Edge Computing Integration

Paper Title: "Multiscale echo self-attention memory network for multivariate time series (TS) classification" Whilst consulting for ThousandEyes (Cisco), our team explored TS techniques for anomaly detection, especially under statistically-contaminated constraints (with multiple modes, common with network measurement stratification). A basis for featurization was the t-digest (robust quantile estimation, as used in elasticsearch, for example). Now that I have turned my attention to edge ("far edge", or "sensor edge" -- ultra-low power), the constraints are more severe. Here my interest turned to "reservoir computing" (in the form of Echo-State Networks [ESN]). This paper addresses a critical challenge in edge computing - how to efficiently process time-series sensor data with limited computational resources while maintaining high accuracy. The authors' combine ESNs with self-attention mechanisms, offering an architecture valuable for resource-constrained edge devices that need to classify complex sensor inputs. ESNs' fixed-weight training enables minimal parameter updates - crucial for edge deployment. Multi-head attention mechanism shows potential for edge optimization through pruning/quantization. Strong performance on multimodal fusion (96.79% on UTD-MHAD combining depth and inertial data) suggests viability for edge NPU deployment. Sensor Fusion Perspective: The architecture naturally handles varying sensor sampling rates and missing data while capturing temporal dependencies across modalities. The key innovation demonstrates that attention mechanisms, typically computationally expensive, can be efficiently combined with ESNs for high-accuracy sensor fusion at the edge. This represents a practical advance for deploying sophisticated sensor fusion algorithms on power-constrained edge devices.

For a large national corporation with a large number of locations and a third-party hosting location, ensuring the safest, fastest, and easiest network configuration for monitoring and operating various Building Automation Systems (BAS) and IoT systems involves a combination of modern networking technologies and best practices. Network Architecture, Centralized Management with Distributed Control, A robust core network at the third-party hosting location to manage central operations. Deploy edge devices at each location for local control and data aggregation. Use SD-WAN (Software-Defined Wide Area Network) to provide centralized management, policy control, and dynamic routing across all locations. SD-WAN enhances security, optimizes bandwidth, and improves connectivity. Ensure redundant internet connections at each location to avoid downtime. Failover Mechanisms: Implement failover mechanisms to switch to backup systems seamlessly during outages. VLANs and Subnets: Use VLANs and subnets to segregate BAS and IoT traffic from other corporate network traffic. Implement micro-segmentation to provide fine-grained security controls within the network. Next-Generation Firewalls (NGFW): Deploy NGFWs to protect against advanced threats. Intrusion Detection and Prevention Systems (IDPS): Implement IDPS to monitor and prevent malicious activities. Secure Remote Access, Use VPNs for secure remote access to the BAS and IoT systems. Zero Trust Network Access (ZTNA): Adopt ZTNA principles to ensure strict identity verification before granting access. Performance Optimization Traffic Prioritization: Use QoS policies to prioritize BAS and IoT traffic to ensure reliable and timely data transmission. Implement edge computing to process data locally and reduce latency. Aggregate data at the edge before sending it to the central location, reducing bandwidth usage. Ease of Management, Use a unified management platform to monitor and manage all network devices, BAS, and IoT systems from a single interface. Automate routine tasks and use orchestration tools to streamline network management. Design the network with scalability in mind to easily add new locations or devices. Integrate with cloud services for scalable data storage and processing. Recommended Technologies and Tools, Cisco Meraki for SD-WAN, security, and centralized management. Palo Alto Networks for advanced firewall and security solutions. AWS IoT or Azure IoT for cloud-based IoT management and edge computing capabilities. Dell EMC or HP Enterprise for robust server and storage solutions. Implementation Strategy, Conduct a thorough assessment of existing infrastructure and requirements. Develop a detailed network design and implementation plan. Implement a pilot at a few selected locations to test the configuration and performance. Gradually roll out the network configuration to all locations.

