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Intelligent Wireless Sensor Network Simulation
Jie Sun PhD Thesis topic
2
Wireless Sensor Network
3
Definitions of Context
– Context
» “Context is any information that can be used to characterize the situation of an entity. An
entity is a person, place, or object that is considered relevant to the interaction between a
user and an application, including the user and the application themselves” (Abowd et al.,
1999)
– Low level context
» corresponds to the raw data acquired by sensors or static data provided by users.
– High level context
» is computed from the low level one, with more informative data associated to the
application and the use
4
Adaptive Context Aware System
Several processes
5
Adaptive Context Aware System
▪ Context acquisition: collecting raw data and metadata that are useful to build the
context.
▪ Context modeling: organisation of the collected data through a specific context data
model. The process gives an interpretation to each raw data. For example, the value 24
becomes the measurement of the outdoor temperature in degree Celsius. This process
builds the low level context. This process is also called annotation or tagging.
▪ Context reasoning: the high level context is computed or inferred from the low level one.
This process can imply different approaches based on machine learning or rule engine.
▪ Context distribution: diffusion of the high level context to the different consumers, for
example, the end user or any system components that are able to adapt their actions
according to the current context.
▪ Context adaptation: actions to adapt any system components according to context
changes
6
New Formalisation of Context: our Definitions
– State
“a qualitative data which changes over times (summarizing a set
of information)”
– Context
“a set of entities characterized by their state, plus all information
that can help to derive any state changes of these entities”
– Observable entity
“entity that is directly observed by sensors”
– Entity of interest
“entity whose characterisation is obtained from one or many other
entities and required by the application”
7
Illustration of our Entity Classes
Two types of entities:
Observable Entity
Entity of Interest
8
Benefits of Our Formalisation
▪ Clarify the reasoning process
▪ “Distribute” the reasoning process in several steps :
– Infer the state of Observables Entities
– Infer the state of Entity of Interest from the state of Observable
Entities
▪ Make the management of rules easier
9
Flood Context Example
Based on (Heathcote, 1998) Primary components of a watershed
outlet
10
Flood Context Example Entities
Observable Entities:
1. Precipitation
2. Watercourse
3. Outlet
Entity of Interest:
1. Flood
11
Flood finite-state machine
12
13
Simulation tool
❑ JADE (Java Agent DEvelopment Framework)
(bellifemine et al., 1999)
❑ Jess rule engine (friedman-hill et al., 2003)
(Bellifemine et al., 1999) JADE--A FIPA-compliant agent framework
(friedman-hill et al., 2003) Jess in Action: Java Rule-Based Systems
14
Simulation tool: Ontology
Based on Semantic Sensor Network ontology
(SSN) (Compton et al., 2012)
15
Rule deducing the NORMAL state of Flood entity
Simulation tool: Jess rules engine
16
◆ French Orgeval watershed managed by Irstea (Agir pour Orgeval.,
2012)
❑ 3 weather stations
❑ 3 stream gauges
❑ Année 2007 (threshold calculation) et 2008 (tests)
◆ Configuration
❑ Aggregation functions for the wireless sensor nodes
❑ Aggregation functions for the DSS
❑ Thresholds for each type of entities
Simulation: Data source
(Agir pour Orgeval., 2012) http://www.agir-pour-orgeval.fr/index.php/schema-regional-eolien-dile-de-france-dou-vient-le-vent/
17
❖ System 1
◆ Classic Context-aware system
❑ Acquisition frequency = Raw data acquiring per minute
❑ Acquisition frequency = Transmission frequency
❖ System 2
◆ Adaptive Context-aware system
❑ Acquisition frequency = Raw data acquiring per minute
Simulation: Defined systems
18
Results: Number of transmitted packets
19
Results: Flood phenomenon states
20
Results: Number of Flood phenomenon state
transitions
21
Results: Examples of flood phenomenon state
timestamp difference
22
❖ Advantages
◆ Unified way to deal with all the components/entities of an observation
process (WSN and studied phenomenon)
◆ Used in different application topics related to agriculture or environment
❖ Future work
◆ Different scenarios to solve the delay of flood risk events
◆ Different scenarios taking into account limited resources
Conclusion
23
This research is partly funded by the grant of the French Auvergne region, and now in
the new Auvergne-Rhone-Alpes region and, the grant of the European Regional
Development Fund (ERDF). The authors would like to thank the team of the Orgeval
observatory for their help.
Thank you for your attention !

