Эротические рассказы

Fog Computing. Группа авторовЧитать онлайн книгу.

Fog Computing - Группа авторов


Скачать книгу
align="center">

      2.4.1 Edge Computing Use Cases

      Edge computing is well suited for IoT deployments where both storing and processing data can be leveraged locally. For example, consider a smart home where the sensory information is stored on the edge device. Simply by doing encryption and storing sensory information locally, edge computing shifts many security concerns from the centralized cloud to its edge devices. In addition, IoT applications consume less bandwidth, and they work even when the connection to the cloud is affected. Furthermore, edge devices may assist in scheduling the operational time of each appliance, minimizing the electricity cost in a smart home [25]. This strategy considers user preferences on each appliance determined from appliance-level energy consumption. All such examples can benefit from the edge computing paradigm and to demonstrate the role of this paradigm in different scenarios, we describe in this section two possible use cases in healthcare [12] and a smart home [3, 15].

      2.4.1.1 A Wearable ECG Sensor

A scenario consisting of a wearable ECG sensor attached to the human body through a smartwatch and a smartphone that acts as an edge device.

      Generally, users prefer smartwatch devices that provide monitoring heart functions while they continue normal physical activities. Due to the limited battery life and storage capacity of the smartwatch, we assume that the data produced by this device is around 1 KB per second and it is stored in the smartphone. Based on this assumption, daily produced data by a wearable device is around 86 MB per day and 2.6 GB monthly. One must note that smartphones have limited battery life and storage capacity. Hence, the smartphone at some point has to transfer the gathered data to another device that provides more storage capacity.

      Referring to Figure 2.5, one can witness that data streaming is realized between the wearable device and the smartphone. Both devices remain connected to each other during the operation time. In case of any critical event, the wearable device interacts with the edge device and notifies the user for any situations. The process (1) start with getting real-time values from a wearable device to the smartphone. The smartphone application checks (2) periodically the wearable device to see if the connection between them is active. In addition, the smartphone may run out of free disk space and one can configure the application for daily synchronization (3) with another storage capability device, or with a central cloud storage or even with a fog node.

      Since the wearable device and the edge device has limited resource capabilities, one must consider the energy consumption of both devices. In such system architecture, the first recommended approach is to decide what data to transmit to the cloud, what to store locally, and the last is to develop better monitoring algorithms. In the other words, when designing such systems, the critical point is to consider the energy consumption, which is affected by three main functions that are realized between devices, such as (1) communication, (2) storage, and (3) processing requirements. Hence, developers have to code software with highly efficient streaming algorithms, storing essential monitoring information, and avoiding continuously data transfers with the central cloud.

      2.4.1.2 Smart Home

      The smart home or smart apartment is an intelligent home network capable of sensing the home's occupants actions, and assisting them by providing appropriate services. In the following scenario, we will describe an example to illustrate a situation under development at the smart home, where an edge device is considered as a mediator between the IoT things deployed in a home environment. The smart home provides resources to the residents that are deployed in rooms. Each room has smart doors, smart windows, sensors (i.e. temperature, humidity, proximity, fire alarm, smoke detector, etc.), radio-frequency identification (RFID) tags, and readers.

      Deploying a huge number of things in a smart home environment results in an impressive amount of produced data. One must consider that the data produced has to be transported to the processing units, assuring privacy and providing high availability. Since personal data must be consumed in the home, an architecture based only on the cloud computing paradigm is not suited for a smart home. In contrast, edge computing is perfect for building a smart home where data reside on an edge device running edge operating system (edgeOS). As a result, all deployed edge devices can be connected and managed easily and data can be processed locally by an edge device.


Скачать книгу
Яндекс.Метрика