Response Delay Research Articles

Analysis of thermal stability for slow burning propellant based on isothermal testing: Self‐accelerating decomposition temperature (SADT) calculation and validation

AbstractBurning rate suppressants (BRSs) refer to a series of additives that reduce the burning rate of propellants, crucial for achieving sustained and stable thrust. This research focuses on assessing the impact of ammonium sulfate and ammonium oxalate on thermal stability and their potential as BRSs. Due to the stronger inhibitory effect of ammonium sulfate on the AP proton transfer process, the activation energy of propellant's first decomposition can be increased from 94.71 kJ mol−1 to 129.69 kJ mol−1 at a 3 % addition level. Based on Semenov model, the self‐accelerated decomposition temperatures (TSADT) were calculated and validated through 7‐day isothermal test. Introducing ammonium sulfate and ammonium oxalate raised the TSADT from 197.31 °C to 220.90 °C and 215.06 °C, respectively, deviating less than 4 % from experimental results. Among the propellants tested, those with ammonium sulfate showed prolonged response delay times (44.43–33.60 h), lower superheating temperatures (222.8–445.5 °C), and reduced mass loss rates (33.0–71.4 %) after 7 days of isothermal storage at 220–240 °C. The consistency between thermal analysis and isothermal test underscores the significant impact of activation energy on thermal stability.

Review of Development and Characteristics Research on Electro-hydraulic Servo System

Background: The nonlinear problem of the electro-hydraulic servo system directly affects the system accuracy, response speed and other indicators, and the resulting problems of control inaccuracy and response delay urgently need to be solved. Method: This article provides an overview of the working principle of electro-hydraulic servo systems, reviews the development history of electro-hydraulic servo systems, and summarizes the current research difficulties and latest patents of electro-hydraulic servo systems from the characteristics of active and passive loading systems,electro-hydraulic servo system multi flexibility characteristics, gap nonlinearity,parameter time-varying characteristics,mathematical modeling,force tracking accuracy control strategy, etc. Result: A large number of scholars have proposed the latest optimization strategies to address the nonlinear problems in various aspects of the electro-hydraulic servo system, effectively improving the control accuracy and static and dynamic characteristics of the electro-hydraulic servo system. Conclusion: By summarizing the latest progress in the study of nonlinear problems in various links of electro-hydraulic servo systems, it has played a guiding role in their future improvement and optimization strategies.

Integrated operating room planning and scheduling: an ILP-Based off-line approach for emergency responsiveness at a local hospital in Naples

This paper addresses a real-world Integrated Operating Room Planning and Scheduling (IORPS) problem encountered by a local hospital in Naples, characterized by stringent emergency management constraints, requiring treatment initiation within a 20-minute time-window. We tackle this problem by an original Integer Linear Programming formulation, capable of dealing with different operating room management strategies (open, block and block-modified). Our work differs from conventional cost-focused models by adopting a patient-oriented objective function, aligning with public hospitals obligations. The proposed method has been validated using real-world data provided by the hospital. The performed experimentation demonstrates the efficiency of the approach, capable of determining the optimal solution within an acceptable computation time that aligns with hospital requirements. Moreover, it also highlights the relevance of using our optimization approach to reduce delays in emergency responsiveness. This confirms its practical usage as a substitute for the current manual procedure.

Learning automata based routing and content delivery for vehicular named data networking

In Vehicular Named Data Networking (VNDN), vehicle mobility leads to frequent reverse-path interruptions and stale forwarding information, which further results in content delivery failures and disenables aggregation. Taking into account these limitations, we propose a learning automata based routing and content delivery solution for VNDN. The novelties of the solution are threefold: (1) Learning automata is leveraged to make forwarding decisions and deliver vehicular contents; (2) Various metrics including connection durations, unsatisfied requests and data response delays are utilized to compute the selection probability of each vehicle in order to select the best vehicle for content delivery; and (3) Aggregation and in-network caching are achieved through stable reverse paths. The typical application of the proposal is that vehicles rapidly access nearby road conditions to maneuver safe driving. The proposal is evaluated quantitatively. Compared with the existing solutions, the proposal reduces the content delivery latency and overheads by nearly 32.54% and 38.3%, respectively.

Use of wireless technologies in IoT projects

When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.

