Mobile Computing Research Articles

DRL‐based computing offloading approach for large‐scale heterogeneous tasks in mobile edge computing

AbstractIn the last few years, the rapid advancement of the Internet of Things (IoT) and the widespread adoption of smart cities have posed new challenges to computing services. Traditional cloud computing models fail to fulfil the rapid response requirement of latency‐sensitive applications, while mobile edge computing (MEC) improves service efficiency and customer experience by transferring computing tasks to servers located at the network edge. However, designing an effective computing offloading strategy in complex scenarios involving multiple computing tasks, nodes, and services remains a pressing issue. In this paper, a computing offloading approach based on Deep Reinforcement Learning (DRL) is proposed for large‐scale heterogeneous computing tasks. First, Markov Decision Processes (MDPs) is used to formulate computing offloading decision and resource allocation problems in large‐scale heterogeneous MEC systems. Subsequently, a comprehensive framework comprising the "end‐edge‐cloud" along with the corresponding time‐overhead and resource allocation models is constructed. Finally, through extensive experiments on real datasets, the proposed approach is demonstrated to outperform existing methods in enhancing service response speed, reducing latency, balancing server loads, and saving energy.

INTEGRATION OF IOT WITH CLOUD, FOG, AND EDGE COMPUTING: A REVIEW

Purpose of review. The paper provides an in-depth exploration of the integration of Internet of Things (IoT) technologies with cloud, fog, and edge computing paradigms, examining the transformative impact on computational architectures. Approach to review. Beginning with an overview of IoT's evolution and its surge in global adoption, the paper emphasizes the increasing importance of integrating cloud, fog, and edge computing to meet the escalating demands for real-time data processing, low-latency communication, and scalable infrastructure in the IoT ecosystem. The survey meticulously dissects each computing paradigm, highlighting the unique characteristics, advantages, and challenges associated with IoT, cloud computing, edge computing, and fog computing. The discussion delves into the individual strengths and limitations of these technologies, addressing issues such as latency, bandwidth consumption, security, and data privacy. Further, the paper explores the synergies between IoT and cloud computing, recognizing cloud computing as a backend solution for processing vast data streams generated by IoT devices. Review results. Challenges related to unreliable data handling and privacy concerns are acknowledged, emphasizing the need for robust security measures and regulatory frameworks. The integration of edge computing with IoT is investigated, showcasing the symbiotic relationship where edge nodes leverage the residual computing capabilities of IoT devices to provide additional services. The challenges associated with the heterogeneity of edge computing systems are highlighted, and the paper presents research on computational offloading as a strategy to minimize latency in mobile edge computing. Fog computing's intermediary role in enhancing bandwidth, reducing latency, and providing scalability for IoT applications is thoroughly examined. Challenges related to security, authentication, and distributed denial of service in fog computing are acknowledged. The paper also explores innovative algorithms addressing resource management challenges in fog-IoT environments. Conclusions. The survey concludes with insights into the collaborative integration of cloud, fog, and edge computing to form a cohesive computational architecture for IoT. The future perspectives section anticipates the role of 6G technology in unlocking the full potential of IoT, emphasizing applications such as telemedicine, smart cities, and enhanced distance learning. Cybersecurity concerns, energy consumption, and standardization challenges are identified as key areas for future research.

The Impact of Cloud Systems on Enhancing Organizational Performance through Innovative Business Models in the Digitalization Era

Abstract Information technology has massively transformed the world of business over the past fifty years - first individual functional areas within companies (“first wave”), later increasingly also cross-divisional value-added processes and trade (“second wave”), advent of the internet and the World Wide Web marked a transformative shift in how information is accessed, shared, and distributed (“third wave”), mobile computing and smartphone (“fourth wave”) and cloud computing that has emerged as a paradigm for delivering computing resources and services over the internet on-demand, providing scalability, flexibility, and cost-effectiveness (“fifth wave”). This paper investigates the transformative impact of cloud systems on increasing organizational performance through innovative business models in the age of digitalization. By employing a mixed methods approach, the study integrates qualitative and quantitative techniques to provide a comprehensive understanding of the research phenomenon.

