1. Copyright : Futuretext Ltd. London 0 International Workshop on Big Data Applications and Principles Madrid By Ajit Jaokar Sep 2014 @ajitjaokar ajit.jaokar@futuretext.com

2. Copyright : Futuretext Ltd. London 1 Ajit Jaokar - www.opengardensblog.futuretext.com 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

3. Copyright : Futuretext Ltd. London 2 Ajit Jaokar - Machine Learning for IoT and Telecoms futuretext applies machine learning techniques to complex problems in the IoT (Internet of Things) and Telecoms domains. We aim to provide a distinct competitive advantage to our customers through application of machine learning techniques Philosophy: Think of NEST. NEST has no interface. It’s interface is based on ‘machine learning’ i.e. it learns and becomes better with use. This will be common with ALL products and will determine the competitive advantage of companies. Its a winner takes all game! Every product will have a ‘self learning’ interface/component and the product which learns best will win!

4. Copyright : Futuretext Ltd. London 3 Ajit Jaokar - IoT Machine Learning IoT and Machine Learning Case studies and applications

5. Copyright : Futuretext Ltd. London 4 Ajit Jaokar - www.futuretext.com @AjitJaokar ajit.jaokar@futuretext.com

6. Copyright : Futuretext Ltd. London 5 Image source: Guardian

7. Copyright : Futuretext Ltd. London 6

8. 7 Ajit Jaokar IOT - THE INDUSTRY- STATE OF PLAY

9. Copyright : Futuretext Ltd. London 8 Ajit Jaokar State of play - 2014 Our industry is exciting – but mature - Now a two horse race for devices with Samsung around 70% of Android Spectrum allocations and ‘G’ cycles are predictable - 5G around 2020 50 billion connected devices by 2020 ITU world Radio communications Conference, November 2015. IOT has taken off.. not because of EU and Corp efforts – but because of Mobile, kickstarter, health apps and iBeacon and ofcourse NEST(acquired by Google)

10. Copyright : Futuretext Ltd. London 9 Ajit Jaokar Stage One: Early innovation 1999 - 2007 Regulatory innovation – net neutrality - Device innovation (Nokia 7110 and Ericsson t68i) - Operator innovation (pricing, bundling, Enterprise) - Connectivity innovation (SMS, BBM) Content innovation (ringtones, games, EMS, MMS) - Ecosystem innovation (iPhone) Stage two: Ecosystem innovation - iPhone and Android (2007 – 2010) Social innovation - Platform innovation - Community innovation - Long tail innovation - Application innovation

11. Copyright : Futuretext Ltd. London 10 Ajit Jaokar Phase three: Market consolidation – 2010 - 2013 And then there were two... Platform innovation and consolidation Security innovation App innovation Phase four – three dimensions – 2014.. Horizontal apps (iPhone and Android) Vertical (across the stack) – hardware, security, Data Network – 5G and pricing

12. Copyright : Futuretext Ltd. London 11 Ajit Jaokar Many of the consumer IOT cases will happen with iBeacon in the next two years

13. Copyright : Futuretext Ltd. London 12 Ajit Jaokar And 5G will provide the WAN connectivity 5G - Source – Ericsson

14. Copyright : Futuretext Ltd. London 13 Ajit Jaokar Samsung Gear Fit named “Best Mobile Device” of Mobile World Congress Notification or Quantification? – Displays (LED, e-paper, Mirasol, OLED and LCD) - Touchscreen or hardware controls? - Battery life and charging

15. Copyright : Futuretext Ltd. London 14 Ajit Jaokar Hotspot 2.0

16. Copyright : Futuretext Ltd. London 15 Ajit Jaokar Three parallel ecosystems IOT is connecting things to the Internet – which is not the same as connecting things to the cellular network! The difference is money.. and customers realise it IOT local/personal (iBeacon, Kickstarter, Health apps) M2M – Machine to Machine IOT – pervasive(5G, Hotspot 2.0) Perspectives 2014 – 2015(radio conf) – 2020(5G, 2020) 2014 – iBeacon (motivate retailers to open WiFi) Hotspot 2.0 – connect cellular and wifi worlds Default wifi and local world? Operator world – (Big)Data, Corporate, pervasive apps – really happen beyond 2020 So 5G will be timed well. The ecosystems will develop and they will be connected by 5G

