User Speed Research Articles

Multitier Service Migration Framework Based on Mobility Prediction in Mobile Edge Computing

Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real‐world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service.

Layer two compression for erroneous wireless

Wireless connection such as 802.11 and cellar networks is preferred for internet connections as its quick set up and mobility. However, radio propagations are prone to signal errors, noises and interferences. Even more powerful modulations and coding were developed; they do not change the nature of error sensitive in wireless link. When network link worsens, the available bandwidth decreases. This exerts problems for the user speed requirement. This paper examines layer two compressions on decreasing link quality. The 802.11 network simulations based on Bianchi model and Shannon theory show that compression technique on layer two increases throughput about 10.27%, although delay increases about two times.

Reconfigurable Microarchitecture-Based PMDC Prototype Development for IoT Edge Computing Utilization

in this article, a Reconfigurable Microarchi- tecture-based PMDC prototype is developed with the help of dynamic pipeline stages. The Reconfigurable Microarchitecture is intended for the Internet of Things (IoT) edge level computing utilization purposes. This microarchitecture is implemented using a reconfigurable computing system with the help of an FPGA board. The reconfigurable microarchitecture is used as a feedback circuit of the PMDC motor to come out with a developed speed control prototype that can be sufficiently integrated with IoT technology. The number of pipeline stages will dynamically change as per user speed requirements. Several methods of speed measurement are tested using different pipeline structures to find out an appropriate method for the IoT edge level. Resource utilization, throughput and latency for those methods are analyzed. Additionally, the reconfigurable microarchitecture gives dynamic power consumption which more suitable for IoT edge level computations. The Real-Time behavior of the developed prototype is carried out with a pseudo-random binary sequence (PRBS) test for its system identification, a particle swarm optimization (PSO) technique for its optimal controller design and edge level computations for its IoT integration procedure.

Fifteen Years of Progress at Zero Velocity: A Review

Fifteen years have passed since the publication of Foxlin's seminal paper “Pedestrian tracking with shoe-mounted inertial sensors”. In addition to popularizing the zero-velocity update, Foxlin also hinted that the optimal parameter tuning of the zero-velocity detector is dependent on, for example, the user's gait speed. As demonstrated by the recent influx of related studies, the question of how to properly design a robust zero-velocity detector is still an open research question. In this review, we first recount the history of foot-mounted inertial navigation and characterize the main sources of error, thereby motivating the need for a robust solution. Following this, we systematically analyze current approaches to robust zero-velocity detection, while categorizing public code and data. The article concludes with a discussion on commercialization along with guidance for future research.

The optimization effect of fuzzy factional-order ordinary differential equation in block chain financial cross-border E-commerce payment mode

To solve the problems of low execution efficiency and insufficient security of traditional electronic payment mode, in this study, an improved mode of cross-border e-commerce payment based on fuzzy fractional-order ordinary differential equation and secure electronic transaction protocol is proposed. Firstly, the competition model of cross-border e-commerce payment platform is constructed using fuzzy fractional order ordinary differential equation. Based on the theory of Hopf bifurcation, the stability of the model is analyzed. Then, the cross-border e-commerce improved payment mode is built based on the secure electronic transaction protocol. Finally, the efficiency and security performance of the improved payment mode are compared with that of the traditional payment mode with face through simulation experiments. The results show that the improved mode takes less time in encryption and decryption than the traditional mode; the time spent on the security payment of the improved mode remains around 1.2 s, while that of the traditional mode is around 2.7 s; the improved mode security payment takes significantly less time than the traditional mode, which indicates that the payment mode proposed in this research based on fuzzy fractional order ordinary differential equation is not only more secure than the traditional payment mode, but also can effectively improve the payment speed of users and greatly enhance their shopping experience.