Computational investigation of transport parameters of ions in gas-filled radiation detectors

In this study, a novel computational method was developed to investigate the transport characteristics of ions in gas-filled radiation detectors, which are closely linked to the behavior of ions in gaseous detectors. Parameters such as polarizability, mean free path, collision frequency, and mean free time, which directly impact ion mobility, were physically manipulated using the Monte Carlo technique, and the results were validated against fundamental physical principles. As an initial scenario, computations were performed on the mobility of Ar+ ions in an argon environment as a carrier gas. The distribution of the radial velocity components of the ion following an ion-gas collision was represented as a two-dimensional histogram with a disc-shaped pattern, and this result was elucidated utilizing the Maxwell-Boltzmann distribution. The findings regarding the mean free time and mean free path of an Ar+ ion in an argon carrier gas align with the results derived from Skullerud's methodologies. These findings hold promise for Garfield++ simulations, enabling the computation of ion mobility and even cluster ions in the mixture, thereby considering their impact on the detector's gain parameters. The results of this investigation offer novel insights into the behavior of ions within detector settings, significantly enhancing our comprehension of the mobility of ions and its effects on radiation detection efficiency.

Insegurança alimentar e nutricional e os comportamentos de risco e proteção para saúde em adolescentes durante a pandemia de covid-19

ABSTRACT The COVID-19 pandemic has reduced access to food and increased food insecurity. The objectives were to analyse the prevalence of Food and Nutritional Insecurity (FNI) in Brazilian adolescents during the COVID-19 pandemic according to sociodemographic characteristics and to examine the association between FNI and risk and protective behaviours in Brazilian adolescents during the that period. Cross-sectional study with data from the ‘ConVid teenagers – Behaviour Survey,’ carried out between June and October 2020, using a self-administered questionnaire via mobile phone or computer. The population was made up of teenagers aged 12 to 17, totalling 9,470. The Prevalence Ratio (PR) and 95% Confidence Interval (95% CI) were used, using Poisson regression with robust variance. The prevalence of FNI (26.1%) was higher among adolescents of black and mixed race/colour and who study in public schools. Adolescents who reported FNI had lower consumption of vegetables and fruits, less physical activity, and greater use of cigarettes and alcohol. FNI was more prevalent in adolescents with worse socioeconomic conditions, and adolescents with FNI showed a higher frequency of health risk behaviours, highlighting the importance of intersectoral public policies to reduce inequalities.

Accuracy of digital measurement for quantitative and qualitative indicators of wound healing and repair: a systematic review protocol

IntroductionChronic wound care remains a critical public health challenge in terms of prevalence, quality of life and healthcare costs on a global scale. Currently used methods to assess the size...

Impact of Technology-Based Learning on Academic Performance of Undergraduate Level Students

Technology-based learning is a new learning method where students are taught using different digital/ICT tools such as mobile phones, computers, the internet, multimedia, tablets, and many others. The present study aimed to determine the impact of technology-based learning on students' academic performance. A quantitative research method was applied, followed by a survey research design to accomplish this study. The study was delimited to Balochistan province. All the public degree-awarding higher educational institutions were considered for the study. The target population for the study was the undergraduate students. The questionnaire was developed in collaboration with all authors. The reliability and validity of the framed questionnaire were checked through pilot testing. The reliability was found to be 0.87. Data were collected personally and with the help of colleagues. The collected data were analyzed using descriptive and inferential statistics with the assistance of SPSS software version 22. The study's findings indicate that technology-based learning develops students' critical thinking, communication, cognitive, and reading skills, motivates students to become problem solvers, and enhances the professional development of the learners. The study concluded that technology-based learning positively impacts students' academic performance at the undergraduate level and also enables the students to cope with modern-world challenges. The study recommends that the government include technology at all levels of education, train its teachers, and provide all the technology resources.

