Relationship Classification of Object to Object communications in - PowerPoint PPT Presentation

Relationship Classification of Object to Object communications in the Internet of Things using Reality Mining 1 D R P A T D O O D Y , D I R E C T O R O F T H E C E N T R E F O R I N N O V A T I O N I N D I S T R I B U T E D S Y S T E M S ( C I D S ) I N S T I T U T E O F T E C H N O L O G Y T R A L E E M R A N D R E W S H I E L D S I R C S E T F U N D E D R E S E A R C H E R C E N T R E F O R I N N O V A T I O N I N D I S T R I B U T E D S Y S T E M S I N S T I T U T E O F T E C H N O L O G Y T R A L E E

Overview 2  Reality Mining?  Applications  Our Projects  Reality mining Applied to IoT  Conclusions

Reality Mining 3  “…The collection and analysis of machine -sensed environmental data pertaining to human social behaviour …”  .. Extracting information from real world sensor data  With the goal of identifying predictable patterns of behaviour.  It was declared to be one of the "10 technologies most likely to change the way we live" by Technology Review Magazine.

Reality Mining 4

Enabled Applications 5 Environmental Monitoring - Noisetube

Real-time Traffic Monitoring 6

Mobile Millennium, UC Berkeley 7

Citysense 8  Shows the overall activity level of the city,  Highlights top activity hotspots in real-time.  Then it links to Yelp and Google to show what venues are operating at those locations.

Market Players 9

Clustering 10  A common property of human social networks are cliques, circles of friends or acquaintances  This inherent tendency to cluster is quantified by the clustering coefficient [Watts and Strogatz (1998)].  Nodes that are clustered together can easily communicate with each other.  Previous research in this area (Ghiasi, et al. 2002) has studied the theoretical aspects of this problem  Applications to energy optimisation.

Small-world networks 11  Objects may only use knowledge of their own acquaintances, to collectively construct paths to the target.  “six degrees of separation” found by the social psychologist Stanley Milgram  This raises a fundamental question  Why should this type of decentralised routing so effective?

Algorithm Considerations 12  Algorithms must take into consideration the characteristics of networks  Energy,  Computation constraints,  Network dynamics, and faults.  K-Nearest Neighbor Algorithm  ART1  Weighted Regression  Case-based reasoning

Clustering - Voronoi Diagram 13  Decision surface formed by the training examples

Regression - Bayesian MLP 14  Several techniques can be used in movement predictions  Artificial Neural Networks,  Bayesian Belief Networks  Hidden Markov Chains  Dynamic Belief Networks  Each technique has its advantages and disadvantages.  Typically use a hybrid model

Degree Distribution 15  Nodes in a network typically do not all have the same number of links, or degree.  For a large number of networks  The World Wide Web [Albert et al. (1999)],  The internet [Faloutsos et al. (1999)]  metabolic networks [Jeong et al. (2000)],  The work listed above assumes a static network topology  Complex IoT networks will continuously changing over time.

CIDS Research 16  Telecommunication Caching  Reality Mining applied to mobile networks  Classifying user groups  Predicting network usage patterns  Using Neural Network and other techniques

Reality Mining in mobile networks 17

Reality Mining in transport networks 18  It is possible to infer an individual’s  Daily commute to work  Amount of time spent at work, at home and traveling  Allowing individuals to make better traveling decisions.  Provides information which will be used to proactively manage the transportation network.  Several clustering algorithms base on artificial intelligence and statistical analysis will need to be considered and evaluated  Adaptive Resonance Theory,  Eigenbehaviours

Enabling technologies 19  The widespread adoption of the Internet of Things will take time  First: in order to connect everyday objects item identification is crucial.  Radio-frequency identification (RFID) offers this functionality.  Second: the ability to detect changes in the physical status of things, using sensor technologies.  Embedded intelligence in the things themselves can further enhance the power of the network  Third: advances in miniaturisation and nanotechnology mean that smaller and smaller things will have the ability to interact and connect.  A combination of all of these developments will create an Internet of Things that connects the world’s objects in both a sensory and an intelligent manner.

Reality mining Applied to IoT 20  Data mining as applied to “business intelligence” applications may play a role  Techniques currently applied to understanding human behaviour and interactions may be applicable to IoT systems.  Reality Mining is one such technique.

Mining the IoT Social Network 21  Relationships between smart object in an IoT network  May have similar properties to humans interacting in a social environment.  When smart objects participate in context-aware applications  Changes in their real-world environment impact on underlying networking structures.  Vast amounts of data being generated by smart objects  Modelled and applied to complex IoT networks

Mining the IoT Social Network 22  Randomness (entropy)  Inherent in human social networks  Entropy of a smart object may be used as a metric

Mining the IoT Social Network 23  Dyadic Inference.  Human social networks respond to surrounding social environment  Smart objects may exhibit similar dyadic properties.  From these properties it is possible to infer  Relationships between multiple smart objects  based on patterns in proximity data.  Smart objects related in such a manner may responds to environmental stimuli

Why do we care? 24  Social Science  Social Network Analysis  Behavioural Modelling  Human Mobility  Systems Research  Transportation  Environmental Modelling  Healthcare

Applications 25  User-Generated Content is a core aspect of the Web  online social networks  Blogs  wikis,  Forums  One of the most successful services allowing this is Twitter:  Possibility is the development of Things-Generated Content where Things (instead of human beings) are provided with "tweet-capabilities"

Challenges 26  Large Datasets  Wal-Mart: 100-400 GB/day of RFID data  CERN LHC: 40 TB/day  Storage is cheap!  Stream data mining

Challenges 27  Abstraction  Low level details  Parallelism  Task distribution  Load balancing  Fault tolerance  Google MapReduce  Framework introduced by Google to support distributed computing on large data sets on clusters of computers

Challenges 28  Privacy  Right to possess your data  Control the use of your data  Right to distribute or dispose of your data

Conclusions 29  The Internet of Things has great promise  Business, policy, and technical challenges must be tackled before these systems are widely embraced.  Early adopters will need to prove that the new sensor driven business models create superior value.  Industry groups and government regulators should study rules on data privacy and data security, particularly for uses that touch on sensitive consumer information.  Software to aggregate and analyse data, must improve to the point where huge volumes of data can be absorbed by human decision makers or synthesised to guide automated systems more appropriately.

Conclusions 30  On the technology side, the cost of sensors and actuators must fall to levels that will spark widespread use.  Networking technologies and the standards that support them must evolve to the point where data can flow freely among sensors, computers, and actuators.  Within companies, big changes in information patterns will have implications for organisational structures, as well as for the way decisions are made, operations are managed, and processes are conceived.  Product development, for example, will need to reflect far greater possibilities for capturing and analysing information.

Q&A 31