Existing evaluations of geo-distributed databases still rely almost exclusively on standard limited workloads such as TPC-C and YCSB+T, which reveal little information about the true cost of wide-area coordination. In this paper, we present a configurable benchmarking framework built around the Product-Parts-Supplier (PPS) workload, and use it to evaluate four representative systems that support geo-distributed transactions: Calvin, SLOG, Detock, and Janus. The experiments run across six realistic and insightful scenarios that vary the transactional load, contention, client count, regional bias, network latency, and packet loss.

The results uncover clear design trade-offs between the systems and demonstrate that our new framework is capable of filling several evaluation holes left by the standard workloads. Our framework introduces important features such as dependent transactions that may abort and retry, longer and tunable read/write sets, and fine-grained control over which regions will participate in the commit. However, the PPS workload comes with some limitations, and thus, the framework does not cover every case. As a consequence, for now, it should complement TPC-C and YCSB+T, not replace them.

TrustChain is a scalable, lightweight blockchain architecture that avoids global consensus by maintaining a personal chain of co-signed interactions for each peer, forming a directed acyclic graph. While its structure makes it promising for mobile and resource constrained environments, its behavior under real world conditions, such as the time it takes to restore connectivity, remains underexplored. This paper presents an implementation of TrustChain in Rust for Android mobile devices and evaluates its robustness under network disconnections across two protocols: UDP and Iroh over QUIC. Robustness is defined as the time elapsed between network connectivity restoration and the successful exchange of the first valid TrustChain message between peers. Experiments involved controlled Wi-Fi interruptions, measuring the reconnection time for each protocol. The findings reveal trade-offs between protocol simplicity and recovery performance. UDP demonstrated consistent low-latency reconnection times between 4 and 6 seconds, averaging 5 seconds, due to its stateless nature and lack of connection recovery overhead. In contrast, Iroh reconnection times ranged from 4.5 to 11.5 seconds, with most values between 6 and 8 seconds, due to QUIC's timeout strategy, DNS-based peer discovery, and relay reconnection overhead. The results provide insights for deploying decentralized systems in mobile contexts and highlight open challenges in peer reconnection for lightweight blockchain protocols. This study also proposes improvements and directions for future work, including multi-peer evaluation, measuring different Wi-Fi congestion levels, or experimentation with other network protocol stacks.

Smartphones offer limited storage and memory, con- straints that conventional blockchains struggle to meet, yet they are also the devices where user- owned transaction chains promise the most value. We present the first empirical study of TrustChain, a DAG-based per-peer ledger running entirely on mobile hardware. A Rust implementation and open- source benchmark evaluate how flush-interval batch- ing (k) and lossless compression affect a single node scaling from 103 to 106 blocks.
On a Galaxy S8 and Pixel-6 emulator, RAM stays below 600 MB and compressed disk use below 0.5 GB at one million blocks (128 B payload). Insert la- tency remains interactive (< 8 ms) with disk:100 flushing; moderate batching (k ≈ 500) cuts CPU load by roughly 45 % without harming durabil- ity. Lightweight compression (LZ4-1, Zstd-1) trims space by 20 to 30 % at a sub-10 ms cost, with diminishing returns at higher levels. End-to-end tests show storage is never the bottleneck, raw UDP achieving a 7 ms median RTT.
Taken together, the implementation and measure- ment dataset provide a concrete reference for deploy- ing DAG-based chains on smartphones and highlight opportunities for advancing mobile blockchain tech- nology.

Blockchain is already a widely adopted solution which can achieve decentralized storage in trustless settings. However, it is infamous for its high energy demands, making it difficult to operate on mobile phones with limited battery life. Among many design decisions in a blockchain implementation, is the choice of the communication protocol used for messaging between the participants. This work systematically evaluates the energy efficiency of custom blockchain implementations: over UDP and over QUIC with Iroh Rust crate for Android devices. The results demonstrate a higher energy demand by the QUIC-based implementation, with up to 34\% faster battery discharge on average compared to the UDP approach. Investigations show different energy usage per CPU and Wi-Fi components, and network traffic profile during the application's operation. These findings highlight the trade-offs related to energy efficiency in designing a blockchain-based system.

Blockchain technologies offer decentralized, secure, and transparent transaction sys tems but face significant scalability challenges, especially in mobile and peer-to-peer environments. Directed Acyclic Graph (DAG)-based protocols, such as Trustchain, present a promising alternative by allowing each agent to maintain a personal chain of relevant transactions, thereby improving throughput and reducing resource consump tion. Despite its theoretical potential, Trustchain has not yet been effectively imple mented for smartphones. To evaluate performance, we provide an implementation over two communication protocols, focusing on throughput. We found that our QUIC-based implementation displays an over three times throughput improvement when keeping a QUIC connection open, and can confidently store at least 28 536-byte blocks per sec ond. However, our UDP-based version outperforms our implementation, confidently storing at least 500 668-byte blocks per second.