🔎 Many industrial operators face the same challenge: "How can we use AI to detect anomalies early enough to prevent unplanned downtime?" That’s a question I often hear in conversations with customers. During a recent visit with Daniel Mantler, our product manager for edge computing, he shared a use case that addresses exactly this challenge. As we all know by now, AI is no longer rocket science. But getting it into real life industrial applications still seeems to be. And that's where our team of experts developed a lean and fast to adapt setup that uses local sensor data to detect for example vibration, temperature, or anomalies directly at the machine. A lightweight machine learning model runs on an edge device and identifies deviations from normal behavior in real time. Because the data is processed on-site, latency is minimal and data sovereignty is maintained. Both aspects are critical in many industrial environments. But the real value lies in the practical benefits for operators: Faster reaction times, reduced dependency on external infrastructure, and the ability to integrate AI into existing systems without needing a team of data scientists. What are your thoughts on integrating ML into edge architectures? I’m keen to hear your thoughts. Let’s use the comments to share perspectives and learn from one another. For those who want to dive deeper into the technical setup and learnings, here’s the full article: 🔗 https://lnkd.in/e8Z5HMCH #artificialintelligence #machinelearning #edgecomputing

Digital Transformation Tip 24/2025: How to Redefine Enterprise Architecture (EA) for Smart Manufacturing?
 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.   Step 1: Transition from a Monolithic to an Agile, API-Driven Architecture ·     Break Down Silos: Move away from traditional, centralized IT/OT structures. Architect a decentralized, microservices-based ecosystem where new digital capabilities (e.g., IoT, AI, digital twins) are plugged in as discrete, interoperable components. ·     Practical Approach: Adopt API-first design principles that allow seamless integration between legacy systems and next-gen digital tools, ensuring rapid adaptability to market shifts.   Step 2: Embed a Data Fabric and Digital Twin Framework ·     Data Fabric: Redefine your EA to incorporate a unified data layer that connects disparate data sources (sensors, ERP, MES) across the shop floor and the corporate system. This fabric enables real-time visibility and decision-making. ·     Digital Twins: Create digital replicas of physical assets to simulate, monitor, and optimize production in real time. ·     Example: Implement digital twins of critical production lines, allowing you to run simulations that predict maintenance needs or process optimizations before any physical intervention is required.   Step 3: Integrate Real-Time IoT and Edge Computing ·     Dynamic Data Streams: Redesign your architecture to support continuous data ingestion from IIoT devices at the edge. This supports instantaneous analytics and operational adjustments. ·     Edge Processing: Deploy edge computing to reduce latency and offload critical computations from the central data center. ·     Practical Example: Deploy edge nodes that pre-process sensor data on-site, ensuring that anomalies are flagged and resolved in real time, reducing downtime and improving production efficiency.   Step 4: Establish an Adaptive Governance Model for Continuous Innovation ·     Agile Governance: Replace static governance frameworks with dynamic, risk-based models that allow for rapid testing, learning, and iteration. ·     Decentralized Control: Empower cross-functional teams to own parts of the digital ecosystem, enabling faster responses to operational challenges. ·     Example: Set up an “innovation sandbox” where teams can quickly prototype new solutions, measure performance against key KPIs, and seamlessly integrate successful pilots into the main architecture. Detailed information is available in Premium Content Newsletter. Image Source: Research Gate Transform Partner – Your Digital Transformation Consultancy

New Whitepaper from Latent AI introducing the " edge continuum" concept. This shift of the AI tech stack towards the edge is key for the next iteration of operationalizing the potential benefits of AI for the warfighter. The key principle of the edge continuum is to utilize distributed computing power and execute data processing as close to the source as possible while preserving the ability to pass harder problems securely and confidently up the continuum as necessary. The edge continuum—a hybrid architecture—distributes AI workloads from the edge to the cloud, bringing processing closer to data sources while leveraging the cloud power for heavier tasks. Moving from “cloud to edge” means we leverage the whole stack and do not heavily rely on centralized cloud computing resources. Key Layers of the Edge Continuum 1. Tactical Edge Devices: Battlefield drones, underwater UUVs, body cams, rugged AI kits Capabilities: Low-SWaP devices running real-time inference for detection, signal flagging, and quick reaction 2. Operational Edge Devices/Nodes: Ground command centers, TOC servers, forward data vans Capabilities: Fuses and filters incoming data, runs preprocessing and context-awareness models 3. Command Edge Devices/Nodes: Battalion-level operations centers, floating ops rooms Capabilities: Aggregates across multiple operational edges, delivers actionable info to commanders 4. Strategic Edge Devices: Cloud hubs, Pentagon/CENTCOM centers, AI model depots Capabilities: High-volume, high-value data aggregation, training, analysis, planning

