Increase In Power Consumption Research Articles

Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller

Concomitant improvements in wireless communication and sensor technologies have increased capabilities of wearable biosensors. These improvements have not transferred to wireless prosthesis/orthosis controllers, in part due to strict latency and power consumption requirements. We used a Bluetooth Low Energy 5.3 (BLE) network to study the influence of the connection interval (10–100 ms) and event length (2500–7500 μs), ranges appropriate for real-time myoelectric prosthesis/orthosis control on the maximum network size, power consumption, and latency. The number of connections increased from 4 to 12 as the connection interval increased from 10 to 50 ms (event length of 2500 μs). For connection intervals ≤50 ms, the number of connections reduced by ≥50% with the increasing event length. At a connection interval of 100 ms, little change was observed in the number of connections vs. event length. Across event lengths, increasing the connection interval from 10 to 100 ms decreased the average power consumed by approximately 16%. Latency measurements showed that an average of one connection interval (maximum of just over two) elapses between the application of the signal at the peripheral node ADC input and its detection on the central node. Overall, reducing the latency using shorter connection intervals reduces the maximum number of connections and increases power consumption.

Using Deep Reinforcement Learning (DRL) for minimizing power consumption in Video-on-Demand (VoD) storage systems

As video streaming services such as Netflix become popular, resolving the problem of high power consumption arising from both large data size and high bandwidth in video storage systems has become important. However, because various factors, such as the power characteristics of heterogeneous storage devices, variable workloads, and disk array models, influence storage power consumption, reducing power consumption with deterministic policies is ineffective. To address this, we present a new deep reinforcement learning (DRL)-based file placement algorithm for replication-based video storage systems, which aims to minimize overall storage power consumption. We first model the video storage system with time-varying streaming workloads as the DRL environment, in which the agent aims to find power-efficient file placement. We then propose a proximal policy optimization (PPO) algorithm, consisting of (1) an action space that determines the placement of each file; (2) an observation space that allows the agent to learn a power-efficient placement based on the current I/O bandwidth utilization; (3) a reward model that assigns a greater penalty for increased power consumption for each action; and (4) an action masking model that supports effective learning by preventing agents from selecting unnecessary actions. Extensive simulations were performed to evaluate the proposed scheme under various solid-state disk (SSD) models and replication configurations. Results show that our scheme reduces storage power consumption by 5% to 25.8% (average 12%) compared to existing benchmark methods known to be effective for file placement.

Exergy efficiency improvement by compression heat recovery for an integrated natural gas liquefaction-CO2 capture-NGL recovery process

For liquefied natual gas production, cryogenic distillation offers lower thermal energy consumption and a more compact footprint for carbon capture unit than the widely used chemical absorption. However, the reduction in thermal energy consumption comes at the cost of increased power consumption. To reduce the power consumption of cryogenic distillation based natural gas lqiuefaction plants, an integrated process for liquefied natual gas production, cryogenic CO2 capture and natural gas liquids recovery is proposed, which is called the base process. This base process is further integrated with a heat recovery unit (Organic Rankine cycle or Kalina cycle) to investigate the optimal utilization of compression heat in the system. The proposed processes are modeled in Aspen HYSYS and optimized using a genetic algorithm installed in Matlab. The results show that specific power consumption of the base case is 0.385 kWh/Nm3 NG. Due to the lack of compression heat recovery, significant exergy loss occurs at the water coolers, accounting for approximately 37 % of the total system exergy destruction. Among the evaluated methods, the supercritical Organic Rankine cycle, using R1234ze as the working fluid, recovers approximately 60 % of the exergy from compression heat with an 8.7 % improvement in system exergy efficiency. The subcritical Organic Rankine cycle, using R600a as the working fluid, achieves a slightly lower exergy efficiency improvement of 7.1 %. The Kalina cycle, while less efficient, still improves the system’s exergy efficiency by 6.3 %. Economic analysis reveals that the total annualized cost of the base process is $8,441,416, with a levelized cost of $0.1411/kg. The subcritical Organic Rankine Cycle provides the most cost-effective solution, with the lowest total annualized cost of $8,305,154 and a levelized cost of $0.1402/kg. The Kalina cycle ranks second in cost-effectiveness, with a total annualized cost of $8,345,914 and a levelized cost of $0.1405/kg NG. Although the supercritical ORC requires the highest capital investment, it proves more cost-effective than the base process due to its reduced annual operating costs.

