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The popularity of unmanned vehicles in numerous areas of employment, combined with the diversity and continuing evolution of their payloads, make the communication solutions utilized by such vehicles the element of a particular importance. In our previous publication, we confirmed a general applicability of wireless local area network (WLAN) technologies as solutions suitable to provide a control loop communication of unmanned surface vehicles (USVs). At the same time, our research indicated that WLAN technologies provide communication resources in excess of what is required for the above task. In this paper, we aim to verify if a WLAN-based USV communication solution can be reliably utilized for both time-sensitive control loop and high-throughput payload communication simultaneously, which could provide significant advantages during USV construction and operation. For this purpose, we analyzed traffic parameters of popular USV payloads, designed a test system to monitor the impact of such traffic sharing a WLAN link with a USV control loop communication and conducted laboratory and field experiments. As initial results indicated the significant impact of payload traffic on the quality of control communication, we have also proposed a method of employing Commercial Off The Shelf (COTS) hardware for this purpose, in a manner which allows the above-mentioned link sharing to operate reliably in changing real-world conditions. The subsequent verification, first in the laboratory and then during a real-world USV field deployment, confirmed the effectiveness of the proposed method.

Differentiated Services Code Point (DSCP) manipulations can distort bandwidth allocation, expose security risks, and degrade performance, yet they are difficult to detect in dynamic traffic. Dynamic traffic flows and sophisticated evasion strategies make such operations difficult to detect. Rule-based and classical machine learning methods cannot detect DSCP-based traffic modifications properly. This study employs deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), to detect DSCP-based manipulations. A labeled dataset comprising normal and manipulated traffic patterns was used for training and validation. An ensemble approach combining CNN, RNN, and LSTM was implemented to enhance detection accuracy. The model demonstrated the highest detection accuracy, achieving 99.28% accuracy and making it the most effective in distinguishing manipulated traffic from legitimate flows. These findings highlight the potential of deep learning in securing QoS mechanisms and mitigating DSCP-based traffic manipulation risks. The proposed model can enhance real-time traffic monitoring, ensure fair bandwidth distribution, and prevent malicious exploitation. Future work should focus on real-world deployment, federated learning for cross-network adaptability, and explainable AI for improved interpretability.

Wireless Body Area Networks (WBANs) are vital for real-time healthcare monitoring. However, existing protocols either overlooked traffic prioritization or rely on approaches that introduces performance drawbacks; multi-hop routing that delay emergency transmission, time-slot allocation that increases latency, priority queue that ignores link quality and raises computational overheads while staving low-priority packets, deadline-based scheduling that triggers retransmissions, or complex data processing that further delay urgent responses. To overcome these limitations, this study introduces the Reliable Traffic Prioritization Scheme (RTPS), a lightweight routing protocol that dynamically prioritizes traffic based on urgency, optimized link selection, and balanced energy usage for long-term monitoring. RTPS employs binary data compression to reduce processing overhead, single-hop transmission for rapid delivery of critical data, and energy-efficient multi-hop routing for normal data using composite link quality metrics. Simulation results demonstrated that RTPS significantly enhanced performance compared to state-of-the-art protocols; Reliable Link Quality, Temperature and Delay Aware Routing (RLTD) and Temperature Aware and Energy-Efficient Routing (TAEERS). Specifically, RTPS reduced queueing delay by 47.47% and 97.9% over RLTD and TAEERS respectively and improved network lifetime by 14.3% over RLTD and more than triple that of TAEERS. It also increased residual energy by 67.35% over RLTD and more than fivefold over TAEERS. Furthermore, RTPS decreased end-to-end delay by 64.76% relative to RLTD, and minimized computational time by 99.95% over TAEERS.

