Internet of Things (IoT) Analytics

The IoT analytics topic section offers comprehensive resources for those deploying complex, high-velocity data back ends for Internet of Things products and shops. Learn the advantages and disadvantages of various analytic approaches such as behavioral, predictive and security analytics. Find guidance on IoT-specific uses of Hadoop and other NoSQL DBMS environments.

June 16, 2021 16 Jun'21
Senators push Amazon, Google on connected IoT device market

During a Senate subcommittee antitrust hearing, Amazon and Google claimed to be making their connected IoT devices more interoperable. Others disagreed.
March 04, 2021 04 Mar'21
Edge AI brings new uses to IoT devices

A Lenovo executive describes AI at the edge, highlighting how this rapidly advancing technology unlocks new automations and capabilities within IoT devices.
December 23, 2019 23 Dec'19
Connected medical devices experts highlight IoT remote monitoring

Medical technology manufacturers were one of the early adopters of IIoT, but that doesn't mean manufacturers know how to get started with IoT remote monitoring.
April 17, 2019 17 Apr'19
IoT device monitoring added to Nyansa Voyance

Nyansa has added IoT traffic analytics to its network monitoring software. The latest version of Nyansa Voyance monitors the activity of IoT devices in healthcare and retail.

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Democratizing machine learning will transform IoT

For the smart home to succeed, what happens in the home, will have to stay in the home and become smarter without the infinite compute capability of the cloud and with features such as voice ... Continue Reading
AI and IoT convergence could herald a new era of technological change

With the right planning, tools and expertise, organizations that combine AI and IoT into AIoT will deliver benefits that only AI can help predict. Continue Reading
deep learning

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Continue Reading

Weigh the pros and cons of technologies, products and projects you are considering.

The value of IoT at the edge

The edge gives organizations a straightforward way to set up an IoT project and gain rapid insights to build cost-effective, data-led strategies. Continue Reading
An intelligent edge: A game changer for IoT

Edge computing drives value in IoT deployments, but only if organizations can overcome the challenges of creating data-driven, low-latency architecture. Continue Reading
Start artificial intelligence for IoT in bite-size pieces

IoT practitioners must follow clear steps to implement an AI analytics process if they want to create an AI application with IoT that improves their deployment's performance. Continue Reading

Learn to apply best practices and optimize your operations.

TinyML at the very edge of IoT shows signs of promise

TinyML can enable machine learning on small devices that exist within IoT systems and experts are currently debating the breadth of its practical real-world uses. Continue Reading
Focus the role of data analytics in IoT on actionable insights

Organizations might find it easier to use the limitations of their IoT data analysis project to guide its creation, such as connectivity or battery power. Continue Reading
Comprehend control loops to unlock analytics for IoT

Applying analytics to IoT quickly becomes a convoluted task. Architects must master the control loop to direct its design and deliver rapid business insights. Continue Reading

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How removing latency can unlock even greater value from IoT

Many applications of IoT require real-time information to give value to customers, whether it's for the safety of autonomous vehicles or cooking the perfect prime rib. Continue Reading
Integrating IoT technology for effective environmental monitoring

IoT technology provides innovative ways to track and mitigate environmental challenges. Organizations can integrate smart concepts for safer surroundings and increased business growth. Continue Reading
IoT-based predictive maintenance staves off machine failures

Predictive maintenance uses sensors, gateways and management systems to predict when equipment failures will occur in manufacturing, oil and gas and food processing industries. Continue Reading