Dual-mode electromagnetic-triboelectric-piezoelectric multifunctional self-charging energy system for efficient capture of kinetic energy

This study presents an innovative dual-mode electromagnetic-triboelectric-piezoelectric multifunctional self-charging energy system (MS-CES) that integrates two different operating modes of electromagnetic generators for contact-separation (CS-EMG) and non-contact (NC-EMG), as well as a triboelectric nanogenerator (TENG) and a piezoelectric nanogenerator (PENG). The system has an energy management circuit that efficiently captures and converts kinetic energy from the environment. We investigate the output performance of each unit of the MS-CES under different vibration frequencies and amplitudes. The units work together through a compact mechanical structure design to achieve high voltage output while shortening charging time based on improved space utilization, thus gaining more efficient energy storage. Polyacrylonitrile (PAN) powder and barium titanate (BTO) composite nanofibre membrane (PAN/BTO) as PENG material further enhances the system's output performance. The system provides a continuous power source for the vibration sensors. It enables human-computer interaction with mobile phones and computers via Bluetooth modules, enabling real-time monitoring and analysis of vibration parameters. Furthermore, we have validated the feasibility of using the MS-CES to power a noninvasive vagus nerve stimulation device, providing new technological means for monitoring and treatment in the medical field.

Lyapunov-guided deep reinforcement learning for delay-aware online task offloading in MEC systems

With the arrival of 5G technology and the popularization of the Internet of Things (IoT), mobile edge computing (MEC) has great potential in handling delay-sensitive and compute-intensive (DSCI) applications. Meanwhile, the need for reduced latency and improved energy efficiency in terminal devices is becoming urgent increasingly. However, the users are affected by channel conditions and bursty computational demands in dynamic MEC environments, which can lead to longer task correspondence times. Therefore, finding an efficient task offloading method in stochastic systems is crucial for optimizing system energy consumption. Additionally, the delay due to frequent user–MEC interactions cannot be overlooked. In this article, we initially frame the task offloading issue as a dynamic optimization issue. The goal is to minimize the system’s long-term energy consumption while ensuring the task queue’s stability over the long term. Using the Lyapunov optimization technique, the task processing deadline problem is converted into a stability control problem for the virtual queue. Then, a novel Lyapunov-guided deep reinforcement learning (DRL) for delay-aware offloading algorithm (LyD2OA) is designed. LyD2OA can figure out the task offloading scheme online, and adaptively offload the task with better network quality. Meanwhile, it ensures that deadlines are not violated when offloading tasks in poor communication environments. In addition, we perform a rigorous mathematical analysis of the performance of Ly2DOA and prove the existence of upper bounds on the virtual queue. It is theoretically proven that LyD2OA enables the system to realize the trade-off between energy consumption and delay. Finally, extensive simulation experiments verify that LyD2OA has good performance in minimizing energy consumption and keeping latency low.

Development of Android App on Scheduling Payments and Financial Management of Workers in Construction Industry

Abstract: A innovation centralized platform designed specifically for labour payment to efficiently manage project management, labour payments and generate detailed financial reports. Mobile computing offers helps to construction workers improve efficiency and minimize the paper works. However, the uptake of the technology is quite low in the industry, with little research on key constraint factors and the priority needs of the construction workforce. In recent year mobile applications have become increasingly popular years and is expected to continue like that as all the tech is moving towards mobile. However, unfortunately, in the face of accelerating user demand, businesses are not validating that their apps are safe, secure and they are more dragged by the speed-to-market pressure as well as the need to maintain a high level of user experience. As the application contains all the payment information tedious writing work can be avoided on site. Just set the payment in the application and are free to go. Extensive time and effort are spent on inspecting and documenting of payments. Managing large number of projects with various subcontractors will be difficult. If the hard copy is lost, entire data set cannot be taken back. In financial updates client also face same problem, to get clients according to their requirements. To bridge this gap and to develop better connection between client, engineers and subcontractors, we need a platform. Project cost based on labour payments and payments pending can be accessed easily. Maintain a history of project payments, weekly payments balance payments tracker