Stability and Hopf Bifurcation of a Delayed Prey-Predator System with Fear, Hunting Cooperative, and Allee Effect

The effect of fear, the hunting cooperative process, and Allee's impact on the behavior of an ecological system are investigated and discussed. The impact of the delay of the prey's response to the predation risk is included. The Leslie-Gower growth is used to describe the growth of the predator population. Firstly, the solutions' existence, positivity, and boundedness within the limits of a suitable region in the parametric space for all time are studied. The stability of all equilibrium points under the surrounding environmental effects is established. Moreover, the occurrence of a Hopf bifurcation is discovered. The stability of the bifurcating periodic dynamic and their dynamical properties are studied. Finally, the obtained theoretical results are confirmed and validated utilizing numerical simulation. It is observed that the system possesses a bi-stable behavior and a Hopf bifurcation.

Investigation of Reynolds number effects on flow stability of a transonic compressor using a dynamic mode decomposition method

With the continuous increase in aircraft flight altitude, low Reynolds number effects in high-altitude environments significantly impact the stability of compressor operation. In this study, dynamic mode decomposition (DMD) of the flow field at various time instances near stall conditions in a transonic compressor was performed to investigate the influence of Reynolds number (Re) effects on critical flow characteristics and instability mechanisms. The results indicate that under different Reynolds number index (RNI) conditions near stall conditions, the frequency of tip leakage flow varies. Under high RNI condition, the influence of tip leakage flow is concentrated mainly from the leading edge to the mid-chord. The stall mode is primarily caused by the interaction of shock with the Tip Leakage Vortex, resulting in the breakdown of the vortex. The development of vortex structures inside the passage leads to a delay in the flow field response to blade passing. Under low RNI condition, the tip clearance leakage flow is stronger, with a relatively reduced influence from the leading edge to the mid-chord and an increased impact from the mid-chord to the trailing edge. The main cause of the stall mode is the circumferential movement of vortex structures, causing secondary leakage over adjacent blade tip clearances. The blockage formed by vortex structures within the passage is weaker, and the flow field response to blade passing remains consistent with the blade passing frequency. However, as stall develops further, the flow field response significantly decreases.

Optimizing Quality Classroom Engagement and Thinking and Rethinking Processes in Social Studies Courses through a Wait-time Model (WTM)

The purpose of this study is to find out how the wait-time provided by professors to the students’ influence their classroom engagement as well as their ability to think and re-think, both in the online and offline set-ups. The problem with wait-time during discussions is that it can hinder the flow of conversation and impede effective communication. One of the problems that confront students is the prolonged pauses or delays in responses that may lead to disengagement, loss of momentum, and decreased overall efficiency in exchanging ideas or information. This delay can be caused by various factors such as technical issues, hesitation in responses, or inadequate facilitation, all of which can contribute to a less productive and engaging discussion. This study used social-observation descriptive design, a form of scientific research. The samples were selected through purposive sampling, include three (3) classes from Bachelor of Secondary Education, majoring in Social Studies totaling 95 respondents and 5 social-science faculty members from the College of Teacher Education, City University of Muntinlupa, Philippines. The Survey on the Average Teacher’s Wait-time Tool and Classroom Discussion through a Wait-time Tool, constructed by the researchers, observation, and interviews were used to gather data from the samples. The study found out that the Average Wait-Time on "Creating Questions" has the longest average wait-time at 2 minutes and 11 while "Remembering Questions" have the shortest wait-time at 41 seconds. There is a strong positive correlation between the average length of wait-time and active classroom engagement. Longer wait-times result in more engaged students, whether in online or face-to-face classes. And the correlation between Wait-Time and Thinking and Rethinking reveals that there is a moderately negative correlation which suggests that the length of wait-time and the thinking and rethinking process have an inverse relationship. Extended wait-times may lead to reduced cognitive activity, indicating a need for balance. These findings emphasize the importance of optimizing wait-time in educational practices to facilitate meaningful engagement and cognitive processing. Balancing the duration of wait-time is essential to harness its benefits effectively. The authors concluded that through quality wait-time, students would be able to further enhance their analytical, critical, and higher order thinking skills.

Applying a clinical staging model in patients affected by schizophrenia spectrum disorder.

