Improving the Performance of MANETs using Machine Learning

Abstract

This work is concerned with the improvement of the overall performance and Quality of Service (QoS) in Mobile Ad-hoc Networks (MANETs). It proposes a 3-cross-layer optimization scheme which maximizes the Completion Ratio (CR) and minimizes the Round-Trip-Time (RTT). It is focusing on optimizing the routing, scheduling and the flow control using machine learning. Specific requirements have been articulated in a number of scenarios and these have been employed to measure the performance of the proposed 3-cross-layer optimization scheme. The open Anglova.net is a scenario which matches the requirement of the research study funded by Armasuisse. This scenario has been used to test advanced cross layer algorithms. The 6-laptop is another scenario which has been used to measure the performance of routing protocols. There was an attempt to contrast these two scenarios, however, and despite that the performance comparison has been made these are considered valid in their own merit. The theoretical performance has been evaluated with fitted radio models. The scope was to provide emulation results from realistic radio models. Omniscient approaches such as the Omniscient Dijkstra Routing balanced (ODRb) and an omniscient suboptimal Time Division Multiple Access (TDMA) schedule are assessed on the dynamic Anglova.Net scenario to evaluate close to optimal performance in real time emulation using the Extendable Mobile Ad-hoc Network Emulator (EMANE). The omniscient agent called Graph Neural Network (GNN-t) is proposed to seek alternative longer routes and reduced congestion. For realistic distributed MANETs, Optimized Link State Routing (OLSR) is investigated and enhanced for multi-hop networks with 24 nodes or less. The distributed routing OLSRv2 is improved using (1) Signal to Interference plus Noise Ratio (SINR) to estimate the link quality, (2) stability of the Multipoint Relays (MPRs) to improve route dissemination and (3) advanced link cost computations to offer reliable routes. The distributed node view graph provided by the routing protocol is exploited to compute schedules to seek theoretical performance. Four scheduling schemes are proposed based on the node view graph: (1) an oblivious to traffic schedule, (2) an advertised traffic based schedule, (3) a slot request algorithm with retransmissions and (4) a ML based 2-hop scheduling scheme. A Flow Control (FC) scheme at the network layer was proposed to reduce the user traffic when node congestion occurs. It maintains the packets on the node during disconnections. OLSR+SINRT, a deterministic and enhanced version of OLSRv2d using SINR information, increases CR from 66% to 76% on the Anglova scenario with fading. Measurements were conducted to assess the performance of OLSR+SINRT (CR=81%) and OLSR (CR=79%) in indoor environments. Results demonstrate major improvements using routing and scheduling with omniscient and distributed solutions in realistic scenarios.

Date of Award	23 Jul 2024
Original language	English
Awarding Institution	Edge Hill University
Sponsors	Armasuisse
Supervisor	Nik Bessis (Director of Studies), MOHSIN RAZA (Supervisor) & Jean Frederic Wagen (Supervisor)