheme stores frequently used data close to users, which reduces energy consumption, server load, and delays in accessing data. This method is an improvement over older techniques, like Edge-Linked Caching (ELC) and Leave Copy Everywhere (LCE), in both efficiency and speed. EELC places frequently accessed data closer to users, which speeds up data access and improves efficiency. This approach also reduces unnecessary data transfers, boosting overall network responsiveness. EELC requires regular updates and monitoring, which takes considerable processing power. This demand can be challenging for devices that have limited resources. [PERSON] and [PERSON] [23] presented a smart content replacement strategy designed for ICN in IoT networks, focusing on better cache management by keeping important data readily available. Traditional IoT networks running on TCP/IP face issues like limited address space, high data traffic, and reduced efficiency. ICN, however, allows in-network caching, where data are stored temporarily in intermediate nodes, supporting faster data retrieval and reducing network load. Traditional caching policies, like First In, First Out (FIFO); Least Recently Used (LRU); and Least Frequently Used (LFU), fail to account for the transient nature of IoT data, leading to frequent eviction of critical content. To address this, the authors introduced a novel replacement policy--evaluating data requests, content freshness, and upcoming request wait times (URWTs) to prioritize cache content effectively. Preemptively tracking these attributes in the Pending Interest Table (PIT) enables their method to remove less critical data while retaining high-value content efficiently. [PERSON] and [PERSON] [24] proposed the Caching Algorithm based on Random Forest (CARF) for low earth orbit satellite mega-constellations, applying ICN to the IoT environment. The CARF uses the \"viscosity\" concept to integrate content popularity and satellite node location predictions to enhance cache placement accuracy. The Random Forest (RF) algorithm in the CARF allows for proactive caching of popular content at ideal satellite locations, effectively reducing access delays. This strategic placement enhances cache hit rates and throughput, ensuring faster data retrieval for users. As a result, the CARF demonstrates superior performance compared to traditional methods, like Leave Copy Everywhere (LCE) and Leave Copy Down (LCD). The method improves caching efficiency but demands significant computational resources and relies on precise satellite location prediction, which may limit its applicability under resource constraints or unexpected satellite path deviations.