Mobile Environment Research Articles

Truthful mechanism for joint resource allocation and task offloading in mobile edge computing

In the context of mobile edge computing (MEC), the delay-sensitive tasks can achieve real-time data processing and analysis by offloading to the MEC servers. The objective is maximizing social welfare in an auction-based model. However, the distances between mobile devices and access points lead to differences in energy consumption. Unfortunately, existing works have not considered both maximizing social welfare and minimizing energy consumption. Motivated by this, we address the problem of joint resource allocation and task offloading in MEC, with heterogeneous MEC servers providing multiple types of resources for mobile devices (MDs) to perform tasks remotely. We split the problem into two sub-problems: winner determination and offloading decision. The first sub-problem determines winners granted the ability to offload tasks to maximize social welfare. The second sub-problem determines how to offload tasks among the MEC servers to minimize energy consumption. In the winner determination problem, we propose a truthful algorithm that drives the system into equilibrium. We then show the approximate ratios for single and multiple MEC servers. In the offloading decision problem, we propose an approximation algorithm. We then show it is a polynomial-time approximation scheme for a single MEC server. Experiment results show that our proposed mechanism finds high-quality solutions in changing mobile environments.

Multi-Objective Optimization of Orchestra Scheduler for Traffic-Aware Networks

The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off.

An Analysis the Energy Consumption of Dynamic Task Offloading and Task Execution in MCC

Cloud computing is a group of distributed computing resources that may run any application. MCC is the result of merging the cloud environment with mobile devices. computational offloading is carried out on the cloud in order to save the processing capacity of handheld devices. There will be a rise in computing as the number of sensors used increases, necessitating greater data analysis. Another problem with mobile environments is the power drain on batteries. One solution is to move the work to a remote location where it can be more efficiently executed. In this off-site setting, you'll find powerful cloud servers with plenty of processing capacity. One way that dense mobile apps were able to move their work to the cloud is through computational offload. why it's important to provide a proper explanation of the time constraint in wireless & distant environments, and how reaction time affects offloading for mobile devices & remote servers.

Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments.

Competing Kinetic Consequences of CO2 on the Oxidative Degradation of Branched Poly(ethylenimine).

Amine-functionalized porous solid materials are effective sorbents for direct air capture (DAC) of CO2. However, they are prone to oxidative degradation in service, increasing the materials cost for widespread implementation. While the identification of oxidation products has given insights into degradation pathways, the roles of some species, like CO2 itself, remain unresolved, with conflicting information in the literature. Here, we investigate the impact of CO2 on the oxidative degradation of poly(ethylenimine)-alumina (PEI/Al2O3) sorbents under conditions encompassing a wide range of CO2-air mixture compositions and temperatures relevant to DAC conditions, thereby reconciling the conflicting data in the literature. Degradation profiles characterized by thermogravimetric analysis, in situ ATR-FTIR, and CO2 capacity measurements reveal nonmonotonic effects of CO2 concentrations and temperatures on oxidation kinetics. Specifically, 0.04% CO2 accelerates PEI/Al2O3 oxidation more at low temperatures (<90 °C) compared to 1% and 5% CO2, but this trend reverses at high temperatures (>90 °C). First-principles metadynamics, machine learning accelerated molecular dynamics simulations, and 1H relaxometry experiments show that chemisorbed CO2 acid-catalyzes critical oxidation reactions, while extensive CO2 uptake reduces PEI branch mobility, slowing radical propagation. These contrasting kinetic effects of CO2 explain the complex degradation profiles observed in this work and in prior literature. Collectively, this work highlights the importance of considering atmospheric components in the design of DAC sorbents and processes. Additionally, it identifies the unconstrained branch mobility and local acid environment as two of the major culprits in the oxidation of amine-based sorbents, suggesting potential strategies to mitigate sorbent degradation.

