Next Generation Opportunistic Networking in Beyond 5G Networks

TL;DR

This paper introduces a novel graph-based approach for opportunistic networking in beyond 5G networks, significantly reducing energy consumption by intelligently scheduling transmissions without precise link knowledge.

Contribution

It proposes a new graph model for network evolution and a scheduling technique that leverages anticipatory mobile networking to optimize energy efficiency in device-centric networks.

Findings

Reduces energy consumption by 60-90% compared to traditional methods.

Performs close to an ideal system with full network knowledge.

Maintains low transmission and computational overheads.

Abstract

Beyond 5G networks are expected to support massive traffic through decentralized solutions and advanced networking mechanisms. This paper aims at contributing towards this vision through the integration of device-centric wireless networks, including Device-to-Device (D2D) communications, and the Next Generation of Opportunistic networking (NGO). This integration offers multiple communication modes such as opportunistic cellular and opportunistic D2D-aided communications. Previous studies have demonstrated the potential and benefits of this integration in terms of energy efficiency, spectral efficiency and traffic offloading. We propose an integration of device-centric wireless networks and NGO that is not driven by a precise knowledge of the presence of the links. The proposed technique utilizes a novel concept of graph to model the evolution of the networking conditions and network connectivity. Uncertainties and future conditions are included in the proposed graph model through anticipatory mobile networking to estimate the transmission energy cost of the different communication modes. Based on these estimates, the devices schedule their transmissions using the most efficient communication mode. These decisions are later revisited in real-time using more precise knowledge about the network state. The conducted evaluation shows that the proposed technique significantly reduces the energy consumption (from 60% to 90% depending on the scenario) compared to traditional single-hop cellular communications and performs closely to an ideal oracle based system with full knowledge of present and future events. The transmission and computational overheads of the proposed technique show small impact on such energy gains.