Long-range Communication Research Articles

Wireless Underground Sensor Communication Using Acoustic Technology.

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin-Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs.

Improved DeepLabV3+ Network Beacon Spot Capture Methods

Improved DeepLabV3+ Network Beacon Spot Capture Methods

Transcription factor shapes chromosomal conformation and regulates gene expression in bacterial adaptation.

Genomic mutations allow bacteria to adapt rapidly to adverse stress environments. The three-dimensional conformation of the genome may also play an important role in transcriptional regulation and environmental adaptation. Here, using chromosome conformation capture, we investigate the high-order architecture of the Zymomonas mobilis chromosome in response to genomic mutation and ambient stimuli (acetic acid and furfural, derived from lignocellulosic hydrolysate). We find that genomic mutation only influences the local chromosome contacts, whereas stress of acetic acid and furfural restrict the long-range contacts and significantly change the chromosome organization at domain scales. Further deciphering the domain feature unveils the important transcription factors, Ferric uptake regulator (Fur) proteins, which act as nucleoid-associated proteins to promote long-range (>200 kb) chromosomal communications and regulate the expression of genes involved in stress response. Our work suggests that ubiquitous transcription factors in prokaryotes mediate chromosome organization and regulate stress-resistance genes in bacterial adaptation.

Extracellular vesicles in non-small cell lung cancer stemness and clinical applications.

Non-small cell lung carcinoma (NSCLC) accounts for 85% of lung cancers, the leading cause of cancer associated deaths in the US and worldwide. Within NSCLC tumors, there is a subpopulation of cancer cells termed cancer stem cells (CSCs) which exhibit stem-like properties that drive NSCLC progression, metastasis, relapse, and therapeutic resistance. Extracellular vesicles (EVs) are membrane-bound nanoparticles secreted by cells that carry vital messages for short- and long-range intercellular communication. Numerous studies have implicated NSCLC CSC-derived EVs in the factors associated with NSCLC lethality. In this review, we have discussed mechanisms of EV-directed cross-talk between CSCs and cells of the tumor microenvironment that promote stemness, tumor progression and metastasis in NSCLC. The mechanistic studies discussed herein have provided insights for developing novel NSCLC diagnostic and prognostic biomarkers and strategies to therapeutically target the NSCLC CSC niche.

AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity.

In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.

High directivity microstrip patch antenna design using binary ebola search optimization for radio frequency identification application

Microstrip patch antenna has gained significant popularity in wireless communication field, because of its compact size, less profile, and ease of installation. It is widely used in various applications such as Radio-Frequency Identification (RFID) systems, where dependable and long-range communication is made possible by the great directivity and efficiency. In this manuscript, a High Directivity Microstrip Patch Antenna design using Binary Ebola Search Optimization for Radio Frequency Identification Application (MPA-RFID-BESO) is proposed.The design of microstrip patch antennas is the difficulty in achieving high directivity while maintaining compact size and low profile. This research explains design and application-specific optimisation of the Microstrip Patch Antenna (MPA) of biomedical utilizing Binary Ebola Search optimization (BESO) algorithm. Microstrip patch antenna is designed to function in double and multi band applications due to its low cost, light weight, and ease of installation. MPA is made with flawed ground construction to lessen cross-polarized radiation emitted by microstrip patches and to achieve the necessary radiation parameters. Here, the cost of the materials is reduced by using a liquid crystal polymer substrate, and aerial performance is enhanced by using the appropriate geometrical parameters. The small design of the BESO optimised improves the performance of antenna as 50 mm×50 mm compact size. The MATLAB program uses high frequency to do the simulation method. The proposed antenna design is a preferable option for applications involving Radio Frequency Identification (RFID). The performance metrics, such as radiation pattern, constant gain, directivity, bandwidth, and antenna efficiency and return loss are measured and compared to the existing techniques. The proposed MPA-BA-BESO design provides 10.51%, 12.85% and 16.04% higher bandwidth and 23.63%, 47.86% and 32.06% higher gain compared with existing designs, like Designing MPA utilizing Moth–Flame optimization approach (MPA-UWB-MFOA), Predicting Rectangular Patch Microstrip Antenna Dimension utilizing Machine Learning (MPA-ANN), Dual Band Antenna Design and Prediction of Resonance Frequency Utilizing Machine Learning Algorithms (DBAD-RF-CNN) respectively. The proposed technology serve as a better design alternative for microstrip patch antennas in communication systems to address biological applications.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

Detecting Halyomorpha halys using a low-power edge-based monitoring system

Smart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature.