Network Stability Based Multicriteria Weighted MPRs Selection Algorithm for Mobile Ad Hoc Networks

Manets are self-governing networks using several mobile nodes, which communicate together through wireless media, without the necessity of prior infrastructure. Therefore, due to highly dynamic and unconfident environments, Manets are weak to guarantee link lifetime, security, reliability and stability. Since, mobility, energy and security are the main factors for improved performance in the network. Paths and relay devices with enhanced stability and security are considered as critical subjects. The study suggests an improved process based on a Multicriteria purpose consistent with numerous systems of measurement. These systems of measurement are energy level, trust measure and mobility of mobile nodes. The concept is to select improved Multipoint relays (MPRs) able to transmit data efficiently, and increase lifetime and reachability. The proposed improvement supports the routing and the selection of paths. The selection of the greatest effective relay nodes can enhance the classification performance. The scheme is based on the OLSR protocol and performance results are defined using the network simulator (NS3) with different movement models. The results showed significant improvements in MCW_OLSR. The proposed version enhances network proficiency for different mobile environments.

Graph-Enhanced Spatio-Temporal Interval Aware Network for Next POI Recommendation in Mobile Environment

Graph-Enhanced Spatio-Temporal Interval Aware Network for Next POI Recommendation in Mobile Environment

Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images

Mobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated.

Application of hybrid SSVEP + P300 brain computer interface to control avatar movement in mobile virtual reality gaming environment

IntroductionThis research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex. MethodsA BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar’s movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement. Results and conclusionsConventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.

An efficient attack detection and prevention approach for secure WSN mobile cloud environment

An efficient attack detection and prevention approach for secure WSN mobile cloud environment

“What the Heck Is Going on in the Group!”: Challenging Journalistic Authority by Pseudo-Professional WhatsApp News Groups

ABSTRACT This paper addresses the ways new entrants and mobile technologies have become significant challenges to the old news’ legitimacy models by focusing on pseudo-professional WhatsApp news groups from a journalistic authority standpoint. Based on a comparative textual analysis of how a breaking news event was handled by a leading Israeli pseudo-professional WhatsApp news group, compared to how it was covered by the Israeli News13 TV news broadcast, the findings indicate how, by adopting professional news practices and establishing unique relations of information exchange with traditional news outlets, journalistic authority is challenged by pseudo-professional WhatsApp news groups in today’s mobile news environment.

Cooperative transmission of UAV swarm using orthogonal time–frequency space modulation

Unmanned aerial vehicles (UAVs) emerge as versatile aerial nodes capable of dynamically filling coverage gaps and enabling mission-critical communication based on their mobile nature. UAV swarms, characterized by the coordinated operation of multiple drones, are employed for a wide range of applications such as surveillance, environmental monitoring, precision farming, and autonomous delivery. While individual UAVs suffer from limited power, group transmissions of UAV swarms can enhance the data rate, reliability, and energy efficiency. However, their mobility may cause severe Doppler spread. The existing orthogonal frequency division multiplexing (OFDM) system exhibits limitations such as susceptibility to the Doppler effect in high-speed mobile environments. To address this issue, orthogonal time–frequency space (OTFS)-aided cooperative transmission (CT) for UAV swarms is considered, which can surmount the aforementioned limitations. The results show the BER improvement of the order of 103 at the SNR value of 10 dB when OTFS modulation is utilized and even better at the higher SNR values. Furthermore, the analytical framework of OTFS-based CT is presented by identifying the key design considerations on the subcarrier spacing, the number of symbols, and the number of subcarriers, which are subject to the maximum speed of the UAV and the cluster size of the UAV swarm. These results provide a significant platform for advancing research in the field of UAV-based CT, especially with the Doppler-resilient OTFS modulation.

모바일 화장품 앱 활용현황 및 소비자의 구매행동이 구매만족도에 미치는 영향

While the mobile environment is rapidly absorbing new beauty-related information, the information of mobile cosmetics apps is playing an important role in changing women's personality, beautiful appearance, and life. The purpose of this study is to understand the characteristics of mobile cosmetics apps from this point of view, and through this, the effect of cosmetics apps' utilization status and cosmetics consumers' purchasing behavior on purchase satisfaction. This study was conducted as an empirical analysis through theoretical consideration and questionnaire method. Previous studies, academic journals, reports, and presentation data were collected and analyzed to understand the characteristics and current status of the cosmetics market, and cosmetics purchase behavior through mobile cosmetics apps was analyzed. A questionnaire was conducted and empirical analysis was conducted to analyze the cosmetics purchase behavior, satisfaction, and usage status of cosmetics apps by consumers through the app. The results of the study are as follows. The group that uses mobile cosmetics apps the most is office workers under their 20s, and the preference for cosmetics apps is Olive Young, which is rich in product information, because it is well suited to the lifestyle of the 20s. Housewives in their 50s used offline more than apps. Through this, it can be seen that people in their 20s who are familiar with smartphones use mobile cosmetics apps more than those in their 50s who have difficulty using smartphones. As a result of analyzing the factors affecting the satisfaction of purchasing cosmetics through mobile cosmetics apps, acquisition of makeup information and easy purchase channels were found to have a great influence on purchase satisfaction.