Semi-Sequential Probabilistic Model for Indoor Localization Enhancement

This paper proposes a semi-sequential probabilistic model (SSP) that applies an additional short term memory to enhance the performance of the probabilistic indoor localization. The conventional probabilistic methods normally treat the locations in the database indiscriminately. In contrast, SSP leverages the information of the previous position to determine the probable location since the user's speed in an indoor environment is bounded and locations near the previous one have higher probability than the other locations. Although the SSP utilizes the previous location information, it does not require the exact moving speed and direction of the user. On-site experiments using the received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for localization demonstrate that SSP reduces the maximum error and boosts the performance of existing probabilistic approaches by 25% - 30%.

Mobility-Aware Analysis of Millimeter Wave Communication Systems With Blockages

Millimeter wave (mmWave) communication enables high rate transmission, but its network performance may be degraded significantly due to blockages between the transmitter and receiver. There have been two approaches to overcome the blockage effect and enhance link reliability: multi-connectivity and Ultra-Dense Network (UDN). Particularly, multi-connectivity under a UDN environment facilitates user-centric communication. It requires dynamic configuration of serving base station groups so that each user experiences high quality services. This paper models user mobility and mobility-aware performance in user-centric mmWave communication systems with multi-connectivity, and proposes a new analytical framework based on the stochastic geometry. To this end, we first derive compact mathematical expressions for state transitions and probabilities of various events that each user experiences. Then we investigate mobility-aware performance in terms of network overhead and downlink throughput. This helps us to understand network operation in depth, and impacts of network density and multi-connection capability on the probability of handover related events. Numerical results verify the accuracy of our analysis and illustrate the correlation between mobility-aware performance and user speed. Our findings provide useful insights into the design of user-centric mmWave communication systems.

Energy and quality of experience aware web browsing for smartphones

In this paper we propose a framework for improving the web browsing experience of users by reducing battery energy consumption and webpage load time, while maintaining the quality of experience for the user. This is made possible through a collaborative protocol between the web server and the web client application that works using HTTP header fields. The innovation herein lies in dynamically creating multiple versions of the webpages that differ in their quality of content and size, and then delivering an appropriate version to the client based on metrics related to the webpage and the user's phone relevant specifications. These metrics include the user's battery consumption profile, user's network speed, the sizes of the different versions of the webpage, and the estimated energy consumption of the versions. We implemented a prototype and validated the system in terms of maintaining the battery profile and enhancing the load time of different webpages. The results indicate that over 50% reduction in power consumption and 80% reduction in speed requirements (and hence download times) can be achieved as a result of switching over to a lower quality version of the webpage that is still in line with the users’ experience.

Enhancing Energy in WBAN through Cognitive Radio Networks

Wireless Body Area Networks (WBAN) provides to measure various physiological and biological data and monitor human body functionalities. In this study, a cognitive radio based coordinator node is designed for Wireless Body Area Networks. Cognitive Radio is capable of connecting various wireless access points with perception and adaptation features. A simulation model has been developed for implementing cognitive radio enabled body area network for analyzing remote health monitoring system for humans. The prominent parameters such as user speed, access point delay, and connection cost are taken into account when selecting the wireless access point. A supervised machine learning technique called Particle Swarm Optimization (PSO) is adapted in the proposed model for improving Energy efficiency and reducing transmission delay in CR enabled WBAN. Simulation is performed using MATLAB and results indicate that the use of proposed PSO for WBAN performs efficiently compared to traditional methods based on MAC protocols.

Telecommunication customer satisfaction using self‐organized network‐based heuristic algorithm

SummaryThe emerging network including long‐term evolution‐advanced (LTE‐A) aims at enhancing the telecommunication customer satisfaction in numerous aspects including system capacity, network coverage, handover management, and quality of service (QoS). Effective handover (HO) management reduces HO failure and hence enhances the data rate and supports user mobility. There are numerous challenges that increase the call drop rate. Among the main challenges that increase the call drop rate, the variable user speeds and variable traffic loads are the major ones. Hence, the time to trigger (TTT), HO margin (HOM), and HO offset (HOO) are used to evaluate the HO management using self‐organized network‐based heuristic algorithm under variable user speeds and variable traffic loads. In recent researches, different HO management techniques were applied to manage the HO decision including fuzzy‐logic tactics and Q‐learning. However, they did not apply intelligent optimization techniques that adapt the variable user speeds and dynamic traffic loads. This paper aims at increasing the telecom customer satisfaction by decreasing the call drop rate using particle swarm optimization (PSO), which adaptively manages the handover control parameters according to the user speed and traffic loads. The simulation results have shown that the proposed optimization tool results in significant call drop rate reduction compared to the ordinary HO management.

Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication

Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner’s Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method.

Experimental study of pedestrian flow mixed with wheelchair users through funnel-shaped bottlenecks

With the aging of the world’s population and the resulting increase of disabilities, the characteristics of pedestrian flow mixed with wheelchair users has been paid more and more attention. In this study, experiments in funnel-shaped bottlenecks were performed to study the impact of the bottleneck shape and the ratio of wheelchair users on the crowd dynamics. It is found that the increase in wheelchairs in the crowd leads to lower moving efficiency and greater congestion impacting the escape time, time-space relationship and time headway. Under low mixing ratios (<2.35%), less congestion occurred in the 45° bottleneck among the four tested angles (0°, 15°, 30°, 45°). The average speeds of the wheelchair users are the fastest in 45° bottleneck (0.310 ± 0.097 m s−1) until the mixing ratio becomes 7.05%. However, the advantage of the angle disappears when the mixing ratio gets higher. The findings in this study are meaningful for the guidance of pedestrian evacuation through bottlenecks with the presence of wheelchair users.

Geometry-Based Analysis of Optimal Handover Parameters for Self-Organizing Networks

Handover failure and ping-pongs are the common thorny issues in modern mobile networks. While handover failures caused by radio link failure (RLF) significantly reduces the reliability of network operation, ping-pongs drastically waste signaling resources. In the upcoming fifth-generation (5G) networks especially, a complex deployment of small cells can exacerbate the two problems, even though the network can be integrated with a self-organizing network (SON), which is an automation-based solution. Due to the coupling of RLFs and ping-pongs as explained in the literature, it is difficult to analyze handovers and minimize both RLFs and ping-pongs simultaneously. In this paper, we model a handover procedure with geometric elements (Apollonian circles and the straight line), and analyze handover performance. The analysis provides an optimal handover setting for minimizing both RLFs and ping-pongs together, whereas previous works only considered trade-offs between them. We show that our analysis accurately estimates the optimal setting by comparing it with an NS-3 simulation. From the analysis, different environments can require different optimal values: fading (as well as interference) limit the optimal values; user speed has a scaling impact; and time-to-trigger has a shifting effect.

Smart Technique for Cache-Assisted Device to Device Communications

The emergence of smart terminals with the consequence of higher network traffic growth rates, has increased the demand for new techniques that are needed to meet user requirements for high-quality communication and social media services. One of these techniques is Device-to-Device (D2D) communication, allowing direct data transmission of popular files between users. This is an advantage during rush hours where there is no access to the traffic via a base station (BS), thereby reducing network loads. In this article, we propose a new caching paradigm for D2D communication to achieve the goal of rising network offloading. A Smart Adaptive Algorithm (SAA) is proposed that smartly selects users with higher probabilities of sharing more data, then places popular files in their cache. This is done to optimize network offloading at minimal consumed energy by the user (user cost), taking into account the user speed, location, predicted zones, interests, and the size of the requested file. The data offloading ratio is taken as a performance metric in evaluating network offloading. It is defined as the proportion of requested data that can be delivered via D2D links. The offloading ratio is calculated in different scenarios in order to assess the influence of proactive caching on core network load. To minimize the complexity of the algorithm, and to respond in a shorter time period to the number of requests, user demand shall be handled with both the base station and the user’s device application. For the simulation and implementation of the proposed algorithm, a human mobility model that predicts different human mobility behaviors is used. The simulation results reveal that the offloading ratio is significantly affected by the size of the file transmitted via D2D connection as well as the user speed. The amount of increase in the offloading ratio due to the increase in the user speed is based on the size of the files transmitted through D2D connection. The smaller files transmitted via D2D connection would result in a significant increase in the offloading ratio, whereas the larger file size would result in a less change in the offloading ratio for higher speed users. The simulation results also reveal that higher speed users share more data at a high bit rates compared to users with lower speeds. Simulation results also show that the proposed SAA has superior performance as compared to other algorithms available in literature.

