Blockchain-based data marketplaces provide immutability and auditability, yet they fundamentally lack guarantees on the correctness of data at the time of inclusion. In industrial IoT environments, where decisions rely on continuous streams of sensor data, this limitation can lead to the permanent storage of faulty or malicious inputs, undermining trust and system reliability. This demonstration presents Proof of Evaluation (PoEval), a novel pre-consensus trust evaluation framework that enforces trust before immutability in permissioned blockchain-based data marketplaces. Unlike conventional approaches that rely solely on consensus for agreement, PoEval introduces a data-centric validation layer that integrates distributed machine learning (ML) evaluators directly into the data admission workflow. This enables independent, model-driven assessment of incoming data prior to ledger inclusion, effectively decoupling data trustworthiness from traditional consensus mechanisms. Through quorum-based aggregation of evaluator decisions, PoEval ensures that only data deemed trustworthy are recorded on-chain. We implement a multi-node prototype and an interactive visualization dashboard that exposes the full evaluation pipeline, including real-time data streams, evaluator outputs, decision aggregation, and blockchain state. The demonstration highlights how PoEval transforms the blockchain from a passive immutable ledger into an active trust enforcement mechanism, significantly improving data quality and reliability compared to conventional workflows.

Blockchains are widely adopted as distributed storage systems to eliminate single points of failure and ensure data immutability. However, while they guarantee that data cannot be altered once recorded, they do not ensure that the data is accurate or trustworthy at the time of inclusion. Existing consensus mechanisms primarily validate agreement among nodes rather than the correctness of the data itself, enabling erroneous or malicious inputs to be permanently embedded in the ledger. This paper proposes Proof of Evaluation (PoEval), a data-centric, trust-by-verification consensus mechanism designed for permissioned blockchains. In PoEval, incoming data is treated as untrusted and must be validated prior to block inclusion. An Evaluation Coordination Committee (ECC) manages evaluator selection and aggregates trust assessments. Selected evaluators independently apply certified machine learning (ML) models to assess data trustworthiness. A quorum-based majority voting mechanism across heterogeneous models determines data eligibility, while node reputation governs block proposal rights. By embedding ML-driven validation within the consensus workflow, PoEval ensures that only verified, high-integrity data is recorded on the blockchain. Experiments using IoT datasets demonstrate that PoEval significantly reduces false acceptance rates and processing latency, and achieves higher data reliability compared to conventional and outlier-aware consensus mechanisms, offering a scalable path toward trust-by-design distributed ledgers.

Internet of Things (IoT) sensors monitor temperature-sensitive goods throughout the supply chain. Nowadays, blockchain is being widely used for traceability, transparency, and immutable storage of this data. However, this approach lacks a mechanism to assess the trustworthiness of the data, and as a result, the reliability of the system is constrained by the quality of the data being added. IoT sensor data can be compromised for various reasons, including sensor malfunctions, deliberate tampering, or human error. This demonstration presents a solution that integrates machine learning techniques with blockchain to enhance data trust in cold chain logistics.

Trust is a foundational principle in blockchain systems, yet the technology does not guarantee the trustworthiness of the data it stores. While blockchain ensures integrity, immutability, and secure consensus, it stores information exactly as received, regardless of its reliability or origin. This limitation introduces risks in decentralized applications, where untrusted data can compromise system behavior and decision-making. Despite the critical need for trust assessment, existing datasets remain limited in scope, often focusing on isolated issues like fraud or smart contract vulnerabilities. To address this gap, we introduce BDT-Benchmark, a comprehensive synthetic dataset designed for machine learning–based trust evaluation across blockchain-integrated environments. BDT-Benchmark spans three core trust dimensions: data source–related, blockchain-internal, and external entity and cross-chain–related features. It includes both benign and malicious records, with representative attacks such as Sybil identities, smart contract exploits, and oracle manipulation modeled using domain-informed statistical distributions. Ambiguous benign data is weakly labeled using Snorkel to scale annotation beyond rule-based heuristics. The dataset is also designed to be extensible, allowing integration with external data-related features from real-world or synthetic sources, enabling broader applicability across domains. We validate the dataset through inter-feature correlation preservation visualizations and machine learning evaluations. Standard classifiers—including Logistic Regression, Random Forest, Naive Bayes and Decision Tree—achieve strong performance across both balanced and imbalanced trust scenarios. BDT-Benchmark serves as a reproducible, extensible resource to benchmark trust assessment models and can support research in secure blockchain integration with IoT, DeFi, and decentralized oracles.

