Edge computing is multiplying in all sectors, including industrial and commercial. There are many use cases, but as operations become more geographically distributed, organizations face various roadblocks on the path to full-scale adoption of edge computing. Most IoT projects fail because of various challenges, which can broadly be categorized as follows:
Standards & Benchmarking
• No defined industry standards for legal, social, and ethical aspects of using edge intelligence.
• Immature benchmarking tools and practices require deep research.
• A mix of legacy and modern solution makes cause integration challenges.
• Information security constraints with the latest technology.
Security & Privacy
• Multitudes of harmful industrial environments affect the usage of mobile devices.
• Lack of comprehensive, secure data flow from edge to core network.
• Lack of standard software framework and toolkits for edge intelligence workflows.
• Hetrogeneity or hardware and platform, and resources in the workflow.
Many IoT devices collecting sensitive information, including personal, health, and financial data, are not designed with security. The primary focuses of the manufacturer are typically ease of use, low cost, and speedy deployment.
However, security comes at the cost of all three, making IoT devices the weakest link and providing an easy-to-exploit attack surface.
There is a need for a standard security framework as a precondition for large-scale edge computing projects. Managing the security of a centralized or cloud data center is far easier than managing the security of thousands of remote and sometimes less accessible edge locations.
In an edge computing network, scaling is not simply adding more servers. It requires increasing all sectors, including computation, network, bandwidth, storage, security, licensing, etc.
While collecting, processing, and storing data at the edge brings a lot of advantages to the business, it also presents new challenges and liabilities.
Growing needs for data storage, computation, and – most importantly – handling of data per defined rules is difficult to manage.
An edge network with millions of connections needs a robust system to handle and accumulate data correctly.
A variety of deployment environments, software, hardware, etc. creates fragmented visualization. Managing an edge network requires more skills, resources, and resolution time as each new custom edge location adds complexity to the system.
Monitoring, managing, viewing, and processing data from multiple edge sources to provide holistic management without creating equipment or domain silos present new technological challenges.