IoT mobile device Data Offloading by Small-Base Station Using Intelligent Software Defined Network

The growing number of IoT devices and the different computing and communication capabilities of IoT devices require an efficient offloading scheme. This offloading scheme need to consider the mobility of IoT devices and helps to intelligently select the optimal server for offloading. An efficient offloading scheme need to take in consideration important factors such as mobility of the IoT user device, speed and direction of the IoT user device as well as the computational capabilities of the user mobile device and the load of nearby servers. Unbalanced load of data or task offloading lead to high latency and poor services. An optimal selection of offloading server will clearly improve latency and QoS. Some new architecture of cellular network suggest the deployment of small-cell base stations (SBS) [1], [2] with a certain computing capabilities which can help offloading task of IoT mobile device or of their nearby SBS. In smart city environment, the mobile IoT device user needs to choose an SBS from several available SBSs within the its communication proximity. In this paper, we propose a Smart Ranking based Task Offloading approach for selecting an SBS and to improve the Quality of Service. This approach uses Q-Learning for SBS selection which will be modelled in Software Defined Network controller to deal with the problem of choosing the SBS in an intelligent way for Task offloading.

Deep Learning Based Real-Time Recognition of Dynamic Finger Gestures Using a Data Glove

In this article, a real-time dynamic finger gesture recognition using a soft sensor embedded data glove is presented, which measures the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joint angles of five fingers. In the gesture recognition field, a challenging problem is that of separating meaningful dynamic gestures from a continuous data stream. Unconscious hand motions or sudden tremors, which can easily lead to segmentation ambiguity, makes this problem difficult. Furthermore, the hand shapes and speeds of users differ when performing the same dynamic gesture, and even those made by one user often vary. To solve the problem of separating meaningful dynamic gestures, we propose a deep learning-based gesture spotting algorithm that detects the start/end of a gesture sequence in a continuous data stream. The gesture spotting algorithm takes window data and estimates a scalar value named gesture progress sequence (GPS). GPS is a quantity that represents gesture progress. Moreover, to solve the gesture variation problem, we propose a sequence simplification algorithm and a deep learning-based gesture recognition algorithm. The proposed three algorithms (gesture spotting algorithm, sequence simplification algorithm, and gesture recognition algorithm) are unified into the real-time gesture recognition system and the system was tested with 11 dynamic finger gestures in real-time. The proposed system took only 6 ms to estimate a GPS and no more than 12 ms to recognize the completed gesture in real-time.

Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets

Ultra-dense networks represent the trend for future wireless 5G networks, which can provide high transmission rates in dense urban environments. However, a massive number of small cells are required to be deployed in such networks, and this requirement increases interference and number of handovers (HOs) in heterogeneous networks (HetNets). In such scenario, mobility management becomes an important issue to guarantee seamless communication while the user moves among cells. In this paper, we propose an auto-tuning optimization (ATO) algorithm that utilizes user speed and received signal reference power to adapt HO margin and time to trigger. The proposed algorithm aims to reduce the number of frequent HOs and HO failure (HOF) ratio. The performance of the proposed algorithm is evaluated through simulation with a two-tier model that consists of 4G and 5G networks. Simulation results show that the average rates of ping-pong HOs and HOF are significantly reduced by the proposed algorithm compared with other algorithms from the literature. In addition, the ATO algorithm achieves a low call drop rate and reduces HO delay and interruption time during user mobility in HetNets.