Clinical staging, already widespread in medicine, represents a new frontier in psychiatry. Our goal was to convert the existing theoretical staging model for schizophrenia into a feasible tool to have a timely assessment of patients' health status applicable in any psychiatric facility. We assessed the empirical soundness of a staging model for schizophrenia spectrum disorders (SSDs), primarily centered on their current status. This model delineated six sequential stages (1, 2A, 2B, 3A, 3B, and 4) based on factors like symptom recurrence, persistence, and progression, including functional decline. Our analysis involved data from 137 individuals affected by SSDs. We examined 22 baseline variables, 23 construct-related variables, and 31 potentially modifiable clinical variables. The latter stages demonstrated significantly poorer outcomes compared to the early stages across various measures, indicating medium to large effect sizes and a dose-response pattern. This pattern confirmed the validity of the model. Notably, stages 2 and 3A exhibited pronounced differences in comparison to other stages, although variables from each validation category also distinguished between consecutive stages, particularly 3A and beyond. Baseline predictors, such as familial predisposition to schizophrenia, neurodevelopmental impairment, childhood adversities, treatment delay, negative symptoms, neurological impairment, and inadequate early response to treatment, independently largely explained the staging variance. The clinical staging model, grounded in the extended course of psychosis, exhibited sound validity and feasibility, even without the use of biological or neuroimaging markers, which could greatly improve the sensitivity of the model. These findings provide insights into stage indicators and predictors of clinical stages from the onset of psychosis.

Application of Information Technology in Human Resource Adit to Employee Performance in CV Prima Rasa Selarasa

The purpose of this research is to analyze how the Application of Information Technology in Human Resources Audits on Employee Performance at CV. Prima Rasa Harmony. The results of this research show that in improving employee performance at CV Prima Rasa Selaras, the use of the Moka and Accurate Lite applications really helps the work, making it easier to process transactions carried out at each outlet without having to do it manually and taking a long time and in the process of using the Moka good application and Accurate Lite, there are still several weaknesses in the application, for example, not being able to refund, not being able to cancel orders and often experiencing response delays which result in the transaction process being slightly disrupted.

Probabilistic assessment of short‐term voltage stability under load and wind uncertainty

AbstractContemporary electricity networks are exposed to operational uncertainties, which may jeopardise the stability of the power grid. More specifically, the increasing penetration level of variable renewable energy generation and uncertainty in load demand are key catalysts for these emerging stability issues. A mathematical relationship is established to track the system voltage trajectory with respect to variations in uncertain inputs (associated with wind speed, system load, and wind power penetration levels). Additionally, it demonstrates the consequences of varying uncertain inputs on the short‐term voltage response across different potential operating conditions. The theoretical proposition has further been verified by the simulation studies with two test power networks in DIgSILENT PowerFactory software. The simulation results revealed that uncertain injection sources significantly impacted the system voltage at the receiving end. High uncertainty in wind speed and system loads increased voltage recovery variation, causing delays in voltage response during low wind speeds and high system loads. Additionally, increased wind power penetration levels expanded voltage recovery uncertainties, resulting in decreased system voltage and potentially leading to voltage violations and instability at 30% wind power levels. Moreover, the results showed that the system's response time increased, and in some cases, it collapsed due to increased system capacity (>80%) and dynamic load (>75%), as well as encountering a large disturbance under uncertain circumstances.

Perceptual decoupling in the sustained attention to response task is unlikely

Researchers dispute the cause of errors in high Go, low No Go target detection tasks, like the Sustained Attention to Response Task (SART). Some researchers propose errors in the SART are due to perceptual decoupling, where a participant is unaware of stimulus identity. This lack of external awareness causes an erroneous response. Other researchers suggest the majority of the errors in the SART are instead due to response leniency, not perceptual decoupling. Response delays may enable a participant who is initially unaware of stimulus identity, perceptually decoupled, to become aware of stimulus identity, or perceptually recoupled. If, however, the stimulus presentation time is shortened to the minimum necessary for stimulus recognition and the stimulus is disrupted with a structured mask, then there should be no time to enable perception to recouple even with a response delay. From the perceptual decoupling perspective, there should be no impact of a response delay on performance in this case. Alternatively if response bias is critical, then even in this case a response delay may impact performance. In this study, we shortened stimulus presentation time and added a structured mask. We examined whether a response delay impacted performance in the SART and tasks where the SART’s response format was reversed. We expected a response delay would only impact signal detection theory bias, c, in the SART, where response leniency is an issue. In the reverse formatted SART, since bias was not expected to be lenient, we expected no impact or minimal impact of a response delay on response bias. These predictions were verified. Response bias is more critical in understanding SART performance, than perceptual decoupling, which is rare if it occurs at all in the SART.

