Managing Massive data of the IoT through cooperative semantic nodes

Managing Massive data of the IoT through cooperative
semantic nodes

Benoit Christophe – Bell Labs Research
benoit.christophe@alcatel-lucent.com
1

COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

The Internet of Things
Vision and definitions
• Extending the Internet to physical entities of interest (EoI)
• A step towards a better automation of user’s tasks (i.e. making user’s life
easier)
Entities of Interest

• Three layers cake (my own
perception)
Access, Observe,
- EoIs are the entities being of interest for Measure, Actuate

some users Connected devices (RFID, sensors, actuators, smart phones)

- Devices (sensors, actuators, etc.)
measure, trigger or actuate on EoIs
- Services and applications combine Combine & Digest
devices to offer meaningful information
Services & Applications
to a user about the EoIs it is interested
in

Goal: to ease the process of associating EoIs and devices
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Hard facts and forecasts

Source: http://gigaom.com/cloud/internet-of-things-will-have-24-billion-devices-by-2020/connecteddevices2020/

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Heterogeneous information produced on a 24/7 basis

Source: http://www.rfglobalnet.com/doc.mvc/The-Internet-Of-Things-Connecting-Everything-0001
Image: http://embedded-computing.com/current-trends-cyber-attacks-mobile-embedded-systems

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Rise of Web platforms for connected devices

• Plethora of services or data offered through the Web
• The “things” become exposed as services on the Web
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Associated problems

• How to find relevant information about an EoI within this giant lake of
generated unstructured data?
• How to combine devices or services to automate realization of complex
tasks?
• Assuming the above, how do we ensure scalability of our search
processes?

6


State of affairs

• Lassila [1] proposed to use Semantic Web technologies to improve
interoperability between devices in order to better automate user’s tasks.
- Many researches [2,3,4,5] in this field tried to describe “things” or “services”
semantically, adopting this vision
- Many European projects (e.g. Sensei, IoT-A) try defining ontological models to
represent their resources
- W3C Semantic Sensors Network Incubator Group (SSN-XG) has developed an
ontology for describing sensors
• However, adding semantic and reasoning process decreases scalability.
Even worst, considering an indoor environment with IoT resources prone
to high mobility makes it hard (impossible?) to maintain a KB in a
reasonable time

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What we believe regarding IoT in indoor environments
• Beyond using Semantic Web to describe IoT resources:
- Consider location as an important criteria when searching for IoT resources
- E.g. when searching for a printer, you probably want to find one near you
- Design a distributed network of nodes
- Where each node is bound to one constituent of a Building (a room, a corridor, etc.)
- Where each node knows its neighborhood
- Where each node contains few but meaningful semantic descriptions of resources
- I.e. a node is bound to a room of a building. Such node contains only the semantic descriptions of
the IoT resources that are in or in the vicinity of the room

- Where each node has local reasoning capabilities: e.g. searching its KB, sharing
descriptions of its IoT resources with its neighbors, and forwarding incoming requests to
its neighbors
- E.g. description of a phone in node “room A” is sent to node “room B” if “room A” and “room B” are
close to each other
- E.g.2. an incoming request that reaches node “room A” is forwarded to node “room B” in case of no
answer

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How do we realize our proposal
Modeling indoor environment

• Create an ontology to model what compose a Building (Floor, Corridor,
Room, etc.)
• Create the properties allowing a constituent of a Building to describe how
it is linked to another constituent (e.g. Room A givesAccessTo RoomB)

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A federated [6] network of cooperating semantic nodes

• Semantic nodes know their neighborhood, store and exchange
descriptions and forward incoming requests
Top node of the federation (in-degree = 0)
N1 Associated to a Place such as « University Building A »

« 2nd Floor »
N2
Non source node (in-degree = 1)

N3

N4 N5

« CS lab. » « Chemistry lab. » N6 N7 N8

Management link (e.g. N1 manages N2)
First cascading process: each node sends its location to its « manager ».
Such data implements the location model and the Place concept.
Second cascading process: top node sends complete location to all nodes

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Sharing knowledge – use of SWRL

• Use SWRL to exchange semantic descriptions of IoT resources between
nodes
- SWRL [7] are conjunctive rules designed on top of an OWL ontology
- SWRL specification contains built-ins allowing operations on integer, dates, etc.
- A subset of SWRL rules are DL-safe (so, decidable)
- SWRL allows defining customized built-ins

• Benefits of using SRWL to define rules throwing knowledge exchange
- A place owner can define its own sharing rules
- Different places can apply a different policy to share knowledge
- Adding a rule does not require to recompile the whole code of a node
- Newly developed built-ins only need to be referenced in the rules

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Sharing knowledge – use of SWRL (2)

