With a large number of networkable devices entering the enterprise, how to smoothly introduce the Internet of Things (IoT) and further strengthen the business application is not easy. The management procedures of each network connection device are inconsistent. Many administrators are very much in the management architecture of the IoT device. It is easy to overlook the considerations of IoT's interoperability, integrity, and security collaboration. However, the following five major challenges must be resolved.
Top 5 challenges that enterprises must consider before introducing the Internet of Things
1, security holes
Everyone knows that security is a key issue for the Internet of Things, but reports indicate that new IoT devices with more computing power and embedded security capabilities are helping to improve the security of the Internet of Things. The new hardware leverages low-power microchips to better run traditional network security applications and protocols. In the face of IoT security issues, targeted IoT security solutions are fundamental to helping you discover anomalies and find devices that might be threatened.
2, the platform problem
The lack of standards and platforms makes developing and deploying complete IoT solutions more difficult than ever, but many vendors now plan to introduce IoT platforms that make it easier to integrate IoT hardware, networks and applications. However, the establishment of multi-vendor solutions has long been the hardest part of developing a complete IoT implementation approach. As a result, some vendors often use “vertical” integration when solving integration problems, combining sensors, devices, analysis, and other components to create a turnkey IoT solution.
3. Expensive and power-consuming network
IoT devices require networks to communicate with each other, but traditional WANs are relatively expensive and consume a lot of power. It is recommended to use a low-cost, low-power wide area network (LPWAN) to provide a cheaper connection or a small battery-powered device.
4. Data that are difficult to analyze
IoT devices can generate massive amounts of data. However, most of the time, most of the data has not been analyzed and used. Artificial intelligence (AI), machine learning, and computer vision are increasingly used to analyze data generated by the Internet of Things and automate operational decisions. This is expected to drive more enterprise IoT applications, including process optimization, predictive maintenance, dynamic routing and scheduling, and security issues.
Analyzing the IoT data in the cloud can cause delays that make it difficult to generate useful real-time alerts and performance in industrial, enterprise, and smart city environments. Currently, data analysis generated by IoT devices is increasingly occurring in the cloud, but not on local servers, on the edge of the network, or even on devices that generate data. Therefore, the delay problem must be considered.
It is predicted that the investment growth of the enterprise/industrial sector for the Internet of Things will break out, accounting for more than half of the global Internet of Things expenditure by 2020. Prior to this, companies should focus on solving the above challenges and create a truly commercially valuable IoT deployment plan.