Edge computing is making a serious comeback in manufacturing—and it’s not just hype. We’ve seen the growing challenges around cloud computing, like unpredictable costs, latency, and lack of control. Edge computing is stepping in to change the game by bringing processing power on-site, right where the data is generated. (I know, I know - this is far from a new concept). Here’s why it matters: ⚡ Real-time data processing: critical for industries relying on AI-driven automation. 🔒 Data sovereignty: keep sensitive production data close, rather than sending it off to the cloud. 💸 Cost control: no unpredictable cloud bills. With edge computing, costs are often fixed and stable, making budgeting and planning significantly easier. But the real magic happens in specific scenarios: 📸 Machine vision at the edge: in manufacturing, real-time defect detection powered by AI means faster quality control, without the lag from cloud processing. 🤖 AI-driven closed-loop automation: think real-time adjustments to machinery, optimizing production lines on the fly based on instant feedback. With edge computing, these systems can self-regulate in real time, significantly reducing downtime and human error. 🏭 Industrial IoT (and the new AI + IoT / AIoT): where sensors, machines, and equipment generate massive amounts of data, edge computing enables instant analysis and decision-making, avoiding delays caused by sending all that data to a distant server. AI is being utilized at the edge (on-premise) to process data locally, allowing for real-time decision-making without reliance on external cloud services. This is essential in applications like machine vision, predictive maintenance, and autonomous systems, where latency must be minimized. In contrast, online providers like OpenAI offer cloud-based AI models that process vast amounts of data in centralized locations, ideal for applications requiring massive computational power, like large-scale language models or AI research. The key difference lies in speed and data control: edge computing enables immediate, localized processing, while cloud AI handles large-scale, remote tasks. #EdgeComputing #Manufacturing #AI #Automation #MachineVision #DataSovereignty #DigitalTransformation

𝗜𝗧/𝗢𝗧 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 -- 𝗧𝗵𝗲 𝗣𝘂𝗿𝘀𝘂𝗶𝘁 𝗼𝗳 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 The separation between the #IT and #OT domains is diminishing. IT, traditionally focused on #DataManagement, #analytics, and enterprise-level operations, is converging with OT, which is responsible for physical processes and equipment. The benefit? The breakdown of #data silos for better interoperability and scalability. 𝗘𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 -#EdgeComputing, thanks to its localized data processing, reduces the reliance on external #cloud connections for critical functions, assuring that operations can continue even during disruptions. -#SCADA systems act as intermediaries, harmonizing data from multiple OT sources before flowing to enterprise systems. -#IIoT platforms streamline data sharing across locations and systems, promoting centralized monitoring. Integrating edge computing with IIoT platforms helps manufacturers scale operations without overloading central systems, ensuring effective data-driven decisions as the volume of operational data grows. 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗶𝗹𝗼𝘀 One of the direct benefits of IT/OT integration is interoperability across different systems and processes. Legacy OT systems, once isolated, are now capable of communicating with IT infrastructure through protocols like #OPC UA and #MQTT, addressing the problem of data silos, which have historically hindered collaboration between both domains. With the use of analytics and #AI, manufacturers can gather insights from previously inaccessible data streams. For example, combining data from OT systems with AI-driven software opens the door for #PredictiveMaintenance strategies to improve overall #Asset Management. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 Scalability is a critical factor. As industries grow, the need for integrated, scalable solutions becomes imperative. Unified network infrastructures, common management platforms, and standardized equipment ensure that IT and OT systems can scale without compromising performance. Cloud platforms and #virtualization technologies are essential to this scaling effort. For instance, virtual controllers offer flexibility by decoupling control software from the underlying hardware, facilitating the remote update and management of systems, and reducing costs associated with hardware dependencies. In addition to scalability, these architectures enable greater flexibility in managing assets and resources; i.e., businesses are able to scale their IT/OT infrastructure in response to production needs while maintaining system reliability and uptime. Source: https://shorturl.at/brwGe ***** ▪ Enjoy this content? Follow me and ring the 🔔 to stay current on #IndustrialAutomation, #IndustrialSoftware, #SmartManufacturing, and #Industry40 Tech Trends & Market Insights!