Information Engineering in the Age of the Internet of Things
1. Information Engineering in the
Age of the Internet of Things
Institute for Communication Systems (ICS)/
5G Innovation Centre
University of Surrey
Guildford, United Kingdom
Digital Catapult, December 2015
2. “A hundred years hence people will be so avid
of every moment of life, life will be so full of
busy delight, that time-saving inventions will
be at a huge premium…”
“…It is not because we shall be hurried in
nerve-shattering anxiety, but because we shall
value at its true worth the refining and restful
influence of leisure, that we shall be impatient
of the minor tasks of every day….”
The March 26, 1906, New Zealand Star :
4. Apollo 11 Command Module (1965) had
64 kilobytes of memory
operated at 0.043MHz.
An iPhone 5s has a CPU running at speeds
of up to 1.3GHz
and has 512MB to 1GB of memory
Cray-1 (1975) produced 80 million Floating
point operations per second (FLOPS)
10 years later, Cray-2 produced 1.9G FLOPS
An iPhone 5s produces 76.8 GFLOPS – nearly
a thousand times more
Cray-2 used 200-kilowatt power
Source: Nick T., PhoneArena.com, 2014
image source: http://blog.opower.com/
5. Computing Power
−More memory and more storage
−"Moore's law" over the history of computing, the
number of transistors in a dense integrated circuit
has doubled approximately every two years.
8. Connectivity and information exchange was
(and is ) the main motivation behind the
Internet; but Content and Services are now
the key elements;
and all started growing rapidly by the
introduction of the World Wide Web (and
linked information and search and discovery
13. Internet of Things: The story so far
Wireless Sensor and
, solutions for
efficiency, routing, …
vertical applications, early
concepts and demos, …
More products, more
solutions for control and
Future: Cloud, Big (IoT) Data
Enhanced Cellular/Wireless Com.
for IoT, Real-world operational
use-cases and Industry and B2B
15. Data in the IoT
− Data is collected by sensory devices and also crowd sensing
− It is time and location dependent.
− It can be noisy and the quality can vary.
− It is often continuous - streaming data.
− There are several important issues such as:
− Device/network management
− Actuation and feedback (command and control)
− Service and entity descriptions.
16. IoT data- challenges
− Multi-modal, distributed and heterogeneous
− Noisy and incomplete
− Time and location dependent
− Dynamic and varies in quality
− Crowdsourced data can be unreliable
− Requires (near-) real-time analysis
− Privacy and security are important issues
− Data can be biased- we need to know our data!
P. Barnaghi, A. Sheth, C. Henson, "From data to actionable knowledge: Big Data Challenges in the Web of Things," IEEE Intelligent Systems,
vol.28 , issue.6, Dec 2013.
17. Making IoT data widely available and (re-)usable
− Machine-readable and/or human interpretable meta-data
− Open IoT data portals (static and streaming)
− Open APIs and discoverable interfaces
− Discoverable data and patterns (and resources)
− (easily) Sharable and connectable data
− Opportunistic and ad-hoc discovery, matching and mash-up
− Quality and fit-for-purpose data
− Trust, privacy and security aware data collection, sharing and
20. A bit of history
− “The Semantic Web is an extension of the current web in
which information is given well-defined meaning, better
enabling computers and people to work in co-operation.“
(Tim Berners-Lee et al, 2001)
Image source: Miller 2004
21. Semantics & the IoT
−The Semantic Sensor (&Actuator) Web is an
extension of the current Web/Internet in which
information is given well-defined meanings, better
enabling objects, devices and people to work in co-
operation and to also enable autonomous
interactions between devices and/or objects.
Some good existing models: SSN Ontology
Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
M. Compton, P. Barnaghi, L. Bermudez, et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics,
But why do we still not have fully
integrated semantic solutions in the IoT?
32. Publishing Semantic annotations
− We need a model (ontology) – this is often the easy part for
a single application.
− Interoperability between the models is a big issue.
− Express-ability vs Complexity is a challenge.
− How and where to add the semantics
− Where to publish and store them
− Semantic descriptions for data, streams, devices (resources)
and entities that are represented by the devices, and
description of the services.
Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html
- Servers provide catalogues of resources to
- A catalogue is an array of URIs.
- Each resource in the catalogue is annotated
with metadata (RDF-like triples).
Perhaps complex models are (sometimes) good
for publishing research papers….
But they are often difficult to implement and use
in real world products.
