Task placement optimization in cloud-edge-fog environments is a challenging problem that requires balancing multiple objectives, such as minimizing latency and energy consumption, while adhering to resource constraints. This paper proposes a framework that integrates Deep Reinforcement Learning with Graph Neural Networks to address these challenges. Specifically, we explore the effectiveness of such architectures, including Graph Convolutional Networks and Message Parsing Neural Networks, within a DRL agent for task allocation. The framework is evaluated on synthetic task flow graphs, representing parallel workflows of varying complexities ($\mathbf{1 0}$ and $\mathbf{1 0 0}$ tasks), and benchmarked against traditional methods such as Genetic Algorithms and a Random Agent. Our results demonstrate that the RL Agent with GCN layers outperforms the MPNN-based RL Agent, GA, and Random Agent in small-scale scenarios while it performs equivalently with the MPNN-based RL Agent in largescale scenarios in which both surpass the heuristic approach and the Random agent.
Wind energy plays a pivotal role in the global shift toward sustainable energy systems. However, the maintenance of wind turbines remains a significant challenge due to their distributed nature, harsh environmental exposure, and the high cost of unplanned downtime. In this work, a novel architecture for predictive maintenance of wind turbines based on continuous acoustic monitoring is presented, based upon OASEES—a decentralized, intelligent, and programmable edge framework that spans the full computing continuum. The proposed system leverages low-cost recording equipment to capture turbine-generated sound data, which are processed locally at the edge using Federated Learning, thus preserving data privacy and reducing communication overhead. A pre-trained deep learning model based on wav2vec is fine-tuned to classify turbine operational states, using labeled acoustic datasets. The effectiveness of the architecture, which, to the best of the authors' knowledge, is among the first to utilize the said distributed learning paradigm for acoustic-based wind turbine predictive maintenance, is validated in a proof-of-concept experimental setting using a publicly available relevant dataset, where both centralized and federated training methods are evaluated. The results demonstrate promising classification accuracy, with the federated model achieving over 78 % accuracy, closely matching the centralized baseline.
Centralized solutions offered by cloud providers are increasingly prevalent in the Internet, despite being single points of failure and threatening the privacy of users by giving access of large amounts of data to a few powerful entities. Decentralized peer-to-peer systems have garnered attention in opposition. However, their limitations, such as high latency and message complexity in searching content constitute a barrier to widespread adoption. This is the case of InterPlanetary File System (IPFS), where the algorithm used to search content comprises two steps: an optimistic and a fallback step. The optimistic step relies on the Bitswap protocol and operates on an unstructured network that tries to find content in a single hop. This solution has low latency but also low success rate. Should a search fail with Bitswap the fallback alternative resorts to the use of a Distributed Hash Table (DHT) to complete the search with a significantly increased latency cost. We propose, implement, and evaluate Goose, an alternative to the Bitswap protocol that exploits principles of informed search to introduce a distributed lightweight indexing scheme and an indirection layer in the unstructured network; increasing the success rate of searches when compared with Bitswap, significantly reducing the reliance on the DHT and improving the median access latency in IPFS by 27% and reducing the number of messages exchanged by up to 67%.
We revisit the concept of order fairness in blockchain systems. Order fairness imposes additional constraints on the actual order of the transactions, preventing adversaries from manipulating the order of transactions to gain undue advantages. While numerous notions of fairness and corresponding fair ordering protocols have been proposed, these efforts typically focus on designing a dedicated protocol tailored to each specific fairness notion. Consequently, it remains unclear whether multiple fairness notions can be realized simultaneously within a single framework. In this work, we propose “2-hop receiver fairness”, a new and unified fairness notion that simultaneously captures approximate sender fairness, block order fairness, and consequence transaction fairness. To achieve 2-hop receiver fairness, we propose TxSort, a generic framework that reduces the fair ordering problem to the asynchronous common subset (ACS), an extensively studied primitive for asynchronous consensus and multiparty computation. In this way, we can integrate the state-of-theart ACS protocols to provide a communication-efficient and round-optimal fair ordering protocol. Specifically, our protocol achieves an amortized communication complexity per transaction (CCpT) of $O\left(n^{2}\right)$, while existing state-of-the-art fair ordering protocols achieve $O\left(n^{3}\right)$ CCpT. As a crucial building block of our framework, we introduce a novel and computation-efficient local algorithm called “pivot quick sort”, which might be of independent interest.
