## 1 Introduction  
The future of the internet will extend far beyond conventional computers and software applications, bringing connectivity to countless everyday items and even devices integratedinto the human body [1; 2]. This transformation, driven by the Internet of Things (IoT), promises a world where almost everything around us, from household appliances to wearable health monitors, is part of a vast, interconnected network [3]. Internet usage primarily focuses on content retrieval and information exchange, often overshadowing two-way host-to-host communication. In the traditional model, users specify a server to request information, restricting the network layer from sourcing data from alternative locations [4]. Information-centric networking (ICN) [5], which uses content-based naming, in-network caching, and name-based routing, provides a more versatile approach [6]. In ICN, users request data by name without specifying their source, allowing any node holding a copy of the data to respond, thus enhancing content accessibility [7]. A central feature of ICN is in-network caching, where intermediary nodes store data ready to be retrieved quickly when requested again [8]. This efficiency depends on factors like cache size, communication cost, and cache policies (including cache decision and eviction criteria) [9]. However, IoT data are often transient and require updates, making data freshness crucial [10]. A caching system must manage data by replacing outdated information with updated content as needed by the user [11]. The accuracy of cached data is crucial for making effective cache decisions [12; 13]. Before, IoT systems only determined the data source and freshness of information. A caching system must manage data by replacing outdated information with updated content that the user needs. Beyond merely retaining recently updated data, the interactions with cached data, including validation of their relevance and timely replacement, ensure that the network delivers accurate and up-to-date information. Robust mechanisms are essential to assess data currentness and validity in IoT systems, considering access frequency and timing. Ensuring data freshness, correctness, and accuracy is crucial for making real-time, critical decisions. Our proposed method goes beyond traditional freshness-based caching strategies by integrating metrics that assess data validity, prioritize updates, and ensure optimized caching actions in dynamic IoT environments. ICN is a network architecture focused on content retrieval rather than traditional end-to-end host communication [12]. It enables efficient data sharing, improved security, and better scalability. This paper proposes an eight-step method to optimize caching and data freshness in ICN-IoT networks. The process begins with routers identifying whether an incoming message is a data request or a data response. For data request messages, the router checks its cache for availability (cache hit) or unavailability (cache miss) of the requested data. If a cache hit occurs, the router calculates the freshness score of the data to verify their validity and decides whether to deliver the data or forward the request to the next hop. For data response messages, the router checks for duplicates in the cache, replacing older entries when a match is found. If no duplicate exists, the router evaluates whether there is space in the cache for the new data; otherwise, it calculates the time validity of cached entries to remove expired data. If no expired data are found, the router eliminates the data entry with the lowest freshness score to accommodate the new data. This systematic process guarantees caching only the most relevant and current information, enhancing network efficiency. It significantly improves response times in ICN-IoT systems. The contributions of this paper are:  
* Enhancing the cache hit ratio by maintaining only recently updated and relevant data in the cache.