One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies’ strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments.

Cloud computing has revolutionized the way businesses access and utilize computational resources, providing unparalleled cost efficiency, scalability, and flexibility. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) comprise the three primary cloud service models; this chapter offers a thorough examination of each. We explore the characteristics, use cases, advantages, and disadvantages of each model, emphasizing the varying degrees of control and responsibility that cloud providers and consumers share. In addition, the chapter delves into significant future trends, such as the increased integration of Artificial Intelligence functionalities, the paramount importance of robust cloud security measures, the convergence of edge and cloud computing, advancements in resource optimization, and the increasing adoption of cloud-native and serverless architectures. It is imperative for organizations to comprehend these models and emergent trends in order to effectively leverage the transformative power of the evolving cloud landscape.

This chapter provides a comprehensive overview of cloud deployment models, including public, private, hybrid, and community clouds, and their profound impact on modern IT infrastructure and the development of inclusive IoT smart systems. The selection of an appropriate cloud model significantly influences critical aspects such as data sovereignty, scalability, resource utilization, and crucially, the accessibility features for individuals with disabilities. We examine the distinct characteristics, advantages, and disadvantages of each model, highlighting how they cater to diverse organizational needs, from cost optimization and rapid provisioning in public clouds to enhanced security and control in private environments. The discussion also addresses the complexities and benefits of hybrid and community cloud approaches, emphasizing their role in enabling flexible, compliant, and collaborative computing solutions. Understanding these models is essential for making informed strategic decisions that foster innovation and ensure equitable access to technology.