An API Recommendation Method for Querying Mobile Computing Problems

Nowadays mobile computing has penetrated into our lives and work. Major companies have invested a lot of material and financial resources in the development of mobile terminals. For developers, querying the right API is extremely important. However, finding the right APIs can be time-consuming and laborious. In this paper, to solve the problems developers may face in the actual development process and improve the development efficiency, we propose RAMC (Recommendation APIs for Mobile Computing), an API recommendation approach leveraging word embedding technique and the information crawling from Stack Overflow posts and Java core packages, to recommend appropriate APIs for developers. Furthermore, RAMC also provides developers with label words, similar questions and relevant code. To evaluate the effectiveness of RAMC, we decided to analyze our system by simulating an instance. By testing a problem encountered during mobile computing development, the RAMC can effectively output the much-related API and tags for developers.

Reliablity and Security for Fog Computing Systems

Fog computing (FC) is a distributed architecture in which computing resources and services are placed on edge devices closer to data sources. This enables more efficient data processing, shorter latency times, and better performance. Fog computing was shown to be a promising solution for addressing the new computing requirements. However, there are still many challenges to overcome to utilize this new computing paradigm, in particular, reliability and security. Following this need, a systematic literature review was conducted to create a list of requirements. As a result, the following four key requirements were formulated: (1) low latency and response times; (2) scalability and resource management; (3) fault tolerance and redundancy; and (4) privacy and security. Low delay and response can be achieved through edge caching, edge real-time analyses and decision making, and mobile edge computing. Scalability and resource management can be enabled by edge federation, virtualization and containerization, and edge resource discovery and orchestration. Fault tolerance and redundancy can be enabled by backup and recovery mechanisms, data replication strategies, and disaster recovery plans, with a residual number system (RNS) being a promising solution. Data security and data privacy are manifested in strong authentication and authorization mechanisms, access control and authorization management, with fully homomorphic encryption (FHE) and the secret sharing system (SSS) being of particular interest.

Dependency task offloading in semantic communications

In the realm of advanced wireless communications, the significance of semantic communication has become increasingly pronounced, particularly in its focus on the meaning and efficiency of information transmission. Given the voluminous nature of task data and the intricacy of task interconnections, executing semantic segmentation tasks directly on user-end devices often falls short due to limitations in processing power. Leveraging mobile edge computing, however, presents a strategic enhancement in performing semantic segmentation tasks by delegating computational workloads to proximal devices, thus diminishing latency and reducing energy expenditure. This document delineates a task offloading framework aimed at refining the management of interdependent tasks, thereby boosting the efficiency of processing complex tasks within environments of semantic communication. The framework abstracts tasks in the form of directed acyclic graphs (DAG) and approaches the optimization challenge through a Markov Decision Process (MDP), targeting an optimal balance between task completion, delay reduction, and energy savings. To adapt to fluctuating network conditions, a novel approach employing the Deep Deterministic Policy Gradient (DTO-DDPG) algorithm is introduced for optimization decision-making. Our empirical tests underscore the framework’s capacity to significantly elevate task completion rates while simultaneously decreasing both energy consumption and latency in scenarios of semantic communication.

Building of Barcode and QR Code Reader Device

This study focuses on building a QR and barcode reader device to scan the barcode or QR code (1D/2D) and display the read information on the Liquid Crystal Display (LCD). With this device we can scan and read all the barcode and QR codes through an OLED display. The proposed device can work in the dark, have a stable performance as well as fast decoding speed. It can easily capture barcodes 1D, 2D on labels, paper, and mobile phone or computer displays sensitively and accurately.

