This document presents IoT-Lite, a lightweight semantic model for annotating data in the Internet of Things. IoT-Lite aims to address issues of heterogeneity and interoperability in IoT systems by providing a simple way to semantically describe sensors, actuators, and other devices. It reuses existing models like SSN and defines best practices for annotation. Evaluations show IoT-Lite imposes minimal overhead on data size and query time compared to other semantic models. The goal of IoT-Lite is to make semantic descriptions transparent and easy to implement for both end users and data producers.

1. IoT-Lite: A Lightweight Semantic Model
for the Internet of Things
1
Maria Bermudez-Edo (University of Granada),
Tarek Elsaleh, Payam Barnaghi (University of Surrey),
Kerry Taylor (The Australian National University/University of Surrey)

2. 2P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology
(IET), I. Borthwick (editor), March 2015.

3. 3
Sensor devices are becoming widely available
- Programmable devices
- Off-the-shelf gadgets/tools

4. Internet of Things: The story so far
RFID based
solutions
Wireless Sensor and
Actuator networks
, solutions for
communication
technologies, energy
efficiency, routing, …
Smart Devices/
Web-enabled
Apps/Services, initial
products,
vertical applications, early
concepts and demos, …
Motion sensor
Motion sensor
ECG sensor
Physical-Cyber-Social
Systems, Linked-data,
semantics, M2M,
More products, more
heterogeneity,
solutions for control and
monitoring, …
Future: Cloud, Big (IoT) Data
Analytics, Interoperability,
Enhanced Cellular/Wireless Com.
for IoT, Real-world operational
use-cases and Industry and B2B
services/applications,
more Standards…

5. Data in the IoT
− Data is collected by sensory devices and also crowd sensing
sources.
− It is time and location dependent.
− It can be noisy and the quality can vary.
− It is often continuous - streaming data.
− Data is gathered from various heterogeneous sources and in
various format and representations.
− Often the value is in integrating data from different sources
and in creating an ecosystem of systems.

6. Device/Data interoperability
6
The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.

7. Heterogeneity, multi-modality and volume are
among the key issues.
We need interoperable and machine-interpretable
solutions…
7

8. Semantic Sensor Web
8
“The semantic sensor Web enables
interoperability and advanced analytics
for situation awareness and other
advanced applications from
heterogeneous sensors.”
(Amit Sheth et al, 2008)

9. 9
Some good existing models:
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.

10. 10
There are several good models and description
frameworks;
The problem is that having good models and
developing ontologies are not enough.
Semantic descriptions are intermediary
solutions, not the end product.
They should be transparent to the end-user and
probably to the data producer as well.

11. Data Lifecycle
11
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,
http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm

12. Semantics in IoT networks
WSN
WSN
WSN
WSN
WSN
Network-enabled
Devices
Semantically
annotate data
12
Gateway
CoAP
HTTP
CoAP
CoAP
HTTP
6LowPAN
Semantically
annotate data
http://mynet1/snodeA23/readTemp?
WSN
MQTT
MQTT
Gateway
network-
enabled
devices
Gateway

13. An overview of IoT-Lite
13

14. An example
14

15. Design Rules (1)
−Design for large-scale.
−Think of who will use the semantics and design for
their needs (keep the minimum required tags).
−Provide means to update and change the semantic
annotations (not covered).
−Create tools for validation and interoperability
testing (TBD).
−Create taxonomies and vocabularies.
15

16. Design Rules (2)
− Re-use existing models.
− Link data and descriptions to other existing resources.
− Define rules and/or best practices for providing the values for
each property.
− Keep it simple.
− Create effective methods, tools and APIs to handle and
process the semantics.
16

17. Evaluations- data size
17
Comparison with the
IoT-A model

18. Evaluations- Query Time
18
Query performed in the experiments

19. Evaluations- Query Time
19
Round Time Trip (RTT) of the queries required
to retrieve the endpoint.

20. IoT-lite ontology
20

21. IoT-Lite
21
http://www.w3.org/Submission/iot-lite/

22. In Conclusion
− The IoT-Lite Ontology provides an extensible way to
describe devices acting as sensors, actuators or tags in terms
of their attributes and associated units of measure, as well as
the device's physical location and area of coverage.
22

23. In Conclusion
23
- Semantic descriptions
are intermediary
solutions, not the end
product.
- They, usually, should be
transparent to the end-
user and probably to the
data producer as well.

24. In Conclusion
−IoT-Lite (or any other similar model) should be
offered with:
−Tools for annotation (similar to SAOPY)
−http://iot.ee.surrey.ac.uk/citypulse/ontologies/sao/saopy.html
−Tools for validation (similar to the SSN validator)
−http://iot.ee.surrey.ac.uk/SSNValidation/
−Best practices
−Sample code and sample datasets
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25. 25

26. Acknowledgment
The research leading to these results has received funding
from the European Commission’s in the Seventh Framework
Programme for the FIWARE project under grant agreement
no. 632893 and in the H2020 for FIESTA-IoT project under
grant agreement no. CNECT-ICT-643943.
26

27. Q&A
− Thank you.
http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/
@pbarnaghi
p.barnaghi@surrey.ac.uk