Mobile Computing Technologies Research Articles

Robust and resilient equilibrium routing mechanism for traffic congestion mitigation built upon correlated equilibrium and distributed optimization

With the rapid development of wireless communication, mobile computing, and GPS technologies, drivers’ route decisions nowadays rely more on navigation services, such as Google or Waze. However, these navigation services don't always come with improved traffic conditions. Individual drivers often make independent and selfish route decisions that are not systematically favorable and thus often result in severe congestions. This study aims to alleviate such problems by exploiting the information gaps between individuals and the central planner (CP). Specifically, we develop a correlated equilibrium routing mechanism (CeRM) for the CP, which drives a group of vehicles’ route choices to an equilibrium with a systematically optimal traffic condition while still satisfying individuals’ selfish nature. Participating drivers would only be better off by following the suggested routing guidance than navigating on their best responses to real-time traffic information. The CeRM is modeled as a nonconvex and nonlinear program involving a large-scale of users. A distributed Augmented Lagrangian algorithm (D-AL) is developed to efficiently solve the CeRM to provide online real-time navigation service, taking advantage of the on-board computation resources of individual vehicles. Considering the D-AL relies on the wireless communications between vehicles and the CP, we proved the convergence robustness of the D-AL against random communication failures and derived the convergence rate upper bound as a function of the communication failure probability. It is noticed that the convergence rate of the D-AL degrades dramatically as the communication failure probability increases, which hampers the applicability of implementing the CeRM in practice. To improve the solution algorithm's resilience in the computation performance, we further designed and proved an acceleration scheme aided D-AL (aD-AL) to expedite the convergence rate under the high likelihood of communication failures. Numerical experiments conducted on the Sioux Falls city network confirmed the D-AL's convergence properties, robustness against random communication failures, and the resilience of the aD-AL to solve the CeRM. The experiments also show that the CeRM results in better system performance (have less system cost) compared with the existing Independent Routing (IR) mechanism and user-oriented Equilibrium Routing (uoER) mechanism.

Edge Computing Resource Allocation Method for Mining 5G Communication System

In the actual production of coal mines, the transmission needs of existing underground applications cannot be met due to a lack of strategies and customized equipment for underground 5G application scenarios, which causes increased underground data processing delay and low transmission efficiency. To solve the problem above, the mobile edge computing (MEC) technology based on the 5G wireless base station is studied, and underground 5G communication capabilities is improved by edge caching and dynamic resource allocation according to the actual situation of coal mines. The experimental result shows that under the premise of maintaining the rated power and transmit power of the existing base station, the average delay of executing tasks is 15ms, which is 50% lower than the average delay of all local execution methods. The average delay is reduced by 37.5% than all MEC execution methods. At the same time, the uplink rate of a single base station can reach 1Gbps and the downlink rate can reach 1.5 Gbps. Our method can significantly improve the reliability of mining 5G communication systems and the rational allocation of resources. Keywords: mining 5G communication; edge computing; resource allocation; deep learning; underground coal mines.

Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.

Secure Aerial Computing: Convergence of Mobile Edge Computing and Blockchain for UAV Networks

The introduction of mobile edge computing (MEC) technology in unmanned aerial vehicle (UAV) networks can provide computing services for mobile users with or without communication infrastructure coverage. However, mobile users’ privacy may be leaked during the computation offloading process due to the information interaction between UAVs and the migration of computation tasks between mobile users and UAVs. To this end, we propose a secure aerial computing architecture that integrates MEC and blockchain technology for UAV networks to effectively ensure the security and privacy of computation offloading between UAVs and mobile users. Under this architecture, a problem of joint optimization of user association, UAV trajectory, block processor scheduling, and computation resource allocation is formulated to minimize the weighted sum of the energy consumption and the delay in completing computation tasks and blockchain tasks processing. To handle this intractable issue, we first decouple the optimization variables and then separate the original problem into multiple subproblems to be solved alternately. In addition, we design a block coordinate descent (BCD)-based algorithm for user association and computation resource allocation, and a successive convex approximation (SCA)-based algorithm to optimize the trajectories of UAVs. Simulation results show that the proposed algorithm has better performance.

