Michael David

Narrowband Internet of Things (NB-IoT) is an important cellular low-power wide-area network (LPWAN) technology for industrial sensing, offering wide-area coverage, licensed-spectrum operation, low device complexity, and long battery life. However, real-time industrial monitoring requires simultaneous consideration of latency, reliability, coverage, energy efficiency, security, and scalability. Although artificial intelligence (AI) has been proposed to adapt NB-IoT operation to changing channel and traffic conditions, the practical benefits and limitations of these approaches remain insufficiently consolidated. This paper presents a critical, PRISMA-inspired review of AI-driven NB-IoT performance optimization for industrial monitoring in Industry 4.0. Following the screening of literature from IEEE Xplore, SpringerLink, ScienceDirect, MDPI, ACM, Wiley, Scopus, and relevant 3GPP documents, 43 sources were retained and classified as core AI-driven NB-IoT studies, non-AI comparator studies, or supporting literature. The review maps AI paradigms to six performance objectives and examines their potential deployment across device, edge, and cloud layers. The synthesis reveals three key insights. First, direct evidence remains limited, with core studies relying predominantly on reinforcement-learning-related methods and simulation-based evaluation. Second, the available evidence does not establish a single AI paradigm as suitable for the entire monitoring pipeline; a layered combination of device-level TinyML, edge-level prediction and constrained control, and cloud-level training represents a promising direction, although end-to-end validation remains lacking. Third, AI overhead—including training, inference, model updates, memory, energy, and communication costs—must be evaluated alongside network performance because it may offset optimization gains. The review links six research gaps to seven future research directions and identifies reproducible testbeds, open datasets, overhead-aware multi-KPI benchmarking, and safe, explainable control as priorities for credible industrial deployment.

Machine-learning-based intrusion detection for Internet of Things systems has often been evaluated through model-centered pipelines that use weakly governed partitioning, limited leakage auditing, and closed-set assumptions. Consequently, reported performance could reflect data-handling artifacts rather than reliable security intelligence. This paper introduces a scientific integrity framework that treats preprocessing as a primary research object for open-set Internet of Things intrusion detection. The framework integrated device-disjoint split governance, feasibility-aware zero-day isolation, quantified leakage control, train-only preprocessing, shared-safe feature selection, diagnostic-harness verification, baseline split comparison, and auditable artifact generation. Applied to the CICIoT-DIAD 2024 corpus with Institute of Electrical and Electronics Engineers Organizationally Unique Identifier-based vendor enrichment, the protocol locked 28 canonical classes, eight semantic attack families, and five policy labels before constructing a device-disjoint, vendor-aware grouped split. When strict device-level zero-day holdout was infeasible, the framework activated an audited row-level fallback that preserved contamination-free holdout isolation without claiming strict device-novel zero-day evaluation. On 35,672,407 flows from 180 files, the accepted run achieved zero device overlap, zero flow-signature Jaccard leakage risk, 100 percent zero-day purity, a Feature Distribution Stability Score of 0.00518, a Device-Feature Dependency Index of 0.00000, an Attack Invariance Score of 0.92964, and an Attack Semantic Consistency Score of 0.90714. The diagnostic harness produced zero hard failures and zero warnings, while baseline comparison showed stronger preprocessing integrity than random stratified and simple device-disjoint splitting. This study did not claim downstream classifier superiority; rather, it established an auditable preprocessing substrate for later classifier-level experiments.

This study explores the application of JAYA optimization algorithms to significantly enhance the performance of indoor optical wireless communication (OWC) systems. By strategically optimizing photo-signal parameters, the system was able to improve signal distribution and reception within a confined space using circular and randomly positioned diffuse spots. The primary objective was to maximize signal-to-noise ratio (SNR) and minimize delay spread (DS), two critical factors that affect transmission quality in OWC systems. Given the challenges posed by background noise and multipath dispersion, an effective optimization strategy was essential to ensure robust signal integrity at the receiver end. Key achievements of JAYA optimization include significant performance gains, such as a 29% improvement in SNR, enhancing signal clarity and reception, and a 23.3% reduction in delay spread, ensuring stable and efficient transmission. System stability also improved, with the standard deviation of SNR improving by up to 5%, leading to a more consistent performance, while the standard deviation of delay spread improved by up to 9.9%, minimizing variations across receivers. Resilience against environmental challenges: Optimization proved effective even in the presence of ambient light noise and complex multipath dispersion effects, reinforcing its adaptability in real-world applications. The findings of this study confirm that JAYA optimization algorithms offer a powerful solution for overcoming noise and dispersion issues in indoor OWC systems, leading to more reliable and high-quality optical wireless communications. These results underscore the importance of algorithmic precision in enhancing system performance, paving the way for further advancements in indoor optical networking technologies.

