How to make data more usable on the Internet of Things

transcript

Payam BarnaghiCentre for Communication Systems Research (CCSR)Faculty of Engineering and Physical Sciences University of SurreyMarch 2013

Network connected Things and Devices

Image courtesy: CISCO

Sensor devices are becoming widely available

- Programmable devices- Off-the-shelf gadgets/tools

More “Things” are being connected

Home/daily-life devicesBusiness and Public infrastructureHealth-care…

People Connecting to Things

Motion sensorMotion sensor

Motion sensor

ECG sensor

Internet

Things Connecting to Things

- Complex and heterogeneous resources and networks

Wireless Sensor Networks (WSN)

Sinknode Gateway

Core networke.g. Internet

Core networke.g. InternetGateway

End-userEnd-user

Computer servicesComputer services

- The networks typically run Low Power Devices- Consist of one or more sensors, could be different type of sensors (or actuators)- The networks typically run Low Power Devices- Consist of one or more sensors, could be different type of sensors (or actuators)

Key characteristics of IoT devices

−Often inexpensive sensors (actuators) equipped with a radio transceiver for various applications, typically low data rate ~ 10-250 kbps.

−Deployed in large numbers−The sensors should coordinate to perform the desired task.−The acquired information (periodic or event-based) is

reported back to the information processing centre (or sometimes in-network processing is required)

−Solutions are application-dependent.

Beyond conventional sensors

− Human as a sensor (citizen sensors)− e.g. tweeting real world data and/or events

− Virtual (software) sensors− e.g. Software agents/services generating/representing

Road block, A3Road block, A3

Suggest a different routeSuggest a different route

Cyber, Physical and Social Data

Citizen Sensors

Source: How Crisis Mapping Saved Lives in Haiti, Ushahidi Haiti Project (UHP).

Cosm- Air Quality Egg

Cosm- data readings

Data formats

Location

Making Sense of Data

In the next few years, sensor networks will produce 10-20 time the amount of data generated by social media. (source: GigaOmni Media)

Things, Data, and lots of it

image courtesy: Smarter Data - I.03_C by Gwen Vanhee

Big Data and IoT

− "Big data" is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set.” (wikipedia)

− Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. (source IBM)

The seduction of data

− Turn 12 terabytes of Tweets created each day into sentiment analysis related to different events/occurrences or relate them to products and services.

− Convert (billions of) smart meter readings to better predict and balance power consumption.

− Analyze thousands of traffic, pollution, weather, congestion, public transport and event sensory data to provide better traffic management.

− Monitor patients, elderly care and much more…

Adapted from: What is Bog Data?, IBM

Do we need all these data?

“Raw data is both an oxymoron and bad data”

Geoff Bowker, 2005

Source: Kate Crawford, "Algorithmic Illusions: Hidden Biases of Big Data", Strata 2013.

IoT Data in the Cloud

Image courtesy: http://images.mathrubhumi.comhttp://www.anacostiaws.org/userfiles/image/Blog-Photos/river2.jpg

Perceptions and Intelligence

Information

Knowledge

Wisdom

Raw sensory data

Structured data (with semantics)

Abstraction and perceptions

Actionable intelligence

Change in communication paradigm

Sinknode Gateway

Core networke.g. Internet End-userEnd-user

DataData

SenderSender

DataData

ReceiverReceiver

A sample data communication in conventional networksA sample data communication in conventional networks

A sample data communication in WSNA sample data communication in WSN

Fire!Fire! Some bits01100011100

Some bits01100011100

− Collaboration and in-network processing− In some applications a single sensor node is not able to handle

the given task or provide the requested information.− Instead of sending the information form various source to an

external network/node, the information can be processed in the network itself.

− e.g. data aggregation, summarisation and then propagating the processed data with reduced size (hence improving energy efficiency by reducing the amount of data to be transmitted).

− Data-centric− Conventional networks often focus on sending data between

two specific nodes each equipped with an address. − Here what is important is data and the observations and

measurements not the node that provides it.

Required mechanisms

“People want answers, not numbers” (Steven Glaser, UC Berkley)

Sinknode Gateway

What is the temperature at home?What is the temperature at home?Freezing!Freezing!

IoT Data alone is not enough

− Domain knowledge− Machine interpretable meta data− Delivery, sharing and representation services− Query, discovery, aggregation services− Publish, subscribe, notification, and access

interfaces/services

Storing, Handling and Processing the Data

Image courtesy: IEEE Spectrum

IoT Data Challenges

− Discovery: finding appropriate device and data sources− Access: Availability and (open) access to IoT resources and

data− Search: querying for data− Integration: dealing with heterogeneous device, networks

and data− Interpretation: translating data to knowledge usable by

people and applications− Scalability: dealing with large number of devices and

myriad of data and computational complexity of interpreting the data.

Interpretation of data

− A primary goal of interconnecting devices and collecting/processing data from them is to create situation awareness and enable applications, machines, and human users to better understand their surrounding environments.

− The understanding of a situation, or context, potentially enables services and applications to make intelligent decisions and to respond to the dynamics of their environments.

Observation and measurement data

Source: W3C Semantic Sensor Networks, SSN Ontology presentation, Laurent Lefort et al.

How to say what a sensor is and what it measures?

Sinknode

Gateway

Data/Service description frameworks

− There are standards such as Sensor Web Enablement (SWE) set developed by the Open Geospatial Consortium that are widely being adopted in industry, government and academia.

− While such frameworks provide some interoperability, semantic technologies are increasingly seen as key enabler for integration of IoT data and broader Web information systems.