17. Copyright : Futuretext Ltd. London 16 Ajit Jaokar IOT – INTERNET OF THINGS

18. Copyright : Futuretext Ltd. London 17 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 For example – the chair may have a pressure sensor indicating that it is occupied Now, if it is able to know who is sitting – it could co-relate more data by connecting to the person’s profile If it is in a cafe, whole new data sets can be co-related (about the venue, about who else is there etc) Thus, IOT is all about Data.. IoT != M2M (M2M is a subset of IoT)

19. Copyright : Futuretext Ltd. London 18 Sensors lead to a LOT of Data (relative to mobile).. (source David wood blog) By 2020, we are expected to have 50 billion connected devices To put in context: The first commercial citywide cellular network was launched in Japan by NTT in 1979 The milestone of 1 billion mobile phone connections was reached in 2002 The 2 billion mobile phone connections milestone was reached in 2005 The 3 billion mobile phone connections milestone was reached in 2007 The 4 billion mobile phone connections milestone was reached in February 2009. Gartner: IoT will unearth more than $1.9 trillion in revenue before 2020; Cisco thinks there will be upwards of 50 billion connected devices by the same date; IDC estimates technology and services revenue will grow worldwide to $7.3 trillion by 2017 (up from $4.8 trillion in 2012).$1.9 trillion50 billion connected devices$7.3 trillion by 2017$4.8 trillion in 2012

20. Copyright : Futuretext Ltd. London 19 So, 50 billion by 2020 is a large number Smart cities can be seen as an application domain of IOT In 2008, for the first time in history, more than half of the world’s population will be living in towns and cities. By 2030 this number will swell to almost 5 billion, with urban growth concentrated in Africa and Asia with many mega-cities(10 million + inhabitants). By 2050, 70% of humanity will live in cities. That’s a profound change and will lead to a different management approach than what is possible today Also, economic wealth of a nation could be seen as – Energy + Entrepreneurship + Connectivity (sensor level + network level + application level) Hence, if IOT is seen as a part of a network, then it is a core component of GDP.

21. Copyright : Futuretext Ltd. London 20 Ajit Jaokar Machine Learning

22. Copyright : Futuretext Ltd. London 21 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.

23. Copyright : Futuretext Ltd. London 22 What Problems Can Machine Learning Address? (source Jason Brownlee) ● Spam Detection: ● Credit Card Fraud Detection Digit Recognition: ● Speech Understanding: ● Face Detection: Product Recommendation: ● Medical Diagnosis: ● Stock Trading: Customer Segmentation Shape Detection.

24. Copyright : Futuretext Ltd. London 23 Types of Problems Classification: Data is labelled meaning it is assigned a class, for example spam/non­spam or fraud/non­fraud. The decision being modelled is to assign labels to new unlabelled pieces of data. This can be thought of as a discrimination problem, modelling the differences or similarities between groups. Regression: Data is labelled with a real value rather than a label. Examples that are easy to understand are time series data like the price of a stock over time. The decision being modelled is the relationships between inputs and outputs. Clustering: Data is not labelled, but can be divided into groups based on similarity and other measures of natural structure in the data. An example from the above list would be organising pictures by faces without names, where the human user has to assign names to groups, like iPhoto on the Mac. ●Rule Extraction: Data is used as the basis for the extraction of propositional rules (antecedent/consequent or if­then). Often necessary to work backwards from a Problem to the algorithm and then work with Data. Hence, you need a depth of domain experience and also algorithm experience

25. Copyright : Futuretext Ltd. London 24 What Algorithms Does Machine Learning Provide? Regression Instance-based Methods Decision Tree Learning Bayesian Kernel Methods Clustering methods Association Rule Learning Artificial Neural Networks Deep Learning Dimensionality Reduction Ensemble Methods

26. Copyright : Futuretext Ltd. London 25 An Algorithmic Perspective Marsland adopts the Mitchell definition of Machine Learning in his book Mach ine Learning: An Algorithmic Perspective. “One of the most interesting features of machine learning is that it lies on the boundary of several different academic disciplines, principally computer science, statistics, mathematics, and engineering (multidisciplinary). …machine learning is usually studied as part of artificial intelligence, which puts it firmly into computer science …understanding why these algorithms work requires a certain amount of statistical and mathematical sophistication that is often missing from computer science undergraduates.”

