How Fog Computing Will Impact IoT Solutions
The Internet of Things (IoT) and current-state cloud computing have created a situation where billions (and growing) of remote sensory devices work perpetually to transmit raw data to centralized cloud data centers for storage and eventual retrieval/analysis. This has caused some concern over whether enterprises, developers and cloud solution designers can build appropriate and secure systems to store the growing surplus of data while allowing quick accessibility to recover the data for useful analytics. Fog computing will offer a significant step toward the solution to this growing concern. The ability of telecom providers to facilitate an enterprise partner’s understanding of specific IoT demands and the level of network support required of those deployments will be an industry differentiator.
- Traditional cloud computing uses centralized, shared data warehousing that usually does not factor in distance, network complexity or compliance; which can create serious challenges with latency, bandwidth, security and reliable accessibility. The immense amount of peripheral data that IoT sensors generate makes it inefficient to transmit these large packets of data back to the cloud. And, while latency may seem irritating when your IoT device is slow in reporting the level of milk in your refrigerator, any delay in transmitting data in a med tech deployment of IoT sensors can be disastrous.
- The concept being explored with fog computing is that the network is more decentralized than with traditional cloud architecture. In a fog computing model, the applications are run to a point at the edge of a network. Ultimately, the goal of fog computing is to improve efficiency while potentially working to reduce the amount of data that is being transmitted back to the cloud to only include specific, requisite data.
- In terms of a manufacturing IoT solution, there might be a deployment where immediate data processing and low-latency ingestion are required of the IoT device. In those scenarios, an enterprise would need faster turnaround on their data than is available by the inevitably slow trip from device layer to routine cloud IoT platforms. An IoT deployment using fog computing would allow real-time processing of data so that any divergence could be detected locally without delay; risk can be mitigated by building pre-determined rulesets into manufacturing processes which use this immediate data flow to monitor internal variance.
- Much like cloud computing, some of the largest obstacles in fog computing continue to be security and privacy. Fog network developers will need to innovate with improved intrusion detection on both the user side and the cloud side. In a fog network paradigm, patented encryption techniques that employ sophisticated algorithms can be run between the fog and cloud to provide improved security. Fog nodes at the edge usually collect data generated by end-user devices, and the utilization of an advanced encryption technique would allow safeguards to data collection at the level of the local gateway without unnecessary decryption. These are just a sampling of the security challenges that will need to be factored into using fog solutions to support an IoT deployment, especially in any large scale, geographically diverse mobility circumstance.
The continuing growth of the IoT and the data that it generates will create an opportunity for managed service vendors who can understand and support their enterprise clients’ specific network needs. Providing holistic telecom support for an enterprise partner’s IoT deployments will allow a managed service vendor to become an integral part of their customer’s overall business success.
Christine Kruze is a telecom management industry thought leader exploring the impact of technology as a business accelerator and social capital enabler. Learn more about expert services to transform your telecom environment.