37. What happens afterwards is more important
− How to use the data (documentation, tools, interfaces)
− How to index and query the annotated data
− How to make the publication suitable for constrained
environments and/or allow them to scale
− How to query them (considering the fact that here we are
dealing with live data and often reducing the processing time
and latency is crucial)
− Linking to other sources
38. The IoT is a dynamic, online and rapidly
Annotation for the (Semantic) Web
Annotation for the IoT
Image sources: ABC Australia and 2dolphins.com
39. Tools and APIs- e.g. Sense2Web
39P. Barnaghi, M. Presser, K. Moessner, "Publishing Linked Sensor Data", in Proc. of the 3rd Int. Workshop on Semantic Sensor Networks (SSN),
40. Tools and API – e.g. FIWARE IoT Discovery Generic
Creating common vocabularies and
taxonomies are also equally important
e.g. event and unit taxonomies, common
formats for representing location,
vocabularies to describe proximity and
relations between Things and their data.
We should accept the fact that sometimes
we do not need (full) semantic
Think of the applications and use-cases
before starting to annotate the data.
45. An example: a discovery
method in the IoT
[#location | #type | time][#location | #type | time]
46. An example: a discovery method in the IoT
S. A. Hoseinitabatabaei, P. Barnaghi, C. Wang, R. Tafazolli, L. Dong, "Method and Apparatus for Scalable Data Discovery in IoT Systems",
US Patents, 2015.
47. 101 Smart City Use-case Scenarios
53. Extraction of events and semantics from social media
Tweets from a city
Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P Sheth, "Extracting City Traffic Events from Social Streams",
ACM Transactions on Intelligent Systems and Technology, 2015
54. Ontology learning from real world data
54Frieder Ganz, Payam Barnaghi, Francois Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems
55. Overall we need semantics and metadata in
the IoT and these play a key role in providing
56. However, we should design and use the
data publication, access, sharing and their
associated metadata models carefully and
consider the constraints and dynamicity
of the IoT environments.
57. Data Lifecycle
Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and
opportunities of data driven systems for building, community and city-scale applications,
59. #1: Design for large-scale and provide tools and APIs.
#2: Think of who will use the data and how when you
design your models.
#3: Provide means to update and change the semantic
60. Smart data collection
− Smart data collection
− Sooner or later we need to
think whether we need to
collect that data, how often
we need to collect it and
− Intelligent data Processing
(selective attention and
(image source: KRISTEN NICOLE, siliconangle.com)
63. #4: Create tools, open APIs and datasets for
validation, evaluation, and interoperability testing.
#5: Create common vocabularies and provide
#6: Of course you can always create a better model,
but try to re-use existing ones as much as you can.
66. Open Data/Open APIs
−Open data is often misinterpreted as free data
and publically available data.
−You can have open data, but with the right
access controls, with trust and privacy.
67. #7: Link your data and descriptions to other existing
#8: Define rules and/or best practices for providing the
values for each attribute.
#9: Remember the widely used (semantic) models on
the Web are simple ones like FOAF.
70. #10: Design for different audience (data consumers,
developers, providers) and think about real impact and
#11: Specify (and encourage others to do the same)
data governance and privacy procedures, explain the
ownership and re-use rules, and give control to the
owners of data
75. #12: Semantics and information engineering are only
one part of the solution and often not the end-product
so the focus of the design should be on creating
effective methods, tools and APIs to handle and process
77. Technical (and non-technical) Challenges
− Creating common models to represent, publish, and
(re-)use and share IoT data.
− creating an IoT data market and data-driven innovation
− Developing standards
− Providing best practices, demonstrators and open data
portals for streaming and dynamic IoT data.
− Provide governance, dependability, reliability, trust and
78. Research challenges
−Transforming raw data to actionable-information.
−Machine learning and data analytics for large-scale, multi-
modal and dynamic (streaming data).
− Making data more accessible and discoverable.
−Energy and computationally efficient data collection,
aggregation and abstraction (for both edge and
79. Research challenges (continued)
−Integration and combination of Physical-Cyber-Social
−Use of data for automated interactions and
autonomous services in different domains.
−Resource-aware and context-aware security, privacy
and trust solutions.
81. In conclusion
− IoT information engineering is different from common models of web data
and/or other types of big data.
− Data collection in the IoT comes at the cost of bandwidth, network,
energy and other resources.
− Data collection, delivery and processing is also depended on multiple
layers of the network.
− We need more resource-aware data analytics methods and cross-layer
optimisations (Deep IoT).
− The solutions should work across different systems and multiple platforms
(Ecosystem of systems).
− Data sources are more than physical (sensory) observation.
− The IoT requires integration and processing of physical-cyber-social data.
− The extracted insights and information should be converted to a feedback
and/or actionable information.
82. Let’s hope
−The Internet of the Future will be
−For everyone, everywhere, available at anytime,
−People will have control on their data
−Data will be used for helping people
−Smart applications will contribute to a better life
and to a better use of of our resources in the
83. Other challenges and topics that I didn't talk about
Networks, test-beds and
84. IET sector briefing report
Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
Public data should be made available to everyone and the basic services (that replace the existing practices) should be free to everyone; it doesn’t mean if we replace a bus time table with a smart phone app then who pays a premium should access it.