The proliferation of connected devices has led to a paradigm shift in cellular standards, typified by the long-term evolution (LTE) standard. The fifth-generation (5G) standard supportsnumerouspromising mobile technologies, including Machine-to-Machine (M2M) and Device-to-Device (D2D) communication, which enable the communication of a large number of intelligent devices and ubiquitous devices. The deployment of M2M devices in the application of smart grid (SG), specifically in power systems, has introduced new compromising challenges in the areas of resource allocation and interference management. The 5G cellular network’s qualityofservice (QoS) and performance deteriorate due to interference brought on by the reduced inter-cell distance and the smooth integration of heterogeneous devices.In this paper, an interference-aware multi-objective particle swarm optimisation(MOPSO)scheme is proposed for M2M communication in SG to mitigate the interference generated as a result of the localisation of M2M devices on the grid. In order to evaluate performance, the MOPSO approach was developed and implemented for smart grid situations based on pre-fault, during-fault, and post-optimisation conditions. The initial step was to use the multi-objective particle swarm optimisation (MOPSO) algorithm to optimise the smart grid network in order to decrease grid interference. According to simulation results, under different pre-fault and post-optimisation settings, the system throughput and signal-to-interference-to-noise ratio (SINR) were greatly increased by 32.69 and 21.94%, respectively. Furthermore, by using MOPSO, the fault clearance time was reduced by 106.06%, reducing the amount of time needed to clear an impending fault with interference. Additionally, the smart grid network’s power loss was improved and maintained at levels comparable to those of the pre-fault conditions. In the subsequent step, the performance of the developed MOPSO technique was compared to that of the non-dominated sorting genetic algorithm (NSGA-II) in terms of convergence in fault clearance time, SINR, and throughput. Simulation results indicated that, in comparison to NSGA-II’s performance, MOPSO throughput and SINR were enhanced by 5.93%, 4.65%, and 0.96%, respectively. In comparison to NSGA-II, the proposed MOPSO converges to the optimal solution more quickly for the various objective functions. The findings provided by the developed MOPSO demonstrate that it can efficiently compete with similar algorithms when tackling problems involving interference optimisation algorithms.

This study explores the application of JAYA optimization algorithms to significantly enhance the performance of indoor optical wireless communication (OWC) systems. By strategically optimizing photo-signal parameters, the system was able to improve signal distribution and reception within a confined space using circular and randomly positioned diffuse spots. The primary objective was to maximize signal-to-noise ratio (SNR) and minimize delay spread (DS), two critical factors affecting transmission quality in OWC systems. Given the challenges posed by background noise and multipath dispersion, an effective optimization strategy was essential to ensure robust signal integrity at the receiver end. Key Achievements of JAYA Optimization include: Significant Performance Gains are 29% improvement in SNR, enhancing signal clarity and reception and 23.3% reduction in delay spread, ensuring stable and efficient transmission. There was also improved System Stability, with the Standard deviation of SNR improved by up to 5%, leading to more consistent performance and the Standard deviation of delay spread improved by up to 9.9%, minimizing variations across receivers. Resilience against Environmental Challenges: Optimization proved effective even in the presence of ambient light noise and complex multipath dispersion effects, reinforcing its adaptability in real-world applications. The findings of this study confirm that JAYA optimization algorithms offer a powerful solution for overcoming noise and dispersion issues in indoor OWC systems, leading to more reliable and high-quality optical wireless communications. These results underscore the importance of algorithmic precision in enhancing system performance, paving the way for further advancements in indoor optical networking technologies.