As modern applications become more global and resource intensive, geo-distributed databases have become critical for fast, reliable data storage. Evaluating the performance of these databases through traditional benchmarks such as TPC-C and YCSB-T is not sufficient to expose all characteristics of the database's performance. A deeper analysis of available benchmarks is needed to determine how to effectively asses geo-distributed databases.

In this paper, we present a modified implementation of the DeathStar hotel benchmark fit for relational databases and use it to evaluate several geo-distributed databases, Detock, SLOG, Janus, and Calvin. We then analyze in detail the unique characteristics of the benchmark and the performance of these databases in the context of the benchmark.

Distributed systems are vital for handling large-scale data and rely on geo-distributed databases to ensure low latency and high availability. Traditional benchmarks, such as TPC-C and YCSB-T, are not designed to handle the complexities of geo-distributed environments and do not allow for configuration of multi-home transaction ratios or dynamic data access patterns. To fill this gap, we implement a benchmark based on the MovR workload and assess its performance on the Detock, Janus, SLOG, and Calvin geo-distributed database systems. Key insights revealed through experiments are that network conditions act as a major bottleneck and high concurrency leads to unsustainable latency spikes which severely limits scalability.

Geo-distributed databases offer the scalability and low latency that contemporary applications demand, but are challenging to implement. It is therefore crucial that they are tested well. Established benchmarks, such as TPC-C and YCSB-T, are limited and do not cover the entire set of workloads that geo-distributed databases are subjected to. In this paper, we discuss the adaptation of DeathStar Movie an existing benchmark for microservice systems for benchmarking these geo-distributed databases. We set up an experiment in which we ran this modified version of the benchmark on four database systems: Detock, SLOG, Calvin, and Janus. The results showed that DeathStar Movie is capable of pushing these systems to their limits. It also showed the different performance characteristics of the systems, stemming from the different ways in which they attempt to overcome the challenges posed by a geo-distributed setting.

In recent years, applications have started using geo-distributed databases, even though their behavior under different workloads remains complex. Therefore, this project analyses how several databases handle transactional workloads using the SmallBank benchmark. We implement and adapt an already existent benchmark, previously used for non-distribuited databases. Furthermore, we use it to evaluate multiple geo-distributed databases highlighting their strengths and weaknesses throughout multiple scenarios designed to stress different parts of the system. We observe that in most scenarios Detock performs slightly better than SLOG, and both outperform Calvin. This aligns with the fact that Detock builds upon SLOG, which itself improves on Calvin. On the other hand, Janus's performance is significantly behind the others due to its communication overhead. However, while the SmallBank benchmark provides an insightful comparison, providing specific advantages compared to previous benchmarks, its adaptation to geo-distributed databases limits its ability to compare scenarios with higher percentages of multi-home and multi-partition transactions.

We present a decentralised alternative to the winner-takes-everything dynamics of social media platforms.

For 25 years there have been continuous attempts to decentralise file sharing, music streaming, video conferencing, and social media. 

None of these hundreds of projects to re-decentralise the Internet have reached the uptake level of YouTube and TikTok. They are rarely easy to use.

We present DeToks, a fully decentralised alternative to Youtube and Tiktok. DeToks is not dependant on any central server or cloud. DeToks is specifically designed to be as decentralised and attack-resilient as Bitcoin and Bittorrent.

Our core contribution is effortless 5G Network Address Translators (NATs) puncturing.

Direct phone-to-phone communication is not available on today's smartphones. DeToks solves this problem.

NATs and carrier-grade NATs block direct communication between smartphones. 

We procured 30+ SIMs card on European 4G/5G mobile networks and measured the carrier-grade NATs behavior. We determined the NAT types (full cone,restricted,symmetric) and their time-out settings. By leveraging provider-aware (Vodafone,Orange,Telia, etc.) NAT puncturing strategies we create direct UDP-based phone-to-phone connectivity. 

We utilise parallelism by opening at least 500 Internet datagram sockets on two devices. By relying on provider-aware IPv4 range allocations, provider-aware port prediction heuristics, high bandwidth probing, and the birthday paradox we can successfully bypass even symmetric NATs. Our communication method achieves peer-to-peer 5G connectivity at the cost of merely some initial delay and bandwidth, without any assistance from third party servers or clouds.

Detoks validates our 5G puncturing work. We demonstrate the feasibility of fully decentralized social media platforms on consumer mobile devices.

