# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### Distributed Computer Systems  
Distributed computing systems are a computing architecture where multiple interconnected computers, or nodes, work together to perform tasks and process data as a unified system. Unlike centralized computing systems where a single server or mainframe handles all processing, distributed systems distribute computational workload across multiple machines, often spread over different geographical locations [23]. This architecture leverages the power of multiple processors or computers to achieve greater performance, reliability, and scalability. In a distributed computer system, nodes communicate and coordinate with each other through a network, or even the internet [24; 25]. Each node in the system can be an individual computer, server, or device contributing to the overall computational power. The nodes collaborate to achieve common objectives, such as processing large volumes of data and running complex algorithms.  
Among the advantages of Distributed Computing Systems is their capacity to replicate horizontally. In order to deal with the augmenting workload of computing assets, extra nodes can be included in the system, therefore adding scalability and flexibility [26]. This approach contrasts with vertical scaling, where increasing the capacity of a single machine is often limited by hardware constraints. Distributed systems can also enhance fault tolerance and reliability by replicating data and functions across multiple nodes [27; 28]. In case one node collapses, others could take its place mitigating the impact on the system altogether, allowing further service level. Distributed systems also facilitate resource sharing and load balancing [29]. Resources (e.g., storage) are thus distributed across nodes, optimizing their utilization and balancing the workload to prevent bottlenecks [30]. This distributed approach allows for efficient management of resources and improved performance, as tasks can be allocated dynamically based on current system demands and node capabilities.  
Distributed computing systems can support various types of distributed architectures and models, including client-server, peer-to-peer (P2P), and cloud computing models. In a client-server model, clients demand services from a central machine that delivers the ensuing reply [31; 32]. In contrast, P2P networks involve a decentralized approach where each knot could perform at once as client and supplier, whilst exchanging information with each other. Cloud computing is a prominent example of a distributed system [33]. It allows on-time entrance, by internet, to a common platform of information technology assets (e.g., servers) [34]. Distributed service providers manage and operate distributed infrastructure, offering user scalability, flexibility, and cost-efficiency. This model allows businesses and individuals to leverage powerful computational resources without investing in and maintaining their hardware. To sum up, the integration of DCS with IoT combines the power of distributed resources with a vast network of interconnected devices, which enhances the effectiveness of IoT deployments, in terms for instance of generating a huge number of information, enhancing cloud, edge, and cloud computing, irrespective of the geographical location, whilst enabling opportune decisions and facilitating the deployment of AI models for large datasets [33]. It offers therefore new possibilities in automation and intelligence as developed below.