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- 1. Template designed by Microsoft e il mondo IoT Erica Barone Microsoft Technical Evangelist Windows & IoT @_ericabarone erica.barone@microsoft.com
- 2. Internet of things Microsoft’s point of view: Internet of Your Things End-to-end IoT scenario: from your sensor to Microsoft Azure Agenda
- 3. Simply: gain value from Your Things Connectivity Data AnalyticsYour Things IoT is not just about collecting data, it’s about how you can use these data to gain “value”
- 4. What kind of «value»? Money Happy Customers New Business Models
- 5. IoT is an Inflection Point Hardware is cheap Connectivity is pervasive Development is easy New Innovative Scenarios Huge benefits fuel demand
- 6. IoT 2010
- 7. IoT 2015
- 8. 26 Billion of connected devices 2020 Forecast* 7.3 Billion of tablets and PCs InsuranceManufacturing Healthcare Vertical leading industries TransportationUtilities Agricolture Other industries gaining value from IoT *Source: Gartner http://www.gartner.com/newsroom/id/2636073
- 10. Predicting future performance from historical data Predictive Analytics Recommendation engines Advertising analysis Weather forecasting for business planning Social network analysis IT infrastructure and web app optimization Legal discovery and document archiving Pricing analysis Fraud detection Churn analysis Equipment monitoring Location-based tracking and services Personalized Insurance Predictive analytics should address the likelihood of something happening in the future, even if it is just an instant later…
- 11. Example • Damage is reported by customer or during weekly restocking routes • Technician must be scheduled to investigate • Process take up to 8 days to fix a broken machine • Sensor data is used to monitor cooler condition in real-time • Broken coolers are identified at time of failure • Lost sales remain due to maintenance lead teams (parts & repair technicians) • Azure ML predicts where, when, & what failures will occur based on sensor data • Spare parts & repairs can be scheduled before machines shut down leading to no lost sales CURRENT SCENARIO REAL-TIME SENSORS SENSORS&MACHINELEARNING Days: Days:Days:
- 13. Microsoft’s view The Internet of Things starts with your things Build on the infrastructure you already have Add more devices to the ones you already own Get more from the data that already exists Stop just running your business. Start making it thrive. Start realizing the potential of the Internet of Your Things.
- 14. Microsoft IoT IoT Editions Power a Broad Range of Devices 20 years of history in embedded devices One Windows platform for all devices Enterprise-ready, Maker-friendly Designed for today’s IoT environments Free IoT Core edition! Cloud-Based IoT Services & Solutions Easy to provision, use and manage Pay as you go, scale as you need Global reach, hyper scale End-to-end security & privacy Windows, Mbed, Linux, iOS, Android, RTOS support Azure IoT
- 15. Windows 10 Windows 10 Phone Small Tablet 2-in-1s (Tablet or Laptop) Desktops & All-in-OnesPhablet Large Tablet Classic Laptop Xbox IoT Surface Hub Holographic
- 16. Windows 10 IoT Editions For Industry Devices Windows 10 Enterprise (IoT) Desktop Shell, Win32 apps 1 GB RAM, 16 GB Storage For Mobile Devices Windows 10 Mobile Enterprise (IoT) Modern Shell Mobile Chassis requirement 512 MB RAM, 4 GB storage For Small Devices Windows 10 IoT Core Dedicated devices No Shell/Store/MS Apps 256MB RAM, 2GB storage Universal Apps Windows Device Services • On all Windows IoT clients • Extends value of Windows for OEMs • OS telemetry, update management, interoperability • Azure IoT-ready Requires desktop or desktop apps– Win32, .NET, WPF, etc. ? Requires a Shell experience, multiple applications, Windows first-party applications, or mobile voice? Otherwise
- 17. Azure IoT services Accelerate your business transformation Microsoft Azure IoT Suite Azure IoT Suite Predictive MaintenanceRemote Monitoring Asset Management And more… Addresses common scenarios: Enables you to Mine data Take actionConnect assets M o n i t o r i n g
- 18. End to End Scenario: From your sensor to Microsoft Azure
- 20. Telemetry system architecture Sensor Device Gateway Cloud 10010 010011 Different technologies Different protocols to send data to the cloud USB HTTP
- 21. Legacy IOT MQTT/COAP/Custom Applications AMQP/HTTP, C, .NET, Java… IP capable devices AMQP/HTTP, C, .NET, Java Low power devices (RTOS) End-to-end stream processing architecture ProcessingCollectionProducers App insights Data analytics State over time Dashboard Service Search Distributed tracing - Azure DBs Azure Storage Service Bus HDInsight Event Hubs Field gateway Custom Cloud gateway Storage Adapters Stream Analytics
- 22. Gateway Sensor Device Gateway Cloud 10010 010011 No Gateway Field Gateway Cloud Gateway • Devices with connection capabilities • Devices with Security Layer support • Applications • Concrete scenarios with several low-cost devices • Scenarios where repair or replace devices and sensors is hard or expensive • Devices which use incopatible protocols (like MQTT or custom protocols)
- 23. HTTP MQTT CoAP Sensor Device Gateway Cloud 10010 010011 IoT main protocols • Client/Server • Synchronous • REST architecture for CRUD operations on resources • No QOS (based on TCP) • HTTPS (HTTP over SSL/TLS) AMQP • Broker/Clients • Multiplexing sessions/links on the same TCP connection • Message • Header (system and custom/user properties) • Opaque body (any format) • Data types system and metadata • QOS (at most once, at least once, exactly once) • SSL/TLS and SASL • HTTP-like (via UDP) • Client/Server • (also) Asynchronous - Observer • QOS with “confirmable” message or not • DTLS (Datagram TLS) • Broker/Clients • Connection oriented via TCP • Very simple, smallest packet (2 bytes) • Payload agnostic • No data types and metadata • Any data format • Topics based • QOS (at most once, at least once, exactly once) • SSL/TLS
- 24. Field Gateway Device Connectivity & Management Analytics & Operationalized Insights Presentation & Business Connectivity Devices RTOS,Linux,Windows,Android,iOS Protocol Adaptation Batch Analytics & Visualizations Azure HDInsight, AzureML, Power BI, Azure Data Factory Hot Path Analytics Azure Stream Analytics, Azure HDInsight Storm Presentation & Business Connectivity App Service, Websites Dynamics, Notification Hubs Hot Path Business Logic Service Fabric & Actor Framework Cloud Gateway Event Hubs & IoT Hub Field Gateway Protocol Adaptation
- 25. Microsoft Azure IoT services Devices Device Connectivity Storage Analytics Presentation & Action Event Hubs SQL Database Machine Learning App Service Service Bus Table/Blob Storage Stream Analytics Power BI External Data Sources DocumentDB HDInsight Notification Hubs External Data Sources Data Factory Mobile Services { }
- 27. Demo – sensors, devices, gateway GSR sensor Heart Rate sensor Seeedstudio Grove Arduino Uno Raspberry Pi
- 29. Teach the system with your own GSR values GSR sensor connected to Analog Input HR sensor connected to Digital Input Sensor Device Gateway Cloud 10010 010011
- 30. HR sensor works as interrupt. The interrupt service routine includes the heart rate calculation Abnormal GSR values: 50 points higher or lower than the threshold ... Sensor Device Gateway Cloud 10010 010011
- 31. Communication between device and gateway Sensor Device Gateway Cloud 10010 010011
- 32. Raspberry Raspberry reads data sent by Arduino from serial port After a check on the serial port, Raspberry wraps the message in order to meet AMQP protocol Sensor Device Gateway Cloud 10010 010011
- 33. Connecting to the cloud In order to send the data to my event hubs I need to insert my subscription details in the code running on Raspberry. Sensor Device Gateway Cloud 10010 010011
- 34. Connection between the gateway and the cloud Sensor Device Gateway Cloud 10010 010011 In this demo, we decided to use AMQP to send data from the gateway to Event Hubs.