The demand for embedded devices, such as intelligent sensors, smartwatches, medical implants, and computing chips in cars, has been rising steadily in the past and is expected to continue for the coming decade. Their high and increasing degree of connectivity expands the attack surface for attackers, which is exaggerated by their physical accessibility. This makes them particularly vulnerable to invasive attacks. Compounding these risks is the growing reliance on third-party software. An example of such risks can be seen in the recent incident where a software solution from the company CrowdStrike, integrated into the Windows Operating System (OS), caused a major global outage that left many airports, hospitals, banks, and government departments worldwide unable to operate. The Guardian reports that this incident affected 8.5 million Windows machines and resulted in an estimated financial loss of 5.4 billion USD for companies in the US alone. Similar risks exist for OSs for embedded devices. These devices do not run general-purpose OSs like Windows, however, in their very nature they are similar. They provide a runtime abstraction for user processes and manage resources for which the OS requires higher privileges. FreeRTOS is a popular embedded OS for resource-constrained devices, also offering real-time capabilities, making it a Real-Time Operating System (RTOS). Meanwhile, RISC-V, a relatively new Instruction Set Architecture (ISA), is gaining popularity due to its open-source philosophy. However, despite the increasing popularity of RISC-V and the widespread use of FreeRTOS, there is a lack of comprehensive security support for FreeRTOS on RISC-V platforms.

This thesis addresses the lack of security for malicious third-party applications and presents a novel FreeRTOS implementation method on a RISC-V embedded system platform. The security of FreeRTOS is enhanced by leveraging the Physical Memory Protection (PMP) feature of RISC-V. The primary contribution involves the FreeRTOS kernel dynamically manage the access rights using PMP, preventing malicious tasks from causing harm. This dramatically improves the security, because the flat memory structure inherent to FreeRTOS is prone to being readily exploited and compromised by attackers. Further, an overview of known attacks on embedded systems and countermeasures is presented, a subset of which are explored in more detail. A secure platform is proposed that aims to demonstrate a holistic approach to integrate the FreeRTOS with PMP support into a System-on-Chip (SoC). Then, the FreeRTOS platform is augmented by a secure boot procedure and a memory encryption unit that ensures the integrity and confidentiality of the external memory. This encryption unit uses the Prince encryption scheme and is elegantly combined with RISC-V’s PMP functionality. Unused bits in the PMP configuration registers are used to control the encryption selectively for single PMP memory regions. Moreover, an address-based encryption mode for this unit is proposed that addresses the weakness that plaintext data is always encrypted to the same ciphertext. This is problematic because an attacker could analyze the structure of the ciphertext and copy the ciphertext of memory cells with known plaintext. The effectiveness of the PMP task isolation is demonstrated by simulating malicious tasks that try to access specific data addresses in the memory of other tasks and the kernel. This setup imitates real-life attack scenarios, assuming the addresses have been exposed to the malicious task through reverse-engineering of the application code. The secure boot is validated by implementing the design on an FPGA and manually tampering the memory, resulting in a failed boot-up. Additionally, the impact of the PMP integration on the real-time capabilities of FreeRTOS is investigated more thoroughly by evaluating real-time metrics: task switch time, access time for shared resources using a mutex (a software construct to avoid access collisions), and dynamic memory allocation. The system design incorporates a RISC-V core named CV32E40S, an open-source core from OpenHW Group. All the security enhancements were run on hardware with a varying number of active PMP regions. In the worst-case scenario, using 64 PMP regions results in a 2.1x increase in task switch time, a 2.4x increase in the time to access shared resources using a mutex, and a 1.7x increase in memory allocation time. The area of the CV32E40S, measured in Look-Up-Tables (LUTs), increases with the number of regions: a 2.2x increase for 16 regions, 3.4x for 32 regions, and 5.4x for 64 regions, compared to the core configuration without PMP. Despite these increases, it remains within an acceptable range relative to the total area of the SoC including cache memory and peripherals.

This thesis presents the development and evaluation of a real-time neural network-based audio classification system designed on an NXP HW board to distinguish emergency response vehicles by their sirens from other vehicles. At the core of the system is a deep learning model that processes audio inputs captured via a microphone, classifying them based on the presence of siren sounds. The system achieves this by extracting audio features and running inference through the designed neural network, followed by post-processing to detect sirens accurately. Audio signals are transformed into mel-spectrograms, which represent the frequency spectrum over time using a specific window size for analysis. The neural network leverages these mel-spectrogram features to perform audio classification.
The deployment of this system involves several key steps. First, the model is trained on diverse audio data, including siren and non-siren sounds. Audio signals are transformed into mel-spectrograms, which capture the frequency spectrum over time. The neural network processes these features to classify the audio based on the presence of siren sounds. The classified results undergo post-processing to enhance detection accuracy. The system is tested in real-world scenarios, demonstrating a turnaround time of less than 3 seconds even under high noise conditions. Various trade-offs are evaluated to improve efficiency, reduce memory size, and minimize latency, ensuring the system meets requirements for model size, latency, and compute cycles. The custom dataset comprises 280 hours of audio, including well-known, publicly available datasets such as ESC-50, Audioset, and UrbanSound. This dataset is enriched with both original and augmented siren sounds and non-siren audio to enhance the model’s learning efficacy and robustness. The system achieves a 96.19% test accuracy in identifying sirens and is suitable for deployment in real-world scenarios, even for SNRs as high as -30 dB. Although the system meets most requirements for model size, latency, and compute cycles, the false positive rate needs improvement. This can be achieved by expanding the dataset and retraining the model.