Sensor Markup Language (SensorML)

Source: http://www.mitre.org/

W3C SSN Ontology

Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn

M. Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.

W3C SSN Ontology

makes observations of this type

Where it is

What it measures

SSN-XG ontologies

SSN-XG annotations

SSN-XG Ontology Scope

Semantics and IoT data

− Creating ontologies and defining data models is not enough− tools to create and annotate data− data handling components

− Complex models and ontologies look good, but− design lightweight versions for constrained environments − think of practical issues− make it as compatible as possible and/or link it to the other

existing ontologies − Domain knowledge and instances

− Common terms and vocabularies − Location, unit of measurement, type, theme, …

− Link it to other resources − Linked-data− URIs and naming

Semantics and sensor data

Source: W. Wang, P. Barnaghi, "Semantic Annotation and Reasoning for Sensor Data", In proceedings of the 4th European Conference on Smart Sensing and Context (EuroSSC2009), 2009.

Semantics and Linked-data

− The principles in designing the linked data are defined as:− using URI’s as names for things;− using HTTP URI’s to enable people to look up those

names;− provide useful RDF information related to URI’s that are

looked up by machine or people;− including RDF statements that link to other URI’s to

enable discovery of other related concepts of the Web of Data;

Linked Sensor data

Linked Open Data

Collectively, the 203 data sets consist of over 25 billion RDF triples, which are interlinked by around 395 million RDF links (September 2010).

Linked IoT Data

Internal location ontology (local)

Lined-data location(external)

Myth and reality

− #1: If we create an Ontology our data is interoperable − Reality: there are/could be a number of ontologies for a domain

− Ontology mapping − Reference ontologies − Standardisation efforts

− #2: Semantic data will make my data machine-understandable and my system will be intelligent.− Reality: it is still meta-data, machines don’t understand it but can interpret it. It

still does need intelligent processing, reasoning mechanism to process and interpret the data.

− #3: It’s a Hype! Ontologies and semantic data are too much overhead; we deal with tiny devices in IoT. − Reality: Ontologies are a way to share and agree on a common vocabulary and

knowledge; at the same time there are machine-interpretable and represented in interoperable and re-usable forms;

− You don’t necessarily need to add semantic metadata in the source- it could be added to the data at a later stage (e.g. in a gateway);

− Legacy applications can ignore it or to be extended to work with it.

Processing Streaming Sensor Data

Symbolic Aggregate Approximation (SAX)

Variable String Length and Vocabulary size.

Length: 10, VocSize: 10 Length: 10, VocSize: 4

“gijigdbabd” “cdddcbaaab”

Green Curve: consists of 100 Samples, Blue Curve: SAX

SAX representation

SAX Pattern (blue) with word length of 20 and a vocabulary of 10 symbolsover the original sensor time-series data (green)

P. Barnaghi, F. Ganz, C. Henson, A. Sheth, "Computing Perception from Sensor Data", in Proc. of the IEEE Sensors 2012, Oct. 2012.

fggfffhfffffgjhghfff

jfhiggfffhfffffgjhgi

fggfffhfffffgjhghfff

Data Processing Framework

fggfffhfffffgjhghfff dddfffffffffffddd cccddddccccdddccc aaaacccaaaaaaaaccccdddcdcdcdcddasddd

PIR Sensor Light Sensor Temperature

Sensor

Raw sensor data stream

Attendance PhoneHot

Temperature Cold

TemperatureBright

Day-time

Night-time

Office room BA0121

On going meeting

Window has been left open

Temporal data(extracted from

SSN descriptions)

Spatial data(extracted from

SSN descriptions)

Thematic data(low level

abstractions)

Parsimonious Covering Theory

Observations

Perceptions

Domain knowledge

SAX Patterns

Raw Sensor Data(Annotated with SSN

Ontology)

Perception Computation

High-level Perceptions

SensorSAX

F. Ganz, P. Barnaghi, F. Carrez, “Information Abstraction for Heterogeneous Real World Internet Data”, Feb. 2013.

Evaluation results of abstraction creation

Data size reduction

Enabling the Internet of Things

- Diversity range of applications- Interacting with large number of devices with various types-Multiple heterogeneous networks-Deluge of data-Processing and interpretation of the IoT data

Challenges and opportunities

− Providing infrastructure − Publishing, sharing, and access solutions on a global scale− Indexing and discovery (data and resources)− Aggregation and fusion− Trust, privacy and security− Data mining and creating actionable knowledge

− Integration into services and applications in e-health, the public sector, retail, manufacturing and personalized apps.− Mobile apps, location-based services, monitoring control etc.

− New business models

Events

Semantic Interop event, European Wireless Conference, Guildford, April 2013. http://www.probe-it.eu/?p=1206

Tutorial at WIMS'13: Data Processing and Semantics for Advanced Internet of Things (IoT) Applications: modeling, annotation, integration, and perception, P. Anantharam, P. Barnaghi, A. Sheth, http://aida.ii.uam.es/wims13/keynotes.php

Dagstuhl seminar on Cyber-Physical-Social Computing, Sept. 30- Oct. 04, 2013, Organizers: Payam Barnaghi, Ramesh Jain, Amit Sheth, Steffen Staab, Markus Strohmaier.

Thank you.

Payam BarnaghiCentre for Communication Systems ResearchFaculty of Engineering and Physical SciencesUniversity of Surreyp.barnahgi@surrey.ac.uk

How to make data more usable on the Internet of Things

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