Incident investigations and response are too slow

Detect anomalies and specific behavior when it happens and kickstart investigations immediately. 3 types of data collection:

• Equipment Data: This is the type of data that concerns the status of the IoT devices. Equipment data is collected in real time to facilitate activities of predictive maintenance. To make equipment data more useful, it should be converged at a centralized platform to offer accessibility to line workers, tactical management, and top executives.
• Submeter Data: Submetering allows property owners to automate the measurement of individual utility usage in multi-user settings. Submeter data can be collected in buildings where multiple tenants use resources like water, electricity, gas, or cable.
• Environmental Data: IoT sensors can measure and monitor environmental data such as: (humidity, temperature, movement, air quality,…)

1. Device Layer: In the IoT architecture, devices that communicate with each other form the primary layer. These devices include sensors that track environmental data, bluetooth devices, low-power radio-based devices, actuators, and so on. IoT devices can be categorized under the following identities:
• A built-in chip-like unique identifier (or UUID) placed inside of a device;
• An identifier that depends on radio IoT data collection systems, for instance—Wi-Fi MAC, Bluetooth, and so forth.
• An identifier located inside the system’s programmable non-volatile memory (EEPROM);
• A Refresh/Bearer token
2. Communication Layer:  This layer lets devices communicate with one another using protocols such as:
• HTTP/HTTPS— This is a basic text-based protocol supported even by low-end 8-bit devices.
• MQTT— Its a protocol designed to handle embedded systems and optimized to support IoT. It is known for a wide community of followers, as well as a robust asset library.
• CoAP— This protocol is based on HTTP semantics, and scores more footprint. Compared to MQTT, CoAP is more difficult to connect to firewalls and has poorer library support.
3. IT Edge Layer: This layer consists of the hardware, firmware, and operating system of your IoT devices.
4. Event Processing Layer: This layer processes and stores data collected from IoT devices. Other processes that occur in this layer include:
• data cleaning
• adding metadata to IoT data
• organizing data insights
• The event processing layer can be built via a database-powered server-side application like a JAX RS tool. You can also create this layer using IoT cloud services to process and store IoT data.
5. Client Communication Layer:  This layer transfers data from the device to the user. It acts as a bridge between back-end databases and front-end interfaces for end users of the data. For many reasons, all these intricate layers of technology come with several hurdles that companies need to overcome. Here are some of them.

Automate investigations for quick insights and responses in seconds. The Importance of IoT Data Collection:

1. Better User Experience
   Automated IoT data collection helps to understand the needs and regularities of the measured data. As you collect data and draw insights, you get a better picture of on-ground usage of your environment real-time situation. By continually revisiting sensor-data and reconstructing your algorithms based on these reports, we get to learn more about the environments and specific dangers. This ultimately puts us in a position to serve better.
2. Asset Maintenance
   Live data enables users to monitor the state of affairs of a complete environmental measuring network. Consequently, any significant wear and tear can be predicted for which measures can be enacted on time.
3. Efficient Use of Resources
   Automated data collection serves the purpose of knowledgeable decision-making. Other than acting on the basis of predetermined standards, the customer can manage and coordinate all activities using live data that eliminates a great deal of uncertainty.
4. Synergy in Systems
   A typical IoT system comprises several components. While these might function smoothly on their own, collectively, there may be some unexpected hiccups. Holistically, this data helps you to grade the overall efficiency of your IoT environment.
5. See everything so you can stop anything
   If you want to stop threats across the environment, you have to have eyes everywhere. OneCloud/OneBox provides deep insight into all data generated in the remote area’s, so you can see threats before they become a problem.
6. Investigate threats in seconds
   Lower your mean time to detect (MTTD) and mean time to respond (MTTR) to threats or incidents, by orchestrating investigation tasks across multiple data or information channels and using playbooks to automate event actions at reaction speed.
7. Amplify your team’s impact
   Orchestrate and automate repetitive audit tasks, investigation and response so your Tread prevention team can do more with the people you already have.