Analytical game strategies for active UAV defense considering response delays

In the realm of aerial warfare, the protection of Unmanned Aerial Vehicles (UAVs) against adversarial threats is crucial. In order to balance the impact of response delays and the demand for onboard applications, this paper derives three analytical game strategies for the active defense of UAVs from differential game theory, accommodating the first-order dynamic delays. The targeted UAV executes evasive maneuvers and launches a defending missile to intercept the attacking missile, which constitutes a UAV-Missile-Defender (UMD) three-body game problem. We explore two distinct operational paradigms: the first involves the UAV and the defender working collaboratively to intercept the incoming threat, while the second prioritizes UAV self-preservation, with independent maneuvering away from potentially sacrificial engagements. Starting with model linearization and order reduction, the Collaborative Interception Strategy (CIS) is first derived via a linear quadratic differential game formulation. Building upon CIS, we further explore two distinct strategies: the Informed Defender Interception Strategy (IDIS), which utilizes UAV maneuvering information, and the Unassisted Defender Interception Strategy (UDIS), which does not rely on UAV maneuvering information. Additionally, we investigate the conditions for the existence of saddle point solutions and their relationship with vehicle maneuverability and response agility. The simulations demonstrate the effectiveness and advantages of the proposed strategies.

Age-related changes in cheek skin movement: A case study of Japanese women.

The majority of conventional studies on skin aging have focused on static conditions. However, in daily life, the facial skin we encounter is constantly in motion due to conversational expressions and changes in facial expressions, causing the skin to alter its position and shape, resulting in a dynamic state. Consequently, it is hypothesized that characteristics of aging not apparent in static conditions may be present in the dynamic state of the skin. Therefore, this study investigates age-related changes in dynamic skin characteristics associated with facial expression alterations. A motion capture system measured the dynamic characteristics (delay and stretchiness of skin movement associated with expression) of the cheek skin in response to facial expressions among 86 Japanese women aged between 20 and 69 years. The findings revealed an increase in the delay of cheek skin response to facial expressions (r=0.24, p<0.05) and a decrease in the stretchiness of the lower cheek area with age (r=0.60, p<0.01). An increasing variance in delay and stretchiness within the same age group was also observed with aging. The findings of this study revealed that skin aging encompasses both static characteristics, such as spots, wrinkles, and sagging, traditionally studied in aging research, and dynamic aging characteristics of the skin that emerge in response to facial expression changes. These dynamic aging characteristics could pave the way for the development of new methodologies in skin aging analysis and potentially improve our understanding and treatment of aging impressions that are visually perceptible in daily life but remain unexplored.

An architecture to improve performance of software-defined optical networks

The software-defined optical network (SDON) is a revolutionary approach in the field of optical networks. The separation of the control plane and data plane in software-defined networking (SDN) provides enhanced security and simplified network administration. Nevertheless, performance and control plane scalability are significant issues in SDN. SDN performance can be evaluated using parameters such as burst loss, delay, channel occupancy, packet loss, throughput, and average response time. The number of messages exchanged between the data plane and the control plane is used as a metric to determine controller scalability. As the network load increases, the controller experiences a higher flow of messages. It causes delay and burst loss in transmitting the burst. Occasionally, bursts exceed the capacity of the fixed-sized burstifier and are discarded because it takes a long time to identify a suitable route for the burst. Hence, it is essential to minimize the volume of messages exchanged between the control plane and the data plane to improve performance and controller scalability. In this paper, we propose a scalable SDN optical network architecture that minimizes the number of messages exchanged between the data plane and the control plane. We proposed mechanisms like channel reservation, transmission cycles, and guard time between cycles to enhance both the speed and the quality of burst transmission. Prior to transmission, resources or channels are allocated to bursts to minimize the possibility of burst collision and loss. The data plane comprises an optical burst switching (OBS) network, and the flow table entries are periodically updated to minimize inter-plane communication. We perform simulations to evaluate and compare the performance of the proposed architecture with the existing state-of-the-art architecture reported in the literature. The proposed architecture performs better than the existing state-of-the-art in terms of metrics including burst loss, delay, channel occupancy, packet loss, throughput, average response time, and reduction in the number of messages exchanged between the data plane and the control plane. Experimental results indicate a 41% reduction in mean burst loss probability and a 40.5% reduction in mean burst sending delay compared to existing architectures. Additionally, 42.1% fewer messages are exchanged between the control plane and the data plane compared to the number of exchanged messages in existing architectures.

New Methods for Optimal Power Allocation and Joint Resource Scheduling in 5G Network which Use Mobile Edge Computing

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore, it is very important to design an energy-efficient offloading strategy. To this end, we first formulate the transmission power allocation (PA) problem for mobile phone users to minimize power consumption. Using a quasi-convex technique, we address the (PA) problem using a (Hybrid GAPSO) algorithm resulting from combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) algorithm. Next, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to minimize the response delay of requests. The (JRORS) problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a dual decision problem and propose a multi-objective particle swarm optimization algorithm referred as (MO-PSO). The simulation results show that (HGAPSO) can save transmission power consumption and has good convergence property, and (MO-PSO) outperforms existing methods in terms of response rate and can maintain good performance in a dynamic network.