Propose an E-CRM Model based on Mobile Computing Technology in Pharma Distribution Industry

In today’s world, the competition between all business areas and companies including pharma distributor companies has increased dramatically, so it is very important for active companies in the pharma distribution industry which deal with a large number of customers in a B2B market to establish a deep and long-term relationship with their customers and manage that relationship effectively. Since the CRM which now is enriched by new emerging technologies in terms of e-CRM and m-CRM is under developing rapidly, it can play a critical role for empowering these companies to strengthen their relationship with their customers. In this research it has been tried to have a complete review of mobile computing technology concept and its effect on CRM. The research methodology is basically qualitative. After a literature review, using qualitative research methods and deep interviews with a group of 8 industry experts, the whole concept of the initial model was derived using Thematic Analysis method. The Grounded Theory approach was applied to extract the main factors and sub-factors of the final model. Additionally, some of \research techniques such as Dematel, ANP and Super Decision software were used to investigate the interdependency, importance and priority of factors and sub-factors. At the last stage a new model for e-CRM in pharma distribution industry based on mobile computing technology has been proposed. The four key components of the model are Quality of Content and Services, Organizational Readiness, Quality of System and Communication, Customer Mobile App.

Deep learning for online computation offloading and resource allocation in NOMA

The limited battery capacity and low computing capability of wireless Internet of Things (IoT) devices can hardly support computation-intensive and delay-sensitive applications. While recent development of wireless power transfer (WPT) and mobile edge computing (MEC) technologies help IoT devices harvest energy and offload computation tasks to edge servers. While it is still challenging to design an efficient offloading policy to improve the performance of the IoT network. In this article, we consider a MEC network that has WPT capability and adopts the non-orthogonal multiple access (NOMA) technology to offload tasks partially. Our goal is to propose an online algorithm to optimize resource allocation under a wireless dynamic channel scenario. In order to obtain the optimal offloading decision and resource allocation efficiently, we propose a Deep Reinforcement learning-based Online Sample-improving (DROS) framework which implements a deep neural network to input the discretized channel gains to obtain the optimal WPT duration. Based on the WPT duration derived by DNN, we design an optimization algorithm to derive the optimal energy proportion for offloading data. Numerical results verify that compared with traditional optimization algorithms, our proposed DROS has significantly sped up convergence for better solutions.

Satellite MEC with Federated Learning: Architectures, Technologies and Challenges

Satellite communication has made great progress in recent years since it is characterized by wide information coverage and can support diverse types of users, which beneficially fulfills the demand of beyond 5G communications. Besides, mobile edge computing (MEC) technologies energize the edge devices with computational abilities to deal with the majority of training tasks without having to upload to the cloud server, which substantially enhances a system's efficiency. In satellite MEC, the raw data of edge users vested in different owners cannot be allowed to be shared, considering data privacy requirements. To address this, federated learning (FL) architecture can be applied to satellite MEC where only parameters and model updates can be transmitted, which avoids the interaction of raw data from diverse sources. In this article, we construct a FL-based satellite MEC architecture, followed by introducing its key techniques in the aspects of resource management and multi-modal data fusion. Furthermore, we study the data privacy and security protection on the FL-aided satellite MEC relying on a blockchain framework. Finally, we portray the challenges of FL-aided satellite MEC systems.

Dynamic Offloading Method for Mobile Edge Computing of Internet of Vehicles Based on Multi-Vehicle Users and Multi-MEC Servers

With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. However, due to the limited resources of the MEC server, it cannot meet the task requests from multiple vehicle users simultaneously. For this reason, making correct and fast offloading decisions to provide users with a service with low latency, low energy consumption, and low cost is still a considerable challenge. Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle users and multiple MEC servers. To solve the problem of minimizing the total cost of the system performing tasks, an Edge-Cloud collaborative, dynamic computation offloading method (ECDDPG) based on a deep deterministic policy gradient is designed. This method is deployed at the edge service layer to make fast offloading decisions for tasks generated by vehicle users. The simulation results show that the performance is better than the Deep Q-network (DQN) method and the Actor-Critic method regarding reward value and convergence. In the face of the change in wireless channel bandwidth and the number of vehicle users, compared with the basic method strategy, the proposed method has better performance in reducing the total computational cost, computing delay, and energy consumption. At the same time, the computational complexity of the system execution tasks is significantly reduced.

Efficient Delay-Aware Task Scheduling for IoT Devices in Mobile Cloud Computing

The development of Internet of Things (IoT) technology depends on technologies such as high-efficiency storage and high computing power. Mobile cloud computing (MCC) technology will be an important foundation for the development of IoT. The efficient scheduling of tasks in IoT devices in MCC environment is challenging. The requirements for task scheduling in MCC are becoming more and more complex. As the core problem in MCC, task scheduling aims to allocate tasks reasonably, achieve optimal scheduling strategies, and complete tasks effectively. In this paper, efficient delay-aware task scheduling algorithm (EDTSA) is proposed, with the optimization goal of minimizing task running time. The matching of tasks and virtual machines is modeled as a bipartite graph. The problem is divided into multiple subproblems to solve the optimal solution separately. The combined solution is used as the initial solution of the local search algorithm. The efficiency of the local search depends on the quality and nature of the initial solution. We can generate multiple initial solutions according to different division criteria. The initial solution is the combination of the optimal solutions of the subproblems, so the quality of the initial solution has been greatly improved and generating multiple initial solutions according to the division can reduce the probability of falling into the local optimal solution. This algorithm also divides the neighborhood to reduce unnecessary searches. Finally, we verify the efficiency and practicability of the algorithm through experiments.

