Edge Computing Applications

Your GIS maps don't talk to your BIM. Your traffic sensors (IoT) don't inform your emergency response. Your drone footage is just ... sitting on a drive. A City Information Model (CIM) fixes this. I've attached the exact framework that successful smart cities like Helsinki and Singapore use. It's not about more data. It's about connecting the data you already have. Here's the simple, 3-stage breakdown 👇 Stage 1: Data Acquisition This is about cataloguing what you already own. - Geographic Info (GIS): Your maps, roads, and utility lines. - Building Info (BIM): 3D models of new and existing structures. - Sensors (IoT): Traffic, air quality, waste management. - Remote Sensing: Drone and satellite imagery. Right now, these are all in separate "drawers." The goal is to bring them to the same "table." Stage 2: Data Processing This is the most critical step. It’s where you break the silos. - Clean & Standardize: Make all data speak the same language using standards like ISO/OGC. - Fuse & Integrate: This is where GIS + BIM + IoT data are merged. Your 3D building model now "knows" its location on the map and its real-time energy use. - Analyze: Use AI to mine patterns. For example: "This intersection always floods when rainfall exceeds 2 inches, and traffic backs up 3 miles. Let's re-route automatically next time."🖐️ Stage 3: Data Application This is why you did the work. Your connected data is now a tool. You can now finally, visualize (meaningful) in 3D. - Optimize Emergency: Deploy first responders with pinpoint accuracy. - Monitor Environment: Track air quality, noise pollution, or energy use. I've attached this framework for you to consider. --------- Follow me for #digitaltwins Links in my profile Florian Huemer

The Peterson Center on Healthcare just released a timely and thought-provoking report titled on the evolving landscape of remote monitoring. As remote physiologic (RPM) and therapeutic monitoring (RTM) gain traction, especially in Medicare and Medicaid, this report asks a critical question: are we paying for what truly works? Key Findings: 📈 Use is growing rapidly: Medicare beneficiaries using RPM jumped from 44,500 in 2019 to 451,000 in 2023. RTM is also rising fast. 💸 Spending is accelerating: RPM spend in Traditional Medicare surged to $194.5M in 2023, with 22% of episodes lasting over 9 months. 🩺 Effectiveness varies widely by condition: -RPM for hypertension shows strong short-term results (up to 6 months). -RTM for musculoskeletal conditions helps when used during focused PT episodes (2–4 months). -RPM for type 2 diabetes shows only modest, short-lived benefit — mostly in patients with very high HbA1c levels (we know this from the last PHTI study) ⏳ Current billing doesn’t match the evidence: Providers can bill indefinitely, even after the clinical benefit has faded (the do-more-make-more problem with FFS). 📊 Data gaps are a big problem: It’s often unclear what’s being monitored, for whom, and why. We have a massive opportunity to align coverage and reimbursement with actual clinical value — ensuring remote monitoring improves outcomes and spending efficiency. As adoption accelerates, it's going to be critical that we develop payment policies and the appropriate clinical models of care to ensure the right tools are reaching the right patients — and only for as long as they help. PDF of full report attached. #DigitalHealth #RemoteMonitoring #ValueBasedCare #healthcare #healthcareonlinkedin #ChronicDiseaseManagement Meg Barron Caroline Pearson

A year ago, I wrote about Google's Media CDN offering and its positioning in the market, which was primarily centered on leveraging Google’s network for large-scale video delivery. As with any service, the initial value proposition is only part of the story. The more telling measure is its subsequent evolution in response to customer usage and industry demands. A year later, Google has made key enhancements to its Media CDN, focusing on adding capacity and operational tooling, as well as onboarding large media and entertainment customers. The fundamental challenge for CDNs remains handling massive, concurrent traffic spikes associated with live streaming. Events over the past year, such as the Super Bowl, FIFA World Cup, and IPL, have continued to set new streaming benchmarks. 𝗢𝗻𝗲 𝗻𝗼𝘁𝗮𝗯𝗹𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝗻 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗠𝗲𝗱𝗶𝗮 𝗖𝗗𝗡 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗮𝘁 𝘀𝗶𝗻𝗰𝗲 𝗲𝗮𝗿𝗹𝘆 𝟮𝟬𝟮𝟱, 𝗶𝘁 𝗵𝗮𝘀 𝘁𝗿𝗶𝗽𝗹𝗲𝗱 𝗶𝘁𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗮 𝗰𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗠𝗲𝗱𝗶𝗮 𝗖𝗗𝗡 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆. Beyond raw capacity, several architectural and commercial updates have been introduced to address common customer pain points around origin performance and budget predictability. Google has added new caching and routing options, including Flexible Shielding, with shield regions in South Africa, the Middle East, and the U.S. The goal is to improve cache offload rates by keeping traffic within a region, thereby avoiding the latency and data-transit costs associated with the "hairpinning" effect of fetching content from a distant origin. It’s worth noting that this is implemented as an add-on feature, allowing customers to choose between optimizing for performance or offloading, in addition to the platform's existing multi-region caching and shielding architecture, which is offered at no cost. Full blog post: https://lnkd.in/eA_giTWw #streamingmedia #googlemediacdn #contentdelivery #infrastructure