Recently, Wireless Body Area Network (WBAN) has witnessed significant attentions in research and product development due to the growing number of sensor-based applications in healthcare domain. Design of efficient and effective Medium Access Control (MAC) protocol is one of the fundamental research themes in WBAN. Static on-demand slot allocation to patient data is the main approach adopted in the design of MAC protocol in literature, without considering the type of patient data specifically the level of severity on patient data. This leads to the degradation of the performance of MAC protocols considering effectiveness and traffic adjustability in realistic medical environments. In this context, this paper proposes a Traffic Priority-Aware MAC (TraPy-MAC) protocol for WBAN. It classifies patient data into emergency and non-emergency categories based on the severity of patient data. The threshold value aided classification considers a number of parameters including type of sensor, body placement location, and data transmission time for allocating dedicated slots patient data. Emergency data are not required to carry out contention and slots are allocated by giving the due importance to threshold value of vital sign data. The contention for slots is made efficient in case of non-emergency data considering threshold value in slot allocation. Moreover, the slot allocation to emergency and non-emergency data are performed parallel resulting in performance gain in channel assignment. Two algorithms namely, Detection of Severity on Vital Sign data (DSVS), and ETS Slots allocation based on the Severity on Vital Sign (ETS-SVS) are developed for calculating threshold value and resolving the conflicts of channel assignment, respectively. Simulations are performed in ns2 and results are compared with the state-of-the-art MAC techniques. Analysis of results attests the benefit of TraPy-MAC in comparison with the state-of-the-art MAC in channel assignment in realistic medical environments.

Packet-switched xHaul networks are a scalable solution enabling convergent transport of diverse types of radio data flows, such as fronthaul / midhaul / backhaul (FH / MH / BH) flows, between remote sites and a central site (hub) in 5G radio access networks (RANs). Such networks can be realized using the cost-efficient Ethernet technology, which enhanced with time-sensitive networking (TSN) features allows for prioritized transmission of latency-sensitive fronthaul flows. Provisioning of multiple types of 5G services of different service requirements in a shared network, commonly referred to as network slicing, requires adequate handling of transported data flows in order to satisfy particular service / slice requirements. In this work, we investigate two traffic prioritization policies, namely, flowaware (FA) and latency-aware (LA), in a packet-switched xHaul network supporting slices of different latency requirements. We evaluate the effectiveness of the policies in a networkplanning case study, where virtualized radio processing resources allocated at the processing pool (PP) facilities, for two slices related to enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services, are subject to optimization. Using numerical experiments, we analyze PP cost savings from applying the LA policy (vs. FA) in various network scenarios. The savings in active PPs reach up to 40% − 60% in ring scenarios and 30% in a mesh network, whereas the gains in overall PP cost are up to 20% for the cost values assumed in the analysis.

The increasing demand for high-speed, reliable, and fair network services in multi-gigabit-wide area networks (WANs) has necessitated the development of advanced bandwidth allocation mechanisms. This paper proposes a Fair and QoS-Aware Dynamic Bandwidth Allocation (FQ-DBA) algorithm designed to address the dual challenges of fairness and Quality of Service (QoS) prioritisation in multi-gigabit networks. FQ-DBA dynamically allocates bandwidth to ensure equitable distribution among users while meeting the stringent QoS requirements of high-priority traffic, such as VoIP and video streaming. The algorithm integrates traffic classification, fairness enforcement, and QoS-aware allocation to optimise network performance. Simulation results demonstrate that FQ-DBA achieves a high fairness index, meets QoS guarantees, and maximises throughput while minimising latency. The proposed framework is scalable, energy-efficient, and compatible with existing network protocols, making it a promising solution for next-generation WANs.

Integration of miniature sensors composes a wireless body area network (WBAN), which enables remote health monitoring. To make this technology widely acceptable in the society, some studies suggest commonly used gadgets such as cell phones or laptops as a hub for WBANs. In these cases, envisaged medical and non-medical applications of WBANs must have the same priority unless in emergency situations. Also, medical applications of WBANs need some strict requirements that are not that important for non-medical applications, such as very low-power consumption or reliability. In addition, channel condition may change in WBANs because of fading effects and this causes packet loss. Therefore proper traffic prioritisation, high reliability and efficient channel utilisation are vitally important issues in these networks. In this study, the authors improve the performance of the medium access control (MAC) protocol of WBANs using an adaptive resource allocation and traffic prioritisation according to the medical situation of user and channel condition. Through adaptively separating and managing the possible traffics of WBANs, the heterogeneous requirements of different applications are provided. Analytical and simulation results show that the proposed MAC protocol outperforms IEEE 802.15.4 and IEEE 802.15.6 MAC protocols in terms of power consumption as well as the channel utilisation and reliability.