Scalability and Performance Analysis of LoRaWAN Networks for IoT Implementation: Comprehensive Review and Research Agenda

The surge in IoT devices has created the need for a scalable and high-performance network infrastructure capable of handling massive data flows and diverse application requirements. Among the various wireless technologies available, with long-range communication capabilities, low power consumption, and cost-effectiveness, the Low-Power Wide-Area Network (LPWAN) has emerged as a highly popular choice for numerous applications. This literature review aims to provide a comprehensive overview of recent developments in scalability and performance analysis of LoRaWAN networks for IoT implementation by reviewing existing research and identifying key challenges and opportunities for the research agenda. In the research on LoRaWAN performance in IoT, five characteristics have been proven and become factors for using LoRaWAN in IoT: low energy power usage, wide coverage, low cost, stable data transmission speed, and system reliability. However, not many have discussed its security. Based on the literature review, LoRaWAN does have some characteristics that make it suitable for large-scale IoT applications. This review will contribute to advancing knowledge in this field, enabling the development of scalable and high-performance IoT systems to meet the demands of the growing IoT ecosystem

Approaches to Characterize and Quantify Extracellular Vesicle Surface Conjugation Efficiency.

Extracellular vesicles (EVs) are cell-secreted nanovesicles that play an important role in long-range cell-cell communication. Although EVs pose a promising alternative to cell-based therapy, targeted in vivo delivery still falls short. Many studies have explored the surface modification of EVs to enhance their targeting capabilities. However, to our knowledge, there are no standardized practices to confirm the successful surface modification of EVs or calculate the degree of conjugation on EV surfaces (conjugation efficiency). These pieces of information are essential in the reproducibility of targeted EV therapeutics and the determination of optimized conjugation conditions for EVs to see significant therapeutic effects in vitro and in vivo. This review will discuss the vast array of techniques adopted, technologies developed, and efficiency definitions made by studies that have calculated EV/nanoparticle surface conjugation efficiency and how differences between studies may contribute to differently reported conjugation efficiencies.

An Environment-Aware Adaptive Data-Gathering Method for Packet-Level Index Modulation in LPWA.

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively.

A hybrid convolutional neural network-transformer method for received signal strength indicator fingerprinting localization in Long Range Wide Area Network

In recent years, low-power wide area networks (LPWANs), particularly Long-Range Wide Area Network (LoRaWAN) technology, are increasingly being adopted into large-scale Internet of Things (IoT) applications thanks to having the ability to offer cost-effective long-range wireless communication at low-power. The need to provide location-stamped communications to IoT applications for meaningful interpretation of physical measurements from IoT devices has increased demand to incorporate location estimation capabilities into LoRaWAN networks. Fingerprint-based localization methods are increasingly becoming popular in LoRaWAN networks because of their relatively high accuracy compared to range-based localization methods. This work proposes hybrid convolutional neural networks (CNNs)-transformer fingerprinting method to localize a node in a LoRaWAN network. CNNs are adopted to complement the strengths of the Transformer by adding the ability to capture local features from input data and consequently allow the Transformer, through the attention mechanism, to effectively learn global dependencies from the input data. Specifically, the proposed method works by first learning the local location features from the input data using the CNNs and passing the resulting information to the transformer encoder to learn global features from the input data. The output of the transformer encoder is then concatenated with information learned at the local level and then passed through the regressor for the final location estimation. With a localization performance of 290.71 m mean error achieved, the proposed method outperformed similar state-of-the-art works in the literature evaluated on the same publicly available LoRaWAN dataset.

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

Design and Implementation of a LoRa Technology - Based Smart Home Monitoring Circuit

This paper offers a detailed design and real-world execution of a complex electrical circuit for smart homes that employs LoRa technology to monitor significant environmental conditions and the operating state of home equipment. The circuit's novel design allows it to monitor power usage and the on/off state of home appliances, in addition to measuring temperature and humidity with extreme precision. With its reputation for long-range and low-power wireless communication, LoRa technology is used by the core microcontroller of the system, which also processes sensor data. This is why the system works particularly effectively in large residences where other wireless technologies would have trouble. The solution ensures consistent and dependable data transmission using LoRa, even in the face of the common architectural obstacles seen in residential environments. To facilitate data analysis and remote monitoring, an electrical circuit was made to be readily linked to a central hub for home automation. A comprehensive assessment procedure was used for implementation in different household environments to determine responsiveness, dependability, and user-centered design. The empirical findings demonstrate how well the circuit works to provide homes with practical advice on how to best regulate device use and optimize the interior temperature, which eventually improves comfort and energy efficiency. To achieve next-generation home automation systems, LoRa-based solutions are critical, and the article concludes by analyzing the consequences of this study on the future landscape of smart-house technologies.

A Review of Recent Patents on LoRaWAN

Background: LoRaWAN technology enables IoT devices with ubiquitous connectivity, which further empowers a broad spectrum of applications, including civil engineering, agriculture, and smart home. Although LoRaWAN has low-power energy consumption and long-range communication capability, it has many features that should be carefully managed to improve its performance. Objective: This paper aimed to review recent patents on improving the performance of LoRaWAN. Methods: The most related patents on improving the performance of LoRaWAN were discussed and analyzed. Results: A total of 71 patents were reviewed. This paper proposed a 2D taxonomy to categorize the existing patents. These patents aimed at focusing on the main components of LoRaWAN, i.e., end node, gateway, and server, and improving the performance of LoRaWAN from six aspects, i.e., energy efficiency, communication range, transmission efficiency, collision management, network security, and interoperability. The methodologies used in the current patent development were also discussed. Conclusion: Tremendous efforts have been devoted to improving the performance of LoRaWAN in recent years. To continue the growth of LoRaWAN technology, future patent development may focus on scalability, cost-effectiveness, and ease of implementation.

A Case Study of Nelder Mead Simplex Optimization Algorithm: Trade-Offs of Sprienski Fractal Bowtie Antenna Parameters

In this study, tri-band antenna design adapted for wireless communication, Internet of Things (IoT) and RFID systems is examined. The simulation results indicate that the proposed antenna has three distinct frequency bands. Band 1 (lower band) covers the frequency range of 1.64-1.78 GHz with a resonance frequency of 1.7 GHz. Band 2 covers the range of 3.06-3.9 GHz with a resonance frequency of 3.4 GHz with a high gain of 10 dBi and a radiation efficiency of 92% for long-range communication. Band 3 radiates from 6.25 to 7.6 GHz with a resonance frequency of 6.62 GHz, which is suitable for higher-frequency applications. The antenna design is simulated and analyzed regarding S11, VSWR, gain, radiation efficiency, and bandwidth. Especially, Band 2 (mid-band) provides notable performance, with its 10 dBi gain and 92% efficiency, which makes the proposed antenna an ideal structure for high-data-rate, long-distance communication systems, and 5G (midband) applications. This study also employs the Nelder-Mead Simplex algorithm to observe the optimization of the physical parameters of the proposed antenna via multiple objective functions. The optimization results outlines that longer the arm length of the proposed antenna causes to decrease the resonance frequency of Band 3. Addition to this, the gain is higher with the lower arm length except for the arm length of 90.467 mm and flare angle of 64.77o. That’s, the trade-off condition occurs between minimum return loss and gain. At this point, it can be concluded from this optimization algorithm results that each objective function should be evaluated separately due to this trade-off condition.

RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks

The ability to offer long-range, high scalability, sustainability, and low-power wireless communication, are the key factors driving the rapid adoption of the LoRaWAN technology in large-scale Internet of Things applications. This situation has created high demand to incorporate location estimation capabilities into large-scale IoT applications to meaningfully interpret physical measurements collected from IoT devices. As a result, research aimed at investigating node localization in LoRaWAN networks is on the rise. The poor localization performance of classical range-based localization approaches in LoRaWAN networks is due to the long-range nature of LoRaWAN and the rich scattering nature of outdoor environments, which affects signal transmission. Because of the ability of fingerprint-based localization methods to effectively learn useful positional information even from noisy RSSI data, this work proposes a fingerprinting-based branched convolutional neural network (CNN) localization method enhanced with squeeze and excitation (SE) blocks to localize a node in LoRaWAN using RSSI data. Results from the experiments conducted to evaluate the performance of the proposed method using a publicly available LoRaWAN dataset prove its effectiveness and robustness in localizing a node with satisfactory results even with a 30% reduction in both the principal component analysis (PCA) variances on the training data and the size of the original sample. A localization accuracy of 284.57 m mean error on the test area was achieved using the Powed data representation, which represents an 8.39% increase in localization accuracy compared to the currently best-performing fingerprint method in the literature, evaluated using the same LoRaWAN dataset.

