Edge Computing Applications

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

A short time-lapse shows a polluted urban canal being transformed into clean, paved, solar-lit infrastructure. What do you think about this work? Beyond the visual contrast, it highlights a deeper challenge in city renovation: not construction capability, but sustained execution. Urban renewal efforts often struggle due to limited visibility, fragmented oversight, and reactive maintenance. This is where AI can play a practical role. How AI helps build a stronger case for city renovation: • Computer vision to detect illegal dumping, blockages, and encroachments • Predictive analytics to identify infrastructure failures before they occur • Real-time tracking of contractors, timelines, and quality of work • Data-driven prioritization of projects based on impact, not politics • Continuous monitoring to ensure assets stay clean and functional Large-scale programs have shown that delivery at scale is possible. The next step is improving precision, accountability, and durability. City renovation should move from episodic cleanups to intelligent, continuously managed systems. Physical infrastructure builds cities. AI helps them stay functional and livable. Divya Gandotra, a content creator focused on social issues, uses the example to challenge the government on urban renewal with help of Ai. #AI #UrbanPlanning #SmartCities #Infrastructure #GovTech #Sustainability

🚀 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

💡 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

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

How to manage EV Charging Stations with an IoT Solution Electric Vehicles are becoming increasingly popular, driving up the demand for reliable and efficient EV charging infrastructure As the EV population grows, effective management of charging stations becomes crucial We recently worked with an EV Charging company on a EV Charging Solution using our Industrial IOT Gateways Some of the things that they were looking to cover where: 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐑𝐞𝐦𝐨𝐭𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: By integrating IoT technology, Charging stations can be equipped with sensors and connectivity for real-time monitoring of charging activities The Operator was aiming to remotely manage and monitor each station's status, ensuring optimal performance, identifying faults, and minimizing downtime 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: IoT enables predictive maintenance for EV charging stations by collecting and analyzing data on equipment performance This predicts potential issues before they escalate, minimizing the risk of unexpected downtime, reducing maintenance costs, and extending the lifespan of charging infrastructure 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: IoT seamlessly integrates with renewable energy sources like solar or wind power Charging stations can intelligently harness clean energy when available, promoting sustainability and reducing the environmental impact of EV charging, aligning with the emphasis on eco-friendly transportation solutions 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞: The data generated by IoT-enabled EV charging stations serves as a valuable resource for business intelligence The Operator wanted to get insights into usage patterns, peak charging times, and popular locations This enables informed decision-making, strategic planning, and optimization of charging infrastructure deployment The IOT-GATE-iMX8 - Industrial IoT Gateway that Anders provided ticked all of the boxes and offered the Connectivity and reliability needed to make sure that the project was a real success Want to find out how we can help on your next EV Charging Project? Find out more in the comments

"ARM CPUs + Apache Kafka = A Perfect Match for Edge AND Cloud" Real-time #datastreaming is no longer limited to powerful servers in central data centers. With the rise of energy-efficient #ARM CPUs, organizations are deploying #ApacheKafka in #edgecomputing, in addition to the widespread hybrid #cloud environments—unlocking new levels of scalability, flexibility, and sustainability. In my blog post, I explore how ARM-based infrastructure—like #AWSGraviton or industrial IoT gateways—pairs with #eventdrivenarchitecture to power use cases across #manufacturing, #retail, #telco, #smartcities, and more. ARM CPUs bring clear benefits to the world of #streamprocessing: - High energy efficiency and low cost - Compact form factors ideal for disconnected edge environments - Strong performance for modern #IoT and #AI workloads The combination of Kafka and ARM enables more cost-efficient and sustainable applications such as: - Predictive maintenance on the factory floor - Offline vehicle telemetry in #transportation and #logistics - Local compliance automation in #healthcare - In-store analytics and loyalty systems in food and retail chains Read the full post with use cases, architecture diagrams, and tips for building cost-effective, resilient, real-time systems at the edge and in the cloud: https://lnkd.in/eeJ6mcaH

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.

Riyadh's Comprehensive Infrastructure Plan: A Key Pillar of Vision 2030 and the Secret to Rapid Execution 🔅 A Transformational Step in Urban Planning and Development Saudi Arabia has unveiled its first comprehensive infrastructure plan for Riyadh, marking a strategic move to reduce traffic congestion, enhance spending efficiency, and ensure project sustainability. 🔅 Seamless Coordination for Efficient Execution This plan serves as a framework to organize all infrastructure projects, helping to identify conflicts between overlapping initiatives, set priorities, and reschedule conflicting projects to ensure seamless execution within defined timelines. 🔅 A Strong Governance and Planning Model Developed with an advanced engineering and planning methodology, the plan includes: 🔸 Reviewing and approving over 837 development plans 🔸 Restructuring phases of 1,737 projects 🔸 Achieving over 100,000 working hours 🔸 Conducting more than 80 coordination workshops 🔸 Automating 66,000 pre-coordinated permits for 2025 The plan reflects a robust governance framework and inter-agency coordination, demonstrated through: 🔸 Agile and responsive management, with 72 representatives from service entities ensuring swift decision-making. 🔸 Digital transformation and process automation, issuing 66,000 pre-coordinated permits for 2025 to enhance efficiency. 🔸 Optimized resource utilization, prioritizing projects to prevent conflicts and improve spending effectiveness. 🔸 Accelerated execution in line with urban expansion, as infrastructure permits surged from 50,000 in 2017 to over 150,000 in 2024. 🔅 Leveraging AI and Smart Planning Strategies One of the key features of this plan is its integration of artificial intelligence and advanced planning strategies, ensuring smarter, more efficient infrastructure management. By harnessing data-driven insights and AI-powered coordination, the plan enhances predictability, decision-making, and long-term sustainability. 🔅 Core Objectives of the Comprehensive Plan The initiative is designed to achieve: 🔸 Enhanced efficiency in planning and execution 🔸 Adoption of environmentally friendly practices 🔸 More effective and precise project implementation 🔸 Strengthened regulatory oversight and project compliance 🔅 Riyadh: A Smart Urban Development Model Expanding over 2,000% in recent decades and now home to 7+ million residents, Riyadh is advancing integrated infrastructure planning, ensuring faster execution, optimized resources, and enhanced quality of life in line with Vision 2030. 🔅 Strong Governance for Efficient Execution This plan fosters seamless collaboration among 15+ governmental entities under a unified leadership council, driving rapid, conflict-free implementation and optimal resource utilization. 🚀 With visionary execution, cities grow smarter, faster, and more advanced! #Riyadh #SaudiVision2030 #InfrastructureDevelopment #ArtificialIntelligence #SmartCities #Sustainability