Predict End-to-End IoT System Behavior
Optimize and plan your Internet of Things (IoT) networks, infrastructure, and storage with Intel® CoFluent™ technology for IoT
Intel® CoFluent™ technology for IoT is an end-to-end planning solution based on system-level modeling and simulation technology. This solution allows you to predict the entire IoT system behavior, using your specific components, configuration options, and other features that make your system unique. This includes predicting the behavior of networks, infrastructure, and storage systems. With the power of simulation, you can optimize your solution for resources and performance—even before any physical deployment occurs.
Explore design spaces and perform “what if” analyses: Estimate processor loads and power consumption under different conditions; study memory requirements and capacity; investigate I/O configurations; predict response times and latencies.
System behavior defines your planning, budget, development schedule, pricing, and market strategy. Use modeling and simulation to: Reduce over-budgeting and over-dimensioning for deployment; reduce business risk; stop introducing unnecessary costs when competing against other vendors.
Simulate your systems more accurately: Reduce the time it takes to convert a proof of concept into a full production deployment; analyze system architecture to meet market needs earlier in the design cycle; anticipate and identify issues early — avoid late-bug discovery; accelerate solution deployment.
Learn how to meet real-world challenges using Intel® CoFluent™ technology for IoT.
Challenge: Data collection from an energy farm. An energy farm has thousands of wind turbines located in a very large area. Each wind turbine has a set of sensors collecting data. A gateway is attached to each wind turbine to collect this data before sending it over a 3G network to a data center.
Solution: Use Intel® CoFluent™ technology to estimate the downlink bandwidth requirements and the carrier connectivity cost of transferring the data over 3G.
Challenge: Energy conservation and management are critical for today’s infrastructures. One key is the ability to manage energy consumption for an office building by optimizing heating/cooling systems to reduce operational costs.
Solution: Use Intel® CoFluent™ technology for IoT to build models that predict and optimize energy consumption based upon occupancy and environment. This includes finding the optimal configuration of gateways to manage 10,000 sensors in the building.
Challenge: A cost-effective and accurate way for retailers to locate products in real time in a retail store.
Solution: Intel® CoFluent™ technology for IoT models were built for a real-time inventory system using RFID tags. These models predicted the number of readers and antennas required to detect RFID tags, and determined the storage requirements for storing RFID tag information in the gateways. The result was optimized solutions for the number of antennas required, the effective use of edge analytics, and maximized hardware utilization — all within minimized network traffic.
Challenge: Predict maintenance requirements of the commuter train in order to improve customer experience and reduce repair costs. This includes capturing how vehicles communicate to the data center over a 3G or Wi-Fi network.
Solution: Model and simulate a train used for public transportation that consists of 20 cars. Each car is equipped with a large number of sensors in order to collect data from monitoring the behavior of the engines, the vibration of the mechanical parts, and so forth. Each car contains one gateway that collects all sensor information related to that particular car, and each gateway forwards the data to a back-end data center. The result is an optimized customer experience with minimized maintenance.
Success Brief: Siemens develops telecom designs using Intel® CoFluent™ studio,...Vorschau | HerunterladenAnzeigen (685.5 KB)
Success Brief: Sensata Technologies creates digital imaging performance models,...Vorschau | HerunterladenAnzeigen (632 KB)
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