# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 1 Introduction  
Nowadays, over 50% of the world's population lives in urban areas [1], and this number is expected to rise to approximately 75% by 2050 [2, 3, 4]. The Internet of Things (IoT) is crucial in transforming these urban areas into Smart Cities. Artificial Intelligence (AI), automation, and digital transformation will be disseminated worldwide, allowing smart devices to facilitate increasingly decentralized control of production [5]. Integrating AI with the IoT will enhance professional learning and provide significant advantages such as efficient remote work, improved security, and reduced business costs [6]. Due to the vast array of devices within the IoT that collect, analyze, and share data across various devices, programs, and platforms, it has garnered attention from the academic, societal, and industrial sectors [7]. IoT is a self-configuring, adaptive, complex network with standard communication protocols that connects _things_ to the Internet [8]. Internet of Everything (IoE) extends beyond the concept of IoT. IoT is the technologyarchitecture facet of IoE, and many studies have focused on central concepts and their relationships in an IoT domain [9]. The IoE is an emerging technology that connects people, processes, data, and things through the Internet [10]. It involves the integration of various technologies such as the IoT, Cloud Computing, big data, and data analytics to create a network of interconnected devices and systems [11]. The IoE has the potential to transform industries and improve the quality of life for individuals by enabling new applications and services [11]. However, IoE inherits the hurdles of IoT, introducing novel challenges that are unlikely to be addressed adequately by the existing middleware solutions tailored specifically for IoT [12]. Current IoT architectures focus mostly on machine-machine collaboration. The IoE brings people to the network, and now we can also support collaboration between humans and between humans and machines. However, how IoE architectures handle these diverse types of interactions remains uncertain within this emerging paradigm. With the rise of urban populations and the increasing integration of IoT into urban infrastructures, the need for efficient and collaborative architectures in IoT and IoE environments is essential. In this work, we analyze the literature using the Rapid Review methodology [13] to analyze collaboration in four prevalent computing architectures in the IoE paradigm [14]: Edge Computing [15], Cloud Computing [16], Blockchain/Web Services [17; 18], and Fog Computing [19]. These architectures offer unique capabilities and constraints that significantly influence collaboration between smart devices. For example, Edge Computing could enhance real-time communication through low-latency processing, while Blockchain can improve cooperation via secure and transparent transaction records [14]. We use the 3C collaboration model [20] to categorize collaboration into communication, coordination, and cooperation to assess how each computing architecture can rely on these components. We expect to contribute to IoT and IoE and related technologies by providing a comparative analysis of Edge Computing, Cloud Computing, Blockchain/Web Services, and Fog Computing, supporting researchers and practitioners in selecting the most suitable architecture for IoT and IoE environments. Smart Cities are a major beneficiary of these insights. Applying the 3C collaboration model to these architectures offers a framework for improving collaborative dynamics in IoT systems. Our findings show that Edge Computing supports real-time, low-latency processing at data sources, while Cloud Computing offers scalable resources for diverse workloads, optimizing coordination and productivity. Effective resource allocation and network configuration in these architectures are essential for cohesive IoT ecosystems, meeting the complex demands of modern IoT applications.