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This week we’re looking at a new IoT sensor technology that could do more with less, IoT Platforms vs Open Source, top connectivity trends for 2026, and more!

IoT deployments are making a whole host of industries more efficient every day, but there is also a quiet mountain of waste: Billions of connected devices now stream data continuously, even when nothing meaningful is happening. A new study from University of Massachusetts Amherst, published in Nature Sensors, argues that this “always-on” approach is becoming unsustainable—and demonstrates an alternative built around event-based, neuromorphic sensing that could dramatically reduce power use and latency at the edge.

The issue is rooted in how most sensors work today. Traditional sensing systems constantly convert analog signals into digital data, transmit that data, and process it downstream—regardless of whether the signal contains anything important. That model made sense when sensors were sparse and power was plentiful. But in dense IoT environments, it leads to wasted energy, unnecessary data movement, and growing delays. The researchers use a simple biological analogy: human skin doesn’t continuously tell the brain “nothing is happening.” It only sends signals when there’s a change worth noticing.

The system developed at UMass Amherst flips that logic. Instead of treating sensing as a continuous data stream, it operates on events—moments when something actually changes. Touch sensors only generate signals when pressure is applied or released, and those signals are processed directly in hardware, without first being converted into long digital data sequences. Computing happens “in memory” using analog techniques, allowing the system to recognize patterns and motion with far less energy and delay than conventional digital pipelines.

What makes this approach especially relevant for IoT is the architectural shift it represents. Rather than pushing raw data upstream for analysis, intelligence is embedded directly at the sensor edge. The system focuses computation only on active regions—ignoring unchanged background signals—and translates temporal activity into spatial patterns that are easier for hardware to interpret. In testing, the researchers showed strong recognition accuracy while reducing the energy-delay cost of inference by more than an order of magnitude compared to traditional digital systems.

The implications extend well beyond touch sensing. Event-based, neuromorphic architectures could reshape how IoT systems handle vision, wearables, robotics, and bio-sensing—anywhere sensors spend most of their time waiting for something to happen. As edge AI becomes more common, this research highlights an important point: efficiency gains won’t come from better models alone. They’ll also depend on smarter sensing strategies that know when to stay quiet—and when to act.

Every year brings a familiar moment of clarity for the technology industry. The signals start to align, the noise fades, and the direction of the year ahead comes into focus. Not because of a single announcement or breakthrough, but because the same themes begin to surface everywhere.

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In this episode of the IoT For All Podcast, Scott Alldridge, CEO of IP Services, joins Ryan Chacon to discuss the state of cybersecurity in IoT. The conversation covers the changing landscape of cybersecurity, the threat of ransomware and real-life horror stories, the pros and cons of AI in cybersecurity, adopting a Zero Trust security model, penetration testing, and the limitations of cyber insurance.

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