DRL based partial offloading for maximizing sum computation rate of FDMA-based wireless powered mobile edge computing

Since most Internet of Things (IoT) nodes are powered by small-capacity batteries and equipped with small computing units, it is difficult for them to process computing-intensive and delay-sensitive tasks. To address these two limitations, a feasible approach is to use both wireless power transmission (WPT) and mobile edge computing (MEC) technologies. Wireless edge devices (EDs) have stable energy supply by capturing radio frequency (RF) energy transmitted by the energy source and offloading computation workload to edge computing servers (ECSs) to enhance the computation rate. In this paper, an FDMA-based wireless-powered MEC network is considered, which consists of an ECS, an RF energy source and multiple EDs. The research goal is to maximize the sum computation rate (SCR) by jointly optimizing the WPT duration, energy allocation for local computation, and computation offloading, and bandwidth allocation among EDs. We first formulate this as a non-convex optimization problem that is hard to address. Then, we decompose it into a top-problem for optimizing the WPT duration and a sub-problem for optimizing the energy allocation and bandwidth allocation under a given WPT duration. An online offloading algorithm based on deep reinforcement learning (DRL) is proposed to quickly obtain the near-optimal WPT duration. For the sub-problem, we design an efficient two-stage algorithm to obtain the optimal solution. The simulation results show that the proposed algorithm can obtain the near-optimal solutions with low computation complexity.

Online Trajectory and Resource Optimization for Stochastic UAV-Enabled MEC Systems

The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.

Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy.

Формирование креативности студентов через использование электронных учебников

Research relevance: article is talking about formation of students' creativity using e-books, especially usual paper books and their usefulness, the growing level of science, and the lack of literature in Kyrgyz language, possibilities in using of websites such as Okuma.kg, Ibooks.oshsu.kg, Biblioteka.oshsu.kg and reasons for need to use electronic issues. Research purpose: to emphasize importance of students, teachers, programmers, designers, Internet bloggers and other teams’ joint work for creation of e-books, which make up the teaching and methodological basis for computer classes. Research materials and methods: to recommend on content of electronic textbooks; becoming them with modern educational material obtained through mobile phones or computer technology; continuity of topics in the texts of materials and practical skills; rules of conducting lessons in any language; topics, checking, evaluation of students independent work. Conclusions: problem of insufficient textbooks, textbooks in schools, colleges and in universities can be solved by active development of modern electronic books, creation of electronic databases on a wide range of educational disciplines and knowledge.

Energy-Aware Computation Management Strategy for Smart Logistic System With MEC

As the most important part of a smart city and long-standing challenging issue, a highly efficient smart logistic system has attracted a great deal of attention in recent years. In particular, unmanned aerial vehicles (UAVs) are ideal solutions for last-mile delivery scenarios in recent years due to their fast speed and easy deployment. However, because of the highly automatic delivery process, UAVs are still constrained by the limited payload, battery, and computing capacity for complex computational tasks. With the aid of the mobile edge computing (MEC) technology, UAVs can offload computational tasks to the MEC computational resources in various types of IoT environments. In spite of the task offloading which can enhance their task process capability, it also brings extra overhead, such as data transfer time and energy consumption. These extra overheads may significantly impact the efficiency and payload of UAV-based delivery systems. Therefore, taking the UAV last-mile delivery system with MEC as an example, this article investigates the energy-aware multi-UAV task computation management problem according to a realistic autonomous delivery network (ADNET). Specifically, we propose a computation management strategy, namely, the MEC-based task offloading and scheduling strategy (TOSS), to provide an integral approach covering both the static task offloading and scheduling algorithm, as well as the dynamic resource conflict resolution algorithm. Grounded on real-world scenarios, our experimental results show that TOSS can achieve a higher payload for UAVs by using minimum energy consumption and task makespan within the given constraints of the deadline compared to the state-of-the-art methods.

