Beyond web/native/hybrid Nunkesser, Robin
2018 IEEE/ACM 5th International Conference on Mobile Software Engineering and Systems (MOBILESoft),
05/2018
Conference Proceeding
Currently, mobile operating systems are dominated by the duopoly of iOS and Android. App projects that intend to reach a high number of customers need to target these two platforms foremost. However, ...iOS and Android do not have an officially supported common development framework. Instead, different development approaches are available for multi-platform development.
The standard taxonomy for different development approaches of mobile applications is: Web Apps, Native Apps, Hybrid Apps. While this made perfect sense for iPhone development, it is not accurate for Android or cross-platform development, for example.
In this paper, a new taxonomy is proposed. Based on the fundamental difference in the tools and programming languages used for the task, six different categories are proposed for everyday use: Endemic Apps, Web Apps, Hybrid Web Apps, Hybrid Bridged Apps, System Language Apps, and Foreign Language Apps. In addition, when a more precise distinction is necessary, a total of three main categories and seven subcategories are defined.
The paper also contains a short overview of the advantages and disadvantages of the approaches mentioned.
Developing applications targeting mobile devices is a complex task involving numerous options, technologies, and trade-offs, mostly due to the proliferation and fragmentation of devices and ...platforms. As a result of this, cross-platform app development has enjoyed the attention of practitioners and academia for the previous decade. Throughout this review, we assess the academic body of knowledge and report on the state of research on the field. We do so with a particular emphasis on core concepts, including those of user experience, device features, performance, and security. Our findings illustrate that the state of research demand for empirical verification of an array of unbacked claims, and that a particular focus on qualitative user-oriented research is essential. Through our outlined taxonomy and state of research overview, we identify research gaps and challenges, and provide numerous suggestions for further research.
The rapid growth of mobile computing is seriously challenged by the resource constrained mobile devices. However, the growth of mobile computing can be enhanced by integrating mobile computing into ...cloud computing, and hence a new paradigm of computing called mobile cloud computing emerges. In here, the data is stored in cloud infrastructure and the actual execution is shifted to cloud environment so that a mobile user is set free from resource constrained issue of existing mobile devices. Moreover, to avail the cloud services, the communications between mobile devices and clouds are held through wireless medium. Thus, some new classes of security and privacy challenges are introduced. The purpose of this survey is to present the main security and privacy challenges in this field which have grown much interest among the academia and research community. Although, there are many challenges, corresponding security solutions have been proposed and identified in literature by many researchers to counter the challenges. We also present these recent works in short. Furthermore, we compare these works based on different security and privacy requirements, and finally present open issues.
Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in ...mobile edge computing is intended to substantially increase this capacity. To deal with this issue, in this article, we design a new efficient lightweight blockchain-enabled radio frequency identification (RFID)-based authentication protocol for supply chains in 5G mobile edge computing environment, called lightweight blockchain-enabled RFID-based authentication protocol (LBRAPS). LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols.
Abstract
The integration of Internet tech and mobile communication tech has changed the form of traditional info and communication tech, which can better serve the real needs of people for real-time ...communication and high-speed sharing and interconnection of data, info and resources. Based on this, this paper first analyses the connotation and characteristics of mobile communication and computer Internet, then studies the integration mode of mobile communication tech and computer Internet tech, and finally gives the trend of the combination of mobile communication tech and computer Internet tech.
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, ...among others. However, continuously executing the entire DNN on mobile devices can quickly deplete their battery. Although task offloading to cloud/edge servers may decrease the mobile device’s computational burden, erratic patterns in channel quality, network, and edge server load can lead to a significant delay in task execution. Recently, approaches based on split computing (SC) have been proposed, where the DNN is split into a head and a tail model, executed respectively on the mobile device and on the edge server. Ultimately, this may reduce bandwidth usage as well as energy consumption. Another approach, called early exiting (EE), trains models to embed multiple “exits” earlier in the architecture, each providing increasingly higher target accuracy. Therefore, the tradeoff between accuracy and delay can be tuned according to the current conditions or application demands. In this article, we provide a comprehensive survey of the state of the art in SC and EE strategies by presenting a comparison of the most relevant approaches. We conclude the article by providing a set of compelling research challenges.
The Review highlighted in this Editorial followed a CAEN Return Home Grant
This is an Editorial for a review that Sosa and coworkers present in the current issue of the Journal of Neurochemistry of ...physiological functions for amyloid precursor protein (APP) at dynamic cellular contact sites such as growth cones, neuronal migration tracts, and synapses. Here, APP physically links the extracellular and intracellular milieus through a multitude of binding partners. From these observations, the authors offer a model of APP as a cell adhesion molecule in the brain, providing a context for understanding its role in Alzheimer's disease and Down syndrome.
The Review highlighted in this Editorial followed a CAEN Return Home Grant
Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are ...generating data that need to be analyzed in real time using deep learning or used to train deep learning models. However, deep learning inference and training require substantial computation resources to run quickly. Edge computing, where a fine mesh of compute nodes are placed close to end devices, is a viable way to meet the high computation and low-latency requirements of deep learning on edge devices and also provides additional benefits in terms of privacy, bandwidth efficiency, and scalability. This paper aims to provide a comprehensive review of the current state of the art at the intersection of deep learning and edge computing. Specifically, it will provide an overview of applications where deep learning is used at the network edge, discuss various approaches for quickly executing deep learning inference across a combination of end devices, edge servers, and the cloud, and describe the methods for training deep learning models across multiple edge devices. It will also discuss open challenges in terms of systems performance, network technologies and management, benchmarks, and privacy. The reader will take away the following concepts from this paper: understanding scenarios where deep learning at the network edge can be useful, understanding common techniques for speeding up deep learning inference and performing distributed training on edge devices, and understanding recent trends and opportunities.
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