# Internet of Things and Distributed Computing Systems in Business Models
## 1 Introduction  
The advent of the Internet of Things (IoT) and Distributed Computing Systems has ushered in a transformative era in business models across various industries. These innovations have redefined the technological environment and fundamentally altered how businesses operate, compete, and deliver customer value [1]. The convergence of IoT and Distributed Computing Systems catalyzes a shift from traditional, centralized business frameworks to more agile, decentralized, and data-driven models. This transformation isdriven by the need for enhanced efficiency, real-time decision-making, and a greater emphasis on customer-centric approaches. [PERSON] et al. [2] define IoT as the interconnected network of physical devices embedded with sensors, software, and other technologies. This innovation enables these devices to collect and exchange data. In addition, the vast network of connected devices generates an unprecedented volume of data, providing businesses with valuable insights into consumer behavior, operational efficiency, and market trends. The ability to monitor, analyze, and act upon real-time data has become a crucial part of modern business strategies. It enables companies to optimize processes, reduce costs, and enhance customer experience. Simultaneously, Distributed Computing Systems have further revolutionized business operations. According to [PERSON] et al. [3], these innovations encompass a range of technologies, including cloud computing, edge computing, and blockchain. These systems distribute computing resources across multiple locations rather than relying on a single centralized server [4]. This decentralization enhances system resilience, reduces latency, and facilitates more efficient resource utilization. Cloud computing, in particular, has democratized access to powerful computing resources, enabling businesses of all sizes to leverage advanced technologies without significant upfront investments. The integration of IoT and Distributed Computing Systems has significantly impacted business models. Traditional businesses, which often relied on hierarchical and linear processes, are now evolving into more dynamic and interconnected entities. The ability to leverage real-time data through IoT devices allows businesses to monitor operations continuously, making adjustments on the fly to optimize performance [5]. Predictive analytics, powered by data from IoT sensors, enable companies to anticipate issues before they occur, reducing downtime and increasing efficiency. Moreover, Distributed Computing Systems facilitate greater flexibility and scalability in business operations. [PERSON] et al. [4] explain that decentralized computing resources ensure companies to quickly adapt to changing demands and scale their operations efficiently. Blockchain technology enhances security and transparency, fostering trust and enabling new collaboration and transactions without traditional intermediaries [6]. These technological advancements also support the creation of personalized customer experiences. Analyzing data from various perspectives allows businesses to gain deeper insights into customer preferences and behavior, allowing for more targeted marketing and tailored product offerings. This customer-centric approach enhances satisfaction, builds loyalty, and drives growth. IoT and DCS have been subject to various criticisms, ranging from technical limitations to economic and social concerns, such as increasing vulnerabilities to cyber attacks, compatibility and interoperability challenges between devices, overwhelming data volume that poses challenges in data processing and storing, high costs of maintenance, and job displacement in sectors like manufacturing [1]. Despite this criticism, the advantages of those systems for business models are overwhelming. To what extent do the Internet of Things and other Distributed Computing Systems enhance business models? This research question guides this piece of literature over the intended literature review.