Blockchain offers cryptographically secure storage for recording transactions. However, one issue with blockchains is the problem of bad data and data reliability, where bad data refers to inaccurate, incomplete, or irrelevant data. This paper investigates how machine learning (ML) can be used to identify inaccurate sensor data added to a blockchain in Internet of Vehicles (IoV) applications. A solution for reducing the inclusion of incorrect data using a reputation-based method is proposed. We suggest that if an accurate ML model can be built for a task that can be completed using the input sensor data, it is possible to use the same model to assess the accuracy of new input data samples for which the actual task outcome is known. A road surface-type classification task is performed using Convolutional Neural Network models on the Passive Vehicular Sensors Datasets, and a pre-trained model is used in a novel solution approach involving edge servers and validators on a blockchain network. Our research shows that ML can be used to identify bad data on the blockchain and to reduce the addition of unreliable data to the blockchain in an IoV context. The proposed solution is generalizable and can be applied to any scenario where an accurate ML model can be devised for a task that can be accomplished using some blockchain input data.

Processing large scientific applications generates a huge amount of data, which makes running experiments in the cloud computing environment very expensive and energy-consuming. To find an optimal solution to the workflow scheduling problem, several approaches have been presented for scheduling workflow on cloud resources. However, more efficient approaches are needed to improve cloud service delivery. In this paper, an energy-efficient virtual machine mapping algorithm (EViMA) is proposed to improve resource management in the cloud computing environment to achieve effective scheduling that reduces cloud data center energy consumption, execution makespan, and execution cost. This ensures that the requirements of cloud users are met, and improves the quality of services offered by cloud providers. Our proposed mechanism considers the heterogeneity of scheduling from both cloud users’ and workflow applications’ perspectives. Through simulation experiments on real workflow datasets, the proposed EViMA can provide better solutions for both cloud users and cloud providers by reducing energy consumption, execution makespan, and execution cost better than the state-of-the-art.

Near Field Communication (NFC) has become prevalent in access control and contactless payment systems, however, there is evidence in the literature to suggest that the technology possesses numerous vulnerabilities. Contactless bank cards are becoming commonplace in society; while there are many benefits from the use of contactless payments, there are also security issues present that could be exploited by a malicious third party. The inherently short operating distance of NFC (typically about 4 cm) is often relied upon as a means of ensuring intentional interaction on the user's part and limiting attack vectors. However, NFC is particularly sensitive to relay attacks, which entirely negate the security usefulness of the short-range aspect of technology. The aim of this article is to demonstrate how standard hardware can be used to exploit the technology to carry out a relay attack. Considering the risk that relay attacks pose, a countermeasure is proposed to mitigate this threat. Our countermeasure yields a 100% detection rate in experiments undertaken - in which over 10,000 contactless transactions were carried out on a range of different contactless cards and devices. In these experiments, there was a false positive rate of 0.38% - 0.86%. As little as 1 in every 250 transactions were falsely classified as being the subject of a relay attack and so the user experience was not significantly impacted. With our countermeasure implemented, transaction time was lengthened by only 0.22 seconds.

