Dao Nhu Ngoc
  Junwook Lee
   The growth in IoT is explosive, impressive-and unsustainable under current architectural approaches. Many IoT deployments face challenges related to latency, network bandwidth, reliability and security, which cannot be addressed in cloud-only models. Fog computing adds a hierarchy of elements between the cloud and endpoint devices, and between devices and gateways, to meet these challenges in a high performance, open and interoperable way. Fog networking consists of a control plane and a data plane. For example, on the data plane, fog computing enables computing services to reside at the edge of the network as opposed to servers in a data-center. Compared to cloud computing, fog computing emphasizes proximity to end-users and client objectives, dense geographical distribution and local resource pooling, latency reduction for quality of service (QoS) and edge analytics/stream mining, resulting in superior user-experience[5] and redundancy in case of failure. Fog networking supports the Internet of Things (IoT) concept, in which most of the devices used by humans on a daily basis will be connected to each other. Examples include phones, wearable health monitoring devices, connected vehicle and augmented reality using devices such as the Google Glass.
/IoT ô븦 mmWave 뿪 Ʈũ ڿ ٽ (Research on Resource Management of mmWave based Fog Radio Access Network for Big Data/IoT) , NRF, 2017.4.01~2020.3.30
Ŭ ͳ Ŭ Ҵ ȭ (A Study on the Optimization for Edge Cloud and Internet Cloud Interworking) , ETRI, 2017.03.01~2017.11.30
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