A centralized frost detection and estimation scheme for Internet-connected domestic refrigerators

Frost accumulation on heat exchange units is a significant problem in refrigeration systems, adversely affecting their operating performance and thereby leading to increased power consumption. Therefore, timely detection and accurate quantification of frost are crucial for effective defrosting strategies. This study presents a novel centralized cloud-based IoT scheme for frost detection and thickness estimation. The image processing is performed on the cloud server to process evaporator coil images for frost thickness quantification. Experiments were conducted on a domestic refrigerator to assess the effectiveness of the proposed image-processing approach and determine latency and processing time. The presented scheme effectively quantifies frost thickness on the evaporator in the 1–5 mm range with a 10.8% error margin. The total inference time, which includes image acquisition, pre-processing, transmission latency, and frost thickness estimation, is approximately 5.15 seconds. The results demonstrate that the proposed image processing method performs comparably to conventional sensors and similar image processing techniques. Moreover, the centralized cloud-based IoT architecture presented effectively meets the scalability demands of consumer refrigerators.

Research on Techniques for Enhancing the Speed of Low-Power Operational Amplifier

In the context of low utilization, this paper explores various techniques for enhancing the speed of operational amplifier (OA). The main text delves into three methods to gain equilibrium: fully differential operational amplifier (FDA), twostage operational transconductance amplifier (OTA), and a novel high-speed calculation system architecture employing a “rhombus crystal tube.” The FDA improves speed by minimizing noise and enhancing bandwidth and output voltage swing, without increasing power consumption. The design of the two-stage OTA combines the characteristics of a differential amplifier and a telescopic amplifier, optimizing gain and slew rate through Miller compensation and noise gain manipulation, thereby achieving high-speed performance. A novel high-speed operational amplifier structure employs diamond transistors to supply substantial current for capacitor charging, enhancing the slew rate and overall speed. Throughout the text, these methods are presented as a means to jointly promote high speed, low power consumption, and the handling of a significant number of transistors. To further increase the speed, the size of the microcrystalline tube is crucial. The research direction is outlined, and while the design equipment is in the foreground, there remains room for progress, especially in applications for large-scale electrical models. Future research aims to enhance the power efficiency of circuits, making them more practical and efficient. This study provides valuable insights into balancing power consumption and speed in advanced electronic devices.

Appropriate Selection of Frequency Range and Damping Ratio to Reduce the Resonance Effect, Lower Power Consumption, and Improve the Accuracy of the Capacitive Accelerometer

Introduction: In this work, we explored the capacitive accelerometer in depth by highlighting its advantages over other accelerometers. However, a major challenge associated with using capacitive accelerometers lies in choosing the appropriate frequency range. Method: Incorrect selection of the frequency range can cause several problems. It can decrease the accuracy of the accelerometer, increase the measurement error, and increase power consumption. To overcome these challenges, we undertook detailed modeling of the physical behaviour of the capacitive accelerometer. This modeling takes into account the mechanical and electrical properties of the sensor, including its mass, its rigidity, and the characteristics of its capacitive circuit. By simulating the developed model, we analyzed how the accelerometer reacts to different frequencies of vibratory movements. Result: This analysis led to the extraction of an important mathematical relationship that links the natural frequency of the accelerometer to the frequency of the vibratory movements to which it is subjected. Using this mathematical relationship, we determined the optimal frequency range for capacitive accelerometer operation. This precise determination of the frequency range makes it possible to significantly reduce the measurement error by avoiding resonance regimes and ensuring that the sensor operates in its maximum precision zone. Conclusion: Additionally, this approach helps improve overall measurement accuracy and minimize sensor power consumption by avoiding inefficient operating conditions.

All-In-One Optoelectronic Transistors for Bio-Inspired Visual System.

