Mobile Industry Research Articles

Fostering Consumer’ Acceptance of SMS-Based Mobile Advertising: A South Asian Perspective

This study aims to investigate the drivers that foster the acceptance of SMS-Based Mobile Advertising among the consumers of Pakistan. The mobile industry in Pakistan is expanding quickly; mobile cellular density increased from 60.4% in 2009–10 to 82.08% in May 2023. Given the quick expansion of the mobile phone industry, advertisers worldwide have poured billions of dollars into SMS-based mobile advertising to draw in mobile consumers. On the other hand, these commercials are poorly received and elicit adverse reactions. Data was meticulously gathered and subjected to thorough CFA and SEM analysis. The model's acceptable fit is confirmed by SEM analysis, and path analysis supports the positive relationships between the endogenous construct and utility, context, and trust. Millennials view mobile devices as highly personal but find mobile advertising obtrusive. This study helps marketers to get insights into the phenomenon of how to raise consumer acceptability levels and help marketers accept their advertising initiatives' desired return on investment.

Exploring the effects of perceived values on consumer usage intention for electric vehicle in Thailand: the mediating effect of satisfaction

Nowadays, electric vehicle innovation plays an important role in the market, transportation, logistics and supply chain in the electric mobility industry, contributing to environmental protection and reducing pollution. The commercial EV launched in Thailand which has been very popular with consumers, but it is also not widely used, and there are not many market surveys about customer usage intention. Therefore, it is necessary to study seriously. This study explores the impact of the perceived value of electric vehicle (EV) features on consumer usage intention to use electric vehicles in Thailand, with the mediating effect of customer satisfaction. The results showed that perceived value from using EV, consumer satisfaction, and usage intention of EV, there is a significant direct and indirect relationship. These findings have contributions and relevant expected benefits.

Smart Parking System Based on Edge-Cloud-Dew Computing Architecture

In a smart parking system, the license plate recognition service controls the car’s entry and exit and plays the core role in the parking lot system. When the Internet is interrupted, the parking lot’s business will also be interrupted. Hence, we proposed an Edge-Cloud-Dew architecture for the mobile industry in order to tackle this critical problem. The architecture has an innovative design, including LAN-level deployment, Platform-as-a-Dew Service (PaaDS), the dew version of license plate recognition, and the dew type of machine learning model training. Based on these designs, the architecture presents many benefits, such as: (1) reduced maintenance and deployment issues and increased dew service reliability and sustainability; (2) effective release of the network constraint on cloud computing and increase in the horizontal and vertical scalability of the system; (3) enhancement of dew computing to resolve the heavy computing process problem; and (4) proposal of a dew type of machine learning training mechanism without requiring periodic retraining, but with acceptable accuracy. Finally, business owners can reduce their burdens when introducing machine learning technology. Our research goal is to make parking systems smarter in edge computing through the integration of cloud and dew architecture technology.

Relay Node-Based Routing Algorithm for Reducing Latency in Industrial Mobile Communication Network

Mobile network nodes perform time transfer during data packet performance in an asynchronous manner. Network nodes get packets for transmission from node sensors that reject the demands of node attackers. When packet loss occurs unexpectedly due to a network removal, it is exceedingly difficult to detect assaults and time forwarding. Attack detection accuracy deteriorates, and packet loss rates rise. A strategy for node mobile intermediate that gets over attacks that identify the forward choosing is provided for improved data forwarding. Network performance is completely damaged by specific attacks, such as communication processes that degrade or lose packets. The algorithm’s architecture ensures that the best nodes are chosen for relaying and that transmission packets that do not drop are declined. To enable the node and create an effective path routing, a process is executed. Decreasing the amount of packet loss and increasing attack efficiency are being identified.

