Mining Network Relationships in the Internet of Things

Transcription

1 Mining Network Relationships in the Internet of Things PAT DOODY, DIRECTOR OF THE CENTRE FOR INNOVATION IN DISTRIBUTED SYSTEMS (CIDS) INSTITUTE OF TECHNOLOGY TRALEE ANDREW SHIELDS IRC FUNDED RESEARCHER CENTRE FOR INNOVATION IN DISTRIBUTED SYSTEMS INSTITUTE OF TECHNOLOGY TRALEE

2 Overview Reality Mining? Reality mining Applied to IoT Driving Applications Conclusions

3 Reality Mining The collection and analysis of machine-sensed environmental data pertaining to human social behaviour [1].. 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 [1] N. Eagle and A. Pentland, Reality mining: sensing complex social systems, Ubiquit Computing, pp , 2006.

4 Reality Mining

5 Reality Mining applied urban transport networks 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,

6 Citysense 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.

7 Reality mining Applied to IoT 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.

8 Small-world networks 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 Why should this type of decentralised routing so effective? Contains a gradient that guides messages toward the target.

9 Clustering 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.

10 Degree Distribution 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.

11 Algorithm Considerations 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

12 Enabling technology and infrastructure 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. What if we can t identify an object can we infer the object type from its behaviour? 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.

13 Mining the IoT Social Network 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

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

15 Mining the IoT Social Network Dyadic Inference. Human social networks respond to surrounding social environment Smart objects may exhibit similar dyadic properties. From these properties it may be 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

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

17 Driving Applications 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"

18 CIDS Research Altobridge Remote Villages Reality Mining applied to mobile networks Classifying user groups Predicting network usage patterns Using neural Network and other techniques Problem: Data backhaul uses satellite

19 Challenges Large Datasets Wal-Mart: GB/day of RFID data CERN LHC: 40 TB/day Storage is cheap! Stream data mining Data Mining Agents

20 Challenges Abstraction Low level details Parallelism Task distribution Load balancing Fault tolerance Google (MapReduce ), Apache (Hadoop, Mahoot, Zookeeper ) etc. Frameworks to support distributed computing on large data sets on clusters of computers

21 Challenges Privacy Right to possess data Control the use of data Right to distribute or dispose of data

22 Conclusions 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.

23 Conclusions 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.