Edge Computing Applications

Army launches G-TEAD Marketplace. Brigade commanders can buy counter-drone systems in 90 days. No RFPs. No committees. November 24, 2025. Global Tactical Edge Acquisition Directorate Activates Digital Procurement Platform. First vendor: Fortem Technologies with DroneHunter interceptors validated in Ukraine. Translation: Amazon for Army tech just went live. The disruption metrics. • Traditional acquisition: 2-3 years • G-TEAD timeline: 90 days max • Decision authority: Tactical commanders • Budget ceiling: None (draws from existing O&M) This isn't procurement reform. It's a procurement revolution. xTechCounter Strike wrapped in Germany. 15 competed. 5 won. Fortem's DroneHunter killed Russian Orlan-10s in Ukraine last month. Now any brigade commander orders them direct. No middlemen. No waiting. The eligibility gates. • CAGE/NCAGE code required • Master Business Agreement signature • Combat validation preferred • NATO partners included • Delivery in 90 days or contract void Three shifts crystallize. Theater beats Pentagon. Brigade commanders bypass acquisition bureaucracy entirely. Commercial beats custom. Ukraine-proven COTS dominates traditional programs. For contractors: Your competition isn't Raytheon anymore. It's whoever validated their tech in Bakhmut last week. Is your solution marketplace-ready or still chasing 5-year PORs? ---------- Like this content? Join our newsletter. Link located below my name 👆

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

🚀 Europe’s Armed Forces Face a 15km 'Death Zone'—Startups Could Be the Key to Surviving It Europe’s militaries are confronting a new battlefield reality: a 15km "zone of total death" identified from the Ukrainian frontlines, where traditional logistics and manned operations have become lethal due to drones, electronic warfare, and precision strikes. At the recent UK-Ukraine Defence Tech Forum, General Valerii Zaluzhnyi put it bluntly: “Classical offensive operations are not just ineffective—they’re suicidal in these zones.” 👉 This challenge demands a radical rethink of logistics at the tactical edge. Troops cannot risk driving trucks into these zones. Instead, quiet, electric Unmanned Ground Vehicles (UGVs) must be deployed to ferry ammunition, supplies, and even evacuate the wounded—taking humans out of harm’s way. But here’s the breakthrough: AI-driven autonomy is making this possible. Startups like TENCORE are scaling rapidly to meet this need, delivering modular UGVs capable of: ✅ Autonomous navigation in GPS- and comms-denied environments using AI-powered perception and route planning ✅ Real-time adaptation to battlefield threats without direct operator control ✅ Modular mission-switching—from logistics to mine-laying to fire support—on a single platform These vehicles are engineered for extreme resilience and flexibility: battery swaps in under 10 seconds, lego-like repairability, and minimal human intervention. But let’s be clear: 👉 Hardware is now table stakes. It’s software that will win the wars of the future. The edge lies in the software layer: AI that can navigate and decide under electronic warfare and jamming Swarming algorithms that enable distributed, coordinated missions Autonomous decision-making at the tactical edge without waiting for command uplinks 🔥 The startup opportunity? Europe’s militaries urgently need: AI-first, software-defined autonomy platforms Interoperable software ecosystems across NATO forces Rapid software iteration matching the speed of battlefield adaptation In today’s wars, humans are the most expensive and vulnerable resource. AI-enabled autonomy isn’t just a buzzword—it’s the frontline’s survival mechanism. The future of defence will be fought in code, deployed on autonomous machines. 💬 If you’re building robotics, AI, autonomy platforms, or distributed software systems, this is your moment. Let’s connect: Europe’s defence ecosystem is ready for bold innovators. #DefenceInnovation #MilitaryLogistics #UGVs #AI #AutonomousSystems #SoftwareDefinedWarfare #StartupOpportunity #EuropeanSecurity #TechForDefence #Ukraine #KARISTA #PSION #NationalSecurity #Geopolitics #DualUseTech #OmniUse #DefenceTech #VentureCapital #Investing #TechCommandInvesting

There are mainly three communication patterns that form the backbone of modern distributed systems. Understanding these patterns is crucial for any software architect or engineer working on scalable applications. Let's dive into these patterns and explore how they shape our systems: 1. Synchronous Communication • Direct, real-time interaction between services • Client initiates request through API Gateway • Services communicate sequentially (A → B → C) • Uses HTTP Sync at each step • Pros: Simple, immediate responses • Cons: Can create bottlenecks, potential for cascading failures Ideal for: Operations requiring immediate, consistent responses 2. Asynchronous One-to-one • Utilizes message queues for communication • Client sends request to API Gateway • Services listen to and receive from queues • Allows for decoupled, non-blocking operations • Pros: Better load handling, fault tolerance • Cons: More complex, eventual consistency Ideal for: High-load scenarios, long-running processes 3. Pub/Sub (Publish/Subscribe) • Employs a central topic for message distribution • Client interacts with API Gateway • Multiple services can subscribe to a single topic • Enables one-to-many communication • Pros: Highly scalable, great for event-driven architectures • Cons: Can be complex to manage, potential message ordering issues Ideal for: Event broadcasting, loosely coupled systems Key Considerations When Choosing a Pattern: • Scalability requirements • Response time needs • System coupling preferences • Fault tolerance and reliability • Complexity of implementation and maintenance The art of system design often involves skillfully combining these patterns to create robust, efficient, and scalable distributed systems. Each pattern has its strengths, and the best architects know how to leverage them for optimal performance. Which pattern do you find most useful ? How do you decide which to use in different scenarios?