A deep learning based approach for image retrieval extraction in mobile edge computing

AbstractDeep learning has been widely explored in 5G applications, including computer vision, the Internet of Things (IoT), and intermedia classification. However, applying the deep learning approach in limited-resource mobile devices is one of the most challenging issues. At the same time, users’ experience in terms of Quality of Service (QoS) (e.g., service latency, outcome accuracy, and achievable data rate) performs poorly while interacting with machine learning applications. Mobile edge computing (MEC) has been introduced as a cooperative approach to bring computation resources in proximity to end-user devices to overcome these limitations. This article aims to design a novel image reiterative extraction algorithm based on convolution neural network (CNN) learning and computational task offloading to support machine learning-based mobile applications in resource-limited and uncertain environments. Accordingly, we leverage the framework of image retrieval extraction and introduce three approaches. First, privacy preservation is strict and aims to protect personal data. Second, network traffic reduction. Third, minimizing feature matching time. Our simulation results associated with real-time experiments on a small-scale MEC server have shown the effectiveness of the proposed deep learning-based approach over existing schemes. The source code is available here: https://github.com/jamalalasadi/CNN_Image_retrieval.

UAVs-assisted QoS guarantee scheme of IoT applications for reliable mobile edge computing

Unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) is an emerging network architecture that has been considered as a promising transformative service paradigm. It enhances the coverage of intelligent mobile communication network and promotes the rapid development of a wide range of internet of things (IoT) applications. However, the ever-increasing data transmission demands given rise by these IoT applications based on MEC have posed significant challenges to the service of quality (QoS) guarantee of UAVs-assisted MEC (UAVs-MEC) network. To address the issue, we propose a novel UAVs-MEC QoS guarantee scheme that can enhances the reliability of IoT applications in UAVs-MEC network. Specifically, a dynamic QoS-aware based service level agreement (SLA) compliance verification model for MEC service is proposed in the scheme. It can monitor and detect the consistency of dynamic QoS of IoT applications through UAVs to ensure the continuous SLA compliance of service provided by MEC. Additionally, an objective weight assignment method and an adaptive update method are presented to improve the accuracy and scalability of the scheme. Finally, the simulation experiments conducted using a real-world dataset show that the proposed scheme can efficiently and effectively verify the SLA compliance of MEC service for guaranteeing QoS of IoT applications in UAVs-MEC context while outperforms other existing SLA violation detection methods.

Understanding Role of Digital Asset and LOSI for Rural Areas: An Evidence-based Study in Indian Context

Digital assets (DAs) are growing to be one of the most promising utilities due to the emerging digital transformation opportunities for society to harness. The innovations in contemporary DAs have led to the use of mobile computing, internet of things, edge computing and services like software as a service, platform as a service and desktop as a service. DAs have the potential to leverage and support other assets defined by the International Fund for Agricultural Development to enhance livelihood prospects in rural areas. The E-government services, aimed at mitigating digital divides and enhancing rural–urban continuum, need appropriate DAs. The UN has been conducting periodical E-government readiness assessments with a focus on Local Online Services Index (LOSI). All the countries participating in this readiness exercise are creating and upgrading through their E-government policies. LOSI is largely dependent on the availability of desired DAs, supply of digital transformation-based services and an emerging demand. The LOSI, introduced in 2018, is currently limited to the municipalities, providing a scope to assess rural areas where digital divides are still prevalent. This article presents a framework to discuss the associated DAs to develop LOSI for the rural areas in the Indian context. The article also presents a use case in the Indian context to understand how the Indian E-government efforts are poised to leverage the DAs to deepen rural digital services. Through the use cases, it is observed that the rural DAs need converged effort of policymakers to have a better LOSI.