Visual perception has profound effects on human decision-making and emotional responses. Replicating the functions of the human visual system through device development has been a constant pursuit in recent years. However, to fully simulate the various functions of the human visual system, it is often necessary to integrate multiple devices with different functions, resulting in complex, large-volume device structures and increased power consumption. Here, an optoelectronic transistor with comprehensive visual functions is introduced. By coupling diverse photoreceptive properties of the channel and electrical regulation through charge injection/ferroelectric switching from the hafnium-based gate, the devices can simulate functions of both photoreceptors in the retina and synapses in the visual cortex. A device array is constructed to confirm the perceptual functions of cone and rod cells. Subsequently, color discrimination and recognition for color images are achieved by combining the tunable perception and synapse functions. Then an intelligent traffic judgment system with this all-in-one device is developed, which is capable of making judgments and decisions regarding traffic signals and pedestrian movements. This work provides a potential solution for developing compact and efficient devices for the next-generation bio-inspired visual system.

Experimental study on hydraulic performance and cavitation characteristics of a R134a refrigerant self-lubricating centrifugal pump

As the primary power equipment in pump flooding cooling systems, the efficiency and performance of mechanical pumps play a crucial role in two-phase cooling systems. A high-speed centrifugal pump with self-lubricating working fluid was designed with a speed of 7500 rpm and a flow coefficient of 0.0506. The hydraulic and cavitation performance of the pump were tested with R134a as the working fluid. The results show that the working fluid pump is capable of efficiently pumping R134a with an efficiency of 40.3% and a head coefficient of 0.988 under the design condition. Within the tested range of inlet temperature from 5°C to 15°C, the flow coefficient from 0.01 to 0.105, and the height of the refrigerant tank from 1.4 m to 5 m, lower net positive suction heads available at the same flow rate will reduce the pump head, increase power consumption, and decrease efficiency. Increasing the pump speed from 3000 rpm to 7500 rpm can improve the pump's performance. At 6000 rpm, the critical cavitation number of the working fluid pump increases with the increase in flow coefficient. At 7500 rpm, the critical cavitation number has a minimum value when the flow coefficient φ = 0.0434. At the design speed and flow rate, both the critical cavitation coefficient and fracture cavitation number increase as the inlet temperature decreases. The inducer can significantly reduce the critical cavitation number of the pump. Finally, an empirical correlation considering the thermodynamic effects is proposed to predict the increase in the critical cavitation number.

Hybridized data encoding based encryption and Diffie Hellman decryption for security enhancement

Information security is extremely important because the modification and interception of the data can lead to loss of data integrity, confidentiality, and availability. The encryption process is a critical component of classic standard encryption techniques such as advanced encryption, standard block ciphers, and data encryption standards. The traditional algorithm requires increased power consumption and memory. Therefore, there is a need to develop an efficient cryptographic operation to promote better security. The encryption process is performed using the Hybridized Data Encoding (HDE) model. Here, the optimal keys can be selected using the Chaotic Coati optimization algorithm (ChCoA). The decryption process can be performed using the Enhanced Diffie Hellman algorithm (EDHA). The resulting achievements will include effective encryption and decryption algorithms that together strengthen data security, facilitate secure data exchange, and provide superior protection against data vulnerabilities. By implementing the proposed work in the Matlab working platform, the performances in terms of encryption time, decryption time, processing time and throughput are analyzed. The proposed model achieved a throughput of 249.1093 (kb/s) and a processing time of 0.55 (sec).

방사성 물질 흡착용 활성탄소섬유 시험방법 연구

The global demand for energy is escalating due to the increased power consumption for AI data centers, leading to the expansion of nuclear power facilities and a subsequent rise in radioactive waste generation. To address this, activated carbon filter systems have been employed in nuclear power plants to adsorb and prevent the atmospheric release of radioactive iodine, a byproduct of these facilities. However, the substantial annual discharge of activated carbon is causing challenges such as a shortage of waste storage facilities. To mitigate this issue, this study investigated the application of activated carbon fiber (ACF) in nuclear power plant filter systems. Adhering to existing standards for radioactive material adsorption, we established a suitable testing environment for ACF and conducted adsorption and removal tests. The results demonstrated a maximum removal efficiency of over 99.918% for radioactive iodine, indicating a performance comparable or superior to that of conventional activated carbon used in current processes. This research is expected to contribute positively to the diversification of materials used for radioactive material adsorption and removal in nuclear power plants, as well as the establishment of relevant standards.