💡 OT DATA: Manufacturers now realize the hard truth - collecting data is easy, but turning it into value at scale is a complex challenge requiring industrial-grade solutions. I've spent time with manufacturers who've been down the DIY path with their shop floor data: 🛠️cobbling together open-source tools, wrestling with security issues, and struggling to scale beyond pilot projects. All while their valuable data remains trapped in operational silos. 🏆What separates winners in this space? True industrial-grade edge computing doesn't just collect data - it transforms operations. Here's what makes Siemens Industrial Edge fundamentally different: 1️⃣ Deployment flexibility: Unlike competitors offering only cloud orchestration, we provide both on-premise AND cloud management, fitting your existing IT infrastructure 2️⃣ Software-defined automation: Our platform extends beyond basic data collection to actual application deployment - including the world's first failsafe virtual PLC 3️⃣ Seamless integration: Edge isn't an island - it connects with Mendix for low-code development, Senseye for predictive maintenance, and our complete portfolio from planning to optimization 4️⃣ Open ecosystem built on OT foundations: We've partnered with leaders like Amazon Web Services (AWS) to bridge IT/OT while maintaining industrial robustness that DIY solutions can't match 📈 The most forward-thinking manufacturers understand this isn't about collecting MORE data, but making data more VALUABLE. They're leveraging platforms built from the ground up for industrial needs. ❓What's your experience with edge computing in manufacturing? Are you getting true value from your operational data or just collecting it? More info at links in first comment below this post👇🏼 #ManufacturingInnovation #IndustrialEdge #OTdata #SiemensXcelerator #DigitalTransformation #ITOT

Netflix's latest tech blog details how 𝗔𝗩𝟭 𝗻𝗼𝘄 𝗽𝗼𝘄𝗲𝗿𝘀 𝗮𝗽𝗽𝗿𝗼𝘅𝗶𝗺𝗮𝘁𝗲𝗹𝘆 𝟯𝟬% 𝗼𝗳 𝗮𝗹𝗹 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝘃𝗶𝗲𝘄𝗶𝗻𝗴 (𝗼𝗻 𝗱𝗲𝗺𝗮𝗻𝗱), following the launch of AV1 support on Android in 2020. While H.264/AVC is still the primary codec across Netflix viewing, the company expects AV1 to become the number one codec very soon. Some other key takeaways from Netflix's post: - AV1 sessions use one-third less bandwidth than both AVC and HEVC, resulting in 45% fewer buffering interruptions - On average, AV1 streaming sessions achieve VMAF scores that are 4.3 points higher than AVC and 0.9 points higher than HEVC sessions - 85% of Netflix's HDR catalog (from the perspective of view-hours) has AV1-HDR10+ coverage, and this number is expected to reach 100% in the next couple of months - The AV1 specification incorporates a unique solution called Film Grain Synthesis (FGS), allowing Netflix to deliver a realistic cinematic film grain experience without the usual data costs - Netflix is evaluating the use of AV1 in live streaming to deliver high-quality live experiences to large audiences without compromising video quality, and to reduce its delivery costs - AV1 offers an opportunity to make graphic overlays highly customizable, since layered coding is supported in AV1’s main profile - Over the past five years (2021–2025), 88% of large-screen devices, including TVs, set-top boxes, and streaming sticks, submitted for Netflix certification have supported AV1, with the vast majority offering full 4K@60fps capability Netflix says it is excited about the forthcoming release of AV2, announced by the Alliance for Open Media in September, with an expected release at the end of this year. Blog post: https://lnkd.in/eaVYqRR6 - #streamingmedia #AV1 #codecs #netflix #SVOD #AVOD #videoworkflow Liwei Guo, Zhi Li, Sheldon Radford, Jeff Watts

Processing data at the edge reflects a shift towards smarter, faster decision-making, where IoT gateways act as the bridge between devices, sensors, and the cloud, ensuring that critical actions can occur close to the source with minimal delay. This capacity changes the way connected systems respond to real-world events. By analyzing information directly where it is generated, latency decreases and the relevance of each decision increases. It is a practical response to the growing demand for speed and reliability in digital infrastructures. Cloud systems still offer the depth and scale required for advanced analytics and storage, yet their effectiveness is strengthened when paired with intelligent processing at the edge. The combination creates an architecture where local and centralised capabilities work in harmony. In this evolution, I see an alignment between technological progress and operational needs, showing a future in which data flows are not only faster but also more context-aware, enabling decisions that are both timely and informed. #EdgeComputing #IoT #CloudComputing #DigitalTransformation #DataProcessing

