Lossless fabrics are widely used in many production data centers, but they can give rise to issues such as head-of-line blocking and congestion spreading during network congestion, which can significantly degrade the performance of data center applications. Additionally, the latency of end-to-end solutions can lead to the buildup of switch queues. To address these challenges, this paper proposes a method called Priority Flow Control-Sensitive (PFC-S). PFC-S monitors buffer occupancy and traffic intensity, performs proactive rerouting, and prevents the impact of PFC congestion diffusion on victim flows. This approach helps maintain low buffer usage levels, thereby enabling control over tail latency. Initial evaluations demonstrate that PFC-S can reduce the average flow completion time and effectively prevent congestion spreading. Moreover, experimental results show that PFC-S provides better protection for victim flows compared to standard PFC, BFC, and HPCC methods.
The sixth generation (6G) of wireless communication envisions a diverse range of use cases, including high-mobility vehicular networks, non-terrestrial satellite links, and ultra-reliable low-latency communication scenarios. Conventional multicarrier waveforms, such as orthogonal frequency division multiplexing (OFDM), demonstrate constraints in highly dynamic and Doppler-rich settings, presenting difficulties in achieving 6G performance standards. In this context, orthogonal time frequency space (OTFS) modulation has emerged as a promising candidate due to its delay-Doppler domain processing and robustness to time-variant channel impairments. This paper provides a unified performance evaluation of OTFS for 6G, investigating its bit error rate (BER), energy efficiency (EE), and peak-to-average power ratio (PAPR) under realistic channel models. EE analysis reveals a strong dependency on circuit power consumption and hardware efficiency, where OTFS maintains superior EE across mobility regimes, particularly in vital vehicular-to-everything (V2X) links. Finally, PAPR evaluations highlight the trade-off between transmit efficiency and signal quality, with OTFS offering a manageable PAPR profile that can be optimized through pulse shaping and power control techniques. Evaluating BER, EE, and PAPR for OTFS under realistic 6G channel models provides a unified perspective on its practical deployment potential. These results underline OTFS as a promising modulation candidate for 6G systems, balancing reliability, energy sustainability, and hardware feasibility in high-speed vehicular networks.
Flying ad-hoc networks (FANETs) assist in high-risk tasks and monitor locations where human reachability is always at risk. FANETs have become one of the most appealing solutions for the Internet of Things in such scenarios. However, the default access mechanism in FANETs, binary exponential back-off, leads to increased collisions and energy utilization due to its lack of network adaptability. The proposed solution is a genetic fuzzy logic backoff (GFLB) algorithm, which determines the contention window size (CWS) based on the collision and success rate of the FANET. The genetic function optimizes the collision-success ratio by minimizing the fitness, while this optimized fitness value and retransmission attempts are then used to predict the CWS for subsequent transmission. A discrete chain Markov model with channel error is developed to evaluate the performance of the GFLB algorithm in terms of success, collisions, delay, and energy utilization under varying densities of unmanned aerial vehicles. The extensive simulation results demonstrate that the proposed GFLB algorithm significantly improves the efficiency and energy usage of end devices in FANETs compared to existing algorithms. The GFLB algorithm effectively reduces collision rates and energy consumption while enhancing the success rate and reducing delay in transmissions.
As efforts to reduce the environmental impact of energy production expand, renewable energy sources are becoming increasingly significant in the global energy balance. Over the anticipated 20-year life of a wind turbine, operation and maintenance (O&M) expenditures are predicted to account for 65%-90% of the overall investment cost, including inflation and crane charges. The higher estimate is based on 600-7500 kW machines in North America, while the lower estimate derives from the Danish fleet of 600 kW turbines. Reliability studies indicate that O&M costs contribute roughly 20%-25% of the levelized cost per kWh. These expenses strongly influence the profitability of wind farms and the competitiveness of wind turbines compared to other renewable energy options, highlighting significant potential for technological improvement. The profitability of a wind farm and the competitiveness of wind turbines compared to other green energy options are strongly influenced by O&M costs. Accurate wind speed and power generation prediction is therefore essential to improve efficiency and reduce investment costs. To address this, machine learning algorithms such as long-short term memory (LSTM), gated recurrent unit (GRU), artificial neural network (ANN), XGBoost, random forest (RF), and support vector machine (SVM) have been applied for forecasting wind speed and predicting power generation. Specifically, LSTM, GRU, and ANN are employed for wind speed forecasting, while XGBoost, RF, and SVM are used for electricity generation prediction. Results show that LSTM and GRU achieve lower root mean squared error than ANN in wind speed forecasting, while RF provides higher accuracy for power generation prediction compared to XGBoost and SVM. Overall, LSTM, GRU, and RF demonstrate strong performance in wind forecasting and power generation prediction.
Existing multi-path routing protocols can meet the service requirements of end-to-end delay and reliability between nodes in the Internet of Things, but they consume more energy. Constrained QoS routing is a new research method for routing protocols, which keeps up with the current trend. By considering the three QoS constraints of end-to-end delay, reliability, and energy consumption, we innovatively use the related techniques of mobile edge computing with machine learning automata to construct a sensor network as a multi-constrained optimal path model in this paper. At the same time, introducing the energy-aware node wake-up mechanism and the reward and punishment mechanism of learning automata, we propose an oriented mobile edge computing node energy-aware optimized QoS constrained routing algorithm. The algorithm can optimize the network energy consumption, extend the network life cycle, and accelerate the convergence by using learning automata. By experimental testing and comparison, the novel constrained Multi-QoS routing approach of energy-aware optimization based on learning automata for mobile edge computing (MQEN) proposed in this paper can reduce end-to-end delay, improve reliability, and decrease energy consumption.