Experimental study of a novel guided sequential immersion cooling system for battery thermal management

Immersion cooling exhibits superior cooling performance compared to traditional battery thermal management systems (BTMS). However, a significant challenge of immersion cooling is the spatial variation of temperature within both the coolant and lithium-ion batteries (LIBs). This research proposes a guided sequential immersion cooling (GSIC) BTMS to address this issue. Experimental studies were conducted to evaluate the heat dissipation performance of the GSIC structure under various conditions, including extreme loads. The results indicate that under the static flow immersion cooling (SFIC) scheme, cooling the tabs significantly influences the overall performance. Immersing the tabs can reduce the maximum battery temperature by 10.403 °C, although the spatial variation of temperature persists. Under forced flow immersion cooling (FFIC) conditions, increasing the coolant flow rate dissipates the heat generated by LIBs more effectively. Even at an extreme discharge rate of 5C, the maximum temperature remains below 45 °C. The average temperature reduction at the tabs is greater than the battery body, and with increased flow rates, the temperature difference between the two can be maintained within 1 °C under all conditions. As the flow rate keeps increasing, the average temperature at the tabs gets even lower than the battery body at low loads. This demonstrates that the GSIC BTMS can suppress the temperature rise at the tabs, which is a critical heat risk. Moreover, the temperature uniformity of the LIBs module is improved. As the flow rate increases, the temperature difference within the LIBs module decreases when the discharge rates is between 1C and 5C. Theoretical analysis confirms that increasing flow rates for low loads can suppress temperature rise, albeit with increased power consumption and reduced cooling efficiency. High flow rates are more suitable for high load conditions of the LIBs. These results validate the feasibility of the GSIC BTMS and provide new insights for the development of immersion cooling BTMS.

Enhanced Coil Design for Inductive Power-Transfer-Based Power Supply in Medium-Voltage Direct Current Sensors

This paper presents an integrated coil design method for inductive power-transfer (IPT) systems. Because a medium-voltage direct current (MVDC) distribution network transmits power at relatively high voltages (typically in the tens of kV), accurate fault diagnosis using high-performance sensors is crucial to improve the safety of MVDC distribution networks. With the increasing power consumption of high-performance sensors, conventional power supplies using optical converters with 5 W-class output characteristics face limitations in achieving the rated output power. Therefore, this paper proposes a safe and reliable power supply method using the principle of IPT to securely maintain the insulation distance between the distribution network and the current sensor-supply line. A 100 W prototype IPT system is investigated, and its feasibility is validated by comparing its performance with conventional optical converters.

Low power techniques for internet of things implementation: A review

The motive of designing a low-power system is to have a long battery life. One can carefully apply low-power design techniques to achieve low power consumption with proper system functioning to increase battery life. One such system where many devices are generally battery-operated and connected to the internet is the Internet of Things (IoT). IoT devices are used widely to gather data to propel actions and transmit the data for data analysis, connectivity, and automation. So much research is focused on IoT communication and computation in IoT devices, which increases power consumption. This survey explores low-power design strategies used at the architectural level. Additionally, it highlights the IoT features that give opportunities to apply low-power design methodologies to achieve less power consumption in IoT. Low-power design techniques are critical for the efficient functioning of Internet of Things (IoT) applications, where devices often operate in resource-constrained environments with limited power sources like batteries. Many applications of IoT are noticed including the domains such as industry, environment, and society, which are highly power-consuming because of the features such as computation, communication, security, and fault tolerance. Researchers have already reported the usefulness of many such techniques including architecture-level low-power design techniques. Still, it is observed that the work carried out is inadequate regarding power consumption, and communication bandwidth bottlenecks. In the present work, a rigorous literature survey is carried out based on low-power design techniques to support sustainable growth of IoT applications across various domains and power optimization.

RETNet: Resolution enhancement Transformer network for magnetic particle imaging based on X-space

Magnetic Particle Imaging (MPI) can visualize the concentration distribution of superparamagnetic iron-oxide nanoparticles (SPIONs) in tissues with the advantages of high sensitivity and high temporal resolution. However, the low spatial resolution of MPI limits its application. Increasing the gradient strength of the selection field can improve the resolution of MPI, but also increase power consumption and noise. A feasible and cost-effective method to address this limitation is to reconstruct high gradient (HG) image from low gradient (LG) image using algorithms. Deep learning has been a powerful tool for improving the resolution of medical imaging techniques. In this study, we propose a Resolution Enhancement Transformer Network (RETNet) for reconstructing HG image with high-resolution from LG image with low-resolution as input, avoiding high power consumption and high noise in the system with HG field. RETNet leverages a shallow feature extractor to capture shallow features, a cross-scale-Transformer (CST) to focus on textural features, a residual-swin-Transformer (RST) to focus on structural features, and an image reconstruction module to aggregate these three types of features and reconstruct the HG image. Textural and structural features extracted can ensure the integrity of the details and the realization of high definition in the reconstructed image. Ablation experiments demonstrate the significant contribution of these two modules to reconstruct the HG image. Comparative experiments, including experiments at noise-free and multiple noise levels, confirm the high robustness of RETNet. Simulation, phantom, and in vivo experiments consistently demonstrate that RETNet outperforms competing methods and effectively improves the resolution of MPI.