Design and Implementation of Earthquake Information Publishing System Based on Mobile Computing and Machine Learning Technology in GIS

This paper proposes a semi-automatic method of geographic information linking based on spatial relationships, entity names, entity categories and other features, combined with semantic and machine learning methods. First, we extracted geographic information from three geographic information sources: Open Street Map, Wikimapia, and Google places. The extracted geographic information is mainly for urban buildings in different regions. Secondly, we analyzed and extracted the characteristics of geographic information data to construct a geographic information ontology, and realized the integration of geographic data through the mapping of geographic information source data and geographic information ontology. Finally, the linking method of fusion classification algorithm support vector machine and K-nearest neighbor method are discussed separately. At the same time, it is compared with the linking method proposed by Samal et al. to comprehensively verify the accuracy of the method proposed in this paper from multiple angles, laying a good foundation for seismic information integration.

Chinese Professional Basketball League Using Mobile Computing and Big Data Technology

The impact of big data technology on the Chinese Men’s Professional Basketball League (CBA) is studied in this study. With the growth of the CBA league in recent years, teams from the western region have emerged. Although there is a trend of “multipolarization” in the competition pattern, it overall depicts a situation in which the east is strong and the west is weak. In addition, the method of recruiting foreign aid workers has improved over time, as has the foreign aid system. The number of foreign aids has increased from one in 1995 to a high of 47, and the quality of foreign aid has improved over time. This not only strengthens the club team, but it also has a significant impact on the CBA league’s competition patterns. The basic factors that affect the changes in the competition pattern are institutional factors, coaching factors, star player factors, foreign aid factors, regional economic factors, reserve talent factors, team style factors, player security factors, and so on, according to research and analysis of the CBA league’s development and changes. Six teams have won the CBA league championship in its 22 seasons. The Bayi and Guangdong teams each won eight times, accounting for more than 70% of the total number of championships.

Adaptive Wireless Video Streaming: Joint Transcoding and Transmission Resource Allocation

The emerging mobile edge computing (MEC) technology has been recently applied to improve adaptive bitrate (ABR) streaming service quality under time-varying wireless channels. In this paper, we consider a heterogeneous multi-user MEC-enabled video streaming network with time-varying wireless channels in sequential time frames. In particular, we aim to design an online joint transcoding and transmission resource allocation algorithm to maximize the ABR streaming user’s quality of experience (QoE) subject to the bandwidth and CPU constraints. The algorithm is “online” in the sense that the bitrate and resource allocation decisions made at each frame depend only on the observation of past events. We formulate the problem as a mixed integer non-linear programming (MINLP) that jointly determines bitrate adaptation, bandwidth allocation, and CPU cycle assignment. To cope with the challenge arising from the coupling decisions of adjacent frames, we propose a low-complexity online algorithm, named OCCA. Specifically, by introducing queueing model constraints, we transform the offline non-conex MINLP problem into a multi-frame problem. Then, we analytically decouple the multi-stage(frame) problem to multiple per-frame convex subproblems that can be solved with high robustness and low computational complexity. We perform simulations with realistic scenarios to evaluate the performance of the proposed algorithm. Results manifest that compared with state-of-the-art approaches, our proposed algorithm can provide 97.84% (on average) extra QoE.

An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology.

Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.

Adaptation of Android and Digital Applications in Education System

Main purpose of this review paper is to emerge early writing tools with new digital writing tools as computers, tablets, and laptops introduce to android application, which recognizes information through QR code in form of audio, video, text, etc., since last few years’ smart phones and wireless internet connection has no ambiguity. Mobile technology plays an important role in combination between advanced mobile technology and computer technology application. Location based interactions or Wi-Fi identification this shows the necessity of digital applications. This paper examines the practical outcomes from study and learning that has been taken place. In education society learning is general with continuing digital revolution in education and business relationships.

Construction of University Sports Flipped Classroom Based on Sports Skills Learning and Mobile Edge Computing-Driven Neural Network

In order to make students more interested in PE learning in PE teaching, it is necessary for PE educators to change the traditional way of thinking, take students as the main body of PE learning, be guided by teachers themselves, and use mobile edge computing and neural network technology as technologies. It enables students to carry out intelligent physical education independently. Whether the teaching is effective does not mean whether the teacher has completed the teaching content seriously, but whether the students have learned something, or whether the students have learned well. Based on the actual teaching practice of flipped sports classroom, this paper takes the “effectiveness” of sports classroom teaching as the breakthrough point, mainly adopts the research methods of comparative analysis, mobile edge computing, and neural network, and combines relevant domestic and foreign research results on the teaching of sports flipped classroom at home and abroad. An in-depth and comprehensive analysis of the effectiveness was carried out. The research results show that it is reliable to build a university sports flipped classroom with a neural network driven by motor skill learning and mobile edge computing as the carrier.