Multimedia delivery support has recently been added to Wireless Sensor Networks (WSN) and has led to increased interest in Wireless Multimedia Sensor Networks (WMSN). WMSNs are expected to be crucial to the success of applications related to the Internet of Things (IoT), such as smart health, smart surveillance, smart homes, etc. Alongside their improved multimedia capabilities, WMSNs inherit WSN limitations such as energy and processing constraints. Additionally, WMSNs have significant Quality of Service (QoS) requirements, since multimedia delivery requires increased network performance in terms of bandwidth, latency, etc. Balancing energy efficiency and QoS is a fundamental challenge for WMSN users and operators alike. This paper proposes Reinforcement Learning based Duty Cycle (rlDC), an innovative learning-based scheme to adjust the duty cycle and contention window of WMSN nodes in order to meet energy efficiency and QoS targets. By employing rlDC, WMSN sensor nodes intelligently adapt their operation according to network delivery performance and application requirements. The proposed rlDC scheme was evaluated under different use cases in a simulation environment, and testing results show it outperforms other state-of-the-art duty-cycle-based protocols for WMSNs.

Instant messaging (IM) applications, even with end-to-end encryption enabled, pose privacy issues due to metadata and pattern leakage. Our goal is to develop a model for a privacy preserving IM application, by designing an IM application that focuses on hiding metadata and discussion patterns. To solve the issue of privacy preservation through the obfuscation of metadata, cryptographic constructions like Oblivious Random Access Machines (ORAM) have been proposed in recent years. However, although they completely hide the user access patterns, they incur high computational costs, often resulting in excessively slow performance in practice. We propose a new federated model, FedORAM, which is the first ORAM scheme that uses a federation of servers to hide metadata for an IM use case. In order to investigate the trade-off between security and performance, we propose two versions of FedORAM: Weak FedORAM and Strong FedORAM. Strong FedORAM uses a tree-based federation architecture to ensure strong obliviousness, but with an increased overhead cost. Weak FedORAM has a more simple federated architecture that only uses Oblivious Transfer (OT) to increase communication speed, but with security consequences. Our results show that both constructions are faster than a similar client-server ORAM scheme. Furthermore, Weak FedORAM has a response time of less than 2 seconds per message for a middle-sized federation.

Performance testing is a critical task to assure optimal experience for users, especially when there are high loads of concurrent users. JMeter is one of the most widely used tools for load and stress testing. With JMeter, it is possible to test the performance of static and dynamic resources on the web. This paper presents DYNAMOJM, a novel tool built on top of JMeter that enables testers to create a dynamic workload for performance testing. This tool implements the DYNAMO approach, which has proven useful to find performance issues more efficiently than static testing techniques.

Providing support for energy efficiency and Quality of Service (QoS) is among the major challenges when designing solutions for video delivery over Wireless Multimedia Sensor Networks (WMSN). As video applications have high bitrate delivery requirements, an effective way is to enable the sensor nodes to transmit data over multiple paths in parallel, for instance by employing Multi-path TCP (MPTCP). However, in a dense network like WMSN, high traffic volume transmission may cause congestion, affecting all paths. In such a case, congestion control algorithms play a key role in order to support high QoS levels, including by employing retransmissions of lost data. However, data retransmissions result in energy consumption increases, which is an aspect of concern for WMSN services. In this paper, we propose eqCCMP, an energy-efficient congestion control scheme for MPTCP. The general idea of eqCCMP is to use low energy consumption paths to deliver data, while making sure good QoS levels are maintained. A fluid model is employed to formulate the operation of eqCCMP. The performance of eqCCMP is evaluated in comparison with other schemes including the vanilla MPTCP and ecMTCP. By considering an extra factor in the congestion avoidance phase for congestion window adjustment, the proposed eqCCMP outperforms the other solutions in terms of throughput and energy efficiency in a simulation environment.

