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    CN4IOT 2016 - 2nd EAI International Conference on Cloud, Networking for IoT Systems

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    Website http://cloudniot.org/2016/show/home | Want to Edit it Edit Freely

    Category Network Management; Databases; Computing Methodologies; Internet of Things

    Deadline: April 22, 2016 | Date: September 14, 2016-September 16, 2016

    Venue/Country: Brindisi, Italy

    Updated: 2016-02-16 20:24:23 (GMT+9)

    Call For Papers - CFP

    The mission of International Conference on Cloud, Networking for IoT systems (CN4IoT) is to serve and promote ongoing re-search activities on the uniform management and operation related to software defined infrastructures. It aims to analyze limits and/or advantages in the exploitation of CN4IoT existing solutions developed for Cloud, Networking and IoT, and to present original and innovative contributions, where the explicit effort in at least two of the three main research fields introduces benefits and advantages clearly proven. Advances in ICT are changing the way we live and interact with each other, making our daily activities fast, easier and more reliable. People use multiple devices at once and keep their mobile devices nearby. Often, such devices have built-in sensors that measure motion, orientation, and environmental conditions, providing raw data with high precision and accuracy useful to monitor changes in the environment.

    The physical world detected by sensors in our mobile and smart devices can be represented through objects (things). Each thing is uniquely identified, exposes specific capability for sensing, processing or actuation, and is interconnected to other things and systems through the Internet (Internet of Things - IoT). In this context, information travels along heterogeneous systems and net-works, leading to the movement of huge amounts of data (Big Data), which need to be annotated, stored, processed, integrated, and presented in a seamless, efficient and easily interpretable form.

    On the other hand, cloud computing is a widely used technology that offers many benefits to both end users and business companies. It is able to provide theoretically unlimited computing and storage capabilities, and efficient communication services for transferring terabyte flows between data centres in an easy way. All these features make cloud computing a promising choice for supporting IoT services and applications. The integration of IoT devices with cloud computing systems is raising the interest of both the academic and industrial communities. Developments in this area pave the way to novel scenarios for IoT applications. Indeed, virtualisation techniques make pools of (virtual) sensors and actuators available as new types of on-demand resources over the Cloud, that can be integrated with other resources and exposed as cloud-based services.

    IoT can significantly benefit from the integration with cloud computing and network infrastructures along with services provided by both big players (e.g., Microsoft, Google, Apple, and Amazon) and small and medium enterprises. Indeed, networking technologies implement both virtual and physical interconnections among cooperating entities and Datacenters, organizing them into a unique computing ecosystem. In such a connected ecosystem, IoT applications can establish elastic relationship driven by per-formance requirements (e.g. information availability, execution time, monetary budget, etc.) and constraints (e.g. input data size, input data streaming rate, number of end-users connecting to that application, output data size, etc.). Thus, networking solutions specifically designed for the exploitation of Cloud services in IoT scenarios assume a crucial role. They allow developers to differentiate circuits aimed at IoT interactions, to increase Cloud scalability and efficiency in service provisioning, and to have both these advantages combined together. In many cases IoT can be seen as personal IoT, where the connection with the Internet is discontinuous, hence local caching facilities are required. The physical analytics can help the discovering of resources and services in applications use case characterized from Small Data. This data collected remotely can be further computated and feeding the Big Data seen in the hybrid model.

    The integration of IoT, Networking and Cloud computing can leverage the rising of new mash-up applications and services inter-acting with a multi-cloud ecosystem, where several cloud providers are interconnected through the network to deliver a universal decentralized computing environment to support IoT scenarios. In order to achieve such next generation IoT applications, one of the major aspects that has to be considered is the networking. In fact, IoT mash-up applications interacting with Cloud systems require the proper setup of traditional (Intranet and Internet), proximal, and location-sensitive networks.

    Potential topics of interest, which can be investigated from different perspectives (social, organizational, technological) include, but are not limited to, the following application domains:

    ? Deployment and autonomic management of densely interconnected and decentralised cloud infrastructures, including the extension of the fog computing paradigm to the edge of the network.

    ? Autonomic Management of Cloud networking in the context of software-defined data centres.

    ? Re-allocation of resources and services (independently of their location) across distributed computing and geographically separated data storage infrastructures

    ? Distributed cloud wireless networking

    ? Software Defined Infrastructure Architectures, Application Programming Interfaces Protocols & Programming languages

    ? Efficient Network and Cloud Function Virtualization (NFV)

    ? Service & Information Orchestration/Chaining and Life Cycle Management

    ? Energy Efficient and Green Software-defined Infrastructures

    ? Software-based Integration of Computing, Storage & Networks

    ? Software-Defined Datacenters and Interdatacenter Networks

    ? Object identity management, discovery services in IoT,

    ? Virtualisation of objects, devices and IoT infrastructures

    ? Adhoc Cloud services, Big Data Analytics

    ? Interconnected smart objects and service infrastructures

    ? Infrastructures, platforms, and software for CN4IoT scenarios

    ? Software engineering methodology for the design of CN4IoTsystems

    ? Dynamic orchestration of cloud resources for IoT systems and applications

    ? Service discovery in CN4IoT- device Discovery, Data Discovery, service Discovery.

    ? Traffic Engineering and Quality of Service (QoS) in CN4IoT

    ? SLA Management for CN4IoT

    ? Monitoring of CN4IoT Systems

    ? Mobility support in CN4IoT

    ? Security and privacy preservation in CN4IoT Systems

    ? Building Trust-based communication and computing systems for IoT applications

    ? HPC-Cloud Platforms for IoT Big Data Analytics

    ? CN4IoT services (Sensing as a Service, Data Analytics as a Service, etc)

    ? Utility Models for CN4IoT metering

    ? IoTMicroservices, Networking and CloudLet computing

    ? Communication challenges in CloudLet computing

    ? Software-defined networking support for IoT device fleets (M2M applications)

    ? Industrial IoT interacting with Clouds and Virtual Networks

    ? Personal IoT and Physical Analytics in IoT Focusing on Applications Use Case with Small Data

    ? Hybrid Sensor/Cloud networking

    ? SDN Switch/Router Architectures/Designs for CN4IoT

    ? Control Architectures/Network Programmability in CN4IoT

    ? Verification/Debugging/ Auditing Tools CN4IoT

    ? Energy-aware and sustainable computing solutions for CN4IoT applications

    ? Economics assessments aimed at Clouds, Data, IoT, Networking and Social in Hybrid Scenarios.

    ? Applications for Smart Cities, BigData Applications, ICT devices used in Factory of the Future, HPC, eHealth, Industrial Pro-cesses, Energy Efficiency Systems, Social Platforms, etc.


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
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