## 4 An Architecture for IoT and Multimedia Data Handling
They never produce any data and the service providers always try to provide their best services to maximize the QoS of the services consumers. The maximum downstream data are used by the content consumers. The third category devices generate and consume data and services. They use both upstream and downstream data. The huge number of end-users and IoT devices are continuously generating and using the generated data instantly, but the huge amount of data needs to be stored in the global cloud data center. The data need to be preprocessed before sending to the global cloud data center. Therefore, we apply EC mechanisms at the edge of the access and core network. Tier 2 shows the devices and the required types of EC mechanisms. The generated data from Tier 1 devices are preprocessed in Tier 2, and Tier 2 contains base stations, access points, and a cache server to support the EC mechanisms. The computational mechanism is applied to the sensor-generated data to reduce the volume of data. We do not mention the computational mechanism because the types of the computation mechanism depend on the objective of the service providers (e.g., the object identification mechanism may apply to the real-time video monitoring sensors or cameras). Similarly, appending the small-sized IoT generated data into a large file based on the sources and sensor types of a certain region. EC appends the small packets for the same destination. Therefore, the network traffic and the use of network resources are optimized. A lot of network resources are available in the Tier 3 devices. The most important resources are the caching capabilities of every router, and their content request message-forwarding mechanism is another level of uniqueness in our proposed architecture. As a result, each router decides caching, forwarding, duplicate content request messages handling. Furthermore, to manage the downstream content flow and data traffic, these router's caching capabilities help the end-users to get their expected content from the nearby cached devices and reduce the communication latencies and enrich the QoS of the end-users. Although the existing caching mechanisms are inefficient for the optimal use of in-network caching capabilities, its use optimizes the network efficiencies and throughput. We describe our cluster-based in-network caching mechanism for the core network to increase the efficiencies of the core network. The clustering mechanism applied to the core network and the remaining Tier architecture will be the same. Content is the salient target for the Tier 4 and the organization of these contents depends on the different service providers. The centralized content server is the pivotal architecture for most of the service providers, but nowadays distributed architecture is becoming an important architecture in order to satisfy the end-users by providing low-latency communications. Although the decentralized architecture increases the end user's satisfaction, they are location-oriented service providers. Therefore, network traffic flows in one way, creates network congestion, and increases the consumption of network bandwidth. Our in-network caching mechanism is distributed and all the devices from all Tiers cache the content based on the content popularity and importance of the content based on the content access time and location.