The growing reliance on big data frameworks such as Hadoop has revolutionised data processing across various domains, enabling large-scale storage and distributed computation. Hadoop is widely employed in real-world applications such as high-performance computation tasks, e-commerce and data analysis in healthcare. However, the efficiency of Hadoop systems is often hampered by faults and anomalies, with stragglers emerging as one of the most prevalent issues. Stragglers disrupt workflows, waste resources and degrade system performance. While existing anomaly detection models employ methods like median analysis or static thresholds, they often struggle with issues such as high false positives, lack of adaptability and poor handling of complex heterogeneous environments. To address these challenges, this paper presents Plabs , a flexible stragglers detection framework for Hadoop. The framework comprises two core components: (1) a Monitoring Module providing real-time tracking of cluster resources and task progress and (2) a Pluggable AI-based straggler detection module, designed for precise straggler task identification. By leveraging advanced monitoring and AI-driven analysis, Plabs offers an automated, flexible and scalable solution for detecting stragglers at run-time in Hadoop clusters. We evaluated Plabs exhaustively with three Machine Learning (ML), two Deep Learning (DL) and two Large Language Models (LLMs) on five different applications in a real testbed environment. Our experiment evaluation shows that DL models outperform others in identifying Hadoop stragglers, achieving superior accuracy and reliability for all the applications.

The growing scale and complexity of distributed systems such as Hadoop produce massive volumes of complex log data, making automated anomaly detection and root cause analysis both essential and increasingly challenging. Traditional rule-based approaches, relying on static thresholds or manual heuristics, struggle to scale due to limited adaptability and high maintenance costs. Transformer-based language models have emerged as powerful tools for modelling log sequences by capturing contextual patterns. To enable efficient adaptation with fewer parameters, techniques such as In-Context Learning (ICL) and Low-Rank Adaptation (LoRA) have been proposed. However, applying these methods to log analysis with Small Language Models (SLMs) introduces several challenges, including high memory and computational overhead (in the case of ICL), limited fine-tuning capacity (with LoRA on smaller models), and poor generalisation across heterogeneous environments. To address these limitations, we propose Lineadapter, a parameter-efficient fine-tuning framework tailored for SLMs in log anomaly detection and root cause analysis. Lineadapter extends convolutional adapter principles to sequential data by integrating lightweight 1D convolutional layers within transformer blocks. This design enables SLMs to adapt effectively to log-specific patterns with minimal computational cost, while preserving the backbone model's representational power. We evaluate LINEADAPTER on multiple real-world system log datasets, comparing its performance with ICL, LoRA, and rule-based baselines. Results show that Lineadapter achieves higher F1-scores and better precision-recall trade-offs, particularly on medium-scale models, establishing it as a scalable, robust, and practical solution for log-based anomaly detection.

The surge in blockchain transaction volume has corresponds with a rise in phishing incidents. Despite the widespread use of graph representation learning in fraud detection, these techniques often fail to fully exploit the directed multi-graph structure and temporal dynamics of blockchain transaction graphs. This oversight, coupled with the neglect of network tail nodes, impairs the accuracy of phishing detection and distorts graph integrity. This study introduces TDM-GAT, a graph neural network that integrates directionality, edge attributes, and temporal information through transaction flow serialization and functional time encoding. This approach significantly enhances the precision of phishing account identification in blockchain networks. Additionally, a PageRank-based biased sampling strategy is implemented to address the long-tailed distribution of graph nodes, ensuring balanced node participation during learning. Evaluations on three distinct datasets show that TDM-GAT outperforms with an AUC exceeding 95% and an accuracy close to 90%, demonstrating a clear advantage in phishing account detection.

Effectively and securely retrieving data from various blockchain networks remains a critical challenge. We propose a novel framework that provides enhanced capabilities for authenticated data retrieval, empowering users and regulatory bodies. Our framework provides a scalable solution for information verification across diverse blockchain systems, enabling a variety of different types of actors to be integrated. Our framework allows metadata from various systems to be combined, also providing support for secure querying.

An emerging evacuation path planning technique that uses Low Power Wide Area Networks (LPWAN) to enable real-time danger prediction and user-oriented path planning can ensure the safe and timely navigation of evacuees in complex scenarios such as cruise ships. However, most existing LPWAN-based evacuation models assume pedestrians’ walking speed remains constant and ignore crowd congestion in corridors before exits, which is not appropriate for rocking ships. To overcome these issues, this paper proposes a congestion-relived guiding framework with dedicated path planning for emergency evacuation on passenger ships. The basic idea is to averagely minimize the total evacuation time while meeting the deadline for ship capsizing under all circumstances by selecting uncrowded paths for each passenger individually. First, we use probability distributions rather than constant numbers to represent walking time (also called delay) along passageways. A worst-case delay bound with a high level of trustworthiness is also estimated for each passageway under the boundary condition of ship capsizing. Next, we predict the congestion of corridors by modeling the spatiotemporal movement of passengers, and then distribute evacuation loads evenly among corridors to alleviate the congestion. The total expected evacuation time of all corridors is finally minimized based on the delay probability distribution and estimated congestion, and the deadline for ship evacuation under all circumstances is met with the worst-case delay bound. Simulation results show that our approach significantly reduces the total escaping time of crowd evacuation by 45% and 34% while improving the navigation success ratio by more than 20% and 80% compared with the state-of-the-art emergency evacuation systems, namely the look-up table guiding scheme and the group-based guiding evacuation scheme, respectively.

Geo-distributed web-applications (GWA) can be deployed across multiple geographically separated datacenters to reduce the latency of access for users. Finding a suitable deployment for a GWA is challenging due to the requirement to consider a number of different parameters, such as host configurations across a federated infrastructure. The ability to evaluate multiple deployment configurations enables an efficient outcome to be determined, balancing resource usage while satisfying user requirements. We proposeGeoDeploy, a framework designed for finding a deployment solution for GWA. We evaluateGeoDeployusing both a formal algorithmic model and a practical cloud-based deployment. We also compare our approach with other existing techniques.

Material reuse and recycling plays a key role in reducing carbon emissions in the architecture and construction sector. A “Material Passport” (MP) is a record describing how a material is used throughout its lifetime, from genesis to termination, recording operations carried out on the material. The granularity of information recorded in a MP can vary, however ensuring that this provenance trail remains immutable is a key requirement. The benefits of using a MP, operations carried out on a MP, and recording of transactions within a distributed Blockchain (parachain) is described. A scenario is used to illustrate how the proposed approach can be used in practice.

We observe significant interest in reinforcement learning methods for real-world sensing-control scenarios driven by the sensor data streams. However, the delay introduced to the data by the communication channels may degrade the system's performance. It is especially crucial in the internet of things (IoT), where devices with constraint resources and low throughput networks are used.

Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g., industrial control systems) that require time-critical communication guarantees. While edge computing can provide immediate analysis of streaming data from Internet of Things devices, those devices lack computing capabilities to guarantee reasonable performance for time-critical applications. To alleviate this critical problem, the prevalent trend is to offload these data analytic tasks from the edge devices to the cloud. However, existing offloading approaches are static in nature as they are unable to adapt varying workload and network conditions. To handle these issues, we present a novel distributed and quality of services based multilevel queue traffic scheduling system that can undertake semiautomatic bandwidth slicing to process time-critical incoming traffic in the edge-cloud environments. Our developed system shows a great enhancement in latency and throughput as well as reduction in energy consumption for edge-cloud environments.

Internet of Things (IoT) allows the creation of virtually endless connections into a global array of distributed intelligence. However, the design, development, and deployment of IoT applications are complex and complicated due to various unwarranted challenges. For instance, addressing the IoT application users' subjective and objective opinions with IoT workflow instances remains a challenge for the design of a more holistic approach. Moreover, the complexity of IoT applications increased exponentially due to the heterogeneous nature of the Edge/Cloud services, utilised with the aim of lowering latency in data transformation and increase re-usability. Hence, in this paper, we present an IoT workflow composition system (IoTWC) to allow IoT users to pipeline their workflows with proposed IoT workflow activity abstract patterns. IoTWC leverages the analytic hierarchy process (AHP) to compose the multi-level IoT workflow that satisfies the requirements of any IoT application. Moreover, the users are befitted with recommended IoT workflow configurations using an AHP based multi-level composition framework. The proposed IoTWC is validated on a user case study to evaluate the coverage of IoT workflow activity abstract patterns and a real-world scenario for smart buildings. The comprehensive analysis shows the effectiveness of IoTWC in terms of IoT workflow abstraction and composition.