INVESTIGATIONS ON THERMAL PERFORMANCE OF AN ELECTRONIC BOARD USING CONDUCTION-BASED FINITE ELEMENT METHOD WITH A NEW MODELING APPROACH

Electronic systems are used in almost all areas of industry, with an increasing power consumption rate. This trend makes thermal management of electronics compulsory in order for proper operation. Several methods can be employed to examine electronics’ thermal behavior. Conduction-based Finite Element Method (FEM) for heat transfer analysis is one of them; providing accurate solutions within short solution times is one of its outstanding advantages. Nevertheless, the fluid inside or around the system, usually air for electronic systems, is not included directly in the conduction-based FEM analysis model. This is an essential deficiency in terms of solution accuracy. If this drawback is overcome, conduction-based FEM will become a preferred analysis method, especially for transient problems under natural convection. In this study, a conduction-based FEM analysis model of an electronic board with two heat-dissipating components inside an enclosure under transient natural convection was developed. The procedure of the model involves the correction of unknown input parameters. An experimental investigation was performed, the results of which were used as reference values for the correction process. These unknown parameters were determined iteratively. The iteration was continued until the results of the analysis and those of the experiment matched. The difference between the results of the analysis and those of the experiment was less than 2-3°C. Some parametrical thermal investigations were performed on the electronic board using the final analysis model.

Design and on-site alert effect of piezoelectric device with amplified displacement for improving clean-energy collection

The installation of road alert devices leads to increased power consumption and electric network complexity on roads, which can be mitigated by cantilever piezoelectric transducers applicable to self-powered road alert devices. However, currently the cantilever piezoelectric transducer does not match the actual pavement deformation. In view of this, the link-type stroke amplification mechanism for road was designed. Then, the factors influencing the magnification were clarified and the size parameters were optimized. Finally, the performance of the piezoelectric transducer array under the amplification mechanism was investigated, and the traffic alert application effect of the amplified displacement road piezoelectric device was tested. The results showed that: The end connecting rod with short length and small distance and the middle connecting rod with longer length were favorable to the magnification. The theoretical magnification of the fabricated stroke amplification mechanism is 2.0. Under its action, the 0.7 mm excitation displacement could result in piezoelectric transducer array electrical outputs of 48 V, 103.96 mW, and power densities of 32.1 W/m2. And the alert piezoelectric self-powered effect was good based on the actual road test. The results will help improve the level of road alert self-powered, and promote the development of clean energy on the road.

Quantum Enhanced Josephson Junction Field-Effect Transistors for Logic Applications