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Intelligent Wireless Sensor Network Simulation

  • 1. Intelligent Wireless Sensor Network Simulation Jie Sun PhD Thesis topic
  • 3. 3 Definitions of Context – Context » “Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and the application themselves” (Abowd et al., 1999) – Low level context » corresponds to the raw data acquired by sensors or static data provided by users. – High level context » is computed from the low level one, with more informative data associated to the application and the use
  • 4. 4 Adaptive Context Aware System Several processes
  • 5. 5 Adaptive Context Aware System ▪ Context acquisition: collecting raw data and metadata that are useful to build the context. ▪ Context modeling: organisation of the collected data through a specific context data model. The process gives an interpretation to each raw data. For example, the value 24 becomes the measurement of the outdoor temperature in degree Celsius. This process builds the low level context. This process is also called annotation or tagging. ▪ Context reasoning: the high level context is computed or inferred from the low level one. This process can imply different approaches based on machine learning or rule engine. ▪ Context distribution: diffusion of the high level context to the different consumers, for example, the end user or any system components that are able to adapt their actions according to the current context. ▪ Context adaptation: actions to adapt any system components according to context changes
  • 6. 6 New Formalisation of Context: our Definitions – State “a qualitative data which changes over times (summarizing a set of information)” – Context “a set of entities characterized by their state, plus all information that can help to derive any state changes of these entities” – Observable entity “entity that is directly observed by sensors” – Entity of interest “entity whose characterisation is obtained from one or many other entities and required by the application”
  • 7. 7 Illustration of our Entity Classes Two types of entities: Observable Entity Entity of Interest
  • 8. 8 Benefits of Our Formalisation ▪ Clarify the reasoning process ▪ “Distribute” the reasoning process in several steps : – Infer the state of Observables Entities – Infer the state of Entity of Interest from the state of Observable Entities ▪ Make the management of rules easier
  • 9. 9 Flood Context Example Based on (Heathcote, 1998) Primary components of a watershed outlet
  • 10. 10 Flood Context Example Entities Observable Entities: 1. Precipitation 2. Watercourse 3. Outlet Entity of Interest: 1. Flood
  • 12. 12
  • 13. 13 Simulation tool ❑ JADE (Java Agent DEvelopment Framework) (bellifemine et al., 1999) ❑ Jess rule engine (friedman-hill et al., 2003) (Bellifemine et al., 1999) JADE--A FIPA-compliant agent framework (friedman-hill et al., 2003) Jess in Action: Java Rule-Based Systems
  • 14. 14 Simulation tool: Ontology Based on Semantic Sensor Network ontology (SSN) (Compton et al., 2012)
  • 15. 15 Rule deducing the NORMAL state of Flood entity Simulation tool: Jess rules engine
  • 16. 16 ◆ French Orgeval watershed managed by Irstea (Agir pour Orgeval., 2012) ❑ 3 weather stations ❑ 3 stream gauges ❑ Année 2007 (threshold calculation) et 2008 (tests) ◆ Configuration ❑ Aggregation functions for the wireless sensor nodes ❑ Aggregation functions for the DSS ❑ Thresholds for each type of entities Simulation: Data source (Agir pour Orgeval., 2012) http://www.agir-pour-orgeval.fr/index.php/schema-regional-eolien-dile-de-france-dou-vient-le-vent/
  • 17. 17 ❖ System 1 ◆ Classic Context-aware system ❑ Acquisition frequency = Raw data acquiring per minute ❑ Acquisition frequency = Transmission frequency ❖ System 2 ◆ Adaptive Context-aware system ❑ Acquisition frequency = Raw data acquiring per minute Simulation: Defined systems
  • 18. 18 Results: Number of transmitted packets
  • 20. 20 Results: Number of Flood phenomenon state transitions
  • 21. 21 Results: Examples of flood phenomenon state timestamp difference
  • 22. 22 ❖ Advantages ◆ Unified way to deal with all the components/entities of an observation process (WSN and studied phenomenon) ◆ Used in different application topics related to agriculture or environment ❖ Future work ◆ Different scenarios to solve the delay of flood risk events ◆ Different scenarios taking into account limited resources Conclusion
  • 23. 23 This research is partly funded by the grant of the French Auvergne region, and now in the new Auvergne-Rhone-Alpes region and, the grant of the European Regional Development Fund (ERDF). The authors would like to thank the team of the Orgeval observatory for their help. Thank you for your attention !