Edge is not a trend; it’s an architecture shift. From $10B in 2023 to $50B+ by 2033... ...the growth isn’t driven by hype. It’s driven by physics. Because once you move from 100 ms to 20 ms, apps feel usable. But to cross 5 ms? You need to compute at the baseband, not the core. Here’s how to engineer edge sites that deliver deterministic low latency.. ...the kind autonomous vehicles, high-frame-rate AR, and critical IoT actually depend on: 1️⃣ Deploy true micro-edge, not retrofitted closets. Use prefabricated, hardened SmartMod™ units from Schneider Electric. Each is factory-integrated for power, cooling, fire, and control. Drop next to STC, Du, or Airtel 5G towers. Size them in 50 kW increments, enough for MEC, AI inference, or on-prem cloud functions. 2️⃣ Terminate fibre and power before you lift a panel. Edge buildouts fail when backhaul and power provisioning lag site readiness. Lock dual feeds (utility + genset), reserve dark fibre with SLA-bound loop latency. Tie telemetry into a regional NOC using EcoStruxure™ IT Expert. 3️⃣ Architect for adversarial environments. At edge, risk profiles flip. You’re no longer behind seven enterprise firewalls. Implement zero-trust gateways at entry points. Segment IoT ingress from control networks. Deploy biometric access control per rack, not just facility. 4️⃣ Design for thermal density and burst load. Run average loads at 65–70% to preserve thermal headroom. Plan cooling for non-linear spikes from MEC caching or edge GPU workloads. Active airflow control, rear-door heat exchangers, or liquid-ready chassis, depending on density. 5️⃣ Treat orchestration as a control system, not a dashboard. With EcoStruxure™, power, cooling, access, and IT converge into a decisioning plane. Don’t just monitor, let the system act. Use real-time data to preempt failure, not just alarm on it. This isn’t edge as a PoC. This is production-grade, SLA-bound, carrier-integrated infrastructure. 5G gives you bandwidth. Edge gives you responsiveness. Without both, your low-latency promise doesn’t land. Ready to design for 5 ms? Let’s draw your first edge map.

An AI-powered implant can now continuously monitor potassium - a silent killer for kidney and heart-failure patients. It’s being built by Proton Intelligence, a Canada-based healthtech startup. And if it works at scale, it could do for potassium what glucose monitors did for diabetes. Here’s why: Many sudden heart rhythm issues and hospitalisations in kidney patients are triggered by dangerous shifts in potassium levels. And potassium is typically checked only during lab visits - sometimes weeks apart. That means doctors are often reacting after the damage is already underway. Proton’s approach is different. They place a tiny sensor just under the skin that continuously tracks potassium in real time. Not once a month. Not after symptoms. All the time. So when potassium spikes or crashes due to diet, missed dialysis, or medication changes - patients and doctors have a chance to act before things spiral. The system connects to a phone app for patients and a dashboard for clinicians. So instead of flying blind between appointments, doctors can see trends. Instead of waiting for symptoms, patients get alerts when something is drifting out of range. Proton has raised $6.95M in seed funding and is currently in clinical trials. The product is still pre-launch, but the direction is clear: continuous potassium monitoring is finally moving closer to real-world care. We’ve already seen this with continuous glucose monitoring - it didn’t just improve diabetes care, it changed how the disease is managed. If potassium becomes just as visible, kidney and heart-failure care could shift from reactive treatment to earlier, safer decisions. Do you think electrolyte monitoring is the next big shift in chronic care? #entrepreneurship #healthtech #innovation