Deep learning models are now widely deployed on edge IoT devices. However, most of these models are trained under supervised conditions and can only recognize seen classes learned from the training stage. Zero-shot learning (ZSL) is a popular method for identifying unseen classes by leveraging the semantic information from both seen and unseen classes. Foundation models (FMs) trained on web-scale data have shown impressive ZSL capability in natural language processing and visual understanding. However, leveraging FMs' generalized knowledge for zero-shot Internet of Things (IoT) sensing using signals such as mmWave, IMU, and Wi-Fi has not been fully investigated. In this work, we align the IoT data embeddings with the semantic embeddings generated by an FM's text encoder for zero-shot IoT sensing. To utilize the physics principles governing the generation of IoT sensor signals to derive more effective prompts for semantic embedding extraction, we propose to use a multi-source information fusion strategy, cross-attention, to combine a hard prompt generated by Large Language Models (LLMs) and a soft prompt consisting of learnable vectors. To address the problem of IoT embeddings biasing to seen classes due to the lack of unseen class data during training, we propose using data augmentation to synthesize unseen class IoT data for fine-tuning the IoT feature extractor and embedding projector. We evaluate our approach on multiple IoT sensing tasks. Experiment results show that our approach achieves an average improvement of 1.0% in open-set detection and 9.5% in generalized zero-shot learning compared with multiple baselines on three datasets.

This study explores the use of Code Churn and Pattern Size (PSIZ) metrics to identify bug-prone areas in Haskell codebases. The primary research questions addressed are whether these metrics can effectively predict areas of code instability and potential bugs. Our contributions include a comprehensive analysis of these metrics across three large Haskell projects, examining the correlation between high metric values and documented bugs. Our findings reveal that Code Churn is a significant indicator of potential bugs, with ’buggy’ files showing markedly higher mean code churn values. However, the PSIZ metric proved ineffective in predicting bug-prone areas, as the mean and median values for both projects and ’buggy’ files were similar and low. These results suggest that while Code Churn is a useful metric for identifying unstable code areas, PSIZ does not offer the same predictive value. Future research should expand the dataset and consider additional metrics to enhance the reliability of these findings.

The classification of bugs in functional languages is an understudied area, as opposed to imperative counterparts, such as Java. This paper acts as an initial step to cover this gap into two complementary directions. First, a dataset of 142 bugs from 10 Haskell FOSS repositories have been classified according to two taxonomies from literature in order to assess how well they handle the differences in programming paradigms. Based on our bug classifications, the first taxonomy has very little variance in types of bugs, with 86% of bugs being classified in the same category. On the other hand, the second taxonomy showed more potential as the bugs were more balanced between categories, but with occasional difficulties in classification, as some bugs fitted more than one type. Second, we performed interviews with four Haskell developers about their experience with bugs and the usefulness of these taxonomies in practice. They argued that while such taxonomies can prove useful, the context in which it is being used is more important. Thus, circumstances such as the size of the project and team, and stages of development need to be taken in consideration before trying to apply any taxonomy.

The Alexandria3K library, a versatile Python-based tool, has been expanded to include the integra- tion of the PubMed dataset, enriching its capabil- ities in the analysis of scientific papers. Origi- nally supporting major datasets like Crossref and US patents, and smaller yet significant datasets like ORCID. The addition of PubMed enables in-depth analysis of medical papers, with medical specific data and articles not yet in the Crossref dataset. This research focused on validating the integration of PubMed into Alexandria3K. To achieve this, two literature surveys were replicated using the com- plete PubMed dataset. The first survey involved querying different pathogens in the dataset for three regions. The results were comparable, although some articles were missed by Alexandria3K but two articles were also missed by the original sur- vey. The second survey revolved around software tools used in medical papers. Although fewer ar- ticles were found with Alexandria3K the ratio for most tools was still comparable. Although a thor- ough manual review of all articles could have fur- ther refined the reevaluation, time constraints pre- vented this step. These replicated surveys demon- strate Alexandria3K’s potential in conducting lit- erature surveys, underscoring the need for manual validation to complement its capabilities.