Background & Motivation
The Internet of Things (IoT) has brought new opportunities for increasing safety, automation, and cost and energy savings. However, these opportunities come with an increase in data generation, which is pushing network capacity to its limits. According to an estimation by IDC, the total amount of data generated by connected devices will exceed 40 trillion gigabytes by 2025. Analyzing data closer to where it is generated, rather than sending it to a data center, can reduce network load, save energy and costs, and satisfy the requirements of low-latency applications.
Edge distributed infrastructure can improve the robustness of systems in the event of a disaster. The localization of data and computation can improve privacy, security, reliability, resilience, and safety, increasing trust in these systems. There is a growing demand for software solutions that can monitor and analyze data flows along the IoT-to-cloud path, enabling the processing of data closer to its source and providing real-time responses for smart IoT applications. These solutions should be able to abstract away the hardware complexity and heterogeneity of edge environments. Experts in the energy sector are calling for a framework for enabling flexibility in energy consumption. Edge resources are currently fragmented, which makes intercommunication and network resource allocation complicated. This is an obstacle to the implementation of novel cloud-to-edge services that could improve performance and robustness, address policy concerns, and adapt to fluctuations in data processing needs and network conditions.
COGNIFOG will provide a software solution as a Cognitive Fog Framework (Cognitive-Fog) for monitoring and analyzing data flows along the IoT-edge-cloud continuum, enabling the processing of data closer to its source and providing real-time responses for many smart IoT applications. This will ultimately improve privacy, security, reliability, resilience, and safety, increasing trust in the technology.