Land registry systems maintain public records of land ownership, transactions, and property boundaries, offering legal documentation and information on titles and associated rights. However, there are challenges in the security, accuracy, efficiency, and transparency of the current registries. The paper proposes the Decentralised Land Registry (DLR), a system that leverages a permissioned blockchain, Hyperledger Fabric, enabling a secure and controlled environment for land registration and transaction validation. The use of smart contracts ensures consensus and immutability and allows support for legitimate property transactions overseen by registry authorities. Decentralised identities and ownership proofs are issued using a Self-Sovereign Identity approach, leveraging Hyperledger Indy as the ledger and Hyperledger Aries as the agent representing government entities and issuing these credentials. This solution would mark a pivotal shift in land registry systems, revolutionising traditional practices by addressing their limitations and fostering a transparent, reliable, and efficient landscape for managing property rights and transactions.
Numerous database replication schemes are built on the crash failure assumption where majority of failures are self-evident as defined in [1]. The study in [1] convincingly refuted this common assumption showing that many of the faults in relational Database Management Systems (DBMSs) cause systematic non-crash failures. Similar results were obtained in the subsequent study [2]. Consequently, the existing database replication solutions, which typically use the same DBMS, are ineffective fault-tolerant mechanisms. Conversely, using diverse DBMSs is a suitable way of protecting against non-crash failures. We have built a middleware-based database replication protocol, DivRep, and deployed it with diverse database servers (DivSQL), for improved fault tolerance. DivSQL provides strict Snapshot Isolation (SI) guarantees, and assumes “incorrect results” failure model (IRFM) – the most realistic one based on the extensive experimental analyses of DBMS faults ([1], [2]). The dependability gain comes with the inherent performance overhead. We provide a comprehensive performance evaluation of DivSQL using 3 diverse DBMSs (two are leaders in the field).
This paper addresses state transfer in blockchain systems, crucial for new or recovering peers. Current systems use periodic snapshots and cryptographic structures like Merkle trees for efficient validation in Byzantine fault tolerant scenarios. Recent improvements use chunk-based data structures (e.g., AVL* trees, Merkle B+trees) to reduce overhead and enable efficient chunk-level validation. This paper introduces B+AVL trees, a novel chunk-based structure that significantly advances the state transfer process. B+AVL trees combine the balancing mechanism of AVL trees with Merkle hashing for validation, enabling efficient and secure state transfer. They are more spaceefficient and simpler to manage than AVL* trees, and they use more compact validation proofs than Merkle B+trees. B+AVL trees represent a major improvement by balancing binary trees and managing chunks in a way that optimizes performance and data validation, ensuring robust state synchronization in blockchain systems. The paper thoroughly assesses B+AVL trees and compares them to competing approaches.
Modern storage systems requirements demand flexible, scalable solutions that address diverse concerns such as data reduction, replication, security, and multi-cloud distribution. Existing solutions often provide these guarantees through monolithic implementations, limiting their adaptability to specific application needs. This paper introduces PolyLayer, a multi-interface, composable and multi-backend storage architecture. It builds on the concept of stackable storage architectures and redesigns these to support commonly used user APIs (e.g., POSIX, Key-value, Object store), while providing support for data persistence across multiple storage backends (i.e., on-premises, cloud services, blockchain). We present the first steps towards the design of such architecture, while implementing a proof-of-concept and evaluating it. Our preliminary results show that the design can effectively be used in real-world scenarios where new functionality is added to a storage system with low overhead over the base system. For instance, we show how anti-tampering mechanisms can be added to a traditional relational database without any change to the database itself or the application using it.
This paper presents a novel processing architecture for unmanned aerial vehicles (UAVs), designed to support small search-and-rescue teams operating in post-disaster environments. The proposed system consists of two main components. The primary component is dedicated to vehicle and person detection and tracking. It utilizes a Raspberry Pi 5 paired with a Coral TPU Accelerator to process input from the UAV's main camera, leveraging a YOLOv11n neural network and the KCF tracking algorithm. This capability is essential for following the rescue team's vehicle and locating survivors. When a network connection is available, this subsystem transmits captured images to a remote server for further analysis. The secondary component is a peripheral vision subsystem, powered by a Raspberry Pi Zero 2 W. It processes input from four peripheral cameras connected to a Luxonis OAK-FFC-4P board. It is tasked with identifying critical conditions and communicating the findings to the main processing unit, which in turn alerts the operator. Once appropriate instructions are received, the UAV is redirected to the specified location to perform a focused search for survivors.
According to the EU State of Cybersecurity report by the European Union Agency for Cybersecurity (ENISA), the number of cybersecurity-related incidents will increase by 24 percent by 2025, with ransomware and DoS/DDoS attacks being the most common. The emergence of new threats [1] and the consolidation of existing ones require doubling of efforts in proactive prevention and a decisive increase in research dedicated to cybersecurity. CyberNEMO (End-to-end Cybersecurity to NEMO meta-OS) project emerges as an evolution of the NEMO (Next Generation Meta Operating System) platform, designed to provide a secure, trustworthy, and robust execution environment across the IoT-Edge-Cloud computing continuum. Leveraging NEMO modular meta-operating system (mOS) framework, CyberNEMO introduces advanced cybersecurity and privacy-preserving mechanisms, emphasizing Zero Trust principles. This paper presents the CyberNEMO architecture, details its core innovative technologies, and describes its validation strategy through diverse living labs-including Smart Energy, Smart Water, Smart Manufacturing, Healthcare, Multimedia Distribution, and Smart Farming scenarios-demonstrating end-to-end cybersecurity and real-time threat mitigation capabilities, aligned with Europe's strategic cybersecurity goals.
Payment channel networks (PCNs) offer a crucial solution to the scalability challenges of blockchain-based transaction systems. However, most existing PCN routing protocols employ a “guess-and-check” approach, which undermines their transaction success rate and efficiency. In this paper, we propose a routing protocol named RCS, based on a novel “Refined Confirm-and-Send” approach. Utilizing PCN topology statistics, RCS performs a refined probing of possible transaction paths and verifies whether a path has sufficient available balance before executing the transaction through it. This method effectively improves the transaction success rate while maintaining restrained overhead. Additionally, to address users' privacy concerns, we design a privacy-preserving version of RCS, named RCS+. RCS+ uses secure comparisons to identify paths with sufficient funds without disclosing channel balances or transaction amounts. Extensive simulations with real-world and synthetic datasets demonstrate that RCS and RCS+ outperform existing state-of-the-art protocols. RCS and RCS+ achieve a $\mathbf{1 0 \%}$ higher transaction success rate compared to the Shortest Path approach, which serves as the core of Lightning Network's current routing mechanism. In terms of overhead, RCS maintains the lowest cost among all tested protocols, e.g., only 20 % of the Flash protocol. While RCS+ incurs marginally higher overhead due to its enhanced privacy guarantees, its cost remains just 30 % of Flash's overhead. Furthermore, RCS/RCS+ exhibits robust adaptability to dynamic changes in PCN topologies, ensuring scalability as the network evolves.
This paper presents the design and implementation of an autonomous UAV-based search and rescue system developed within the Horizon Europe project P2CODE. The proposed system leverages a modular and scalable architecture integrating edge-based real-time video processing, AI-based human detection, asynchronous message communication, and persistent state logging, all orchestrated through a web-based operator interface. Central to the system is a swarm intelligence algorithm that partitions the search area among multiple UAVs, taking into account factors such as battery levels and initial positions to generate balanced and coherent flight paths. By combining a Divide Areas based on Robots' initial Positions (DARP) method with a Spanning Tree Coverage (STC) algorithm, the system ensures efficient and complete coverage of large outdoor regions. The operational workflow supports both fully autonomous exploration and reactive human-in-the-loop intervention in response to real-time detections. This work contributes a practical blueprint for large-scale, multi-agent coordination in dynamic and unstructured environments, advancing the state of the art in autonomous search and rescue missions.
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in inspection and monitoring missions, yet onboard computation and communication impose significant energy burdens that limit flight time and operational scope. In this work, we introduce a novel, blockchain-enabled framework-grounded in the Distributed Autonomous Organization (DAO) paradigm-for orchestrating distributed analytics across a swarm of UAVs. Leveraging the OASEES project's smart-contract architecture, each drone embeds a Metrics Module for real-time power monitoring, a Behavioral Module for adaptive control, and a Blockchain Agent that autonomously proposes, votes on, and executes collective decisions. Three concurrent threads-Proposal Trigger, Voting, and Action Execution-enable fully decentralized governance of swarm behavior: from detecting critical energy thresholds and formulating swarm-wide conservation maneuvers, to executing approved strategies across all members. We validate our framework in a UAV-based infrastructure inspection scenario, employing a YOLOv5 object-detection pipeline to classify four corrosion classes on a telecommunications mast under three video-capture modalities (short-distance, long-distance, and horizontally concatenated streams). Across all configurations, our system achieves near-perfect precision, recall, and mean Average Precision (mAP50-95 $\approx 0.995$), demonstrating both the efficacy of distributed workload inference and the feasibility of treating a single drone as a multi-feed processor. These results underscore the potential of DAO-driven UAV swarms for energy-aware, resilient aerial analytics, and pave the way for fully decentralized 5G/6G-enabled airborne networks.
Ensuring fairness in blockchain-based data trading presents significant challenges, as the transparency of blockchain can expose sensitive details and compromise fairness. Fairness ensures that the seller receives payment only if they provide the correct data, and the buyer gains access to the data only after making the payment. Existing approaches face limitations in efficiency, particularly when applied to large-scale data. Moreover, preserving privacy has also been a significant challenge in blockchain. In this paper, we introduce zkMarket, a privacy-preserving fair trade system on the blockchain. We ensure fairness by integrating encryption with zk-SNARKs, enabling verifiable proofs for fair trading. However, applying zk-SNARKs directly can be computationally expensive for the prover. To address this, we improve efficiency by leveraging our novel matrix-formed PRG (MatPRG) and commit-and-prove SNARK (CP-SNARK), making the data registration process more concise and significantly reducing the seller's proving time. To ensure transaction privacy, zkMarket is built upon an anonymous transfer protocol. Experimental results demonstrate that zkMarket significantly reduces the computational overhead associated with traditional blockchain solutions while maintaining robust security and privacy. Specifically, our evaluation quantifies this high efficiency: the seller can register 1 MB of data in 2.8 seconds, the buyer can generate the trade transaction in 0.2 seconds, and the seller can finalize the trade within 0.4 seconds.
HotStuff stands out as the first Byzantine Fault Tolerant (BFT) state machine replication protocol to achieve linear communication complexity while maintaining optimistic responsiveness and fairness. However, it is required at least three phases to commit a block, which leads to high latency. Recent studies have focused on developing two-phase variants of HotStuff to reduce its block commitment latency. Nonetheless, two-phase HotStuff suffers from the liveness issue, forcing these protocols to sacrifice one of the three fundamental properties of HotStuff when addressing this problem, thereby facing a trade-off among these properties. In this paper, we introduce a novel protocol, Harmonia, which, to the best of our knowledge, is the first BFT protocol to simultaneously achieve linear communication complexity, optimistic responsiveness, fairness, and the minimum commit latency of two phases. Harmonia overcomes the liveness issue of two-phase HotStuff through its specialized block unlocking mechanism while preserving all the original properties of HotStuff. Experimental results show that Harmonia consistently outperforms HotStuff across various metrics.
Account shuffling is a crucial method to solve the problems of high cross-shard transaction (TX) ratio and load imbalance in sharded blockchain. However, most existing methods primarily focus on account partitioning, with insufficient attention to account migration, resulting in limited improvements in throughput and latency. Therefore, we propose an efficient account shuffling mechanism based on lightweight migration in sharded blockchain (ASLM-Shard). Specifically, we first propose a migration-aware label propagation algorithm (MA-LPA) to improve the effect of account partitioning by balancing the relationship among account migration overhead, cross-shard TX ratio and load imbalance. Then, we adopt a sparse Merkle tree (SMT) to store account states to support flexible state verification, and propose a transaction-aware lightweight account migration (TLAM) method that leverages a “Lock-Mint” strategy to minimize migration costs while ensuring security. Extensive experimental results show that, compared with the SOTA baseline, ASLM-Shard improves system throughput by up to 27.8% and reduces TX latency by up to 81.1% when the account partitioning strategy is fixed, it also achieves up to 17.9% higher throughput and 23.4% lower latency when the migration method is fixed.
Monitoring Key Performance Indicator (KPI) trends in a timely manner enables early detection of performance degradation, which is critical to maintaining cloud service reliability. However, incipient anomalies that subtly precede KPI degradation are notoriously difficult to detect due to interference from noise and the high-dimensional, correlated nature of monitoring data. In modern production environments, KPIs are recorded as massive multivariate time series (MTS), where intricate temporal and inter-metric dependencies further obscure early signals of system instability. Unfortunately, existing fault detection methods either rely on oversimplified statistical assumptions that ignore these dependencies or depend on largescale supervised training, which is rarely feasible in dynamic, label-scarce settings. We introduce Heimdallr, an unsupervised detection framework tailored to uncover early-stage anomalies that causally affect KPIs. The core design of Heimdallr is a KPI-oriented monitoring and attribution mechanism that models and partitions the latent space based on KPI behavior. This design enables not only the early detection of anomalies but also causal attribution to specific KPI shifts, thereby enhancing system observability and resilience. Heimdallr is built upon two key innovations: Dynamic-inner Related Component Analysis (DiRCA), a latent structure modeling technique that captures dynamic temporal dependencies across metrics, enabling interpretable representations of underlying system behavior; KPI Feature Ensemble Monitoring Network (KFEMNet), a three-layer hierarchical architecture that, following DiRCA's decomposition, extracts fine-grained deviations and detects incipient anomalies with high sensitivity. Extensive experiments on both synthetic and real-world datasets convincingly demonstrate that Heimdallr consistently outperforms existing state-of-the-art methods, achieving higher early detection accuracy and lower false alarm rates, while maintaining low overhead and high interpretability suitable for production deployment.
To adapt to the growth of data access resiliency, distributed cloud storage systems are increasingly deploying erasure coding methods with redundancy conversion which adjust their parameters to encode and repair constantly expanding data sets. However, while new parity blocks are generated for updated data groups, any parity blocks created from previous data group updates are not exploited further, or even entirely discarded. This paper proposes EDIC (Elastic Diagonally Interleaved Coding), a middleware that optimizes fault tolerance for converted data groups. EDIC deploys diagonally interleaved coding, an advanced erasure coding method, enhanced by leveraging multiple versions of parity blocks, inspired by the principles of Elastic Reed-Solomon codes. EDIC exploits the parity blocks of a previous version of a converted data group to repair the original, non-converted blocks within the group, while the new parity blocks will cover the recently added blocks, incurring only a minimal storage overhead.
Resilient key management is particularly important for systems whose functionality depends on the high availability of cryptographically controlled data. A natural approach to achieving resilience implies the use of more distributed nodes as key managers, so that, in case something happens with one, some remain available. However, such distributed systems may experience network partitions, i.e. network splits that separate nodes in different disconnected islands. In this paper, we propose a decentralized key management solution that continues to operate in the event of network partitions and also conflict-free reintegrates back into a single system after partitions merge. Our solution encodes keys as conflict-free replicated data types and distributes the knowledge about them through a combination of push- and pull-based epidemics, and gossip protocol. We introduce a proof-of-concept implementation and evaluate it under different scenarios of network partitions and recoveries, concluding that the system correctly converges in limited time after partition events, that the overhead of spreading information about keys is bounded and that the latency of key-related client requests is acceptably low.
Microservices architecture, known for its advantages in scalability and availability, presents challenges in pinpointing failure causes due to its inherent complexity. Traditional root cause analysis (RCA) methods often lack comprehensive insights into anomaly detection and exhibit shortcomings in finegrained anomaly localization. To address these gaps, we propose RootScan, an innovative framework tailored for fine-grained and interpretable root cause analysis in microservices. Our approach features an event-based observability model that unifies multimodal data (traces, metrics, and logs) into a structured event flow, providing a holistic view of system behavior. Then, we leverage the interpretability of generative networks to detect anomalies from the event flow level to feature level, yielding valuable clues for subsequent root cause analysis. Further, we explore anomaly propagation patterns to precisely trace root causes at fine-grained levels (container, microservice and component). We validate our proposal using an open-source dataset and benchmark microservice environment. Experimental results demonstrate the superiority of our approach, markedly enhancing RCA accuracy and reliability in microservices environments.
