The concept of combining the resource-bound last-mile sensors of any Internet-of-Things-related application with computational capabilities is receiving increasing attention from researchers and practitioners. Recent technological advances in embedded devices has led to the production of small-sized heterogeneous multi-core processors that incorporate pattern machine engines on-the-chip and support the execution of power-efficient algorithms. We are now capable of analyzing the data collected from the sensors on the spot, classify the data, detect abnormal events and produce advanced alerts. The capability to locally process the data allows to transmit to the cloud infrastructure and store only the segments that correspond to an abnormal behavior. In this way the embedded device would conserve battery power and minimize memory requirements. Therefore, the so-called Fog computing approach extends the cloud computing paradigm by migrating data processing closer to production site, accelerates system responsiveness to events along with its overall awareness, by eliminating the data round-trip to the cloud. Offloading large datasets to the core network is no longer a necessity, consequently leading to improved resource utilization, protection of private and confidential information and quality of experience (QoE). Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture.