Implementing Fog Computing for IoT Ecosystems
Implementing Fog Computing for IoT Ecosystems
This post examines how fog computing and edge devices will augment the cloud to find a new harmony for smart devices and applications.
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Industry 4.0 empowers industrial users to adopt and leverage the data gathering and analytics capabilities of cloud computing for predictive analysis, reducing maintenance downtime, centralized storage, and remote management. But with the rapid increase of IoT-powered applications in the last couple of years and a lack of proper network bandwidth allocation and utilization, cloud computing faces the following limitations in wide adoption of the cloud, such as:
Subscription-based cloud support
Unused silicon power on edge devices (Router, Gateway).
Huge amounts of raw data pushed to the cloud, resulting in high latency.
Always dependent on Internet connections.
Over-utilization of network bandwidth.
Fog computing came in as an aid to overcome the above-mentioned limitations and bottlenecks by providing a decentralized architecture and serving as an extension to cloud computing by collaborating with one or more edge node devices providing the subsequent amount of localized control, configuration, management, and much more for end devices.
Business Impact of Fog Computing on IoT Solutions
Fog computing came into existence to serve as an extension of cloud computing services and not as a replacement for them. Adopting fog computing to your existing IoT solution will have the following business advantages:
Off the grid: Leveraging the benefits of fog computing, one can enable their IoT solution to control, manage, and administer your local edge device network without external dependencies on cloud-based services, which provide freedom from subscription-based cloud services.
Global distributed network: Fog computing-empowered edge nodes or gateways provide distributed networks with the power of local decision-making and temporary data storage for analysis. This kind of distribution ensures that even if cloud services are not available, your IoT solution would be able to function locally with some limited restrictions.
Better bandwidth utilization: Fog computing empowers edge nodes, processes the raw data obtained from the end devices locally and periodically pushes the processed data to a central mainframe and thus ensures the most optimal usage of network bandwidth.
Real-time operation and low latency: Fog computing bifurcates data based on time criticality and ensures that the most time-critical data are processed locally without the intervention of a central mainframe — thus enabling real-time operations and very low latency.
Optimal usage of edge node resources: Fog computing-enabled edge nodes are designed with the aspect of maximally leveraging edge node resources to overcome the limitations over cloud computing and optimal usage of network bandwidth.
Fog Computing Application
Fog computing can play a crucial role when applied to the following domains.
Lighting industries are undergoing a revolutionary transition from wired interfaces to wireless interfaces. Fog computing-empowered edge nodes enable smart lighting OEMs to provide a complete solution independent of cloud providers. Fog computing-enabled light solutions enable the OEMs with:
Local control, monitoring, and management of end devices.
Using localization features along with sunset sunrise data, one can schedule lights to turn on/off.
Expand their global reach in the most remote areas.
Consolidate reports on energy consumption with per-device granularity.
Smart energy has become a particular interest considering the industry shift in production of energy and conservation of natural resources, constant monitoring of end devices used in wind farms, solar farms, and water and gas distribution networks. When the power of fog computing is coupled with the existing IoT solution in smart energy, OEMs can achieve:
Real-time fault detection with low latency.
Edge node-enabled data analytics.
Geo-distributed networks allowing the pinpointing of fault regions.
Demand analysis using M2M interactions.
On-demand-based automatic distribution switches.
With the advancements of IoT-enabled devices, smart agriculture has become a niche area of interest for all IoT-centric cloud providers. Farmers are moving toward smart farming practices that generate a huge amount of data from soil sensors, temperature sensors, humidity sensors, motion sensors, and ambient light sensors. When the power of fog computing is coupled with existing IoT solutions in smart agriculture, solution providers can achieve:
Using localization features, fog computing can predict ideal harvesting time.
Expanding their global reach in the most remote rural areas without Internet connectivity.
Generating crop health analysis reports locally.
Livestock monitoring, health analysis, and location tracking.
Fog computing-empowered smart transportation applications can be achieved by providing inter-fog communication over a distributed network. With inter-fog communication, smart transportation solutions:
Edge nodes can independently manage traffic lights in real time based on traffic analysis.
Street light control locally based on time and weather.
Real-time traffic reports and suggestions for alternate routes in case of congestion.
Inter-vehicle communication and connected cars
Future of Fog Computing
As mentioned earlier, fog computing came to facilitate and overcome the few limitations of cloud computing and not as a complete replacement for it.
Fog computing, in the future, can utilize the power of machine learning and artificial intelligence by leveraging the computing power on local edge nodes and providing precise results and analytics unique to each user.
Fog, in the future, will give rise to hybrid computing models where the edge nodes will be used for real-time analysis while the cloud would be used for persistent data storage. With hybrid computing models in place, IoT solutions can target both real-time applications and avoid the bottlenecks of cloud computing.
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