Nowadays, digital communications are pervasive and as such, they carry a huge amount of both professional and private information all around the world. Given the knowledge that can be extracted from such information, its confidentiality is of utmost importance for both companies and individuals. Recent news related to massive breaches of privacy by both external actors such as government agencies, rogue teams; and internal actors such as communication services providers (i.e., Google, Apple, Facebook, Amazon, Microsoft) have exacerbated the need for more secure communication technologies. Although message content can be encrypted end-to-end by so-called off-the-record techniques', message metadata such as sender, recipient, time sent and size can still leak a lot of information about communicating parties. Oblivious RAM (ORAM) systems form a promising new branch of research for hiding metadata from the hosting servers, but they have not yet been deployed in production environments. Due to their complexity and performance penalty, they can currently be used only for very simple client-server applications such as instant messaging (IM). In this context, we show accessing metadata on a messaging server can leak information that could be concealed by ORAM systems. More specifically, we show the differences observed in metadata collection between a classic XMPP server and two ORAM-based servers. In order to assess those systems, we have designed a new attack based on live forensic techniques to retrieve metadata from the RAM of a running IM server. We have used two datasets of instant messages for carrying out this assessment. Our experimental results highlight the leak of metadata from a standard messaging server and can also be used for testing the security of an ORAM-based messaging server.

Performance testing is a critical task to ensure an acceptable user experience with software systems, especially when there are high numbers of concurrent users. Selecting an appropriate test workload is a challenging and time-consuming process that relies heavily on the testers’ expertise. Not only are workloads application-dependent, but also it is usually unclear how large a workload must be to expose any performance issues that exist in an application. Previous research has proposed to dynamically adapt the test workloads in real-time based on the application behavior. By reducing the need for the trial-and-error test cycles required when using static workloads, dynamic workload adaptation can reduce the effort and expertise needed to carry out performance testing. However, such approaches usually require testers to properly configure several parameters in order to be effective in identifying workload-dependent performance bugs, which may hinder their usability among practitioners. To address this issue, this paper examines the different criteria needed to conduct performance testing efficiently using dynamic workload adaptation. We present the results of comprehensively evaluating one such approach, providing insights into how to tune it properly in order to obtain better outcomes based on different scenarios. We also study the effects of varying its configuration and how this can affect the results obtained.

Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.

Congestion is a major cause of energy wastage and Quality of Service (QoS) degradation in wireless communication systems. Saving energy and maintaining high QoS levels are especially important in Wireless Multimedia Sensor Networks (WMSN), due to the limited sensor node energy resources and QoS-related application requirements. This paper proposes an energy efficient and QoS-aware congestion control scheme for reliable communications over WMSNs (eqCC). The proposed solution makes use of QoS feedback and current battery energy levels of sensor nodes in order to adapt sending data rate. We employ reinforcement learning by formulating the problem in terms of a Markov Decision Process (MDP) and solve it using the Q-Learning technique. The proposed eqCC is validated using simulations and is compared with classic TCP and Flush, another congestion control algorithm for Wireless Sensor Networks (WSN). The results show how eqCC outperforms the other solutions under high and low network load.

Microscopic traffic simulation is the most accurate tool for predictive analytics in urban environments. However, the amount of workload (i.e., cars simulated simultaneously) can be challenging for classical systems, particularly for scenarios requiring faster than real-time processing (e.g., for emergency units having to make quick decisions on traffic management). This challenge can be tackled with distributed simulations by sharing the load between simulation engines running on different computing nodes, hence balancing the processing power required. This paper studies the performance of dSUMO, i.e., a distributed microscopic traffic simulator. dSUMO is fully decentralized and can dynamically balance the workload between its computing nodes, hence showing important improvements against classical, centralized and not dynamic, solutions.