27. Copyright : Futuretext Ltd. London 26 Definition of Machine Learning A one­sentence definition is: “Machine Learning is the training of a model from data that generalizes a decision against a performance measure.” 1) Training a model suggests training examples. 2) A model suggests state acquired through experience. 3) Generalizes a decision suggests the capability to make a decision based on inputs and anticipating unseen inputs in the future for which a decision will be required. 4)Finally, against a performance measure suggests a targeted need and directed quality to the model being prepared.

28. Copyright : Futuretext Ltd. London 27 Key concepts Data Instance: A single row of data is called an instance. It is an observation from the domain. Feature: A single column of data is called a feature. It is an component of an observation an d is also called an attribute of a data instance. Some features may be inputs to a model (the predictors) and others may be outputs or the features to be predicted. Data Type: Features have a data type. They may be real or integer valued or may have a categorical or ordinal value. You can have strings, dates, times, and more complex types, but typically they are reduced to real or categorical values w hen working with traditional machine learning methods. Datasets: A collection of instances is a dataset and when working with machine learning methods we typically need a few datasets for different purposes. Training Dataset: A dataset that we feed into our machine learning algorithm to train our model. Testing Dataset: A dataset that we use to validate the accurac y of our model but is not used to train the model. It may be called the validation dataset.

29. Copyright : Futuretext Ltd. London 28 Learning Machine learning is indeed about automated learning with algorithms. In this section we will consider a few high­level concepts about learning. Induction: Machine learning algorithms learn through a process called induction or inductive learning. Induction is a reasoning process that makes generalizations (a model) from specific information (training data). Generalization: Generalization is required because the model that is prepared by a machine learning algorithm needs to make predictions or decisions based on specific data instances that were not seen during training. Over­ Learning: When a model learns the training data too closely and does not generalize, this i s called over­ learning.result is poor performance on data other than the training dataset. This is also call ed over­fitting. Under­ Learning: When a model has not learned enough structure from the database because the learning process was terminated early, this is called under­ learning.The result is good generalization but poor performance on all data, including the tra ining dataset. This is also called under­fitting.

30. Copyright : Futuretext Ltd. London 29 Online Learning: Online learning is when a method is updated with data instances from the domain as they become available. Online learning requires methods that are robust to noisy data but can produce models that are more in tune with the current state of the domain Offline Learning: Offline learning is when a method is created on pre­prepared data and is then used operationally on unobserved data. The training process can be controlled and can tuned carefully because the scope of the training data is known. The model is not updated after it has been prepared and performance may de crease if the domain changes. Supervised Learning: This is a learning process for generalizing on problems where a prediction is required.A "teaching process" compares predictions by t he model to known answersand makes corrections in the model. Unsupervised Learning: This is a learning process for generalizing the structure in the data where no prediction is required. Natural structures are identified and exploited for relating instances to each other.

31. Copyright : Futuretext Ltd. London 30 Source: Oracle

32. Copyright : Futuretext Ltd. London 31 Source: Oracle

33. Copyright : Futuretext Ltd. London 32 Source: Oracle

34. Copyright : Futuretext Ltd. London 33 Source: Oracle

35. Copyright : Futuretext Ltd. London 34 Recap machine learning with IoT Supervised Learning In supervised learning, a labeled training set (i.e., predefined inputs and known outputs) is used to build the system model. This model is used to represent the learned relation between the input, output and system parameters K-nearest neighbor (k-NN): This supervised learning algorithm classifies a data sample (called a query point) based on the labels (i.e., the output values) of the near data samples. For example, missing readings of a sensor node can be predicted using the average measurements of neighboring sensors within specific diameter limits. There are several functions to determine the nearest set of nodes. A simple method is to use the Euclidean distance between different sensors Decision tree (DT): It is a classification method for predicting labels of data by iterating the input data through a learning tree During this process, the feature properties are compared relative to decision conditions to reach a specific category. For example, DT provides a simple, but efficient method to identify link reliability in WSNs by identifying a few critical features such as loss rate, corruption rate, mean time to failure (MTTF) and mean time to restore (MTTR).

36. Copyright : Futuretext Ltd. London 35 Neural networks (NNs): This learning algorithm could be constructed by cascading chains of decision units (e.g., perceptrons or radial basis functions) used to recognize non-linear and complex functions. In WSNs, using neural networks in distributed manners is still not so pervasive due to the high computational requirements for learning the network weights, as well as the high management overhead. However, in centralized solutions, neural networks can learn multiple outputs and decision boundaries at once which makes them suitable for solving several network challenges using the same model. Support vector machines (SVMs): It is a machine learning algorithm that learns to classify data points using labeled training samples. For example, one approach for detecting malicious behavior of a node is by using SVM to investigate temporal and spatial correlations of data. To illustrate, given WSN's observations as points in the feature space, SVM divides the space into parts. These parts are separated by as wide as possible margins (i.e., separation gaps), and new reading will be classified based on which side of the gaps they fall on as shown

37. Copyright : Futuretext Ltd. London 36 Bayesian statistics: Unlike most machine learning algorithms, Bayesian inference requires a relatively small number of training samples One application of Bayesian inference in WSNs is assessing event consistency (θ) using incomplete data sets (D) by investigating prior knowledge about the environment. Unsupervised Learning Unsupervised learners are not provided with labels (i.e., there is no output vector). Basically, the goal of an unsupervised learning algorithm is to classify the sample set into different groups by investigating the similarity between them. this theme of learning algorithms is widely used in node clustering and data aggregation problems

38. Copyright : Futuretext Ltd. London 37 K-means clustering: The k-means algorithm is used to recognize data into different classes (known as clusters). This unsupervised learning algorithm is widely used in sensor node clustering problem due to its linear complexity and simple implementation. The k-means steps to resolve such node clustering problem are (a) randomly choose k nodes to be the initial centroids for different clusters; (b) label each node with the closest centroid using a distance function; (c) re-compute the centroids using the current node memberships and (d) stop if the convergence condition is valid (e.g., a predefined threshold for the sum of distances between nodes and their perspective centroids), otherwise go back to step (b).

39. Copyright : Futuretext Ltd. London 38 Principal component analysis (PCA): It is a multivariate method for data compression and dimensionality reduction that aims to extract important information from data and present it as a set of new orthogonal variables called principal components. For example, PCA reduces the amount of transmitted data among sensor nodes by finding a small set of uncorrelated linear combinations of original readings. Furthermore, the PCA method simplifies the problem solving by considering only few conditions in very large variable problems (i.e., tuning big data into tiny data representation) Reinforcement Learning : Reinforcement learning enables an agent (e.g., a sensor node) to learn by interacting with its environment. The agent will learn to take the best actions that maximize its long-term rewards by using its own experience. The most well-known reinforcement learning technique is Q-learning. an agent regularly updates its achieved rewards based on the taken action at a given state.

40. Copyright : Futuretext Ltd. London 39 Ajit Jaokar IoT and Machine Learning

41. Copyright : Futuretext Ltd. London Basic idea of machine learning is to build a mathematical model based on training data(learning stage) – predict results for new data(prediction stage) and tweak the model based on new conditions What type of model? Predicitive, Classification, Clustering, Decision Oriented, Associative IoT and Machine Learning  On one hand - IoT creates a lot of contextual data which complements existing processes  On the other hand – the Sheer scale of IoT calls for unique solutions Types of problems: Apply existing Machine Learning algorithms to IoT data Use IoT data to complement existing processes Use the scale of IoT data to gain new insights Consider some unique characteristics of IoT data (ex streaming) 40

42. Copyright : Futuretext Ltd. London 41 IoT : from traditional computing to.. Gone from making Smart things smarter(traditional computing) to a)Making dumb things smarter.. and b)living things more robust 3 Domains: Consumer, Enterprise, Public infrastructure 1) Consumer – bio sensors(real time tracking), Quantified self – focussing on benefits 2) Enterprise – Complex machinery (preventative maintenance), asset efficiency – reducing assets, increasing efficiency of existing assets. More from transactions to relationships(real time context awareness). 3) Public infrastructure(Dynamically adjust traffic lights). Dis-economies of scale(bad things also scale in cities) – Thanks John Hagel III

43. Copyright : Futuretext Ltd. London 42 Three key areas: 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 Four examples of differences: Sensor fusion - Deep Learning - Real time - Streaming

44. Copyright : Futuretext Ltd. London Sensor fusion  Sensor fusion is the combining of sensory data or data derived from sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The data sources for a fusion process are not specified to originate from identical sensors. Sensor fusion is a term that covers a number of methods and algorithms, including: Central Limit Theorem, Kalman filter, Bayesian networks, Dempster-Shafer Example: http://www.camgian.com/ http://www.egburt.com/http://www.camgian.com/http://www.egburt.com/ 43

45. Copyright : Futuretext Ltd. London Deep learning  Google's acquisition of DeepMind Technologies DeepMind Technologies  In 2011, Stanford computer science professor Andrew Ng founded Google’s Google Brain project, which created a neural network trained with deep learning algorithms, which famously proved capable of recognizing high level concepts, such as cats, after watching just YouTube videos--and without ever having been told what a “cat” is.recognizing high level concepts, such as cats  A smart-object recognition algorithm that doesn’t need humans http://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need- humans A feature construction method for general object recognition (Kirt Lillywhite, Dah-JyeLee n, BeauTippetts, JamesArchibald) http://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need- humans 44

46. Copyright : Futuretext Ltd. London 45 Real time: Beyond ‘Hadoop’ (non hadoopable) the BDAS stack BDAS Berkeley data analytics stack Spark – an open source, in-memory, cluster computing framework. Integrated with Hadoop(can work with files stored in HDFS) Written in Scala

47. Copyright : Futuretext Ltd. London 46 Real time (Stream processing)

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49. Copyright : Futuretext Ltd. London 48 Spark – an open source, in-memory, cluster computing framework. Integrated with Hadoop(can work with files stored in HDFS) Written in Scala Spark comes with tools: Interactive query analysis (Shark), Graph processing and analysis (Bagel) and Real-time analysis (Spark Streaming). RDDs(Resilient Distributed Data sets): are the fundamental data objects used in Spark..RDDs are distributed objects that can be cached in-memory, across a cluster of compute nodes. Scalesto 100sof nodes. Canachievesecondscale latencies

50. Copyright : Futuretext Ltd. London 49 Source: Tathagata Das (TD) UC Berkeley

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52. Copyright : Futuretext Ltd. London 51

53. Copyright : Futuretext Ltd. London 52 Survey paper: Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications Mohammad Abu Alsheikh et al School of Computer Engineering, Nanyang Technological University, Singapore 639798 Event detection and Query processing Monitoring can be classified as: event-driven, continuous, or query-driven Fundamentally, machine learning offers solutions to restrict query areas and assess event validity for efficient event detection and query processing mechanisms. Advantages: Optimize limited resources ex storage and processing Assess accuracy using simple classifiers. Narrow down the search region (avoid flooding the network) More than a threshold detection (simplest case) Event recognition through Bayesian algorithms: Krishnamachari and Iyengar Use of WSNs for detecting environmental phenomenon in a distributed manner. Readings will be considered as faulty if their values exceed a specific threshold. This study employs decentralized Bayesian learning that detects up to 95 percent of the faults, and will result in recognizing the event region.

54. Copyright : Futuretext Ltd. London 53 Zappi et al A real-time approach for activity recognition using WSNs that accurately detects body gesture and motion. Initially, the nodes, that are spread throughout the body, detect the organ motion using an accelerometer sensor with three axis measurements (positive, negative and null), where these measurements are used by a hidden Markov model (HMM) to predict the activity at each sensor. Sensor activation and selection rely on the sensor's potential contributions in classifier accuracy (i.e., select the sensors that provide the most informative description of the gesture). To generate a final gesture decision, a naive Bayes classifier is used to combine the independent node predictions so as to maximize the posterior probability of the Bayes theorem.

55. Copyright : Futuretext Ltd. London 54

56. Copyright : Futuretext Ltd. London 55 Forest fire detection through neural network: WSNs were actively used in fire detection and rescue systems Yu et al. presented a real-time forest fire detection scheme based on a neural network method. Data processing will be distributed to cluster heads, and only important information will be aggregated to a final decision maker. Although the idea is creative and beneficial to the environment, the classification task and system core are hardly interpretable when introducing such systems to decision makers.

57. Copyright : Futuretext Ltd. London 56 Query processing through k-nearest neighbors: K-nearest neighbor query is considered as a highly effective query processing technique in WSNs. Winter et al. developed an in-network query processing solution using the k-nearest neighbor algorithm, namely the “K-NN Boundary Tr ee” (KBT) algorithm. Each node that is aware of its location will determine its k-NN search region whenever a query is received from the application manager. Jayaraman et al. extended the query processing design. “3D-KNN” is a query processing scheme for WSNs that adopts the k-nearest neighbor algorithm. This approach restricts the query region to bound at least k- nearest nodes deployed within a 3D space. In addition, signal-to-noise ratio (SNR) and distance measurements are used to refine the k-nearest neighbor. The primary concerns of such k-NN-based algorithms for query processing are the requirement of large memory footprint to store every collected sample and the high processing delay in large scale sensor networks.

58. Copyright : Futuretext Ltd. London 57 Distributed event detection for disaster management using decision tree: Bahrepour et al. developed decision tree-based event detection and recognition for sensor network disaster prevention systems. The main application of this decentralized mechanism is the fire detection in residential areas. The final event detection decision is made by using a simple vote from the highest reputation nodes.

59. Copyright : Futuretext Ltd. London 58 Query optimization using principal component analysis (PCA): Malik et al. optimized traditional query processing in WSNs using data attributes and PCA, thus reducing the overhead of such a process. PCA has been used to dynamically detect important attributes (i.e., dominant principal components) among the whole correlated data set. proposed algorithm in four fundamental steps. SQL request, which contains the human intelligible attributes, is sent to the database management and optimization system. Here, the original query is optimized where the high-variance components are extracted from historical data using the PCA algorithm The optimized query is diffused to the wireless sensor network to extract the sensory data. Later, the original attributes (i.e., human intelligible attributes) can be extracted from the optimized attributes by reversing the process of PCA.

60. Copyright : Futuretext Ltd. London 59 Supposedly, the algorithm guarantees 25 percent improvement in energy saving of the network nodes while achieving 93 percent of accuracy rates. However, this enhancement is at the cost of accuracy of the collected data (as some of the data components will be ignored). Therefore, this solution may not be ideal for the applications with high accuracy and precision requirements.

61. Copyright : Futuretext Ltd. London 60 Localization and Objects Targeting Localization is the process of determining the geographic coordinates of network's nodes and components. Position awareness of sensor nodes is an important capability, since most sensor network operations are typically based on the location In most large scale systems, it is financially infeasible to use global positioning system (GPS) hardware in each node for this purpose. Moreover, GPS service may not be available in the observed environment (e.g., indoor). Relative location measurement is sufficient for certain uses. However, by using the absolute locations for a small group of nodes, relative locations can be transformed into absolute ones

62. Copyright : Futuretext Ltd. London 61 Sensor nodes may encounter changes in their location after deployment (e.g., due to movement). be summarized as follows: The benefits of using machine learning algorithms in sensor node localization process are Converting the relative locations of nodes to absolute ones using few anchor points. This will eliminate the need for range measurement hardware to obtain distance estimations. In surveillance and object targeting systems, machine learning can be used to divide the monitored sites into a number of clusters, where each cluster represents specific location indicator.

63. Copyright : Futuretext Ltd. London 62 Bayesian node localization: Morelande et al. [21] used a Bayesian algorithm to develop a localization scheme for WSNs using only few anchor points. This study focuses on the enhancement of progressive correction, which is a method for predicting samples from likelihoods to get closer to the posterior likelihood. The idea of using the Bayesian algorithm for localization is appealing as it can handle incomplete data sets by investigating prior knowledge and probabilities. [21],

64. Copyright : Futuretext Ltd. London 63 Robust location-aware activity recognition: Lu and Fu addressed the problem of sensor and activity localization in smart homes without direct sensing. The activities of interest include using the phone, listening to the music, using the refrigerator, studying, etc. The proposed framework, named “Ambient Intelligence Compliant Object” (AICO), uses multiple naive Bayes classifiers to determine the resident' s current location and evaluate the reliability of the system by detecting any malfunctioned sensors. The designers must predefine a set of supported activities in advance. There are also unsupervised machine learning algorithms for automatic feature extraction such as the deep learning methods and the non-negative matrix factorization algorithm where activities are determined without prior training

65. Copyright : Futuretext Ltd. London 64 Localization based on neural network: Shareef et al compared three localization schemes that are based on different types of neural networks. In particular, this study considers WSN localization using multi-layer perceptron (MLP), radial basis function (RBF), and recurrent neural networks (RNN). In summary, the RBF neural network results in the minimum error at the cost of high resource requirements. In contrast, MLP consumes the minimum computational and memory resources.

66. Copyright : Futuretext Ltd. London 65 Security and Anomaly Intrusion Detection Save node's energy and significantly expand WSN lifetime by preventing the transmission of the outlier, misleading data. Enhance network reliability by eliminating faulty and malicious readings. In the same way, avoiding the discovery of unexpected knowledge that will be converted to important, and often critical actions. Online learning and prevention (without human intervention) of malicious attacks and vulnerabilities.

67. Copyright : Futuretext Ltd. London 66 Outlier detection using Bayesian belief network: Janakiram et al. used Bayesian belief networks (BBNs) to develop an outlier detection scheme. Given that the majority of node's neighbors will have similar readings (i.e., temporal and spatial correlations), it is reasonable to use this phenomenon to build conditional dependencies among nodes' readings. BBNs infer the conditional relationships among the observations to discover any potential outliers in the collected data. Furthermore, this method can be used to evaluate missing values. Outlier detection using k-nearest neighbors: Branch et al. developed an in-network outlier detection method in WSNs using k- nearest neighbors. Moreover, any missing nodes' readings will be replaced by the average value of the k-nearest nodes. However, such non-parametric, k-NN-based algorithm requires large memory to store every collected readings from the monitored environment.

68. Copyright : Futuretext Ltd. London 67 Quality of Service, Data Integrity and Fault Detection advantages: Different machine learning classifiers are used to recognize different types of streams, thus eliminating the need for flow-aware management techniques. The requirements for QoS guarantee, data integrity and fault detection depend on the network service and application. Machine learning methods are able to handle much of this while ensuring efficient resource utilization, mainly bandwidth and power utilization.

69. Copyright : Futuretext Ltd. London 68 QoS estimation using neural network: Snow et al. introduced a method to estimate a sensor network dependability metric using a neural network method. Dependability is a metric that represents availability, reliability, maintainability, and survivability of a sensor network. Several attributes are used to estimate such a metric including mean time between failure (MTBF) and mean time to repair (MTTR). Moustapha and Selmic introduced a dynamic fault detection model for WSNs. This model captures the nodes' dynamic behavior and their effects on other nodes. In addition, neural network learning, which is trained using back-propagation method, was used for node identification and fault detection. This study results in an effective nonlinear sensor model that suits applications with fault detection requirements.

70. Copyright : Futuretext Ltd. London 69 Air quality monitoring using neural networks: Postolache et al. proposed a neural networks-based method for measuring air pollution levels using inexpensive gas sensor nodes, while eliminating the effects of temperature and humidity on sensor readings. This solution detects the air quality and gas concentration using neural networks implemented using JavaScript (JS). As a result, the solution is able to distribute processing between web server and end user computers (i.e., a combination of client and server side scripts). Intelligent lighting control using neural networks: Gao et al. introduced a new standard for lighting control in smart building using the neural network algorithm. A radial basis function (RBF) neural network is used to extract a new mathematical expression, called “Illuminance Matrix” (I- matrix), to measure the degree of illuminance in the lighted area. Fundamentally, in the field of lighting control, converting the collected data from the photosensors to a form that is suitable for digital signal processing is a crucial issue and can highly affect the performance of the developed system. The article shows that using the I-matrix scheme can achieve about 60% more accuracy compared to the standard methods.

71. Copyright : Futuretext Ltd. London 70 Ajit Jaokar - www.futuretext.com @AjitJaokar ajit.jaokar@futuretext.com