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The rapid growth of big data frameworks and applications such as smart cities, smart vehicles, healthcare, and manufacturing has pushed edge computing among the major topics in academia and industry. With the increasing demand for high availability in such systems, system requirements tend to increase over time. The stringent requirements of IoT systems have recently suggested the architectural placement of a computing entity closer to the network edge. This architectural shifting has many benefits, such as process optimization and interaction speed. For example, if a wearable ECG sensor were to use the cloud instead of the edge it will consistently send all data up to the centralized cloud. As a consequence, in such a scenario it will cause high communication latency and unreliable availability between the sensor and the centralized cloud. In real-time, safety-critical IoT use-cases, devices must comply to stringent constraints to avoid any fatal events. In this scenario, the latency delay introduced by sending the data to the cloud and back is inadmissible. Thus, in case of a critical event detected by the sensor, a local decision must be taken by the edge device, rather than sending data to the cloud.

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].