Quality of Service Management for Fast Data Transmission in Industrial Mobile Communications Using PSO-Based Computer Vision Technique

Mobile ad hoc networks (MANETs) have rapidly expanded in recent years, mostly as a result of the mobile devices’ obvious low cost, heterogeneity, and flexibility. When communication networks are down or inaccessible, sensors can quickly create a reliable network that can be used as a rescue data system. Industrial mobile communication has developed into a significant study area for both industry and academia in recent years. The need for data interchange between various smart devices with various latency flows is enormous. Nevertheless, there has not been as much research done in this area. The suggested work suggests a fuzzy-based improved PSO optimized in MANET to alleviate the drawbacks of the conventional routing approach. The suggested study offers a numerical modelling that can be used to carry out adaptive transmission optimization with a variety of programmable module structures and guarantee cost-effective route establishment with greater throughput, goodput, and lowest delay requirements. To find the best route, the proposed approach combines energy-optimized route construction with data-driven cluster head (CH) selection based on swarm intelligence. Particle swarm optimization- (PSO-) based clustering achieves improved delay, goodput, throughput, and path difference degree as compared to other conventional approaches, according to the extensive simulation results. The energy efficiency of a network that is decentralized is more important. The MANET device’s energy efficiency helps to extend battery life and improve network performance. This research demonstrates how the fuzzy-based improved PSO optimized in MANET helps to raise the network’s energy efficiency. As a result, network energy conservation improves network performance and battery life. Additionally, this method enhances the quality of service methodology. End-to-end delay, energy consumption, packet delivery ratio, and normalized routing overhead are measured and compared between the simulation and conventional routing protocols.

A Review of Select Patented Technologies for Cooling of High Heat Flux Power Semiconductor Devices

The development of wide band-gap power electronics over the last two decades has stimulated a tremendous amount of research into high performance liquid cooling solutions for high heat flux (200-1000 W/cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> ) power semiconductor devices. A patent literature search for electronics cooling technologies was conducted in the late-2000 time frame and promising technologies for high performance thermal management were identified, at that time, for research and development. These promising technologies were separated into four categories classified as single-phase (i.e., liquid) jet impingement cooling, microchannel cooling, phase change or two-phase cooling, and near-junction including direct chip cooling. In this Letter, a perspective is provided on select patents from our group that stem from research into these technology areas from 2010 to 2022. Additionally, the thermal-fluid performance capability of each technology is briefly summarized. As a whole, the Letter provides a demonstration of the historical significance of power electronics cooling technology on the mobility industry and briefly outlines future directions for research and development.

Polymer electrolyte design strategies for high-performance and safe lithium-ion batteries: Recent developments and future prospects

Although lithium-ion batteries have gained widespread use in high-performance and mobile industries, concerns about their safety due to the low boiling point of their organic liquid electrolyte have posed challenges to their further development. In response, solid polymer electrolytes have emerged as a promising alternative, characterized by low flammability, flexibility, and high safety relative to liquid electrolytes. However, commercialization has been hindered by limitations in Li-ion conductivity and mechanical properties. Recent research efforts have focused on addressing these limitations to improve the performance and safety of polymer-based Li-ion batteries. This review discusses the utilization of polymer materials to enhance battery safety and overcome previous challenges, with a particular emphasis on the design of robust artificial interfaces to increase battery stability. Furthermore, we discuss the prospects for the future of polymer-based battery industries.

Polypropylene/silica nanocomposite membranes for lithium‐ion battery separator: Studies on interfacial tuning of pore structure, mechanical and thermal properties

AbstractEnergy storage and mobility industries use polypropylene (PP) nanocomposites. The energy storage applications of newly made microporous SiO2 filled, porous PP nanocomposites combining PP, natural rubber, and SiO2 nanoparticles then subsequently removing natural rubber from the nanocomposite were explored in this paper. SEM pictures showed how the pore morphology was altered by nanofillers. Mechanical tensile tests reveal that the SiO2 nanofiller enhances the mechanical properties of microporous polypropylene. XRD and DSC experiments have shown that SiO2 nanoparticles works as a nucleating agent but has no impact on the crystallinity of PP. The electrolyte absorption, porosity, ionic conductivity, wettability and charge–discharge performance were investigated for PP/SiO2 nanocomposite separator. SiO2 nanoparticles in the matrix have a significant impact on the wettability of PP membrane, since the presence of SiO2 nanoparticle reduces the contact angle from 24° to 15°. SiO2 filled PP membrane outperformed microporous PP and celgard separator in terms of electrolyte uptake (230%) and porosity (62%). In comparison to microporous PP, the PP/SiO2 membrane demonstrated a better ionic conductivity (5.9 × 10−4 S cm−1). Finally, Li/PP‐SiO2/LiFePO4 coin cells (2320 type) were fabricated and delivered a discharge capacity of 138 mAh g−1.

