Journal Information
Mobile Networks and Applications
http://www.springer.com/engineering/signals/journal/11036
Impact Factor:
1.538
Publisher:
Springer
ISSN:
1383-469X
Viewed:
3873
Tracked:
8

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Call For Papers
Mobile Networks and Applications' technical scope covers mobility solutions that provide communication technologies and mobile services, which enables users to access resources and share information freely, anytime anywhere. The emerging symbiosis of wireless communication, the ever more powerful mobile devices, with the back-end resources of the cloud, making the user fully location independent.

The journal addresses the convergence of mobility, computing and information organization, services and management. In approving Special Issues, the Journal places an equal emphasis on the various areas of nomadic computing, data management, related software and hardware technologies, and mobile user services, alongside more `classical' topics in wireless and mobile networking. The journal documents practical and theoretical results which make a fundamental contribution.
Last updated by Dou Sun in 2016-12-13
Special Issues
Special Issue on Machine Learning and Intelligent Communications (MLICOM 2017)
Submission Date: 2017-07-01

Along with the fast developing of modern communication and signal processing technologies, the amount of high quality wireless services is required and increasing exponentially. According to the prediction of Cisco VNI Mobile Forecast 2016, Global mobile data traffic will increase nearly eightfold between 2015 and 2020, and mobile network connection speeds will increase more than threefold by 2020. Hence, there are still big gap between the future requirements and current communications, networking, and signal processing technologies. In this view, we are aiming to discuss the potential advantages of adopting some intelligent algorithms into the next generation communication systems. As an emerging discipline, machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence, and explores the study and construction of algorithms that can learn from and make predictions on complicated scenarios. We hope the integrating of machine learning algorithms and other intelligent algorithms into communication systems will improve the quality of service and make the systems smart, intelligent, and efficient. We invite high quality original research papers describing recent and expected challenges or discoveries along with potential intelligent solutions for future wireless communications and networks. Both original, unpublished contributions and survey/tutorial types of articles are encouraged. Topics of interest include, but are not limited to, the following scope: - Machine learning - Intelligent positioning and navigation - Intelligent Multimedia Processing and Security - Intelligent Wireless Networks and Security - Cognitive Radio and Intelligent Networking - Intelligent Internet of Things - Intelligent Satellite Communications - Intelligent Image Processing - Green Communications - Intelligent Ad-hoc Sensor Networks - Resource Allocation in Wireless and Cloud Networks - Intelligent Radar Signal Processing - Network Coding and Cooperative Communications - Advanced Signal Processing
Last updated by Dou Sun in 2017-06-14
Special Issue on Mobilware 2017: Mobile Network Software-ization and Mobile Middleware
Submission Date: 2017-07-01

The recent advances in wireless communications and the proliferation of powerful mobile devices have enabled smart cyber-physical environments where people and devices can seamlessly interact and where mass-market users are willing to receive/contribute to a wide range of mobile services, everywhere and anytime. A key enabler of these pervasive ubiquitous environments is the advancement of software and middleware technologies in various mobile-related sectors, ranging from effective synergic management of wireless communications to mobility/adaptivity support in operating systems, from horizontal support to crowdsourcing in different application domains to dynamic offloading to cloud resources, only to mention a few. Topics of interest include, but are not limited to, the following scope: - IoT and SDN in mobile networks - Fog Computing in wired/wireless and mobile environments - Fog/Cloud Computing service continuum - Containerization support for mobile environments - Security and privacy of mobile-based monitoring systems - Big data and cloud computing scalable processing of mobile sensors-generated datastreams - Applications and testbeds in vehicular networks, home networks, and Industrial IoT - New middleware concepts for mobile devices - Mobile middleware enabling machine-tomachine communication - Mobile crowdsourcing and people-centric collaborative sensing - Smart space/city middleware and applications - Middleware for mobile cloud computing (e.g., seamless offloading) - End-to-end architectures for seamless ubiquitous service provisioning and deployment - Synergic integration of heterogeneous wired and wireless networks - Opportunistic, delay-tolerant, and store-carry-forward techniques for mobile and wireless networking - Energy-efficient applications, services, and middleware - Modeling, simulation, and performance evaluation of mobile wireless systems and services - Trustworthiness, security, and privacy of mobile and wireless systems - Impact of IMS, RCS, RCS-e, EPC, LTE, LTE Direct on the evolution of mobile middleware
Last updated by Dou Sun in 2017-04-13
Special Issue on Recent Advances in IoT as a Service
Submission Date: 2017-07-15