• Example of rules that we used to test our approach
- When a resource has reached (resp. left) a place P, notify all accessible places
about this fact
- Example of use: enable a node receiving an incoming request to answer “I do not have
corresponding object but my neighbor has it”
- When it has been learnt that any mobile resource reaches a place P2 after
having reached a place P1 and if a resource has just joined P1 notify P2 that a
resource will join.
- Example of use: P2 anticipates the work required when a resource joins, loading its
semantic description by advance

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Sharing knowledge – Overall process

• Consider a node willing to share knowledge with a set of peers
- Apply Dijkstra algorithm on the « graph » associated to the federation of nodes
to find the path to follow from the source to the destination
- Create a message routed from the source to the destination and containing
knowledge to share (RDF triples or a pointer to a semantic description file)
- Implementation uses RDF triple stores to store information about EoIs and devices
- Consequently, implementation of the message is based on SPARQL1.1 syntax (allowing
to update triples stores)

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Overall picture
• When a described entity is added
Described entities
- Parse the triples of its description
- Update local triple store
Semantic profiles - Rebuilt KB (so re-run rules)

{t1,t2,…,tn}
- With results of the sharing knowledge rules
RDF API
- Build path for each neighbor to be updated
{t1,t2,…,tn}
All nec. triples - Generate SPARQL1.1 message
KB Manager - Send message
{r1,r2,…,rk} TS
{r1,r2,…,rk}
Msg1: L= {N3,N2} C= {r1,r2,rk}
Result Msg2: L= {N3,N4,N5,N6} C= {r4,r7}
Dispatcher …
N3
N4

Indoor Location N
instance
N2 5
N1 N
{r1,r2,rk} TS 6 TS {r4,r7}
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Experimentation

• 26 modeled « places » bound to our premises
• Several sets of different sizes
- Containing semantic profiles of devices and EoIs (using models presented in
earlier works [8,9])
- Set sizes ranging from 1 to 100000 RDF triples

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Experimentation (2)

• Semantic node concept implemented as a Web application (running in a
Tomcat server)
- Fully implemented in Java
- Use of OWLDB (further replaced by Sesame) to store triples
- Use of the OWL API + HermiT reasoner to process semantic decriptions of
entities
- Use of JGraphT library to compute paths between different nodes
• Computers (2.23GHtz, 2GB RAM) installed in the modelled “places”, each
one running a Tomcat server with one instance of semantic node

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Experimentation (3)

• 2 types of measures
- Evaluating the time taken to update triple stores
- Evaluating the time taken to query a triple store
- Both follow an exponential curve, validating the fact that searching or
maintaining a unique KB would be impossible.

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Conclusions

• Use of a federated network of cooperative semantic nodes
- Each one with a triple store, storing meaningful information
• Double cascading process to let all nodes be aware of their neighbors
• Sharing knowledge process
- Based on SWRL rules
- Using SPARQL1.1 to update triple stores
• First measures shown
- Sharing the results compiled by one node follows an exponential curve (function
of the number of results to share)
- Querying a triple store also follows an exponential curve (function of the
number of triples contained by the store)

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References

1. O. Lassila, “Applying semantic web in mobile and ubiquitous computing: Will policy-awareness help”,
4th International Semantic Web Conference, 2005.
2. R. Masuoka, B. Parsia, and Y. Labrou, “Task computing - the semantic web meets pervasive
computing”, 2nd International Semantic Web Conference (ISWC2003), 2003.
3. O. Lassila and M. Adler, “Semantic gadgets: Ubiquitous computing meets the semantic web”, in
Spinning the Semantic Web, 2003.
4. A. Katasonov, O. Kaykova, O. Khriyenko, S. Nikitin, and V. Y. Terziyan, “Smart semantic middleware for
the internet of things” in ICINCO-ICSO, 2008.
5. D. Pfisterer, K. Rmer, D. Bimschas, O. Kleine, R. Mietz, C. Truong, H. Hasemann, A. Krller, M. Pagel, M.
Hauswirth, M. Karnstedt, M. Leggieri, A. Passant, and R. Richardson, “Spitfire: toward a semantic web
of things” IEEE Communications Magazine, 2011.
6. D. Heimbigner and D. McLeod, “A federated architecture for information management”, ACM Trans. Inf.
Syst. 1985.
7. SWRL A Semantic Web Rule Language combining OWL and RuleML,
http://www.w3.org/Submission/SWRL/
8. B. Christophe, V. Verdot, and V. Toubiana, “Searching the web of things”, in Semantic Computing
(ICSC), 2011.
9. B. Christophe, “Semantic profiles to model the web of things”, in Semantics Knowledge and Grid
(SKG), 2011.

19


Thanks

Benoit Christophe
Bell Labs Research
Alcatel-Lucent Bell Labs France

benoit.christophe@alcatel-lucent.com

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Managing Massive data of the IoT through cooperative semantic nodes

Managing Massive data of the IoT through cooperative semantic nodes

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