Alan Odaklı Vektör Kontrol Yaklaşımı ile Elektrikli Scooter BLDCM için Kayan Kip Tabanlı Çekiş Kontrol Sistemi Tasarımı

Nowadays brushless DC motors (BLDCMs) are becoming indispensable components as the electrification revolution in the mobility industry is happening. Electric kick scooters, so-called e-scooters, are among these micro-mobility vehicles which are powered by these motors. Due to the uncertain and nonlinear features, the controller performance developed for these motors degrades. For these reasons, a chattering-reduced cascaded Sliding Mode Control (SMC) scheme to effectively track reference motor speed in the outer loop by eliminating torque ripples in the inner loop current control was designed. Field-oriented Control (FOC) methodology was used to implement the SMC in the BLDCM. An exponential reaching law algorithm was proposed for sliding surfaces of the inner and outer loop controllers. The suitability and performance of electric scooter-hub motors were analyzed in terms of traction control. A cascaded speed and torque controller produced significantly favorable results representing minimized torque and current ripples, and operation over a wide speed range.

Backdoor Attack on Deep Neural Networks Triggered by Fault Injection Attack on Image Sensor Interface

A backdoor attack is a type of attack method that induces deep neural network (DNN) misclassification. The adversary who aims to trigger the backdoor attack inputs the image with a specific pattern (the adversarial mark) into the DNN model (backdoor model). In general, the adversary mark is created on the physical object input to an image by capturing a photo. With this conventional method, the success of the backdoor attack is not stable because the size and position change depending on the shooting environment. So far, we have proposed a method of creating an adversarial mark for triggering backdoor attacks by means of a fault injection attack on the mobile industry processor interface (MIPI), which is the image sensor interface. We propose the image tampering model, with which the adversarial mark can be generated in the actual fault injection to create the adversarial mark pattern. Then, the backdoor model was trained with poison data images, which the proposed simulation model created. We conducted a backdoor attack experiment using a backdoor model trained on a dataset containing 5% poison data. The clean data accuracy in normal operation was 91%; nevertheless, the attack success rate with fault injection was 83%.

Preview the Products on Display at Display Week 2023

Preview the Products on Display at Display Week 2023

The Growing Function of Understanding Wireless Network Technologies for Upcoming Generation Digital Stream Processing

With the development of the fifth generation (5G) and sixth generation (6G) networks, wireless networks are beginning accomplish persistent large scale obtainment, and communication. Modern cellular technology and the developing new era are both seen favourably for the smart grid. The 5G and 6G networks are being developed and prepared for deployment by the mobile industry. The development of IoT and other intelligent automation applications is being significantly fueled by the growing wireless networks, which are becoming more widely accessible. Network densification, fast throughput, precise location, and energy efficiency criteria will be increasingly demanding in future wireless communications. One of the core areas of wireless networking research in the future will be how to increase productivity while reducing expenses. Approaching this goal in a way that allows for the ability to learn from experience is crucial. Transfer learning (TL) promotes new activities and domains to pick up knowledge from more seasoned tasks and domains so that new tasks may be completed more quickly and effectively. The connection and similarity information between various jobs in several domains of wireless communications can assist TL conserve energy and increase efficiency. Applying TL to upcoming 6G communications is thus a very important subject.

Microstructural Characterization of Al/CNTs Nanocomposites after Cold Rolling.

