Based on this motivation, this paper describes the concept of Fog Computing in detail, discusses the main obstacles for Fog Computing adoption, and derives open research challenges.įace recognition is increasingly deployed as a means to unobtrusively verify the identity of people. However, Fog Computing as a deployment platform has not yet found widespread adoption this, we believe, could be helped through a consistent use of the service-oriented computing paradigm for fog infrastructure services. This natural extension of the cloud towards the edge is typically referred to as Fog Computing and has lately found a lot of attention. Future application domains such as the Internet of Things, autonomous driving, or future 5G mobile apps, however, require low latency access which is typically achieved by moving computation towards the edge of the network. While this is very convenient for developers, it also comes with relatively high access latency for end users. State-of-the-art applications are typically deployed on top of cloud services which offer the illusion of infinite resources, elastic scalability, and a simple pay-per-use billing model. This transformation of the surveillance system can be used to check and maintain integrity, management of blurring keys, and provide authorization rights to access video data. The system is coupled with a private Blockchain network that integrates into the surveillance system. The proposed solution consumes less resources and provides better privacy preserving functionality. To meet users' privacy protection demands, a reversible blurring is performed on the privacy sensitive objects detected in the captured video stream. The proposed system is event driven and resource efficient as it utilizes motion detection to detect intrusions and filters unnecessary data in the surveillance system. To address these issues, in this paper a distributed edge-fog node based video surveillance system is proposed for smart home environments for privacy preservation of individuals. The existing centralised security systems can also be misused to collect the personal information for example the collected information could be used to launch cyber frauds using collected biometric identities. Such monitoring has a potential to cause serious violation of privacy of individuals or individual rights as their movements are continuously observed. Security surveillance of home or office premises is usually performed by deploying a number of video cameras to continuously monitor the environment. In simulation of proposed LEO satellite communication networks, we show how QoS depends on orbital parameters and that our proposed method can take these effects into account where the existing approach cannot. Further, we extend the existing discrete resource placement methods to allow placement with QoS constraints. In this paper, we show how the existing research on resource placement on a 2D torus can be applied to this problem by leveraging the unique topology of LEO satellite networks. To implement and use the LEO Edge efficiently, methods for server and service placement are required that help select an optimal subset of satellites as server or service replica locations. Current proposals assume compute resources or service replicas at every LEO satellite, which requires high upfront investments and can lead to over-provisioning. The LEO Edge promises low-latency, high-bandwidth access to compute and storage resources for a global base of clients and IoT devices regardless of their geographical location. With the advent of large LEO satellite communication networks to provide global broadband Internet access, interest in providing edge computing resources within LEO networks has emerged.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |