The digitization of brownfield manufacturing environments, particularly within Small and Medium-sized Enterprises (SMEs), is fundamentally constrained by the lack of robust, scalable, and cost-effective communication infrastructure capable of retrofitting legacy equipment. While Industry 4.0 applications demand reliable, low-latency data acquisition, existing solutions often fail to balance cost-effectiveness with the performance demands of industrial settings. This paper introduces the design, optimization, and empirical evaluation of a highly cost-effective Industrial Internet of Things (IIoT) communication framework tailored for challenging industrial environments. Utilizing a conventional hierarchical edge-to-cloud architecture, this study leverages existing Wi-Fi infrastructure and the MQTT protocol to enable decentralized, parallel data acquisition while optimizing localized sensor density through aggregator nodes. The hardware design utilizes custom ESP32-based nodes, achieving a unit cost of approximately $74.00. Rather than proposing a fundamentally novel topology, the core scientific contribution of this work is the provision of an applied deployment blueprint and the extraction of transferable analytical insights for accessible MQTT/Wi-Fi deployment. Performance was quantified through a facility-level pilot deployment of 19 nodes in an operational industrial bakery characterized by significant electromagnetic interference and physical obstructions. The results demonstrate stable operation, achieving a system uptime of 99.92% over a 30-day period. Network performance metrics confirm the framework’s suitability for high-fidelity monitoring, recording an average end-to-end latency of 185 ms and a low jitter of 86 ms. Furthermore, the scalability of the proposed architecture was explored through Discrete-Event Simulation (DES), suggesting acceptable monitoring performance for approximately 200 concurrent nodes under stated assumptions. Security trade-offs were theoretically analyzed, justifying a defense-in-depth strategy that prioritizes system availability and latency budgets over device-level encryption in resource-constrained environments. This study provides an applied deployment blueprint for establishing the foundational communication layer required for localized Industry 4.0 applications in brownfield settings.
Mobile ad hoc cloud computing allows a mobile task to run on its source device, a nearby cooperative mobile node, an edge server, or a remote cloud. This paper presents a fully specified hybrid Cheetah Optimizer–Manta Ray Foraging Optimization (CO–MRFO) scheduler for priority-aware task assignment. MRFO performs global exploration, CO refines promising schedules, and a linearly decreasing individual-level probability controls their use. Each candidate is decoded into a one-hot task–node assignment and repaired when resource or connectivity constraints are violated. The minimization objective combines normalized response time, node-dependent energy, priority-weighted deadline violations, and feasibility penalties. Over 30 paired runs with 100 tasks, CO–MRFO obtained an objective value of 12.84 ± 0.31, runtime of 8.74 s, total energy of 412.6 J, deadline-miss ratio of 0.42%, and throughput of 114.8 tasks/s. Relative to GWO, it reduced the objective by 14.7%, runtime by 14.6%, energy by 10.8%, and deadline misses by 78.1%. Statistical, ablation, scalability, sensitivity, and service-level analyses support the contribution of the hybrid switching, priority term, and node-dependent energy model within the simulated environment.
High-dimensional network traffic data pose a significant challenge for the design of efficient and accurate Network Attack Detection Systems (NADS). Choosing features is essential to minimize redundancy, improve generalization, and facilitate real-time analysis. In this work, we propose an Entropy-Guided Jaya (EGJ) optimization framework for feature selection in DDoS detection tasks. The framework employs a weighted composite entropy measure, combining Shannon entropy and conditional entropy, to initialize the Jaya population with informative feature subsets. This initialization is followed by iterative optimization using a binarized and reinforced variant of the Jaya algorithm, which balances exploration and exploitation in the high-dimensional search space. To evaluate the discriminative power of the selected features, we employ the XGBoost classifier implemented on the H2O platform, chosen for its scalability and robustness on large-scale security datasets. Experimental evaluation on the CIC-DDoS2019 benchmark demonstrates that the proposed method achieves substantial feature reduction while maintaining high recall, precision, and F1-score. It attains an average accuracy of 93%. The results confirm that entropy-guided feature selection, combined with ensemble-based classification, offers a lightweight yet effective approach to secure modern networks against DDoS attacks.
Petros Stylianou, Elio Faddoul, Mohamed Selim Korium, Ioannis Krikidis
Structured datasets are essential for advancing simultaneous wireless information and power transfer (SWIPT) research, enabling the reproducible analysis of waveform encoding and energy harvesting performance. This dataset contains experimental measurements from a point-to-point SWIPT system using multisine radio frequency waveforms, where information is encoded through the number of carriers and energy is harvested at the receiver. Measurements include harvested power and signal parameters across multiple transmitter-receiver distances, power levels, and waveform configurations. Metadata describes all experimental protocols, hardware calibration, and labeling procedures to ensure transparency and accessibility. Dataset generation combines software-defined radio transmission, rectenna-based power harvesting, and systematic parameter variation. The dataset supports machine-learning model development for signal classification and enables studies of the relationship between multisine waveform properties, classification performance, and energy transfer efficiency. It is intended for researchers who are developing wireless communication systems, low-power IoT devices, and SWIPT optimization methodologies.
This pape investigates the performance of a fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) network to enable edge-enhanced connectivity in short-packet communications. A multi-user downlink framework is proposed, wherein cell-edge users exploit the spatial diversity offered by FAS to mitigate severe path loss and interference, while central users employ successive interference cancellation for multi-layer signal decoding. The analysis focuses on evaluating the average block error rate, the sum rate performance, and the influence of power allocation parameters. Compared with the conventional FAS-NOMA system, the proposed scheme effectively mitigates the fading effect in edge channels through dynamic antenna port selection and properly accommodates dispersed weak signals, significantly enhancing the communication reliability for cell-edge users. Simulation results demonstrate the effectiveness of the proposed scheme. The findings confirm that the proposed FAS-NOMA framework provides a flexible and effective solution for short-packet edge access, offering a favorable trade-off between reliability and throughput for latency-critical applications such as URLLC and IoT.
