Utilizing Edge Computing

A Detroit plant's $12M assembly line crashed. The cloud dashboard showed green. The diagnosis, 12 seconds late: "Timeout error." The bill: $47,000. The culprit? A construction crew two blocks away. The hero? A dusty PC the size of a pizza box. Here's what happened: They'd done everything "right": 1,200 sensors streaming to the cloud for "real-time" analytics. Then, Tuesday at 2:47 AM, a welding robot stuttered. By the time data uploaded to Virginia, processed, and pinged back, 247 chassis were scrap. Eight minutes later: total seizure. A mundane fiber cut was all it took. Meanwhile, in a forgotten server room, a grizzled controls engineer named Marcus ran his "rogue" edge setup. While the cloud smiled, his industrial PC had already stopped the neighboring line. 12 millisecond decisions. No internet required. Six months of side-by-side data were almost insulting: • Latency: Cloud: 8-15 seconds. Edge: 8-15 milliseconds. • Downtime: Cloud-dependent: 47 hours. Edge: Zero. • Bandwidth cost: Cloud: $3,200/month. Edge: $87/month. • Security: Cloud: 3 CVE scares. Edge: Data never left the building. The kicker? The floor team trusted Marcus's box. When it screamed "bearing failure," they listened. When the cloud sent its 47th "low priority" alert, they muted it. The lesson I share with every manufacturer: The cloud plans tomorrow's strategy brilliantly. Edge computing runs today's factory. It's the difference between a consultant emailing from Chicago and a foreman slamming the emergency stop before you blink. That plant migrated 80% of critical ops to edge. The result? Zero defects since. Yesterday's fiber cut? Didn't notice. Stop streaming your factory's heartbeat to a data center 900 miles away. The smartest decision is a local one, where steel meets weld, sensor meets machine, decision meets millisecond. Over-clouding manufacturing is like using a weather satellite to decide if you need an umbrella right now. Your thoughts?

"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

🚚 In logistics, speed isn’t just an advantage—it’s survival. Every second counts. And that’s why more and more businesses are turning to edge computing. Instead of sending all data to a central cloud, edge computing processes it right where it's generated—in trucks, warehouses, and even smart containers. Why does this matter? Because real-time data means real-time decisions: ✅ A delivery truck can reroute instantly to avoid traffic ✅ A robot in the warehouse can react to inventory shifts in the moment ✅ A temperature-controlled shipment can trigger alerts before anything spoils Edge computing is not just fast—it’s proactive, local, and intelligent. And in supply chain operations, that can mean fewer delays, lower costs, and happier customers. From my experience in tech innovation, the most resilient logistics teams are the ones that move decision-making closer to the action. Cloud + Edge = A winning combo for modern operations. Where do you see the biggest opportunity for edge computing in your industry? Let’s share ideas in the comments, and follow me for more! #EdgeComputing #SmartLogistics #SupplyChainInnovation

💥 Figma’s $300K-per-day AWS Bill Is a Wake-Up Call Figma’s S-1 IPO filing shocked the tech world: they spend around $300,000 every single day on AWS—amounting to $100 million annually, or 12% of their revenue. That’s not just high; it’s dangerously vulnerable to price hikes, outages, and vendor holdovers. This extreme cloud dependency touches on two core risks: Financial drain—It’s a recurring expense that directly eats into profitability. Vendor lock-in—Reliance on a single provider means loss of control, and significant risk if terms change or access is revoked. What Does This Mean for Africa—and Why Edge Computing Is Our Superpower In Africa, heavy cloud bills like Figma’s are simply unsustainable—data center costs, bandwidth, and infrastructure are major constraints. But here’s the opportunity: Edge computing works locally, not through remote servers. Voice-first Natural Machine Interfaces (NMIs) let people interact easily—vital in areas with illiteracy. Offline-capable AI at the edge enables reliable, low-cost service, even in low-connectivity regions. Together, this creates the perfect formula for cost-effective, inclusive innovation. Let’s Flip the Script What if instead of paying AWS millions, we used localized edge infrastructure to power AI-driven education, healthcare, agriculture, and more? Imagine: A farmer receiving actionable voice prompts via an app powered locally, not up in the cloud. Schools hosting AI tutors in a Wi-Fi radius—no internet needed, just quality learning. Local businesses running voice-first assistive apps at minimal cost, with no recurring massive cloud bills. That’s not just smart—it’s transformative. Edge + NMI = Real innovation with real impact. Your Turn Cloud isn’t inherently bad—but uncontrolled dependency is risky. Africa’s path forward lies in affordable infrastructure, localized AI, and inclusive interfaces. Are you innovating on the edge? Building voice-native solutions? Let’s connect and collaborate! #CloudComputing #EdgeAI #AfricanInnovation #TechForGood #CostOptimization #Inclusion #VoiceAI #Figma