Optimising performances of LoRa based IoT enabled wireless sensor network for smart agriculture

The significance of maintaining food security is particularly emphasized in regions such as Malaysia, where there is a substantial reliance on imports of fruits and vegetables. The local farming industry requires advancements in technology to address the complexities of food production. This motivates the exploration of implementing Internet of Things (IoT) solutions to reduce reliance on manpower and enhance farming efficiency. However, the current integrated IoT solutions for smart farms can be expensive and complicated, rendering them redundant for small-area farms that do not require advanced technologies or machine learning. This study aims to optimize IoT LoRa parameter settings, focusing on providing affordable solutions tailored for fruit and vegetable farms. The main goal is to address the common issue faced by farmers—investing in expensive IoT-enabled wireless sensors without realizing a return on investment. To overcome this challenge, a simple and cost-effective IoT solution is proposed, utilising sensors and readily available applications to achieve efficient farming practises. The main objective is to develop an integrated IoT solution tailored for fruit and vegetable farms, facilitating improved productivity and growth. The solution incorporates sensors to monitor and control various aspects of crop cultivation, collecting essential data. The research development here also aims to inspire low-cost IoT solutions suitable for small-scale food plantation farms by using the long-range physical communication technique (LoRa) protocol. Leveraging IoT technologies in a simplified manner allows local farming industries to boost productivity, lower operational costs, and effectively address food security concerns. In addition to the IoT technology development for smart farms, the research here also investigates the various factors affecting the performance of LoRa-enabled IoT solutions for smart farms, including LoRa frequency, distance, environment, and the performance of the IoT system under different weather conditions, showed a significant effect on system performance (p ≤ 0.05). This comprehensive approach aims to contribute valuable insights for the development and implementation of cost-effective IoT solutions tailored for small-scale agricultural practices. In conclusion, this project demonstrates the efficiency of LoRa-based IoT in agriculture and highlights areas for improvement. Future directions involve improving scalability, streamlining the user interface, and adding sophisticated features to support the development of productive, networked, and environmentally friendly farming methods.

A model for cooperativity of kinesin-4 motors by communicating through the microtubule track

Kinesin-4 motor proteins at nanomolar concentrations can accumulate at microtubule plus ends, regulating the microtubule length. Recent experimental studies showed that at picomolar motor concentrations, under which nearly no motor crowding can occur, the run length of the kinesin-4 motor increases whereas the velocity decreases with the motor concentration, indicating the presence of a long-range communication between the motors through the microtubule track. However, the mechanism of the long-range communication is illusive. Here, based on the proposal that large conformational changes of the tubulin induced by the strong interaction with a kinesin head can propagate along the microtubule such that influence the tubulin-binding energy of other motors distant away, the dynamics of the kinesin-4 motor at the picomolar motor concentrations is studied theoretically, explaining quantitatively the experimental data. Moreover, the lifetime of the motor when multiple motors are jammed near the plus ends at high motor concentrations is studied theoretically.

Optimizing Hybrid V2X Communication: An Intelligent Technology Selection Algorithm Using 5G, C-V2X PC5 and DSRC

Cooperative communications advancements in Vehicular-to-Everything (V2X) are bolstering the autonomous driving paradigm. V2X nodes are connected through communication technology, such as a short-range communication mode (Dedicated Short Range Communication (DSRC) and Cellular-V2X) or a long-range communication mode (Uu). Conventional vehicular networks employ static wireless vehicular communication technology without considering the traffic load on any individual V2X communication technology and the traffic dynamics in the vicinity of the V2X node, and are hence inefficient. In this study, we investigate hybrid V2X communication and propose an autonomous and intelligent technology selection algorithm using a decision tree. The algorithm uses the information from the received Cooperative Intelligent Transport Systems (C-ITS) Cooperative Awareness Messages (CAMs) to collect statistics such as inter vehicular distance, one-way end-to-end latency and CAM density. These statistics are then used as input for the decision tree for selecting the appropriate technology (DSRC, C-V2X PC5 or 5G) for the subsequent scheduled C-ITS message transmission. The assessment of the intelligent hybrid V2X algorithm’s performance in our V2X test setup demonstrates enhancements in one-way end-to-end latency, reliability, and packet delivery rate when contrasted with the conventional utilization of static technology.