Mile High Video Spotlight: Adeia’s Low-Latency Streaming Innovations At Mile High Video 2025, VP of Advanced R&D Chris Phillips detailed Adeia's approach to low-latency streaming, showcasing three key technologies: • Low Latency Streaming: Adeia minimizes delay by optimizing video segment prediction and buffering. This ensures consistent playback quality even under fluctuating network conditions, delivering a seamless viewing experience. • Encoding Optimization: Adeia uses machine learning to dynamically adjust encoding parameters based on real-time network feedback. This balances video quality and bandwidth efficiency, reducing buffering without compromising visual fidelity. • Selective L4S Markings: Adeia leverages Low Latency, Low Loss, Scalable Throughput (L4S) technology by selectively marking packets to prioritize latency-sensitive video data. This reduces delay and packet loss, enhancing reliability over congested networks. Adeia also presented a paper, “On Ultra-Low Latency Multimedia Delivery: An Approach for Selective L4S Enablement,” exploring how selective L4S marking can enhance low-latency streaming, paving the way for next-generation video delivery solutions. Chris shared his bullish outlook on VVC (Versatile Video Coding), emphasizing its potential for improved compression efficiency and enhanced video quality. For a deeper dive into Adeia’s low-latency streaming technologies, read the full interview or watch the video, both at the link below.

Indian cities face severe urban challenges like waterlogging in Bangalore, air pollution in Delhi NCR, traffic congestion in Pune, and the combination of all these issues in Mumbai. Despite citizens paying high taxes, cities remain unsafe and unlivable. Sustainable solutions are essential to address these problems and create resilient, livable urban spaces. Sustainable Solutions for Urban Challenges 1. Green Infrastructure for Waterlogging Waterlogging in cities like Bangalore can be tackled with green infrastructure solutions, such as permeable pavements, rain gardens, and restoring natural water bodies. Sponge city principles—where cities absorb rainwater effectively can reduce flooding and improve water management. 2. Clean Energy and Electric Vehicles for Air Pollution Delhi NCR's air pollution can be mitigated by promoting clean energy (solar, wind) and the transition to electric vehicles (EVs). Investments in EV infrastructure and public transportation are key, alongside greening initiatives to reduce air pollution and improve air quality. 3. Smart Mobility for Traffic Reduction Traffic congestion in Pune can be addressed through smart mobility solutions, such as expanding public transport (metro, buses), intelligent traffic management, and encouraging cycling and walking. Telecommuting and mixed-use urban planning can reduce long commutes and ease traffic pressure. 4. Climate-Resilient Urban Planning Mumbai's multiple challenges, including flooding and congestion, require climate-resilient infrastructure. Comprehensive urban planning should incorporate sustainable land-use, green building codes, and affordable housing to handle population pressures. Smart city technologies can optimize essential services like energy and waste management. 5. Circular Economy and Waste Management Sustainable waste management, driven by a circular economy approach, can reduce landfills through recycling and waste-to-energy conversion. Decentralized waste treatment plants can help cities minimize their environmental impact and manage waste more efficiently. 6. Policy Reform and Civic Engagement Governments need to implement policy reforms that prioritize sustainability, offering incentives for green technologies and enforcing stricter emissions controls. Public-private partnerships can support urban sustainability projects, while civic engagement ensures that communities actively participate in local sustainability initiatives like water conservation and urban greening. Conclusion The solution to India’s urban issues lies in sustainable development—integrating clean energy, smart mobility, green infrastructure, and strong policy frameworks. With comprehensive planning and active public participation, Indian cities can become safer, more livable spaces for the future. #SustainableCities #GreenInfrastructure #CleanEnergy #SmartMobility #UrbanResilience Kindly share your views?

