GDTN: Genome-Based Delay Tolerant Network Formation in Heterogeneous 5G Using Inter-UA Collaboration

Abstract

With a more Internet-savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter-collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay-tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA). The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real-time testbed and simulation environment.

Delay-Constrained Routing Based on Stochastic Model for Flying Ad Hoc Networks

“…We define the end if (8) end for (9) for each node j ∈ A(S t i ) do (10) Uses (5) and (11) to compute q ij and r ij ; (11) end for (12) Uses (7) 6 Mobile Information Systems termination time υ k T to be the stopping time when packet k is terminated within delay constraint and υ k s to be the stopping timeout of delay threshold. e notation μ k,n denotes the transmission gain when the packet k is transmitted at slot n, and μ k,n < ∞.…”

“…e routing methods proposed for delay tolerant network (DTN), such as EPI-DEMIC [4], BUBBLE [5], GDTN [6], and PROPHET [7], are destined to handle the recurrent disconnections of the links due to the high degree of nodes' mobility. In most cases, this category of routing methods uses the technique of store-carry-and-forward when the senders lose connectivity with their neighbors to deliver the packets to destination.…”

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

“…a Ss,k ≤ 1 w Mm , w Ss ≤ 1 1 ≤ k ≤ Num_UEs, 1 ≤ n ≤ Num _SBSs (22) Since the optimization problem formulated is a NP hard problem, we obtain the solution of the optimization problem using particle swarm optimization [42,43], which is a widely used bio-inspired algorithm, like genome-based approach [44]. The particle swarm optimization is easy to implement and calculated easily within a short time.…”

“…After that, local best fitness pbest and position were obtained, and global fitness gbest and position were obtained. bio-inspired algorithm, like genome-based approach [44]. The particle swarm optimization is easy to implement and calculated easily within a short time.…”

An Approach to Data Analysis in 5G Networks

“…Meanwhile, there are reports [1,32] and projects [21,[33][34][35][36][37] that introduce analysis capacities to cover 5G requirements. In this way, METIS Project [28] takes into account SON concept in order to provide a new level of adaptability to 5G infrastructures.…”