기술융합형 웹툰 서비스의 지속적 사용에 영향을 미치는 요인 연구

This study aims to identify the factors influencing the continuous use of technology-converged webtoons by users through the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2). The influence of webtoons as a media form is increasing, and they are evolving into technology-converged webtoons, such as smarttoons optimized for mobile environments and cuttoons designed for sharing on social media. The study investigated the factors influencing the continued use of technology -converged webtoons by categorizing them into technical attributes (effort expectancy, social influence, facilitating conditions, recommendation services) and psychological factors (hedonic motivation, price value, habit). The research findings revealed that, within the technical attributes, effort expectancy positively impacted the sustained use of technology-converged webtoons. Furthermore, within the psychological factors, hedonic motivation, price value, facilitating conditions, and habit were found to have a positive influence on the continuous use of technology-converged webtoons. Through this study, it was determined that users of technology-converged webtoons are more likely to continue using webtoons due to psychological factors rather than technical attributes. Additionally, the study highlighted the importance of the price value factor, noting that users perceive the price of technology -converged webtoons as “reasonable,” offering “good value for money” and providing “utility.” Based on these findings, it is expected that this study will serve as valuable data for the future developmental growth of the webtoon industry.

A Novel intelligent SAV oriented QL-based task offloading in mobile edge environments

Edge computing is a novel and potential computing model which moves storage and computing capabilities to the network edge, substantially decreasing service latency and network traffic. The existing Internet of Things (IoT) network offloading algorithm faces limitations such as a fixed number of applications, high edge-to-edge delay, and reliance on a single Mobile Edge Computing (MEC) server, posing security and privacy concerns. Moreover, resource-constrained mobile devices need more effective data integration and compression strategies. Addressing these challenges, this study suggests an approach based on deep reinforcement learning (DRL), specifically the SAV (State Action Value) Oriented QL (Q-Learning) based Task Offloading method, to optimise task offloading and resource allocation in edge-cloud computing. The model aims to empower Mobile Devices (MDs) to develop optimal offloading decisions for long-term Quality Perception, utilising a neural network to establish the relationship between MD state and action value. The paper introduces a Recurrent Extended Memory Network (REMN) to capture dynamic workload behaviour at Edge nodes (ENs). It incorporates Quality Mapping, Quality Estimation, and a Quality-Aware DRL Task Offloading Algorithm to improve the accuracy and efficiency of the offloading procedure in MEC systems. This systematic approach improves overall system performance and enables MDs to leverage ENs for neural network training, reducing computational burdens. As a result, it can accomplish a more significant number of tasks, reducing latency from 0.74 ms to 7.168 ms and decreasing energy consumption from 270 J to 1820.39 J for tasks ranging from 10 to 50, respectively.

Real-time map rendering and interaction: a stylized hierarchical symbol model

ABSTRACT Digital maps, serving as pivotal instruments in the realm of geographic information services, adeptly encapsulate spatial data through refined graphics and symbols. Widely used across various domains such as smart devices, in-vehicle navigation systems, unmanned aerial vehicles (UAV), electronic gaming, and virtual reality, these maps face the ongoing challenge of delivering real-time, precise, and efficacious map rendering and interaction experiences. In response to this challenge, this study introduces a Stylized Hierarchical Symbol structure (SHS) and a Geographic Feature Color-weighted Rendering technique (GFCR). The approach is further complemented by incorporating Adaptive Hierarchy-Weighted caching (AHie), Catalan Number-based Caching access (CatNCa), and Grid-based Dynamic caching loading (GriD). Experimental results demonstrate the method's effectiveness in addressing issues related to timeliness, accuracy, and logical correctness in mobile map environments, enhancing the real-time interaction experience of mobile digital maps. Notably, this method imposes modest hardware and storage requirements, making it applicable to a wide range of scenarios including natural resource planning, intricate road landscapes, route navigation challenges, and emerging fields such as autonomous driving, virtual reality, and the metaverse.

Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident

ABSTRACT Electric vehicles (EVs) have played a significant role in sustainability, and EVs fire accidents have raised doubts in recent years. To solve the mobile analytical equipment limitation in EVs fire accident and help staff receive prompt results at on-spot inspection, we provide a lightweight but accurate Transformer that can ideally adapt to the mobile environment. First, we built on the simple Segformer and extended it to aggregate the representations of amorphous objects, such as fire traces, in image recognition. Second, we used shunt-based self-attention (SSA) to enhance the model for capturing multi-scale contextual information and help distinguish the deformed level of EVs after combustion. Third, we redesigned a simple multi-level information aggregation (MIA) decoder to obtain the relationship between pixels in the channel dimensions by a weighted aggregation. Furthermore, to foster image trace recognition, we put forwards and evaluated the accuracy of models on electric vehicle fire traces (EVFTrace), a dataset of images of burnt EVs. On EVFTrace, the mean intersection over union (mIoU) achieves 72.24%. The float point operations (Flops) and parameters (Params) achieve 114.83 G and 89.5 M. Our model shows excellent efficiency and accuracy for burnt EVs segmentation tasks.

Multidimensional Spectral Phasors of LAURDAN's Excitation-Emission Matrices: The Ultimate Sensor for Lipid Phases?

The impact of lipid diversity on the lateral organization of biological membranes remains a topic of debate. While the existence of domains in lamellar membranes is well-established, the nonlamellar phases occurring in biological systems are less explored due to technical constraints. Here, we present the measurement of the excitation-emission matrices (EEM) of LAURDAN in several lipid structures. LAURDAN is a fluorescence probe widely used for characterizing lipid assemblies. The EEMs were analyzed by multidimensional spectral phasors (MdSP), an approach that seizes information from both the excitation and emission spectra. We developed a computer algorithm to construct EEM data based on a model for LAURDAN's photophysics. The MdSP calculated from the simulated EEMs reveals that all feasible possibilities lie inside a universal triangle in the phasor's plot. We use this triangle to propose a ternary representation for the phasors, allowing a better assessment of LAURDAN's surroundings in terms of hydration, water mobility, and local electronic environment. Building upon this foundation, we constructed a theoretical "phase map" that can assess both lamellar and nonlamellar membranes. We thoroughly validated this theory using well-known lipid mixtures under different phase-state conditions and enzymatically generated systems. Our results confirm that the use of MdSP is a powerful tool for obtaining quantitative information on both lamellar and nonlamellar structures. This study not only advances our understanding of the impact of lipid diversity on membrane organization but also provides a robust and general framework for the assessment of fluorescence properties that can be further extended to other probes.

Enhancing Collaborative Learning in Mobile Environments through Interactive Virtual Reality Simulations

This study investigates the potential of enhancing collaborative learning in mobile environments through the integration of interactive virtual reality (VR) simulations. With the ubiquity of mobile devices and advancements in VR technology, there is a growing interest in using immersive experiences to promote collaborative learning among students. The study explores the design and implementation of interactive VR simulations customized for mobile platforms. The goal is to create engaging and immersive learning experiences that foster collaboration and knowledge sharing. By immersing students in virtual environments where they can interact with digital objects and manipulate scenarios, the study aims to facilitate active participation and teamwork, thereby enhancing learning outcomes. Furthermore, the study examines the impact of interactive VR simulations on student engagement, motivation, and knowledge retention in collaborative learning settings. Through empirical studies and user evaluations, the effectiveness of interactive VR simulations as a tool for collaborative learning in mobile environments is assessed. The findings provide valuable insights into the design and pedagogical integration of VR technologies in mobile learning contexts. They offer guidance for educators and instructional designers who aim to leverage the potential of immersive experiences to improve collaborative learning outcomes.

Prioritization-based delay sensitive task offloading in SDN-integrated mobile IoT network

Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms.