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Data Science for Internet of Things with Ajit Jaokar

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Data Science for Internet of Things with Ajit Jaokar

  1. 1. Copyright : Futuretext Ltd. London0 @ajitjaokar
  2. 2. Copyright : Futuretext Ltd. London1 Getting started in maths: for-getting-started-in-statistics-for-programmersdata-science.html Lipstick Robot(Deep learning) great-way-to-explain-deep-learning.html Evolution of Deep learning models: learning-models.html internet-of-things-practitioner-course-march-2016.html
  3. 3. Copyright : Futuretext Ltd. London2 Ajit Jaokar Roadmap and Big Picture
  4. 4. Copyright : Futuretext Ltd. London3 Ajit Jaokar - Data Science for IoT @Oxford Uni + UPM(Smart cities) + Online Next book part of Stanford Uni course In 2015, Ajit was included in 16 Top Data Science bloggers on Data Science Central, Top 100 blogs on KDnuggets and Top 50 people to follow on Twitter by IoT central for IoT. World Economic Forum Spoken at MWC(5 times), CEBIT, CTIA, Web 2.0, CNN, BBC, Oxford Uni, Uni St Gallen, European Parliament. @feynlabs – teaching kids Computer Science. Adivsory – Connected Liverpool
  5. 5. Copyright : Futuretext Ltd. London4 Data Science for Internet of Things – practitioner course – March 2016 Now running in it’s second batch .. Welcome to the world’s first course that helps you to become a Data Scientist for the Internet Of Things ..
  6. 6. Copyright : Futuretext Ltd. London5 Ajit Jaokar The Big Picture – The Data Science and IoT landscape
  7. 7. Copyright : Futuretext Ltd. London Internet of Things CNN, RNN Data Lake Event Based analysis Rules/ Workflow Edge Processing Engine Rules/ Workflow Alerts Trigger s Actions Cloud / Data LakeEdge Device Event Collector Predictive Alerts Stream Processing System Event Store Analytics Model Build Model HDFS Batch Processing System Validate Event Sequence CNN, RNN Data Lake Event Based analysis CEP CEP CEP
  8. 8. Copyright : Futuretext Ltd. London7 Ajit Jaokar INTERNET OF THINGS
  9. 9. Copyright : Futuretext Ltd. London8 As the term Internet of Things implies (IOT) – IOT is about Smart objects For an object (say a chair) to be ‘smart’ it must have three things - An Identity (to be uniquely identifiable – via iPv6) - A communication mechanism(i.e. a radio) and - A set of sensors / actuators + Physical context(ex location) Social context + Decisions at the ‘edge’ ex with sensor fusion and even in offline mode Workflow – (IFTTT) often also at the edge – Thus, IOT is all about Data .. IoT != M2M (M2M is a subset of IoT)
  10. 10. Copyright : Futuretext Ltd. London9 Ajit Jaokar Many of the consumer IOT cases will happen with iBeacon in the next two years
  11. 11. Copyright : Futuretext Ltd. London10 Ajit Jaokar And 5G will provide the WAN connectivity 5G - Source – Ericsson
  12. 12. Copyright : Futuretext Ltd. London Closed Loop Message – Response System Senso rs Rules/ Workflow Edge Processor Rules/ Workflow Analytic Workbench: Operational Investigative, Predictive Analytics and Machine Learning Possible Specialized Store Enterprise Apps: ERP, CRM, and other enterprise apps Alerts Trigger Actions Cloud Based Central Repository Source: allseenalliance_4x3_template_24sept2014.pdf
  13. 13. Copyright : Futuretext Ltd. London12 iOt relates to Automation in three key areas based on Sensing and Predicting a) Move from exception handling to patterns of exceptions over time.(are some exceptions occurring repeatedly? Do I need to redsign my product, Is that a new product?) – b) Move from optimization to disruption – ownership to rental ship (Where are all these dynamic assets?) c) Move to self learning: Robotics: From assembly line to self learning robots(Boston Dynamics), autonomous helicopters
  14. 14. Copyright : Futuretext Ltd. London13 Machines generate Data - Types of Big Data Status Data almost everything will have a status data. This will create vast amounts of data – much of it will be summarized at the ‘edge’ Location Data: Almost everything will have location data even if that location is static. Things will be in transit (where is my product/car etc etc) Machines taking action: Thermostat is automatically reduced Actionable Data: Data in human actionable form – workflow – IFTTT Machines learning by themselves in areas where there are no ‘rules’ – Most interesting space – best example is Deep Learning
  15. 15. Copyright : Futuretext Ltd. London14 Data Science for IoT: The role of hardware in analytics Processing at the Edge (which Cisco and others have called Fog Computing). Alternately, we see entirely new classes of hardware specifically involved in Data Science for IoT(such as synapse chip for Deep learning)
  16. 16. Copyright : Futuretext Ltd. London15 Edge computing
  17. 17. Copyright : Futuretext Ltd. London16 Different Data Formats  POS data  Social media  External feeds  Payments  Log data  Telephone conversations  RFID Scans  Events  Emails  Sensors  Free-form text  Geospatial  Audio  Still images/videos  Transactions  Call center notes Adapted from Ravi Kalakota PhD
  18. 18. Copyright : Futuretext Ltd. London IoT Reference Stack Portal Dashboard API Manageme ntEvent Processing and Analytics Aggregation / Bus Layer ESB and Message Broker Devices Communications MQTT / HTTP/COAP DeviceMgr Identity&AccessManagement Protocols Standards Industrial Internet Consumer Governance Smart Grid Manufacturi ng Logistic& Transpor tation Robotics Connecte d Car Wearabl es Health Public Safety Smart Cities Retail
  19. 19. Copyright : Futuretext Ltd. London Multiple Protocols of IOT HTTP/ REST, MQTT, COAP, etc TCP, UDP IPV6, IPV6 w 6LOWPAN, etc Wireless (802.15.4, Wifi, BLE, etc.) Higher layer protocols ‒ Application ‒ Transport ‒ Network Higher layer protocols ‒ Link layer
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  28. 28. Copyright : Futuretext Ltd. London27 Ajit Jaokar MACHINE LEARNING
  29. 29. Copyright : Futuretext Ltd. London28 What is Machine Learning? Mitchell's Machine Learning Tom Mitchell in his book Machine Learning “The field of machine learning is c oncerned with the question of how to construct computer programs that automatically improve with experience.” formally: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Think of it as a design tool where we need to understand: What data to collect for the experience (E) What decisions the software needs to make (T) and How we will evaluate its results (P). A programmers perspective: Machine Learning involves: a) Training of a model from data b) Predicts/ Extrapolates a decision c) Against a performance measure.
  30. 30. Copyright : Futuretext Ltd. London29 Technique Applicability Algorithms Classification Most commonly used technique for predicting a specific outcome such as response / no-response, high / medium / low-value customer, likely to buy / not buy. Logistic Regression —classic statistical technique but now available inside the Oracle Database and supports text and transactional data Naive Bayes —Fast, simple, commonly applicable Support Vector Machine— Next generation, supports text and wide data Decision Tree —Popular, provides human-readable rules Source: Oracle
  31. 31. Copyright : Futuretext Ltd. London30 Regression Technique for predicting a continuous numerical outcome such as customer lifetime value, house value, process yield rates. Multiple Regression — classic statistical technique but now available inside the Oracle Database and supports text and transactional data Support Vector Machine —Next generation, supports text and wide data Attribute Importance Ranks attributes according to strength of relationship with target attribute. Use cases include finding factors most associated with customers who respond to an offer, factors most associated with healthy patients. Minimum Description Length—Considers each attribute as a simple predictive model of the target class Source: Oracle
  32. 32. Copyright : Futuretext Ltd. London31 Anomaly Detection Identifies unusual or suspicious cases based on deviation from the norm. Common examples include health care fraud, expense report fraud, and tax compliance. One-Class Support Vector Machine —Trains on "normal" cases to flag unusual cases Clustering Useful for exploring data and finding natural groupings. Members of a cluster are more like each other than they are like members of a different cluster. Common examples include finding new customer segments, and life sciences discovery. Enhanced K-Means— Supports text mining, hierarchical clustering, distance based Orthogonal Partitioning Clustering—Hierarchical clustering, density based Expectation Maximization— Clustering technique that performs well in mixed data (dense and sparse) data mining problems. Source: Oracle
  33. 33. Copyright : Futuretext Ltd. London32 Association Finds rules associated with frequently co-occuring items, used for market basket analysis, cross-sell, root cause analysis. Useful for product bundling, in- store placement, and defect analysis. Apriori—Industry standard for market basket analysis Feature Selection and Extraction Produces new attributes as linear combination of existing attributes. Applicable for text data, latent semantic analysis, data compression, data decomposition and projection, and pattern recognition. Non-negative Matrix Factorization—Next generation, maps the original data into the new set of attributes Principal Components Analysis (PCA)—creates new fewer composite attributes that respresent all the attributes. Singular Vector Decomposition— established feature extraction method that has a wide range of applications. Source: Oracle
  34. 34. Copyright : Futuretext Ltd. London33 Ajit Jaokar KEY CONCEPTS – DATA SCIENCE AND IOT Deep learning Big Data Complex event Processing Streaming
  35. 35. Copyright : Futuretext Ltd. London34 Ajit Jaokar DEEP LEARNING
  36. 36. Copyright : Futuretext Ltd. London Internet of Things CNN, RNN Data Lake Event Based analysis Rules/ Workflow Edge Processing Engine Rules/ Workflow Alerts Trigger s Actions Cloud / Data LakeEdge Device Event Collector Predictive Alerts Stream Processing System Event Store Analytics Model Build Model HDFS Batch Processing System Validate Event Sequence CNN, RNN Data Lake Event Based analysis CEP CEP CEP
  37. 37. Copyright : Futuretext Ltd. London36 And its coming to mobile phones! .
  38. 38. Copyright : Futuretext Ltd. London37  In a groundbreaking paper published today in Nature, a team of researchers led by DeepMind co-founder Demis Hassabis reported developing a deep neural network that was able to learn to play such games at an expert level. What makes this achievement all the more impressive is that the program was not given any background knowledge about the games. It just had access to the score and the pixels on the screen.  It didn’t know about bats, balls, lasers or any of the other things we humans need to know about in order to play the games.  But by playing lots and lots of games many times over, the computer learnt first how to play, and then how to play well.
  39. 39. Copyright : Futuretext Ltd. London38 Deep Learning and Feature learning Deep Learning can be hence seen as a more complete, hierarchical and a ‘bottom up’ way for feature extraction and without human intervention. Source: ELEG 5040 Advanced Topics on Signal Processing (Introduction to Deep Learning) by Xiaogang Wang
  40. 40. Copyright : Futuretext Ltd. London39
  41. 41. Copyright : Futuretext Ltd. London40 Ajit Jaokar Big Data – Hadoop, Spark
  42. 42. Copyright : Futuretext Ltd. London Internet of Things CNN, RNN Data Lake Event Based analysis Rules/ Workflow Edge Processing Engine Rules/ Workflow Alerts Trigger s Actions Cloud / Data LakeEdge Device Event Collector Predictive Alerts Stream Processing System Event Store Analytics Model Build Model HDFS Batch Processing System Validate Event Sequence CNN, RNN Data Lake Event Based analysis CEP CEP CEP
  43. 43. Copyright : Futuretext Ltd. London42 Diagram courtesy of Mark Grover.
  44. 44. Copyright : Futuretext Ltd. London HDFS Databa seDashbo ards Kafka Flume HDFS ZeroM Q Twitter Spark Streaming Spark streaming
  45. 45. Copyright : Futuretext Ltd. London Optional Storage And Queries Real-time Feeds Stream Processing Application Alerts Actions Memory Disk Source: The 8 Requirements of Real-Time Stream Processing By Michael Stonebraker et al
  46. 46. Copyright : Futuretext Ltd. London Kafka Producers Brokers Consumers Front End Front End Front End Service Hadoop Clusters Security systems Real-time monitorin g Other consumer service Data warehous e
  47. 47. Copyright : Futuretext Ltd. London NoSql HDFSData Sources Stream Processing Architecture based on Apache Spark Adapted from
  48. 48. Copyright : Futuretext Ltd. London47 Ajit Jaokar Complex Event Processing
  49. 49. Copyright : Futuretext Ltd. London Internet of Things CNN, RNN Data Lake Event Based analysis Rules/ Workflow Edge Processing Engine Rules/ Workflow Alerts Trigger s Actions Cloud / Data LakeEdge Device Event Collector Predictive Alerts Stream Processing System Event Store Analytics Model Build Model HDFS Batch Processing System Validate Event Sequence CNN, RNN Data Lake Event Based analysis CEP CEP CEP
  50. 50. Copyright : Futuretext Ltd. London49 For example: • Complex event processing involves combining outputs of multiple sensors and inferring events from readings even when the event is not directly observed by a specific sensor. For Complex event processing, we also need to add statistical models such as likelihood, confidence and probability using techniques like Bayesian networks, neural networks, Dempster-Shafer methods, kalman filters etc (ex care home – image Guardian)
  51. 51. Copyright : Futuretext Ltd. London Quaternions Heading Pitch, roll and yawLinear acceleration Gravity Sensor fusion algorithm Inputs Outputs 3 –axis earth magnetic field 3 –axis linear acceleration 3 –axis angular rate Source: ST microsystems
  52. 52. Copyright : Futuretext Ltd. London51 Ajit Jaokar Methodology for Data Science for IoT
  53. 53. Copyright : Futuretext Ltd. London52 Creating an open methodology for Internet of Things (IoT) Analytics: Data science for Internet of Things January 9, 2016 By ajit Leave a Comment
  54. 54. Copyright : Futuretext Ltd. London53 There is no specific methodology to solve Data Science for IoT (IoT Analytics) problems. This leads to some initial questions: Should there be a distinct methodology to solve Data Science problems for IoT? Are IoT problems for Data Science unique enough to warrant a specific approach? What existing methodologies should we draw upon? On one hand , A Data Science for IoT problem is a typical Data Science problem. On the other hand, there are some unique considerations to IoT – for example in the use of Hardware, High Data volumes, Use of CEP(Complex event processing), impact of verticals(like automotive), Impact of streaming data etc.
  55. 55. Copyright : Futuretext Ltd. London54 Background and inspiration Some initial background: Data mining has well known methodologies such as Crisp DM. Hilary Mason and others have also proposed specific methodologies for Data Science . Kaggle problems have a specific approach to solving them . With techniques like PFA(Portable format for Analytics) provide a way of formalizing and moving Analytics models. All these strategies also apply to IoT. IoT itself has methodologies like Ignite IoT – but these do not cover IoT analytics in detail. A methodology for IoT analytics(Data Science for IoT) should cover the unique aspects of each step in Data Science. For example: It is more than the choice of the model family. The choice of the model family (ANN, SVM, Trees, etc) is only one of the many choices to make – Others include :
  56. 56. Copyright : Futuretext Ltd. London55 a) Choice of the model structure – optimisation methodology (CV, Bootstrap, etc) b) Choice of the model parameter optimisation algorithm (joint gradients vs. conjugate gradients ) c) Preprocessing of the data (centring, reduction, functional reduction, log- transform, etc.) d) How to deal with missing data (case deletion, imputation, etc.) e) How to detect and deal with suspect data (distance-based outlier detection, density-based, etc.) f) How to choose relevant features (filters, wrappers, embedded method ?) g) How to measure prediction performances (mean square error, mean absolute error, misclassification rate, lift, precision/recall, etc.) source Methodology and standards for data analysis with machine learning tools Damien Fran¸cois ∗
  57. 57. Copyright : Futuretext Ltd. London56 The methodology could also cover - Exploratory analysis of data Hypothesis testing (“Given a sample and an apparent effect, what is the probability of seeing such an effect by chance?” ) and other ideas .. Who? Ajit Jaokar – futuretext Jean-Jacques (JJ) Bernard, management & technology consultant Shiva soleimani – student - Isfahan university
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  59. 59. Copyright : Futuretext Ltd. London58 Data Science for Internet of Things – practitioner course – March 2016 Now running in it’s second batch .. Welcome to the world’s first course that helps you to become a Data Scientist for the Internet Of Things ..
  60. 60. Copyright : Futuretext Ltd. London59 Weekly schedule Concepts Week 0 March 15 Orientation, introductions, Personal learning plans, Platform signup Week 1 mar 21 Foundations:An analytics Driven Organization – IoT and Machine Learning - Data Science for IoT – Unique characteristics – Data Science for IoT – why now? Mar 28 Machine Learning concepts Deep Learning concepts Apr 4 An introduction to IoT (Internet of Things) Apr 11 IoT platforms – From sensor to Cloud Apr 18 Concepts of Big Data Part One Apr 25 Concepts of Big Data Part Two May 2 Market drivers for IoT May 9 Choosing a model – what technique to Use? May 16 Use Cases and IoT datasets (these will continue throughout the course) May 23 Time series and NoSQL databases
  61. 61. Copyright : Futuretext Ltd. London60 May 30 Streaming analytics part One June 6 Streaming analytics part two June 13 Deep learning part one June 20 Deep learning part two June 2 7 Machine learning algorithms – part one July 4 Machine learning algorithms – part two July 11 Mathematical foundations – part one July 18 Mathematical foundations – part two July To Dec 31 Project Contact us at to signup
  62. 62. Copyright : Futuretext Ltd. London61 Programming Week 0 Mar 15 Orientation, introductions, Personal learning plans, Platform signup Week 1 mar 21 Mar 28 Apr 4 Intro to R, Installations, Basics of R Apr 11 Apr 18 Data Frames in R & Tabular Data Apr 25 May 2 Data Processing & Data Visualization in R May 9 May 16 Scala basics May 23 May 30 Spark batch processing I
  63. 63. Copyright : Futuretext Ltd. London62 June 6 June 13 Spark Batch Processing II June 20 June 2 7 Spark SQL July 4 July 11 Spark Streaming July 18 July To Dec 31 Projects Contact us at to signup
  64. 64. Copyright : Futuretext Ltd. London63 @ajitjaokar
  65. 65. Copyright : Futuretext Ltd. London64 A Reference Architecture for the Internet of Things Daniel Karzel, Hannelore Marginean, Tuan-Si Tran adapted from defined by IoT-A The IoT interconnects the Things in order to exchange information to fulfill tasks for the users. Ideas of fridges communicating not only with your smart-phone, but with the producer's server farm or an energy power plant will soon become reality. Terminology: • Thing: An object of our everyday life placed in our everyday environment. A thing can be a car, fridge but can also be abstracted to a complete house or city depending on the use case. • Device: A sensor, actuator or tag. Usually the device is part of a thing. The thing processes the devices’ context information and communicates selected information to other things. Furthermore, the thing can pass actions to actuators. • Interoperability and Integration components • Context aware components • Middleware components(load balancing etc) • Security
  66. 66. Copyright : Futuretext Ltd. London65 Anind K. Dey’s context toolkit. The context toolkit was designed on an application level, as it was designed for Geographical Information Systems (GIS). In the IoT we have to extend the context toolkit towards the intercommunication between things. However, the basic idea of goal, context information and resulting actions remains in the IoT world.
  67. 67. Copyright : Futuretext Ltd. London66 In the IoT world we don’t only define the goal on the user level (i.e. by application), but things themselves can work towards certain goals without actively including the user. In the end the devices still serve the user but they act autonomously in the background – which is exactly the idea of ubiquitous computing. Context defines the state of an environment (usually the user’s environment) in a certain place at a certain time. The context model usually distinguishes between context elements and context situation. Context elements define specific context, usually on the device level. A context element can be for example a temperature value at a certain time and location.
  68. 68. Copyright : Futuretext Ltd. London67
  69. 69. Copyright : Futuretext Ltd. London68 Location and time are context elements themselves, but they play a special role as they are needed to locate sensor values in space and time. Without knowing where and when a temperature was measured the temperature does not help much for making conclusions. The context situation is an aggregation of context elements. The context situation is thus a view on the environment in a certain location at a certain time. Similarly to the context model you can also define an action model that defines what things can trigger (e.g. open a window, take a photo). Actions can only be triggered with the combination of context information (e.g. a context situation) and defined goals. Goals are usually depicted as rules of a rule engine (e.g. IF temperature > 25* THEN open window).
  70. 70. Copyright : Futuretext Ltd. London69
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  72. 72. Copyright : Futuretext Ltd. London71 Consists of 6 layers. Besides these layers there are two “cross-section- layers” that affect all other layers, namely “Security” and “Management”.
  73. 73. Copyright : Futuretext Ltd. London72 The device integration layer connects all the different device types and consumes device measurements as well as it communicates actions (on device level). This layer can be seen as a translator that speaks many languages. The output of the sensors and tags depends on the protocol they implement. The input of the actuators is also defined by the protocol they implement.