- 35. Azure Event Hubs Event Producers > 1M Producers > 1GB/sec Aggregate Throughput Up to 32 partitions via portal, more on request Partitions Direct PartitionKey Hash Throughput Units: • 1 ≤ TUs ≤ Partition Count • TU: 1 MB/s writes, 2 MB/s reads Receivers AMQP 1.0 Credit-based flow control Client-side cursors Offset by Id or Timestamp Sensor Device Gateway Cloud 10010 010011
- 36. AzureSQLDB AzureEventHubs AzureBlobStorage INPUT Source of Events Azure BlobStorage Azure EventHubs ReferenceData Queryrunscontinuouslyagainstincomingstreamofevents Events Have a defined schema and are temporal (sequenced in time) Azure Stream Analytics Sensor Device Gateway Cloud 10010 010011
- 37. Tumbling Window 1 5 4 26 8 6 5 Time (secs) 1 5 4 26 8 6 A 20-second Tumbling Window 3 6 1 5 3 6 1 Tumbling windows: • Repeat • Are non-overlapping SELECT TollId, COUNT(*) FROM EntryStream TIMESTAMP BY EntryTime GROUP BY TollId, TumblingWindow(second, 20) Query: Count the total number of vehicles entering each toll booth every interval of 20 seconds. An event can belong to only one tumbling window
- 38. SELECT Max(time) as time, Max(temp) as TempMax, Min(temp) as TempMin, Avg(temp) as tempAvg FROM DevicesInput TIMESTAMP BY time GROUP BY TumblingWindow(Minute, 1) SELECT 'LIE' as alertType, 'Why are you lying?!?!? :)' as message, dspl as dsplAlert, Avg(lght)as hr, max(time) as timeStart, max(time) as timeEnd, Max(temp)as tempMax, Min(temp) as tempMin, Avg(gsrth)as gsrth FROM DevicesInput TIMESTAMPBY time GROUP BY dspl, TumblingWindow(Second, 5) HAVING (tempMin < (gsrth-50) OR tempMax > (gsrth+50)) AND (hr > 80) ehdevices ehdevices ehalerts Stream Analytics Web App Sensor Device Gateway Cloud 10010 010011
- 39. Azure Web App Raw data shown with graphs come directly from Event Hubs Aggregations and Alerts are generated by Stream Analytics
- 41. Questions?
- 42. Thank you Grazie a tutti per la partecipazione Riceverete il link per il download a slide e demo via email nei prossimi giorni Per contattarmi @_ericabarone erica.barone@microsoft.com
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- Per il 2020 la connettività dei dispositivi diventerà uno STD, anche grazie alla riduzione dei costi delle componenti. Si potranno usare componenti dal costo inferiore a 1 dollaro da applicare agli oggetti per farli comunicare con internet e altri oggetti. Avremo anche nuovi modelli di business: se si pensa alle assicurazioni, si può pensare a un monitoraggio di cert parametri sulla base dei quali è possibile calcolare la quota da pagare (usage-based insurance) Ci saranno poi tecnologie sensor-specific (ad esempio nel manufacturing) e tecnologie diffuse (smart buildings)
- Key goal of slide: One of the key goals of data insight is to get predictive. Through the central collection and processing of data, machine learning and algorithms can be applied to the data set to analyze the historical data and arrive at reasonable accuracy levels for future events. This predictive approach can be applied in a number of ways, from simple notification, to alert and notification depending in the situation.
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- Key goal of slide: As we look at a deeper layer of detail for a scenario, it could look like this. The producers are potentially like the 4 listed here. Apps, older devices, IP connected or smaller devices. These may connect directly to Event Hubs in Azure, or they may hop through either Field or Cloud gateways where translation is performed. As this diagram illustrates, the destination in Azure, Event Hubs, enables the data to take a route of 1 of 2 choices. Either the data can be processed by tools like Stream Analytics, Storm or something custom, or it can be formatted for delivery to a specified storage location. If Storage is the destination, there are a number of options, or even combinations that can be utilized. DB, Table, Blob, HDI or even back to a Service Bus could be engaged as a return loop for command and control scenarios. Finally, the data travels through this infrastructure and may be presented in any number of ways
- infine è necessario visualizzare i dati che sono stati elaborati in modo tale da poter effettuare delle valutazioni utili per il proprio business. I servizi Azure che permettono la visualizzazione sono molteplici: App Service: in particolare con Web App e Mobile App servizi come il Notification Hub permettono di inviare una notifica nel caso in cui ci sia un valore per cui si debba prestare una particolare attenzione all’interno del flusso oppure altri strumenti più complessi che MS mette a disposizione come il CRM Dynamics, che possono essere utilizzati per la presentazione dei dati in realtà enterprise
- riassunto di tutti i servizi che possono essere applicati in una soluzione IOT end-to-end molti di questi li abbiamo già affrontati mi vorrei soffermare sui servizi di storage che non abbiamo visto finora questi permettono di gestire l’archiviazione dei dati che sono letti dai device
- Inserire una foto con tutti gli HW (+Band se riusciamo a metterlo)
- Stream Analytics mi serve per fare un analisi del flusso dei dati in tempo reale. Non analizza il dato singolo, ma il flusso, senza perdere le caratteristiche. Ecco perchè usarlo per la telemetria Un’applicazione tipica per l’analisi dei flussi ha una struttura come questa. I principali component sono: Input – sorgenti degli eventi. Le sorgenti “proprie” di uno scenario di questo tipo sono device, macchine, applicazioni, sensori, etc. In ogni caso, ASA non è direttamente connesso a questi. La prima interfaccia con I dati che provengono da queste sorgenti è EH. ASA è ottimizzato per prendere il flusso di dati da EH e dagli Azure Blob. Azure Blob Storage is the likely place where log data is stored. The list of input sources that ASA directly integrates with may increase in the future, but Azure Event Hubs and Azure Blob Storage will be the primary sources. Query – componente principale di ASA. Le query implementano la logica di analisi. Sono un set di trasformazioni che vengono applicate al flusso in inut per produrre un flusso di output. Le query sono l’unico element che gli sviluppatori di ASA devono effettivamente sviluppare/scrivere. Tutto il resto viene completamente gestito attraverso il portale di gestione di Azure. Una cosa molto importante da notare è che ASA supporta un linguaggio simile al SQL per le query, ma a differenza di un DB tradizionale queste query sono eseguite continuamente sullo streaming dei dati. Si fermano solo nel caso in cui vengano fermate manualmente o vanno in pausa nel momento in cui non ci sono dati da processare Output – Quando una query viene eseguita, questa produce dati continuamente. Questi risultati possono essere memorizzati in Blob Storage, EH o in SQL Database su Azure. L’input processato e memorizzato in uno di questi servizi può essere riutilizzato come input di un nuovo processo. È quindi possibile concatenare tra loro diversi processi per effettuare una serie di trasformazioni sui dati che provengono dall’esterno. Posso avere 2 input in stream analytics: il flusso corrente e un flusso che avevo memorizzato prima ad esempio in un blob storage. Stream Analytics può fare un’analisi continua del flusso sttuale rispetto a quello storico.
- Tumbling windows specify a repeating, non-overlapping time interval of a fixed size. Syntax: TUMBLINGWINDOW(timeunit, windowsize) Timeunit – day, hour, minute, second, millisecond, microsecond, nanosecond. Windowsize – a bigInteger that described the size (width) of a window. Note that because tumbling windows are non-overlapping each event can only belong to one tumbling window. The query just counts the numbers of vehicles passing the toll station every 20 seconds, grouped by Toll Id.
- Screenshot del website nel quadrato a dx
- Stringa di connessione all’event hubs