New Research: Optimizing AI Inference with Edge Computing We're excited to share our latest research on how edge computing can improve AI performance! Our team has been exploring how offloading tokenization and RAG (Retrieval-Augmented Generation) to the edge can dramatically improve performance. Key Findings: - Tokenization at the edge: ~20ms latency improvement - RAG at the edge: Up to 145% faster (340ms+ improvement in some cases) - Significant payload size reduction and server load optimization As AI inference becomes the new bottleneck, distributing computation closer to users is crucial. Edge computing isn't just for caching; it could be a game-changer for AI performance. This research was led by Khaled Maâmra, our brilliant Research Scientist, who recently joined Edgee. Khaled brings deep expertise in AI optimization and edge computing, and we're thrilled to have him driving our technical innovation forward. 📖 Read the full article to discover what's possible at the edge: https://lnkd.in/eKBtaTmh

Excited to introduce 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐛𝐨𝐨𝐬𝐭𝐢𝐧𝐠. Imagine harnessing the power of large AI models for real-time, low-latency applications on wearable devices like headsets and earbuds. Here’s the catch: wearable devices can’t locally run large resource-heavy models. On the other hand, running the large model remotely (on say a phone or cloud) can break real-time requirements for apps like augmented reality and audio due to wireless and Internet communication delay. We introduce knowledge boosting, where a large model remotely gives hints to a small on-device model, even when the large model operates on time-delayed older information. This boosts performance without breaking real-time requirements. Our streaming neural network, processing 8 ms chunks, showed impressive results on speech tasks, even with communication delays up to 48 ms (InterSpeech'24). Knowledge boosting bridges the performance gap between small and large models, making powerful low-latency AI accessible to edge devices. 📄 Paper: https://lnkd.in/g9BYks7p 💻 Code: https://lnkd.in/gUGnqhZs 🌐 Project page: https://lnkd.in/gJnVmK6v #Edgecomputing #ModelCollaboration #InterSpeech #LowLatency #Realtime #Wearables #EfficientML

Edge capability and conditional transmission ... How edge computing on LPWAN devices extends the battery life by factor of 4 As industrial IoT systems continue to scale across critical infrastructure—pipelines, reservoirs, remote assets, and urban utilities—one question persists across all engineering teams: "How do we make the device smarter without draining the battery faster or make the firmware more complex?" The answer is not in more power—it’s in more intelligence at the edge. > What Is #EdgeCapability in #LPWAN Devices? Edge capability refers to the ability of the device to process and analyze data locally, before deciding whether to transmit it over the network. This is a critical advancement in the design of battery-powered LPWAN devices—whether #LoRaWAN, #NB-IoT, or #LTE-M. Instead of blindly transmitting data at fixed intervals, smart edge devices evaluate conditions such as: - Threshold violations (e.g., pressure above X bar) - Anomalous patterns (e.g., sudden temperature spike) - Predictive failure signals (via trend detection) Only when action is needed, do they transmit. > Why Conditional Transmission Changes the Game Let’s take a real-world example from our deployments at Ellenex: - Scenario A: Traditional Mode Transmit every 15 minutes (fixed schedule) 96 transmissions/day Average battery life: < 1 year - Scenario B: Edge Mode with Conditional Transmission Sample every 5 minutes Transmit only when threshold conditions are met or at max once per day 1–5 transmissions/day depending on conditions Average battery life: 3.5–4 years By eliminating unnecessary network sessions, power-hungry radio activations, and overhead from MAC layer interactions, energy usage drops dramatically. > Implications for Industrial Use Cases Water Utilities can detect leaks without flooding the network with data. Smart Agriculture devices react only to critical soil moisture levels, not morning dew. Asset Monitoring for pressure, level, vibration, or flow becomes cost-effective in remote areas. And most importantly: maintenance intervals are extended dramatically. Battery replacements become rare events, not monthly line items. > What This Means for Product Designers When we design LPWAN devices at Ellenex, edge intelligence is not optional—it’s a core requirement. Every mA-hour counts. We, at Ellenex Industrial IoT, design products with: - Smart wakeup logic - Configurable edge thresholds - Modular firmware to enable OTA updates of local logic Because the edge is not just about faster insights—it’s about operational viability. Final Thought Nowadays, data is only valuable when it's actionable—and battery life is only long when data knows when not to leave the device. Edge capability + conditional transmission provides longer life, smarter systems, and scalable deployments. If you're still pushing data every 15 minutes—it is time to re-think 🤔 . #monitoring #IoT #ellenex #EdgeComputing #LPWAN #batterylife