The deformation behaviour of aluminium reinforced by carbon nanotubes (Al/CNTs) nanocomposites during cold rolling was investigated in this work. Deformation processes after production by conventional powder metallurgy routes may be an efficient approach to improve the microstructure and mechanical properties by decreasing the porosity. Metal matrix nanocomposites have enormous potential to produce advanced components, mainly in the mobility industry, with powder metallurgy being one of the most reported production processes. For this reason, it is increasingly important to study the deformation behaviour of nanocomposites. In this context, nanocomposites were produced via powder metallurgy. Advanced characterization techniques carried out the microstructural characterization of the as-received powders and produced nanocomposites. The microstructural characterization of the as-received powders and produced nanocomposites was carried out through optical microscopy (OM), and scanning and transmission electron microscopy (SEM and TEM), complemented by electron backscattered diffraction (EBSD). The powder metallurgy route followed by cold rolling is reliable for Al/CNTs nanocomposites. The microstructural characterization shows that the nanocomposites exhibit a different crystallographic orientation than the Al matrix. CNTs in the matrix influence grain rotation during sintering and deformation. Mechanical characterization revealed that during deformation, there is an initial decrease in the hardness and tensile strength for the Al/CNTs and Al matrix. The initial decrease was attributed to the Bauschinger effect being more significant for the nanocomposites. The difference in the mechanical properties of the nanocomposites and Al matrix was attributed to distinct texture evolution during cold rolling.

AN EXPLORATORY STUDY ON THE COMPONENTS OF AI IMPACTING CUSTOMER RETENTION IN TELECOM INDUSTRY

Consumer behavior in the mobile industry, particularly consumer behaviors, has become a prominent marketing research topic. Every consumer has distinct life experiences that influence their shopping decisions. Age, employment, lifestyle, personality, and self-concept are social and personal elements that can affect a buyer's final selection. Based on tests performed on data collected, several outcomes were discovered. In this research, the participants were chosen using the technique described, and their data were analyzed using descriptive and inferential statistics. The Chi-Square test was used to investigate the relationship between companies and their usage of optimal service techniques. According to the study's findings, AI-powered solutions may aid organizations in encouraging customers to take action at each stage of the user life cycle.

Sharing economy: car sharing systems

The basis for the development of the sharing economy is that certain of our objects are unused most of the time, they just take up space, and in many cases their ownership involves unnecessarily high costs. This encouraged people to save and look for cheaper solutions, which could also result in an alternative source of income. As a result of this new approach, we become more environmentally conscious customers and the realization of sustainable consumption becomes possible. The sharing economy has appeared in several industries and achieved significant results in the last few years, but the mobility industry is the area that can boast the most significant results.

NeuE: Automated Neural Network Ensembles for Edge Intelligence

Artificial Intelligence (AI) applications have been established in the mobile industry and are decisively determining the progress in entrepreneurial value creation. This paper explores the potential of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge Computing</i> to enhance the performance of AI applications. In particular, a DNN ensemble formation (DEF) problem is studied which judiciously recruits members for DNN ensembles considering the device heterogeneity, computing resource limitation, and service deadline of edge computing systems, in an attempt to optimize the performance of edge AI services. We design a novel algorithm called Neural Ensemble (NeuE) to solve the DEF problem. NeuE involves an online learning process that learns the in-practice performance of DNN ensembles and adaptively forms DNN ensembles according to the features of admitted tasks. It leverages the framework of contextual multi-armed bandit and follows the constraints of computing resource limitation and service deadline. We also show theoretically that NeuE provides asymptotic optimality. However, NeuE suffers from poor scalability due to exponentially-growing ensemble decision space. We then propose a variant of NeuE, called NeuE-S, to expedite NeuE. NeuE-S identifies representative ensemble decisions using similarities of ensemble decisions and carries out learning with a reduced decision space. We show via theoretical analysis that NeuE-S drastically reduces the computation complexity with negligible performance loss. We implement our method on an edge computing testbed. The results show that our method dramatically improves the performance of edge AI services.

Innovations at the level of operational process within the framework of furniture manufacturing SME

Innovation within furniture manufacturing SMEs, as opposed to large companies, involves both a number of advantages and disadvantages. Among the main advantages of innovation in small enterprises or especially start-ups in the field of the mobile industry, there is the agility of employees and fresh and unconventional thinking, but which also requires a steering and monitoring mechanism. In this sense, it is necessary to establish at the level of a small enterprise the culture of innovation of operational processes, which involves the enterprise's ability to exploit all resources, especially employees, who can produce innovation, and as a result, will ensure the prosperity of the entity's activity.

Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

Mobile Industrial Internet of Things (IIoT) applications have achieved the explosive growth in recent years. The mobile IIoT has flourished and become the backbone of the industry, laying a solid foundation for the interconnection of all things. The variety of application scenarios has brought serious challenges to mobile IIoT networks, which face complex and changeable communication environments. Ensuring data secure transmission is critical for mobile IIoT networks. This paper investigates the data secure transmission performance prediction of mobile IIoT networks. To cut down computational complexity, we propose a data secure transmission scheme employing Transmit Antenna Selection (TAS). The novel secrecy performance expressions are first derived. Then, to realize real-time secrecy analysis, we design an improved Convolutional Neural Network (CNN) model, and propose an intelligent data secure transmission performance prediction algorithm. For mobile signals, the important features may be removed by the pooling layers. This will lead to negative effects on the secrecy performance prediction. A novel nine-layer improved CNN model is designed. Out of the input and output layers, it removes the pooling layer and contains six convolution layers. Elman, Back-Propagation (BP) and LeNet methods are employed to compare with the proposed algorithm. Through simulation analysis, good prediction accuracy is achieved by the CNN algorithm. The prediction accuracy obtains a 59% increase.

A dynamic capability evaluation of emerging business models for new mobility

The ‘new mobility’ is claimed to promise improved transport services with reduced socioeconomic and environmental impacts whilst at the same time creating high-value business opportunities. The present article contributes to understanding the latter part of this promise through analysing the sources of value creation in the new mobility industry. It makes a novel application of a business model concepts - the Dynamic Capabilities approach - to transport research. Drawing on findings from expert interviews with professionals in innovation clusters, the findings reveal that new mobility companies benefit from an inherent dynamism and a continuous learning culture. The participation in networks and alliances let companies reach broader market solutions through cross-regional collaboration. These ecosystems are well equipped to address wider mobility needs founded on managing data. For managers in the new mobility, key conclusions are that establishing a concise value proposition and expanding key resources such as staff skills or data management procedures are crucial for business sustainability.

On the injection molding of thick‐walled thermoplastic vulcanizates: linking static and dynamic mechanical properties with morphology

AbstractElastomers are widely used in the automotive industry in anti‐vibration components. Commonly, elastomeric anti‐vibration components are made of thermoset rubbers. However, new drivers related to the requirements of the new sustainable mobility industry are pushing towards the substitution of thermoset rubbers by thermoplastic elastomers. Among thermoplastic elastomers, thermoplastic vulcanizates (TPVs) represent an interesting material choice for anti‐vibration products due to their light weight, recyclability, ease of processing by means of injection molding and design flexibility. In this work, the correlation between static and dynamic mechanical properties and the morphology of injection molded thick‐walled TPVs is investigated. Interestingly, when comparing TPVs of different hardnesses, the softest TPV demonstrated an improvement in elastic ability to recover of up to 50% in transverse to flow direction as well as a highly anisotropic viscoelastic behavior. These phenomena are interpreted in terms of the orientation of ethylene‐propylene‐diene particles together with polypropylene crystals and chain alignment observed in microstructural characterization tests. This study opens new opportunities to develop TPV‐based anti‐vibration components with superior elasticity and customizable static and dynamic mechanical properties by considering the anisotropic character of soft TPV materials in product design and development strategies. © 2022 Society of Industrial Chemistry.