As we are striding into the initial era of the Internet of Things (IoT), a key question is how we make the most of IoT for all stakeholders, including platform providers, IoT application developers, end-users, large and small organizations (such as city councils, enterprises) that wish to provide better services, and manufacturers of smart devices. The amount of smart devices immersed in everyday life, from manufacturing to clothing, is growing every day in terms of power, processing and network connectivity. The sheer size and variety of contextual data that they produce, along with the actions they can take on their environment, is enormous. It remains to be answered how all this potential will come to bear; this special issue focuses on the discussion on the challenges posed by these trends. The "Everything as a Service" deployment paradigm will enable the easy adoption of IoT based services and applications by end users, while forcing providers of smart objects and middleware platforms to architect their solutions accordingly. To maximize impact and adoption, the barrier-to-entry should be lowered by making development of new applications and the ingestion and exposure of smart objects as easy as possible. Original submissions, not under any concurrent reviews, are solicited in all areas related to advances on applications, methods and approaches envisioning to address the new challenges in the IoT as a service. Topics of interest include, but are not limited to, the following scope: - Smart objects as a service - IoT marketplace-for offering IoT based applications and services - API economy - for easier and tighter integration - Semantic Web technologies for IoT: registry, storage - IoT delivery platforms - existing and emerging platform and architectures for exposing and interacting with IoT - Federated IoT support - Non Functional Requirements (NFR) for IoT - security, scalability, responsiveness and more - Standardization - standard areas needed - Business models - IoT DevOps - IoT context based analytics - IoT application deployment success stories - Mobile First IoT: mobile backend as a services (MBaaS) and smartphone as data prosumers
Last updated by Dou Sun in 2017-06-14
Special Issue on Recent Advances in Mining Intelligence and Context-Awareness on IoT-based Platforms
Submission Date: 2017-08-15

To develop innovative and intelligent machines that are fit for futuristic use, it is necessary to take a holistic approach in order to recognize various situations and environmental issues. To do this, various types of machine learning mechanisms have been developed during past decades.Recently, deep Learning has been proposed as a new area of Machine Learning and Mining researches, which have been investigated with the objective of moving Machine Learning closer to one of its original goals. Deep learning is being tried to apply for various recognition fields where researchers feel difficulty, but can achieve very promising result such as Alphago by Google deep mind. This kind of learning mechanism can cooperate with various sensors, which are able to gather large information. Also, the development of sensor networks, particularly in the last years, has extended their applicability in various domains, such as heritage preservation, environmental motoring and human activity recognition. Especially, to achieve highly natural interpretation of the environmental situation, various kinds of sensors should be widely employed in recognition system. Therefore, integration of sensor data with intelligent machine learning scheme is a natural choice and henceforth the sensor-based recognition technology is emerging as an important field of research including artificial intelligence (AI). This special issue aims to highlight the latest research results and advances on algorithms and technologies for various sensor-based recognition systems. It will include related topics and demonstrate original research work in this field of research. It will also cover the results of investigation on these topics featuring novel solutions and discuss the future trend of research in this domain. The MIKE2017 will be an interdisciplinary conference that brings together researchers and practitioners from the domains of learning algorithms, data mining, machine learning, knowledge exploration, large-scale data analytics, big data, soft computing, information systems, and so on. The selected outstanding papers from MIKE2017 will be recommended to this special issue. Topics of interest include, but are not limited to, the following scope: - Advances in Machine Learning - Algorithms for Intelligent Learning - Analogical, cognitive, and creative reasoning - Business Intelligence - Case Based Recommender Systems - Collective Learning and Tagging - Context and location aware techniques - Cross / Multi Language content mining - Crowdsourcing & Crowd Mining Algorithms and Systems - Big Data Search (HANA, Terracotta, Hadoop) - Visual analytics for text mining /exploration Visual Exploration - Real-time applications of data mining - Learning mechanisms from wireless sensor systems - Self-learning structure and framework - Security problem in multimedia data mining and distribution - Emotion and expression recognition algorithms for human interactive applications - Bioinformatics - Reasoning and Learning - Distributed and Peer-to-peer Search - Distributed Mining of Human expertise - Image Processing and Understanding - IoT solutions and platforms for information mining - Human sensors-based applications for intelligent context recognition - Real-time signal processing algorithms for recognition system
Last updated by Dou Sun in 2017-05-23
Special Issue on Emerging solutions in big data and cloud technologies for mobile networks
Submission Date: 2017-10-29

This special issue aims to bring together researchers interested in formulating and implementing innovative solutions under big data and cloud technologies for mobile networks. Data analytics, trust management, massive data management are few of the well established research areas which has gained increased importance over the past years. The technological advancements enrich the creativity of researchers which helps in attaining efficient adaptive solutions. Such rich adaptive solutions open the way to new dimensions of security violations in mobile networks. Hence, the focus of this special issue is to the interest of interdisciplinary target audience spanning: any recent challenge applicable for mobile networks. Topics of interest include, but are not limited to, the following scope: - Mobile cloud platforms - Integration solutions for mobile, cloud and big data infrastructures - Management of massive data using mobile clouds - Resource management and scheduling in mobile clouds - New programming models for mobile networks - Adaptive software for big data in mobile networks - Resilience issues in mobile clouds - Security challenges in mobile cloud environment - Next generation mobile computing services and applications - Migration mechanisms in mobile cloud computing environment - Network framework design for mobile cloud service requirements - Application of mobile computing based solutions in cloud and big data environments - Mobile system software enhancements - Big data applications in mobile clouds - Collaborative infrastructures for analytics in mobile networks - Protocols and emerging standards for mobile networks in cloud and big data environment - Trust management challenges in mobile cloud - Resource optimization in mobile cloud environment
Last updated by Dou Sun in 2017-06-14
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