Optimising the data centres of large IT organisations is complex as (i) they are composed of various hosting departments with their own preferences and (ii) reassignment solutions can be evaluated from various independent dimensions. But in reality, the problem is even more challenging as companies can now choose from a pool of cloud services to host some of their workloads. This hybrid search space seems intractable, as each workload placement decision (seen as running in a virtual machine on a server) is required to answer many questions: can we host it internally? In which hosting department? Are the capital allocators of this hosting department ok with this placement? How much does it save us and is it safe? Is there a better option in the Cloud? Etc. In this paper, we define the multi-objective VM reassignment problem for hybrid and decentralised data centres. We also propose H2–D2, a solution that uses a multi-layer architecture and a metaheuristic algorithm to suggest reassignment solutions that are evaluated by the various hosting departments (according to their preferences). We compare H2–D2 against state-of-the-art multi-objective algorithms and find that H2–D2 outperforms them both in terms of quantity (approx 30% more than the second-best algorithm on average) and quality of solutions (19% better than the second-best on average).

Lately Video Sensor Networks (VSN) are increasingly being used in the context of smart cities, smart homes, for environment monitoring, surveillance, etc. In such system, the trade-off between Quality of Service (QoS) and energy consumption is always a big issue. As the wireless transmission part plays the dominant role in power consumption, many researches propose energy saving schemes based on the adjustment of duty cycle by adaptively switching between wake-up/sleep state of nodes. However, the main drawback of this method is that it affects streaming quality in terms of throughput and delay. Therefore, one of the most important challenges when designing an energy-aware VSN is to keep the balance between energy consumption and video delivery quality. This paper proposes an Enhanced scheme for Adaptive Multimedia Delivery (eAMD) that dynamically adjusts the wake-up/sleep duration of video sensor nodes based on the node remaining battery levels and network performance. A Markov Decision Process (MDP)-based framework is used to formulate the problem and an innovative algorithm based on Q-Learning is proposed to find the optimal policy for video sensor nodes. Using both a systematic and algorithmic approach, our proposed system architecture and algorithms hold the potential to improve the trade-off between video streaming quality and energy efficiency in comparison with other state-of-the-art adaptive video based algorithms.

Vehicular communication systems have significant benefits. Their adoption and use in motor vehicles can help prevent road traffic accidents, increase route efficiency and fuel economy, and positively impact the user experience. As autonomous vehicles and inter-vehicular communication systems are becoming increasingly commonplace, the need for robust and secure wireless communication between vehicles is essential. The black hole attack is an attack vector that can significantly reduce the availability of Vehicular Ad Hoc Networks (VANETs) and prevent communication between vehicles entirely. We propose a solution to help secure these networks against this vulnerability by detecting the attack and removing the malicious node from the network. We validated our proposal using a simulation model in NS-3; results show that our system can detect, stop, and prevent black hole attacks in VANETs.

The dissemination of textual personal information has become an important driver of innovation. However, due to the possible content of sensitive information, this data must be anonymized. A commonly-used technique to anonymize data is generalization. Nevertheless, its effectiveness can be hampered by the Value Generalization Hierarchies (VGHs) used as poorly-specified VGHs can decrease the usefulness of the resulting data. To tackle this problem, in our previous work we presented the Generalization Semantic Loss (GSL), a metric that captures the quality of categorical VGHs in terms of semantic consistency and taxonomic organization. We validated the accuracy of GSL using an intrinsic evaluation with respect to a gold standard ontology. In this paper, we extend our previous work by conducting an extrinsic evaluation of GSL with respect to the performance that VGHs have in anonymization (using data utility metrics). We show how GSL can be used to perform an a priori assessment of the VGHs' effectiveness for anonymization. In this manner, data publishers can quantitatively compare the quality of various VGHs and identify (before anonymization) those that better retain the semantics of the original data. Consequently, the utility of the anonymized datasets can be improved without sacrificing the privacy goal. Our results demonstrate the accuracy of GSL, as the quality of VGHs measured with GSL strongly correlates with the utility of the anonymized data. Results also show the benefits that an a priori VGH assessment strategy brings to the anonymization process in terms of time-savings and a reduction in the dependency on expert knowledge. Finally, GSL also proved to be lightweight in terms of computational resources.