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.

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.

Inconsistencies in the absorption cross section of ozone have been observed. Hence, for accurate measurement, we have reported the combined effects of varying optical path-length and temperature on the ozone gas absorption cross section (OACS) at 334.15nm. Adopting optical absorption spectroscopy, results of the (OACS) have been simulated using spectralcalc simulator with HITRAN 12 has the latest line list. OACS increased by 52.27% as the temperature increased from 100K to 350K while it was slightly affected by a 0.007% decrease varying the path-length from 0.75cm-130cm.

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.

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.

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.