Exploring the Potential of Deep Learning and Blockchain for Intrusion Detection Systems: A Comprehensive Review

Detecting intrusions in real-time within cloud networks presents a multifaceted challenge involving intricate processes such as feature representation, intrusion type classification and post-processing procedures. Distributed Intrusion Detection Systems (DIDSs) constitute a complex landscape characterized by diverse contextual nuances, distinct functional advantages and limitations specific to deployment scenarios. Despite these challenges, DIDS offers immense potential for addressing evolving intrusion detection challenges through tailored contextual adaptations and unique functional advantages. Moreover, exploring the limitations associated with different deployment contexts facilitates targeted improvements and refinements, unlocking new avenues for innovation in intrusion detection technologies. Notably, deep learning (DL) integrated with blockchain technology emerges as a superior approach in terms of security, while bioinspired models excel in Quality of Service (QoS). These models demonstrate higher accuracy across various network scenarios, underscoring their efficacy in intrusion detection. Additionally, edge-based models exhibit high accuracy and scalability with reduced delay, complexity and cost in real-time network environments. The fusion of these models holds promise for enhancing classification performance across diverse attack types, offering avenues for future research exploration. This text conducts a comprehensive comparison of performance metrics, including accuracy, response delay, computational complexity, scalability and deployment costs. The proposed Novel DIDS Rank (NDR) streamlines model selection by considering these metrics, enabling users to make well-informed decisions based on multiple performance aspects simultaneously. This unified ranking approach facilitates the identification of DIDS that achieves high accuracy and scalability while minimizing response delay, cost and complexity across varied deployment scenarios.

Benefits, barriers and facilitators for out-of-hospital point of care testing: a qualitative study

Background Heightened pressures on hospitals and Emergency Medical Services (EMS) due to growing demand and staff shortages have led to prolonged ambulance response times and delays in handing over the care of EMS patients on arrival at an Emergency Department. These delays jeopardise patient safety and staff wellbeing. Point of care (POC) tests in EMS have been proposed to facilitate effective on-scene decision-making, reduced conveyance, improved clinical outcomes, enhanced system efficiency and patient experience. Despite an acceleration in POC testing during the Covid-19 pandemic, limited evidence exists for integrating POC tests into routine EMS practice. The aim of this research was to explore the impact, benefits, barriers, and facilitators of POC testing in United Kingdom (UK) EMS, alongside factors influencing future research on POC testing implementation. Methods Convenience and snowballing sample techniques were used to recruit a diverse stakeholder group, including patient and public participants, for online semi-structured interviews between June and July 2023. Interviews were recorded, transcribed verbatim and thematically analysed using the framework method. The codes were pre-selected using the outcomes of a prior stakeholder event and double coded by the research team. Results A total of 25 participants took part in semi-structured interviews. Whilst most participants identified clear potential benefits associated with the implementation of further POC tests within UK EMS, barriers that need to be considered in future research were also highlighted. Three themes were identified: enhancing patient care and system efficiency through POC testing; navigating implementation challenges: balancing barriers and facilitators for successful implementation; building the foundations: key considerations for future research. Conclusions Our study indicates that although the adoption of further POC testing was viewed positively, with substantial potential for impact, it will be essential to carefully address the barriers identified, along with insights from prior research, to overcome the associated challenges effectively.

GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning

Routing optimization, as a significant part of Traffic Engineering (TE), plays an important role in balancing network traffic and improving quality of service. With the application of Machine Learning (ML) in various fields, many neural network-based routing optimization solutions have been proposed. However, most existing ML-based methods need to retrain the model when confronted with a network unseen during training, which incurs significant time overhead and response delay. To improve the generalization ability of the routing model, in this paper, we innovatively propose a routing optimization method GROM which combines Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN), to directly generate routing policies under different and unseen network topologies without retraining. Specifically, for handling different network topologies, we transform the traffic-splitting ratio into element-level output of GNN model. To make the DRL agent easier to converge and well generalize to unseen topologies, we discretize the huge continuous traffic-splitting action space. Extensive simulation results on five real-world network topologies demonstrate that GROM can rapidly generate routing policies under different network topologies and has superior generalization ability.

Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality

In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%.