As buses often share space with other vehicles, their travel time and reliability are affected by the flow of traffic. This can be mitigated by prioritization measures as bus streets or bus lanes, but such measures are not always possible due to limited space or too large impact on other traffic. An alternative is dynamic bus lanes, which only are reserved for buses when needed. The aim of this article is to explore the feasibility of dynamic bus lanes through a case study of a main arterial towards the city centre of a low/medium sized city with multiple bus lines arriving at different intersections along the main arterial. This is investigated by setting up and calibrating a microscopic traffic simulation model for a real world case. The simulation model is used to study impacts of dynamic bus lanes compared to two cases: current operations of mixed traffic (no bus lane) as baseline, and dedicated bus lane. The simulation results demonstrate that dynamic bus lanes can contribute to keeping a good punctuality even at higher traffic demands, this due to decreased travel time variability. However, the total travel time for all travellers (independent of mode) increase (even if buses are assumed to be full).

The Fine  −  Kinney is a risk assessment method widely used in many industries due to its ease of use and quantitative risk evaluation. As in other methods, it is a method that recommends taking a series of control measures for operational safety. However, it is not always possible to implement control measures based on the determined priorities of the risks. It is considered that determining the priorities of these measures depends on many criteria such as applicability, functionality, performance, and integrity. Therefore, this study has studied the prioritization of control measures in Fine − Kinney-based risk assessment. The criteria affecting the prioritization of control measures are hierarchically structured, and the importance weights of the criteria are determined by the Bayesian Best–Worst Method (BBWM). The priorities of control measures were determined with the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) method. The proposed model has been applied to the risk assessment process in a petrol station’s liquid fuel tank area. According to the results obtained with BBWM, the most important criterion affecting the prioritization of control measures is the applicability criterion. It has an importance weight of about 42%. It is followed by performance with 31%, functionality with 18%, and integrity with 10%, respectively. FVIKOR results show that the “Periodic control of the ventilation device” measure is the top priority for Fine − Kinney risk assessment. “The absence of any ducts or sewer pits that may cause gas accumulation in the tank area and near the dispenser; Yellow line marking of entry and exit and vehicle roads; Placing of speed limit warning signs” has been determined as a secondary priority. On conclusion, this proposed model is expected to bring a new perspective to the work of occupational health and safety analysts, since the priority suggested by Fine − Kinney risk analysis methods is not always in the same order as the one in the stage of taking action, and the source, budget, and cost/benefit ratio of the measure affect this situation in practice.

Every year, millions die in road accidents globally, imposing significant economic and humanitarian costs. While road traffic accidents are a major health concern, many developing countries, including Ethiopia, struggle to address this issue effectively. Ethiopia ranks second in East Africa for severe road traffic accidents, highlighting the need for improved injury reduction strategies. This study introduces a novel approach by chronologically identifying and prioritizing accident black spots in the studied area, Ethiopia. This method provides a valuable tool for transportation authorities and traffic police to target high-risk areas for immediate intervention. Focusing on the Dembecha-Injibara highway segment, the study employs both descriptive and inferential analyses, using the Zegeer method to calculate accident rates. It also uses factors of weight contributing to road traffic accidents and their severity to rank accident-prone areas. The findings reveal that areas near Finote Selam, Banja, and Burie are highly prone to severe accidents, with specific accident frequencies and priority values identified. Recommendations are offered to address these high-risk areas and mitigate severe traffic accidents in the study region.