The need of data centers for cloud and network computing has dramatically increased power consumption. In 2014, data centers in the U.S. consumed an estimated 70 billion kWh, representing about 1.8% of total U.S. electricity consumption [“United States Data Center Energy Usage Report," published in June 2016 and supported by the Federal Energy Management Program of the U.S. Department of Energy]. Worldwide, the International Energy Agency estimates that currently about 1% of all global electricity is used by data centers. It is expected that by 2030 the electricity use of information and communications technology may exceed 20% of all global electricity. The microelectronics industry has been focusing on aggressively shrinking the size of logic and memory devices to reduce power consumption. However, this scaling is going to eventually stop as we approach the fundamental materials limit. Moreover, the power dissipation due to the increased number of transistors is also expected to be a limiting factor for processor performance.Superconducting computing has been suggested as a promising approach for low-energy, power-efficient circuit applications. Moreover, using superconducting interconnects can further reduce power consumption. Josephson junction field-effect transistors (JJFET, Fig. a) have recently emerged as a promising candidate for superconducting computing. JJFETs are particularly useful for low power consumption applications as they are operated with, in the superconducting regime, zero voltage drop across its source and drain. Feasibility of using JJFETs for logic operations has been explored [1]. Moreover, high noise margin has been demonstrated for the logic inputs [2]. Compared to single flux quantum approach, JJFET circuits can use designs similar to conventional silicon MOSFETs [1].For JJFETs to perform logic operations, the gain-factor (αR) value must be larger than 1. Here αR = dIc/d(Vg–Vt) x πΔ/Ic, Δ is the superconducting gap, Vg the gate bias voltage, Vt the threshold voltage. Ic ∝ exp(-L/ξc) is the critical supercurrent, where L is the channel length and ξc the carrier coherence length [3]. In a conventional JJFET, ξc ∝ (Vg–Vt)0.5 (Fig. b), and thus dIc/d(Vg–Vt) is small (Fig. c). This translates to a requirement of superconducting transition temperature of ~ 400K for αR larger than 1, far exceeding any recorded critical temperatures. As such, it is impossible to use conventional JJFETs for logic operations.Here, we propose a novel type of JJFET based on quantum phase transition, such as the excitonic insulator (EI) transition in a type-II InAs/GaSb heterostructure [4,5], for low-energy, power-efficient logic applications. The nature of the collective phenomenon in the EI quantum phase transition can provide a sharp transition of the supercurrent states (e.g., ξc ∝ (Vg–Vt)5 and dIc/d(Vg–Vt) very large, as shown in Figs. b and c, respectively) which will enable αR larger than 1 with an easy-to-achieve superconducting transition temperature of ~ 40K. In this talk, we will present some preliminary results demonstrating that indeed the gain factor in these quantum enhanced JJFETs can be greatly improved, thus making them a promising candidate for logic applications.Sandia National Laboratories is a multimission laboratory managed and operated by NTESS LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. DOE’s NNSA under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. DOE or the United States Government.

Secure Triggering Frame-Based Dynamic Power Saving Mechanism against Battery Draining Attack in Wi-Fi-Enabled Sensor Networks.

Wireless local area networks (WLANs) have recently evolved into technologies featuring extremely high throughput and ultra-high reliability. As WLANs are predominantly utilized in Internet of Things (IoT) and Wi-Fi-enabled sensor applications powered by coin cell batteries, these high-efficiency, high-performance technologies often cause significant battery depletion. The introduction of the trigger frame-based uplink transmission method, designed to enhance network throughput, lacks adequate security measures, enabling attackers to manipulate trigger frames. Devices receiving such frames must respond immediately; however, if a device receives a fake trigger frame, it fails to enter sleep mode, continuously sending response signals and thereby increasing power consumption. This problem is specifically acute in next-generation devices that support multi-link operation (MLO), capable of simultaneous transmission and reception across multiple links, rendering them more susceptible to battery draining attacks than conventional single-link devices. To address this, this paper introduces a Secure Triggering Frame-Based Dynamic Power Saving Mechanism (STF-DPSM) specifically designed for multi-link environments. Experimental results indicate that even in a multi-link environment with only two links, the STF-DPSM improves energy efficiency by an average of approximately 55.69% over conventional methods and reduces delay times by an average of approximately 44.7% compared with methods that consistently utilize encryption/decryption and integrity checks.

QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

Strengthening the energy efficiency ratio of warm deep gas-assisted hydrate production through optimizing water circulation

Utilizing the geothermal energy from the deep gas layer to enhance the hydrate decomposition is a potential clean production approach, avoiding the vast costs and severe pollution in the traditional heat injection scheme. In this work, a novel method was proposed to promote hydrate decomposition by employing circulating water to transport heat from the deep gas layer. Results showed that increasing the water flow rate from 9.82 mL/min to 39.28 mL/min could significantly improve the production efficiency by 27.27 %. Yet, the energy efficiency ratio (EER) slightly decreased by 3.31 % due to the increased power consumption of the water pump. A hybrid water flow rate was thus used with a lower rate in the later production stage when the heat absorption of hydrate decomposition was declining accordingly. This was found to successively improve the EER by 2.68 % while maintaining comparable production efficiency; a further cycling regulation (intermittent circulating) of the hybrid water flow rate could significantly enhance the EER by 16.23 %. The results could guide the efficient utilization of natural geothermal energy to facilitate the large-scale gas production from the natural gas hydrates when heat supply from the surrounding sediments was limited.