From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

A Comprehensive Review of D2D Communication in 5G and B5G Networks

The evolution of Device-to-device (D2D) communication represents a significant breakthrough within the realm of mobile technology, particularly in the context of 5G and beyond 5G (B5G) networks. This innovation streamlines the process of data transfer between devices that are in close physical proximity to each other. D2D communication capitalizes on the capabilities of nearby devices to communicate directly with one another, thereby optimizing the efficient utilization of available network resources, reducing latency, enhancing data transmission speed, and increasing the overall network capacity. In essence, it empowers more effective and rapid data sharing among neighboring devices, which is especially advantageous within the advanced landscape of mobile networks such as 5G and B5G. The development of D2D communication is largely driven by mobile operators who gather and leverage short-range communications data to propel this technology forward. This data is vital for maintaining proximity-based services and enhancing network performance. The primary objective of this research is to provide a comprehensive overview of recent progress in different aspects of D2D communication, including the discovery process, mode selection methods, interference management, power allocation, and how D2D is employed in 5G technologies. Furthermore, the study also underscores the unresolved issues and identifies the challenges associated with D2D communication, shedding light on areas that need further exploration and developmen

Joint optimization of transmission-order selection and channel allocation for bidirectional wireless links-part II: algorithms

This is the second in a two-part series of papers on transmission order (TO) optimization in the presence of channel allocation (CA), i.e., joint optimization of the TO selection and CA problem, for interfering bidirectional wireless links. Part I of this paper thoroughly analyzes the joint optimization problem from a game theoretic perspective for a general deterministic setting. Here in Part II, we present novel distributed and centralized CA-TO algorithms, together with their performance analysis, for Device-to-Device (D2D) communications underlaying cellular networks based on the findings in Part I of this paper. Here, TO is a novel dimension for optimization. In Part II, we propose and analyze novel two distributed and one centralized joint CA-TO algorithms. Our investigations show that: i) our algorithms contain many of the existing TO algorithms and CA algorithms as its special cases and can thus be considered as a general framework for the joint CA and TO optimization. The computer simulations for TDD-based D2D communications underlaying cellular network show that the proposed distributed and centralized joint CA-TO algorithms remarkably outperform the reference algorithms.IEEE Communications Societ

Performance evaluation of synergic operation of algorithms enabling opportunistic networks - D4.3

Deliverable D4.3 del projecte OneFITPreprin