I've witnessed incredible Edge AI projects stumble, not because of a lack of groundbreaking models, but because they neglected the gritty reality of real-world deployment. It's tempting to focus solely on the intelligence, but the true differentiator lies in building systems that are reliably efficient and adapt seamlessly under pressure. After years of shipping IoT solutions, I've distilled these 15 non-negotiable design principles. These aren't just technical considerations; they're lessons learned from countless hours of debugging, optimizing, and ensuring devices perform flawlessly where it matters most: ➞ 1. Latency-First Design: Prioritize real-time decision-making. Edge AI exists to respond fast, not wait for the cloud. ➞ 2. Offline-First Reliability: Design assuming the internet will fail. Systems must continue to work locally. ➞ 3. Compute-Aware Models: Choose models that fit device limits (CPU/GPU/NPU), not the latest hype. ➞ 4. Memory Efficiency: Optimize RAM and storage usage to avoid crashes and ensure stable performance. ➞ 5. Power-Aware Inference: Respect battery and energy constraints at all times, especially in mobile or remote environments. ➞ 6. Thermal Stability: Heat reduces performance - design for throttling and harsh environmental conditions. ➞ 7. Data Filtering at Source: Don’t send raw streams. Filter, compress, and extract features locally before transmitting. ➞ 8. Event-Driven Processing: Trigger AI actions only when needed (state or threshold changes) to save compute cost. ➞ 9. Model Compression: Use quantization, pruning, and distillation to shrink models for edge devices. ➞ 10. Edge-to-Cloud Sync Strategy: Sync only what matters - summaries, learned insights, or anomalies. ➞ 11. Human Override Safety: For critical systems, always include manual control and an emergency kill switch. ➞ 12. Secure Device Identity: Each device must have strong authentication, certificates, and trust verification. ➞ 13. OTA Update Discipline: Enable safe over-the-air model updates with rollback and version control. ➞ 14. Fleet Observability: Monitor latency, drift, and device performance across the entire fleet in real time. ➞ 15. Continuous Drift Monitoring: Edge environments evolve - detect data drift and retrain models proactively. The projects that truly win in Edge AI aren't just powerful; they're fast, resilient, and adaptive. These principles are how you build systems that think locally, act instantly, and scale globally with confidence. 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow Nick Tudor for more insights on AI + IoT that actually ship.

Many manufacturers today have invested heavily in data infrastructure: PLCs, SCADA, MES, historians, dashboards. Yet when you dig into the architecture, especially on high-speed or complex lines, a common gap emerges. Critical short-duration events are not being captured accurately or with enough context to drive actionable insights. This is not due to lack of technology. Modern PLCs, edge devices, and platforms are more than capable. The problem is architectural. Many plants still rely on SCADA and MES systems that poll PLCs at relatively slow intervals, typically 1000 milliseconds. That polling interval creates a blind spot. Meanwhile, PLC scan cycles typically run between 3 and 5 milliseconds. In high-speed lines, servo-based systems, robotics, and motion applications, critical events happen on sub-second timescales. Operator inputs, cascading alarms, motion faults, and intermittent product jams often occur and resolve in less than a second. If these events are not buffered properly at the PLC layer or edge, they are simply lost to higher-level systems. This leads to a familiar pattern. • OEE reports that do not explain why downtime occurred • Fault logs that fail to show which fault triggered first • Product loss and yield issues that cannot be traced to specific machine behaviors • Maintenance teams spending hours reviewing PLC logic and guesswork post-mortems The bigger risk is that leadership decisions get made on incomplete data. Continuous improvement efforts stall. Predictive maintenance strategies fail to get off the ground. McKinsey & Company data suggests that manufacturers who close this gap and build modern data architectures can reduce unplanned downtime by up to 50% and improve productivity by 10 to 20%. But this requires capturing data with the right fidelity, at the right layer, and with the right context. From my experience, this is true not only on high-speed systems where products are moving faster than the eye can see and $100,000 high-speed cameras are used to diagnose failures. It is equally true on slower lines where operators and engineers struggle to explain recurring issues because key data is missing. If you are running below 60 percent OEE, you likely have more foundational work to do first. But if your goal is to move from reactive to proactive operations, to reduce variability, and to enable next-generation capabilities like advanced analytics and machine learning, this is an architectural conversation that needs to happen. I work with manufacturers who want to modernize these architectures and close this visibility gap. If you are looking at these challenges or want to benchmark your current architecture against best practices, feel free to reach out. I would be happy to share insights and lessons learned.