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

𝗗𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴. 𝗔𝗽𝗽𝗹𝘆 𝗜𝘁. The AI headlines are exciting. But if you're a founder, engineer, or educator in manufacturing, here's the question that actually matters: 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘵𝘰𝘥𝘢𝘺 𝘁𝗼 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲𝘀𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻? Let’s get tactical. 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Tool to try: Lenovo’s LeForecast A foundation model for time-series forecasting. Trained on manufacturing-specific datasets. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re battling supply chain volatility and need better inventory planning. 👉 Tip: Start by connecting your ERP data. Don’t wait for perfect integration: small wins snowball. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗻𝗲𝘅𝘁 𝗿𝗼𝗯𝗼𝘁 Tools behind the scenes: NVIDIA Omniverse, Microsoft Azure Digital Twins Schaeffler + Accenture used these to simulate humanoid robots (like Agility’s Digit) inside full-scale virtual factories. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re considering automation but can’t afford to mess up your live floor. 👉 Tip: Simulate your current workflows first. Even without a robot, you’ll find inefficiencies you didn’t know existed. 𝟯. 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝟮𝟬𝟮𝟬𝘀 Example: GM uses AI to scan weld quality, detect microcracks, and spot battery defects: before they become recalls. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re relying on spot checks or human-only inspections. 👉 Tip: Start with one defect type. Use computer vision (CV) models trained with edge devices like NVIDIA Jetson or AWS Panorama. 𝟰. 𝗘𝗱𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲 Why it matters: If your AI system reacts in seconds instead of milliseconds, it's too late for safety-critical tasks. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're in high-speed assembly lines, robotics, or anything safety-regulated. 👉 Tip: Evaluate edge-ready AI platforms like Lenovo ThinkEdge or Honeywell’s new containerized UOC systems. 𝟱. 𝗕𝗲 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 The EU AI Act is live. China is doubling down on "self-reliant AI." The U.S.? Deregulating. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're deploying GenAI, predictive models, or automation tools across borders. 👉 Tip: Start tagging your AI systems by risk level. This will save you time (and fines) later. Here are 5 actionable moves manufacturers can make today to level up with AI: pulled straight from the trenches of Hannover Messe, GM's plant floor, and what we’re building at DigiFab.ai. ✅ Forecast with tools like LeForecast ✅ Simulate before automating with digital twins ✅ Bring AI into your QA pipeline ✅ Push intelligence to the edge ✅ Get ahead of compliance rules (especially if you operate globally) 🧠 Each of these is something you can pilot now: not next quarter. Happy to share what’s worked (and what hasn’t). 👇 Save and repost. #AI #Manufacturing #DigitalTwins #EdgeAI #IndustrialAI #DigiFabAI

AI at the Edge: Smaller Deployments Delivering Big Results The shift to edge AI is no longer theoretical—it’s happening now, and I’ve seen its power firsthand in industries like retail, manufacturing, and healthcare. Take Lenovo's recent ThinkEdge SE100 announcement at MWC 2025. This 85% smaller, GPU-ready device is a hands-on example of how edge AI is driving significant business value for companies of all sizes, thanks to deployments that are tactical, cost-effective, and scalable. I recently worked with a retail client who needed to solve two major pain points: keeping track of inventory in real time and improving loss prevention at self-checkouts. Rather than relying on heavy, cloud-based solutions, they rolled out an edge AI deployment using a small, rugged inferencing server. Within weeks, they saw massive improvements in inventory accuracy and fewer incidents of loss. By processing data directly on-site, latency was eliminated, and they were making actionable decisions in seconds. This aligns perfectly with what the ThinkEdge SE100 is designed to do: handle AI workloads like object detection, video analytics, and real-time inferencing locally, saving costs and enabling faster, smarter decision-making. The real value of AI at the edge is how it empowers businesses to respond to problems immediately, without relying on expensive or bandwidth-heavy data center models. The rugged, scalable nature of edge solutions like the SE100 also makes them adaptable across industries: Retailers** can power smarter inventory management and loss prevention. Manufacturers** can ensure quality control and monitor production in real time. Healthcare** providers can automate processes and improve efficiency in remote offices. The sustainability of these edge systems also stands out. With lower energy use (<140W even with GPUs equipped) and innovations like recycled materials and smaller packaging, they’re showing how AI can deliver results responsibly while supporting sustainability goals. Edge AI deployments like this aren’t just small innovations—they’re the key to unlocking big value across industries. By keeping data local, reducing latency, and lowering costs, businesses can bring the power of AI directly to where the work actually happens. How do you see edge AI transforming your business? If you’ve stepped into tactical, edge-focused deployments, I’d love to hear about the results you’re seeing. #AI #EdgeComputing #LenovoThinkEdgeSE100 #DigitalTransformation #Innovation