## 2 Related Work  
The data caching method is designed to optimize data storage and retrieval by selectively caching data at strategic points within a network [14]. In this approach, nodes in the network temporarily store data based on specific criteria, such as data freshness, access frequency, and relevance, allowing for faster data access and reduced communication overhead. When a request is made, cached data can be retrieved from an intermediate node rather than the source, significantly lowering latency and network traffic [15]. This method incorporates caching policies to determine when to store, update, or remove data, focusing on maximizing cache utility and efficiency. This section discusses previous papers and the motivation for conducting this research. [PERSON] and [PERSON] [16] proposed a distributed probabilistic caching strategy where cache nodes adjust their caching probability based on the distance between the producer and the consumer and the data's freshness. In this approach, the closer a caching node is to the producer, the fresher the data it can provide. Data received directly from the producer are the freshest but involve higher communication costs due to more hops. Conversely, data retrieved from an intermediate router's cache have lower freshness but incur a reduced multi-hop retrieval cost. This method seeks to balance the tradeoff between \"data freshness\" and the cost of multi-hop data retrieval to optimize overall data access in a multi-hop network path. [PERSON] and [PERSON] [17] proposed a consumer-driven algorithm to confirm information freshness in content-centric networking. This paper first caches data packets with a freshness parameter defined by the data source. In the second step, a consumer requests data based on a specified freshness level. The freshness parameter at the source is important and can either boost or reduce network performance. In this paper, the authors try to reduce negative impacts by having the data producer respond based on the consumer's specified requirements. The benefit of this proposed method is that consumers can control the freshness of the data, which improves data relevance and freshness. [PERSON] and [PERSON] [18] presented a probabilistic caching strategy for IoT (Pcasting) systems, considering data freshness, energy, and each IoT sensor's cache space. This proposed method helps decrease energy consumption and delays in data retrieval. The Pcasting method is flexible and compatible with various routing protocols. It can also be adjusted to include extra parameters when needed. The results show that this strategy significantly boosts network efficiency. It provides a flexible, energy-efficient caching solution for IoT environments. [PERSON] and [PERSON] [19] suggested a model for in-network caching in the IoT that balances communication costs with data freshness. This paper first explores the benefits of caching temporary data in internet routers. It considers both the rate of data requests and the data's lifetime. Data lifetime refers to the duration for which data stay valid. In the second phase, when a router receives data, it checks freshness by looking at the data's arrival time, generation time, and expected lifetime. This evaluation helps ensure that only relevant data are stored. [PERSON] and [PERSON] [20] proposed a caching decision algorithm called the Multi-Attribute Cache Decision Strategy (MACDS) for ICN-IoT. The MACDS algorithm considers several important factors, such as energy use, memory space, data freshness, and the hop count between nodes. These attributes allow the MACDS to make smart decisions on data caching locations. The proposed method optimizes resource efficiency while ensuring data accessibility, making it highly suitable for IoT environments with frequent resource constraints. It helps maintain up-to-date information across connected devices. The MACDS algorithm boosts energy efficiency by cutting down on unnecessary data transfers.