Performance Evaluation of A2-A4-RSRQ and A3-RSRP Handover Algorithms in LTE Network

In LTE Network, users can move freely in the network through fast and seamless handover (HO). This research focuses on intra-LTE handover which occurs using interface X2 to move an EU between two eNBs, i.e. source eNB and target eNB without any changes in MME and SGW at EPC level. Two popular algorithms of intra-LTE handover namely A2-A4-RSRQ and A3-RSRP were evaluated and compared through simulations as well as direct measurements in the field. Simulation is conducted using NS3 simulation tool where performances of various scenarios from both algorithms were evaluated. The performance metrics studied include the average number of HOs that occur, throughput and optimized ratio. Simulations carried out for various scenarios in term of EU numbers, user speeds, and channel conditions. In addition, the results of one-month measurement of three eNBs were also presented. The measurement results are then compared and used to verify the simulation results. Furthermore, by using the optimizing ratio metric, the optimal pair of parameter values of Threshold as well as Offset and Handover Margin (HOM) along with Time-to-Trigger (TTT) are sought for the A2-A4-RSRQ and A3-RSRP respectively.

Dialogue System of Controlling Robot Based on the Theory of Finite-State Automata

The article discusses the system of dialogue control manipulation robots. The analysis of the basic methods of automatic speech recognition, speech understanding, dialogue management, voice response synthesis in dialogue systems has been carried out. Three types of dialogue management are considered as "system initiative", "user initiative" and "combined initiative". A system of object-oriented dialog control of a robot based on the theory of finite state machines with using a deep neural network is proposed. The main difference of the proposed system lies in the separate implementation of the dialogue process and robot’s actions, which is close to the pace of natural dialogue control. This method of constructing a dialogue control robot allows system to automatically correct the result of speech recognition, robot’s actions based on tasks. The necessity of correcting the result of speech recognition and robot’s actions may be caused by the users’ accent, working environment noise or incorrect voice commands. The process of correcting speech recognition results and robot’s actions consists of three stages, respectively, in a special mode and a general mode. The special mode allows users to directly control the manipulator by voice commands. The general mode extends the capabilities of users, allowing them to get additional information in real time. At the first stage, continuous speech recognition is built by using a deep neural network, taking into account the accents and speech speeds of various users. Continuous speech recognition is a real-time voice to text conversion. At the second stage, the correction of the speech recognition result by managing the dialogue based on the theory of finite automata. At the third stage, the actions of the robot are corrected depending on the operating state of the robot and the dialogue management process. In order to realize a natural dialogue between users and robots, the problem is solved in creating a small database of possible dialogues and using various training data. In the experiments, the dialogue system is used to control the KUKA manipulator (KRC4 control) to put the desired block in the specified location, implemented in the Python environment using the RoboDK software. The processes and results of experiments confirming the operability of the interactive robot control system are given. A fairly high accuracy (92 %) and an automatic speech recognition rate close to the rate of natural speech were obtained.

EFFECTS OF LOADING SPEED ON THE SITE TRAFFIC CONVERSION

The subject of this article is the impact of site loading speed to increase user conversions. The goal is to determine what is the optimal site loading speed for users and further site optimization. Tasks: identify and optimize the components that take the longest load time. The methods used are: optimization of the server part of the site; Configuring Apache and Ngix Server Using server side gzip compression Using CDNs to download popular JavaScript libraries server-side caching settings; database optimization; optimization of TCP, TLS, HTTP / 2; client side optimization. The following results are obtained. Using the methods described above, on the example of a working draft, it was possible to optimize the site loading speed by 40%. Taking statistics for the period of 7 days, with the same amount of traffic, the conversion of users to customers increased from 7% to 15%. The number of failures on the first visit to the site decreased by 30%. Conclusions. In the course of work, the correlation between the speed of loading the site and the conversion of users to customers was confirmed. By optimizing the speed of loading the site, it was possible to increase the conversion of users by 2 times and reduce the percentage of failure on the first visit to the site.