Traffic polarization in networks refers to the phenomenon where traffic tends to concentrate along specific routes or edges when doing multipath routing, leading to imbalanced flow patterns. This spatial distribution of traffic can result in congested and overburdened links, while other routes remain underutilized. Such imbalanced traffic distribution can lead to network bottlenecks, reduced throughput, and compromised Quality of Service for critical applications. These issues emphasize the urgent necessity to address traffic polarization and its detrimental impact on network efficiency and resilience. In this master thesis, we introduce a novel approach to tackle the problem of hash polarization and evaluate the performance of our implementation. Perhaps influenced by the RFC 2992 document, previous works always use the whole value of the hash result to do the multipath routing decisions, and therefore try to mitigate the polarization problem by developing more functions or reusing them. However, we investigate if the polarizion issue can be solved by utilizing different parts of the hash result. In this case, the most critical problem would be how to choose the bits of the hash result for the multipath routing decisions. Unfortunately, during the experiment, we discovered that the optimal performance design is influenced by many factors in the network topology and traffic demand pattern, making it difficult to summarize a universal law. Nevertheless, our research has proposed a mechanism called “bit-awareness”, which can significantly alleviate the problem of selecting overlapping bits, and hence addresses the polarization issue. / Trafikpolarisering i nätverk hänvisar till fenomenet där trafik tenderar att koncentreras längs specifika rutter eller kanter när man gör flervägsdirigering, vilket leder till obalanserade flödesmönster. Denna rumsliga fördelning av trafik kan resultera i överbelastade och överbelastade länkar, medan andra vägar förblir underutnyttjade. Sådan obalanserad trafikdistribution kan leda till nätverksflaskhalsar, minskad genomströmning och försämrad tjänstekvalitet för kritiska applikationer. Dessa frågor betonar det akuta behovet av att ta itu med trafikpolarisering och dess skadliga inverkan på nätverkseffektivitet och motståndskraft. I denna masteruppsats introducerar vi ett nytt tillvägagångssätt för att tackla problemet med hashpolarisering och utvärdera prestandan för vår implementering. Kanske påverkat av RFC 2992-dokumentet, skulle tidigare arbeten använda hela värdet av hashresultatet för att fatta beslut om flervägsdirigering och därför försöka mildra polariseringsproblemet genom att utveckla fler funktioner eller återanvända dem. Vi undrar dock om problemet kan lösas genom att använda olika delar av hashresultatet. I det här fallet skulle det mest avgörande problemet vara hur man väljer bitarna i hashresultatet för besluten om flervägsdirigering. Tyvärr upptäckte vi under experimentet att den optimala prestandadesignen påverkas av många faktorer i nätverkstopologin och trafikefterfrågan, vilket gör det svårt att sammanfatta en universell lag. Ändå har vår forskning föreslagit en mekanism som kallas ”bit-medvetenhet”, som avsevärt kan lindra problemet med att välja överlappande bitar, och därmed adresserar polariseringsfrågan.

With increasing computational capabilities and advances in robotics, technology is at the verge of the next industrial revolution. An growing number of tasks can be performed by artificial intelligence and agile robots. This impacts almost every part of the economy, including agriculture, transportation, industrial manufacturing and even social interactions. In all applications of automated machines, communication is a critical component to enable cooperation between machines and exchange of sensor and control signals. The mobility and scale at which these automated machines are deployed also challenges todays communication systems. These complex cyber-physical systems consisting of up to hundreds of mobile machines require highly reliable connectivity to operate safely and efficiently. Current automation systems use wired communication to guarantee low latency connectivity. But wired connections cannot be used to connect mobile robots and are also problematic to deploy at scale. Therefore, wireless connectivity is a necessity. On the other hand, it is subject to many external influences and cannot reach the same level of reliability as the wired communication systems. This thesis aims to address this problem by proposing methods to combine multiple unreliable wireless connections to a stable channel. The foundation for this work is Caterpillar Random Linear Network Coding (CRLNC), a new variant of network code designed to achieve low latency. CRLNC performs similar to block codes in recovery of lost packets, but with a significantly decreased latency. CRLNC with Feedback (CRLNC-FB) integrates a Selective-Repeat ARQ (SR-ARQ) to optimize the tradeoff between delay and throughput of reliable communication. The proposed protocol allows to slightly increase the overhead to reduce the packet delay at the receiver. With CRLNC, delay can be reduced by more than 50 % with only a 10 % reduction in throughput. Finally, CRLNC is combined with a statistical multipath scheduler to optimize the reliability and service availability in wireless network with multiple unreliable paths. This multipath CRLNC scheme improves the reliability of a fixed-rate packet stream by 10 % in a system model based on real-world measurements of LTE and WiFi. All the proposed protocols have been implemented in the software library NCKernel. With NCKernel, these protocols could be evaluated in simulated and emulated networks, and were also deployed in several real-world testbeds and demonstrators.:Abstract 2 Acknowledgements 6 1 Introduction 7 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Use Cases and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Opportunities of Multipath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 State of the Art of Multipath Communication 19 2.1 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Application Layer and Session Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.6 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 NCKernel: Network Coding Protocol Framework 27 3.1 Theory that matters! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.1 Socket Buffers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.2 En-/Re-/Decoder API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.3 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.4 Timers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.5 Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5 Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4 Low-Latency Network Coding 35 4.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Random Linear Network Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.3 Low Latency Network Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.4 CRLNC: Caterpillar Random Linear Network Coding . . . . . . . . . . . . . . . . . . 38 4.4.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.4.2 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.4.3 Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.5.3 Packet Loss Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.5.4 Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.5.5 Window Size Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5 Delay-Throughput Tradeoff 55 5.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 Network Coding with ARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3 CRLNC-FB: CRLNC with Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.3.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.2 Decoding and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.3 Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.3 Systematic Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.4 Coded Packet Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5.4.5 Comparison with other Protocols . . . . . . . . . . . . . . . . . . . . . . . . 67 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6 Multipath for Reliable Low-Latency Packet Streams 73 6.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.1 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.2 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.3 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3.4 Reliability Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.4 Multipath CRLNC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 6.4.1 Window Size for Heterogeneous Paths . . . . . . . . . . . . . . . . . . . . . 77 6.4.2 Packet Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.1 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.2 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7 Conclusion 94 7.1 Results and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.2 Future Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Acronyms 99 Publications 101 Bibliography 103

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