Multi-agent reinforcement learning based computation offloading and resource allocation for LEO Satellite edge computing networks

Due to the limitations caused by geographical conditions and economic requirements, it is difficult to provide computing services by terrestrial networks for mobile terminals in remote areas. To address this issue, mobile edge computing (MEC) servers can be deployed in the low earth orbit (LEO) satellites to act as a complement and accommodate the unserved terminals. However, offloading computing tasks to servers in satellites may increase the energy consumption of ground terminals. Considering the limited battery capacity of ground terminals, how to perform the computation offloading and resource allocation are key challenges in the LEO satellite edge computing networks. Therefore, in this paper, we investigate the energy minimization problem for LEO satellite edge computing networks, where a multi-agent deep reinforcement learning algorithm with global rewards is proposed to optimize the transmit power, CPU frequency, bit allocation, offloading decision and bandwidth allocation via a decentralized method. Simulation results show that our proposed algorithm can converge faster. Most importantly, compared with the random algorithm, the proximal policy optimization (PPO) algorithm, and the deep deterministic policy gradient (DDPG) algorithm, the ground terminals’ energy consumption can be effectively reduced by our proposed algorithm.

Implementasi Sistem Antrian Pasien Berbasis Website Pada Klinik Sehat Tamba Kelurahan Cilangkap

Clinics are health facilities that provide basic medical services, diagnosis, treatment, and care to patients with minor or non-emergency medical conditions. The development of information technology has triggered the need for information systems in the medical field to improve health services. One important factor in increasing patient satisfaction is minimizing waiting time. Long queues can reduce clinic efficiency and affect patient satisfaction. This study aims to implement a website-based patient queue system at the Tamba Sehat Clinic, with the hope of increasing service efficiency and patient satisfaction. In implementing the website queuing system, researchers used qualitative methods to understand the effectiveness of health services and patient experiences. Data was collected through interviews, observations, and literature studies. The implementation of the Website-Based Patient Queuing System at the Tamba Sehat Clinic has had a significant impact. People can now easily get a queue number online via mobile phone or computer, eliminating the need to queue directly at the clinic location. Tamba Sehat Clinic, which was established in January 2023, has seen increased efficiency with this system. Patients can quickly and efficiently get a queue number, ensuring that their time is spent productively. Additionally, adopting this technology helps clinics improve the quality of service to prospective patients, creating a more comfortable and efficient experience for those in need of healthcare services. Thus, the implementation of a website-based patient queue system has brought real benefits to the community and clinics.

SFL-TUM: Energy efficient SFRL method for large scale AI model's task offloading in UAV-assisted MEC networks

The convergence of mobile edge computing (MEC) network with unmanned aerial vehicles (UAVs) presents an auspicious opportunity to revolutionize wireless communication and facilitate high-speed internet access in remote regions for mobile devices (MDs) as well as large scale artificial intelligence (AI) models. However, the substantial amount of data produced by the UAVs-assisted MEC network necessitates the integration of efficient distributed learning techniques in AI models. In recent times, distributed learning algorithms, including federated reinforcement learning (FRL) and split learning (SL), have been explored for the purpose of learning machine learning (ML) models that are distributed by sharing model parameters, as opposed to large raw data-sets as seen in traditional centralized learning algorithms. To implement the hybrid method, the model is first trained locally on each UAV-assisted MEC network using SL. Subsequently, the model parameters that have been encrypted are sent to a central server for federated averaging. Finally, after the model has been updated, it is distributed to each UAV-assisted MEC network for local fine-tuning. Our simulations indicate that the proposed split and federated reinforcement learning (SFRL) framework yields comparable high-test accuracy performance while consuming less energy compared to extant distributed learning algorithms. Furthermore, the SFRL algorithm efficiently realizes energy-efficient selection between the SL and FRL methods under different distributions. Numerical results shows that the proposed scheme improves the accuracy by 29.31% and reduced the energy consumption by around 67.34% and time delay by about 7.37%. as compared to the existing baseline schemes.