The emergence of 5G and the upcoming 6G wireless communication technologies driven the need for innovative network architectures, such as Heterogeneous Networks (HetNets), which integrate diverse components to boost network capacity and performance. Mobile Edge Computing (MEC) offers a promising solution for low-latency computation requirements for different application and use cases at the network edge, but effective task offloading in dynamic HetNet environments remains challenging due to factors like user mobility, channel conditions, and frequent handovers. This paper proposes a Graph Convolutional Network-Assisted Task Offloading (GCN-ATO) algorithm, which leverages a trained GCN model to predict Key Performance Indicators (KPIs) based on current network conditions and user positions, thereby optimizing task offloading decisions. By reducing unnecessary data transmission and enhancing resource utilization, the GCN-ATO algorithm improves energy efficiency and task execution latency. We evaluate our approach using real-world mobility and network traces, demonstrating significant reductions in energy consumption compared to baseline schemes, while preserving task execution deadlines and system performance.

The Internet of Things (IoT) is overrunning different domains and applications, where the use of wireless sensors and mobile devices is indispensable in such a mobile environment. These heterogeneous devices may generate a tremendous amount of content. Information-Centric Network (ICN) paradigm has been proposed to meet today’s users and application requirements. The in-network caching is a fundamental feature supported by design in ICN that improves network performance by providing ubiquitous caching in the network layer. Since most IoT devices are resource-constrained with limitations in communication, processing, energy, and memory; the energy-efficiency is a prime concern in IoT deployment. Different factors may affect energy efficiency in ICN-based wireless IoT networks such as transport (communication), caching, and energy limitation. This research paper attempts to focus on the in-network caching in wireless IoT to maximize the energy-efficiency. We propose an Energy-aware caching placement scheme (EaCP) that aims to maximize the energy-saving by trading-off between content transmission energy and content caching energy. Compared to other strategies, the simulation results show significant improvements while ensuring low data replication and a high cache hit ratio.

Internet usability is expanded form just human-to-human interactions toward different communication types, while the communication itself is shifting from the host-centric model to the content-centric paradigm. The 5G and beyond networks promise not only to support such changes but also to provide massive data exchange and connectivity with high reliability. The next-generation networking technologies are the key enabled for 5G that aim at building a new ecosystem. One promising piece of this ecosystem is the information-centric network (ICN), which is a future network architecture that tends to tackle the current host-centric model issues. It natively supports several features, including abstraction content naming and transparent in-network content caching that contribute to improve network performance, reduce traffic, and improve the latency. In this article, we first provide a potential road map by introducing different next-generation active technologies to enable the big picture of 5G, including mobile-edge computing (MEC), software-defined networking (SDN), and network function virtualization (NFV). Then, we discuss the need for ICN and its coexistence within this ecosystem. Later, we present an in-depth review of the recent content naming schemes and a comprehensive review of in-network content caching solutions. We classify these solutions into different classes based on the used technologies and their working principle. Finally, we highlight some research challenges and propose promising directions for the research community.

The tremendous growth of today's connecting devices and generated content lead to an increasing load of current Internet infrastructure with challenging requirements. 5G technology promises to provide high bandwidth and ultra reliable low-latency communications, while Information-Centric Networking (ICN) promises to replace the current host-centric paradigm. ICN provides ubiquitous and transparent in-network content caching in order to enhance network performance and reduce content retrieval latency. In this regard, several cache strategies have been proposed, most of them are neither distributed in nature nor scalable in large-scale networks. In this paper, we design a distributed and efficient content caching scheme for 5G-enabled ICN networks, namely Cache and Split (CnS). CnS is designed to make a trade-off between the cache utilization and the content delivery time based on the number of received demands and content popularity. We evaluate our scheme using various performance metrics against different caching strategies. The obtained results prove an improvement in the cache utilization, with fast data retrieval, and enhancements in the content cache distribution.

The 5G networks are considered as new wireless technologies that promise to provides Ultra-Reliable Low Latency Communication. In doing so, it uses various coverage techniques with high access point density that rise various complexity in the handle and manage the mobility of users. Information-Centric Network (ICN) is an emerging paradigm that promises to replace the current IP network. The content in ICN is first-class citizens instead of the host. ICN names the content rather than the owner, and the demands are driven by the receiver. Although this change contributes to the ease of consumers' mobility, producer mobility is considered as a challenging issue in ICN. In this paper, we study the issue of producer mobility, discuss the existing issues and challenges, and then design a Label-based technique that takes the benefits of location-free naming to enhance the producer mobility in a seamless and easy manner. The simulation results show the high performance of our proposed architecture in terms of seamless handover and low data miss.