Over the weekend I had the idea to push the boundaries of what AI "outside the box" could look like and go to a place few people are talking about. With some capable homebrew hardware, I deployed two local machines (mini-PCs with both Nvidia & AMD eGPUs) running LLMs like OpenAI-OSS-20B, and then connected them to the off-grid Meshtastic LoRa mesh network. The result? AI agents that function completely independent of the internet and datacenters. By combining: • Low-cost local inference • Off-grid LoRa mesh networking with Meshtastic • Distributed hardware at the edge We now have a system that can: • Serve AI queries over a resilient mesh regardless of infrastructure status • Operate where no cellular or Wi-Fi exists • Capable of being powered indefinitely by a 500w solar/battery combo • Maintain control, privacy, and uptime without relying on centralized infrastructure This has huge implications for: • Remote communities • Disaster response & emergency comms • Off-grid research outposts • Low-latency & resilient local services Still early days, but the potential for AI that doesn’t need massive datacenters or even the internet is real. Not to mention surprisingly easy and affordable. Let the off-grid AI era begin! #AI #EdgeComputing #OffGridTech #DecentralizedAI #LocalInference

#AI doesn’t always need the cloud. When split second decisions are made in the OR or in a diagnostics lab, AI needs to be operating on the edge.   At Cyient, we’re driving that shift with #TinyML by optimizing deep learning models to run fast and efficiently on the edge.   Why it matters: Edge computing is projected to reach $43B by 2030, growing at 38% CAGR and On-device AI can cut clinical response times by up to 70%. Cyient TinyML unlocks real-time intelligence where connectivity is limited and speed is important.   Our latest whitepaper breaks down how we applied techniques like quantization and pruning to compress models (VGG16, MobileNet, and more) across use cases in radiology, dermatology, and even fashion retail.   You’ll find a tested blueprint for delivering high-performance AI at the edge, without compromising accuracy.   📄 Get the whitepaper: https://lnkd.in/gKcTN5hx   Building edge-ready AI? Let’s make it faster, lighter, and smarter—together.   #TinyML #EdgeAI #HealthcareAI #AIOnTheEdge #IoTDevices #Cyient #AIOptimization #DigitalEngineering

Monday Musings: Edge or Cloud? While this comes up a lot, the question isn't "edge or cloud" anymore. It's "which data goes where and when?" And I've learned that getting the location wrong can render an otherwise brilliant solution ineffective. Here's the thing: a 2-second delay doesn't matter when you're analyzing last quarter's sales trends. But when you're monitoring a warehouse forklift in real-time? Two seconds means the vehicle has already moved three feet. By the time a cloud-based alert arrives, the potential collision has either happened or been avoided. Location matters. We utilize cloud computing when we require substantial processing power and can tolerate some latency. Pattern analysis across multiple facilities, training AI models on massive datasets, generating insights from weeks of accumulated data - that all happens in the cloud. Edge computing wins when speed and reliability are non-negotiable. Real-time process monitoring, immediate safety alerts, and operations in locations with unreliable internet. A cloud-only solution would go blind every time connectivity failed. Quality control can't afford that. Edge devices detect defects in real time, even when offline. When the connection is restored, they sync with the cloud for deeper analysis and model improvement. Bandwidth economics matter too. One retail client has hundreds of cameras. Streaming all that video to the cloud 24/7? Their monthly bill would be astronomical. Solution: process locally, only send interesting events to the cloud. Customer enters the store? Send it up. Empty aisles at 3 AM? Keep it local. Privacy considerations also drive edge decisions. Some companies won't allow sensitive operational data to leave their premises. Edge processing keeps it contained while still delivering insights. But edge isn't perfect. Those local devices need maintenance. Software updates get complex across 50 locations. And you're limited by whatever processing power you've installed on-site. The reality? Most sophisticated AI systems today are hybrid. Process locally for speed, reliability, or privacy. Use the cloud for heavy computation, long-term storage, and cross-location insights. I've stopped thinking in terms of binary choices, and we've started designing workflows that direct data to where it's processed most effectively. The technology choice should be invisible to users. They just want their AI agent to work—whether it's thinking at the edge, in the cloud, or both. And honestly, that's the whole point. Match the technology to the business need, not the other way around. #EdgeComputing #CloudComputing #AI #AgenticAI #ProcessIntelligence #TechnologyStrategy