Clustering in wireless sensor networks (WSNs) offers numerous desirable properties, including load balancing, energy conservation, and distributed key management. Secure Clustering requires it to detect compromised nodes and remove them from clusters during setup. Suppose some nodes are attacked and pass the filtering. In that case, they can modify some nodes to adopt a different clustering perspective, as well as initiate new clusters to degrade the overall cluster quality. To address these issues, a new method, Secretary Bird with Self-Organizing Maps (SBWSOM), has been designed to detect and eliminate malicious nodes while efficiently providing data. First, the appropriate sensor nodes were constructed in Python. Second, the malicious node was located and destroyed, and the Cluster Head (CH) was picked based on parameters such as remaining energy, network level, and base station (BS) location. Furthermore, the data rates of chosen CHs have been confirmed and sent to empty nodes. Lastly, the values compared and studied were Latency, throughput, packet delivery ratio (PDR), energy consumption, and transmission loss. The evaluation of this proposal demonstrated improved data transfer, with a throughput of 0.91, an energy consumption of 0.46 mJ, and a packet delivery ratio of 96.3%. Also, the transmit loss was 4.20%, and Latency was 6.04 ms. Overall, this method performed well, with significant improvement over previous models.
Driver behavior, vehicle dynamics, and operating conditions strongly influence fuel consumption in open-pit mining operations. This study proposes a driver behavior–aware fuel optimization framework that integrates a digital twin architecture with reinforcement learning to improve fuel efficiency of heavy-duty haul trucks. The framework combines a data-driven vehicle dynamics surrogate, explicit modeling of driver behavior, and proximal policy optimization to enable safe and scalable policy learning within a realistic simulation environment. Historical telematics data were used to construct the digital twin and evaluate the learned policy under controlled operating conditions. Experimental results show that the reinforcement learning agent produces substantially smoother driving behavior, characterized by stable speed regulation and elimination of aggressive acceleration and braking events. Compared to historical operator driving, fuel consumption per kilometer, computed using rollout-level aggregation of cumulative fuel consumption and total traveled distance, was reduced from 4.45 L/km to 3.02 L/km, corresponding to a 32.05% improvement in fuel efficiency. The results demonstrate that explicitly modeling driver behavior within a digital twin-based reinforcement learning framework can yield significant fuel savings while maintaining realistic and interpretable driving strategies. The proposed approach provides a promising foundation for the development of decision-support and driver assistance systems aimed at improving energy efficiency in open-pit haulage operations.
This paper presents a fully integrated, real-time assistive system that combines voice-based object recognition with a generative conversational interface, specifically designed to enhance elderly care through edge AI deployment. The proposed framework enables intuitive human–robot interaction in domestic environments by fusing natural language understanding, optimized visual detection, and local generative response. Voice commands are processed through a speech-to-text pipeline using the Google Web Speech API, with keyword extraction triggering object detection via a quantized YOLOv8n model accelerated through TensorRT with FP16 inference on an NVIDIA Jetson Nano. In parallel, a locally deployed generative AI assistant, executed entirely on-device, provides empathetic dialogue to support social engagement and emotional well-being. The proposed system adopts a hybrid edge architecture in which object detection, robot control, and LLM-based dialogue generation are executed on-device, while speech-to-text transcription relies on a cloud-based service. This generative interface is implemented as an LLM-based Emotional Assistant. The system achieves 13 FPS with an inference latency of 70 ms for object detection, 94.3% speech recognition accuracy, and an F1-score of 0.69 at a 0.5 confidence threshold. All AI components are executed on-board, preserving privacy for on- device processing while maintaining real-time responsiveness. Experimental validation confirms the effectiveness of deploying multimodal AI, including generative models, on resource-constrained hardware. This work lays the foundation for autonomous, voice-guided care robots that not only assist in locating objects but also engage users socially, promoting greater autonomy and quality of life for older adults.
Blockchain technology has emerged as a transformative tool for secure data sharing across decentralised systems, particularly in finance, healthcare, and governance. However, despite its promise, the widespread adoption of blockchain platforms remains constrained by unresolved security threats and architecture-specific vulnerabilities. This paper presents a systematic literature review (SLR) that critically evaluates the security risks and countermeasures associated with blockchain-based data sharing models. The review focuses on three widely referenced platforms, Ethereum, Hyperledger Fabric, and MedRec, chosen for their relevance to public, permissioned, and healthcare-oriented blockchain deployments, respectively. The review analyzed 30 peer-reviewed publications from 2018 to 2025 sourced from IEEE Xplore, SpringerLink, ScienceDirect, and other digital libraries. Empirical insights from the reviewed literature indicate that Sybil attacks remain prevalent on public blockchains, although adaptive Proof-of-Stake protocols are reported to reduce their success rate considerably. Front-running and Miner Extractable Value–related behaviors are frequently observed in Ethereum-based decentralised finance ecosystems, often resulting in significant financial losses. Unauthorised access persists as a major concern, particularly for software wallets, which are commonly exposed to phishing and malware attacks. The findings underscore unique trade-offs across platforms: Ethereum supports transparency but is prone to transaction manipulation; Hyperledger ensures strong access control yet faces insider threat challenges; and MedRec enhances patient privacy but lacks robust mobile integration. This study provides a structured synthesis of existing threats, platform-level responses, and design trade-offs, offering guidance for stakeholders aiming to strengthen security in blockchain-based data-sharing infrastructures.
Network Intrusion Detection Systems (NIDS) play a vital role in modern cybersecurity by leveraging artificial intelligence (AI) in particular deep learning (DL) and machine learning (ML) to detect and mitigate malicious activities. However, these AI-driven systems are highly vulnerable to adversarial attacks, where small, imperceptible perturbations in input data can deceive models and significantly reduce detection accuracy. This raises critical concerns about the security and reliability of intrusion detection, especially in real-world scenarios where attackers exploit adversarial transferability to bypass defenses. This research investigates the threat posed by black-box adversarial attacks via surrogate models, focusing on the ability of adversarial examples to transfer across different architectures. This study simulates real-world adversarial threats, demonstrating how attacks crafted on one model can effectively deceive another, compromising NIDS security. A comparative study is conducted on two widely used AI models: an Artificial Neural Network (ANN) and a Convolutional Neural Network (CNN), both trained on the CICIDS 2019 dataset. The study evaluates the robustness of these models against two gradient-based adversarial attack methods, Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), to determine their susceptibility under black box adversarial conditions. Experimental results indicate that CNN-based NIDS are more vulnerable to adversarial attacks than ANN-based models, with adversarial examples successfully transferring across architectures. These findings highlight the critical risks associated with adversarial transferability, underscoring the need for enhanced security measures to strengthen AI-driven intrusion detection systems against evolving cyber threats.