Lightweight communication protocols for wireless sensor networks (WSNs) are unfolding for machine to machine (M2M) communications and thus there is always going to be a possible conflict of interest on which protocol is best suited for any particular application. The two protocols of interest in this study are the message queue telemetry transport protocol for sensor network (MQTT-SN), a variant of message queue telemetry transport (MQTT) protocol and the constrained application protocol (CoAP). There have been studies that reveal that these protocols perform differently based on the underlying network conditions. CoAP experience lower delays than MQTT for higher packet loss and higher delays for lower packet loss. MQTT default communication via a broker is easier to scale compared to CoAP direct request-response paradigm. Although this is a huge advantage over CoAP, it presents the single point-of-failure problem. In this paper we propose an integration of MQTT-CoAP protocol using an abstraction layer that enables both MQTT-SN and CoAP protocol to be used in the same sensor node. Resources are managed by directly modifying sensor node configuration using CoAP protocol. Performance evaluation of these protocols under the integrated scenario shows acceptable levels of latency and energy consumption for internet of thing (IoT) operations.

When interference is reduced, the benefits of using a macrocell and femtocell heterogeneous network (Macro-Femto) heterogeneous network (HetNet) can be increased to their full potential. In this study, Enhanced Active Power Control (EAPC), Active Power Control (APC), and Power Control (PC1) interference mitigation strategies are applied, and their performances in uplink and downlink transmission of 5G Non-Stand-Alone (NSA) architecture are compared. According to the findings of a MATLAB simulation, the EAPC technique utilized a lower amount of transmit power for the Macro User Equipment (MUE), the Home User Equipment (HUE), and the femtocell logical node (Hen-gNB), in comparison to the APC and PC1 techniques. While PC1 approach required less en-gNB transmission power. The MUE, HUE, hen-gNB, and en-gNB throughput of the EAPC approach was much higher. This work will enable wireless system designers and network engineers know the appropriate technique to utilize to achieve desired Quality of Service (QoS) while conserving network resources.

A graphene-polyvinyl alcohol (PVA) composite saturable absorption is demonstrated at 2000 nm region. Graphene suspension is produced using low-cost electrochemical exfoliation process. The suspension is mixed with PVA host polymer in 1:1 ratio and left evaporated at room temperature which finally produced graphene-PVA thin film. Thulium doped fiber (TDF) gain medium has been shown to produce a stable Q-switched pulse with a highest repetition rate of 54 kHz, a short pulse duration of 2.89 µs, a maximum peak power of 16 mW, and an estimated maximum pulse energy of 49 nJ. Apparently, at 2000 nm region, superior performances of graphene-PVA composite have been recorded which was largely contributed by meticulous composite preparation and homogenous mixture with PVA host.

Ozone gas is a greenhouse gas. Accurate measurement of its concentration is dependent on the right value of the ozone gas absorption cross-section. In the literature, discrepancies and inconsistencies have been however linked with ozone gas absorption cross-section. In the literature, information on the pressure effect on pressures less than 100 mbar and greater than 100 but less than 1000 mbar is not available for the visible spectrum. Thus, creating an information gap which this manuscript is intended to fill up. This is the problem that has been addressed in this present work. The method of simulation with SpectralCalc is the method adopted for the present work. HITRAN 2012 simulator, available on spectralcalc.com, was used in simulating the ozone gas absorption cross-section to determine the simultaneous effect of optical path length and pressure at two peak wavelengths in the visible spectrum. Simulation outcomes were obtained for an optical path length of 10 cm to 120 cm shows that the optimum absorption cross-section value of 5.1084×10-25 m2/molecule at 603 nm and 4.7182×10-25 m2/molecule at 575 nm for gas cells length between 10 cm and 120 cm are obtained at peak points. Pressure values at which ozone gas absorption cross-section becomes a constant value of 5.1058×10-25 m2/molecule at 603 nm and 4.7158×10-25 m2/molecule at 575 nm is optical path length dependent. The percentage difference between 5.1084×10-25 m2/molecule and 5.1058×10-25 m2/molecule is 0.05% for all lengths of gas cells considered. Similarly, the percentage difference between 4.7182×10-25 m2/molecule and 4.7158×10-25 m2/molecule is also 0.05% for all lengths of gas cells considered. These results are relevant for high accuracy and high precision ozone gas measurements. Furthermore, efficient measurement of ozone gas is a direct enhancement of green communication.