  74. 74. Copyright : Futuretext Ltd. London73 The device management is in charge of taking device registrations and sensor-measurements from the device integration layer. Furthermore it communicates status changes for actuators down to the device integration layer. The device integration layer then just validates that the status change (i.e. the action) is conform with the actuator and then translates the status change to the actuator.
  75. 75. Copyright : Futuretext Ltd. London74 The data management can be seen as a central database that holds all data of a “thing”, but this is only one possible implementation. For larger things within the system (e.g. a device life-cycle monitoring system collecting data from other things) data management might be a data warehouse or even a complete data farm. The implementation of the data management layer thus strongly depends on the use-case for the specific thing.
  76. 76. Copyright : Futuretext Ltd. London75 The context management defines the central business logic and is responsible for six tasks: 1. Define the goals of the thing. 2. Consume the context situation(s) of other things 3. Produce the (own) context situation of the thing. 4. Evaluate the (own) context situation towards the goal. 5. Trigger actions that help to fulfill the goal according to the evaluated rules. 6. Publish context situations for other things.
  77. 77. Copyright : Futuretext Ltd. London76 According to these tasks we can divide the context management into eight components as shown below.
  78. 78. Copyright : Futuretext Ltd. London77 Rule Engine & Artificial Intelligence (AI): Define and manage all of the rules necessary for context evaluation. This includes the goal (which is basically as set of rules) as well as rules for creating the context situation and actions. Context Situation Integration Module: Listens to context situations of other things and integrates the incoming context situations. Action Integration Module: Incoming actions of other things are evaluated and passed on to the device management layer by this component. Rules have to be considered, that define in which situations an action received from another thing can be passed on for triggering an actuator. Context Situation Creator Module: Collects data from the system and builds the context situation(s). This can also be driven by rules. Action Creator Module: Similar to the context situation creator module, action objects have to be created once triggered during rule evaluation.
  79. 79. Copyright : Futuretext Ltd. London78 Context Situation Publisher Module: Provide context situations to the thing integration layer. According to the sophistication level of the implementation the context situation publisher can provide a set of context situations for different things that are subscribed or one context situation for everybody. The context situation publisher module has to take care of data permission levels towards other things. Only trusted other things should receive selected context information. Furthermore this module has to take care of defining the context situation schemas that are communicated to other things that want to subscribe. The schema is used to evaluate whether a thing is capable of communicating with another thing. Action Publisher Module: Similar to the context situation publisher module this module is responsible to communicate actions to the thing integration layer to be communicated to other things. Additionally the action schema(s) are managed by this component.
  80. 80. Copyright : Futuretext Ltd. London79 Context Evaluation Module: Evaluates the rules using the (current) context situation and triggers actions that are communicated down to the devices or to the action creator module. The action creator module in turn passes the created actions to the action publisher that communicates the actions to other things. One way to simply evaluate rules is to build decision trees from the rules defined by the rule engine. The concrete architecture and complexity of offered functionality strongly depends on the use case for the thing under development. Especially the rule engine & artificial intelligence component might not have to be very sophisticated for less intelligent things (e.g. a fridge). For things that collect context information from other systems these components will, however, be very sophisticated. Higher sophistication can be for example data science and data mining techniques.
  81. 81. Copyright : Futuretext Ltd. London80 The thing integration layer is responsible for finding other things and communicating with them. Once two things found each other they have to undergo a registration mechanism. The thing integration layer has to evaluate if the communication with the thing to be partnered with is possible. For this purpose the context situation and/or action schemata have to be compared. These are provided by the context management layer. If the schema-match is evaluated positively, the thing can notify the other thing upon new context situation or action creation. The context situations and actions to be communicated to other things are provided by the context management layer. The thing registration can be done in a central component or by the thing itself (e.g. auto-discovery network scan).
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  83. 83. Copyright : Futuretext Ltd. London82 The application integration layer connects the user to the thing. Applications that are (directly) on top of the architecture are located here. The application integration can be seen as a service layer, or even as a simple UI on top of the stack. The concrete implementation of the layer depends on the use case.
  84. 84. Copyright : Futuretext Ltd. London83 @ajitjaokar