=Paper=
{{Paper
|id=None
|storemode=property
|title=A Distributed Reasoning Platform to Preserve Energy in Wireless Sensor Networks
|pdfUrl=https://ceur-ws.org/Vol-1035/iswc2013_demo_44.pdf
|volume=Vol-1035
|dblpUrl=https://dblp.org/rec/conf/semweb/OngenaeVWT13
}}
==A Distributed Reasoning Platform to Preserve Energy in Wireless Sensor Networks==
https://ceur-ws.org/Vol-1035/iswc2013_demo_44.pdf
A Distributed Reasoning Platform to Preserve
Energy in Wireless Sensor Networks
Femke Ongenae1 , Stijn Verstichel1 , Maarten Wijnants2 , and Filip De Turck1
1
Department of Information Technology (INTEC), Ghent University - iMinds,
Gaston Crommenlaan 8 bus 201, B-9050 Ghent, Belgium
Femke.Ongenae@intec.ugent.be
2
Expertise centre for Digital Media (EDM), Hasselt University - iMinds,
Wetenschapspark 2, 3590 Diepenbeek, Belgium
Maarten.Wijnants@uhasselt.be
Abstract. A distributed reasoning platform is presented to reduce the
energy consumption of Wireless Sensor Networks (WSNs) offering geospa-
tial services by minimizing the amount of wireless communication. It
combines local, rule-based reasoning on the sensors and gateways with
global, ontology-based reasoning on the back-end servers. The Seman-
tic Sensor Network (SNN) Ontology was extended to model the WSN
energy consumption. One exemplary prototype is presented, namely the
Garbage Bin Tampering Monitor (GBTM).
1 Introduction
The GreenWeCan [1] project investigates a “green” wireless city access network
infrastructure able to offer geospatial services by aggregating data from multiple
sources, in a scalable and cost-effective way, and minimizing energy consump-
tion as well as the human exposure to electromagnetic radiation. The machines
in a WSN range from heavily resource-constrained sensors to powerful back-end
servers. These WSNs often use a hierarchical approach with a sink that intercon-
nects the sensors and the back-end. Power management is important in WSNs.
Sensors are often battery-operated, so their autonomy must be maximized. As
radio transmissions needed for communication are costly operations [2], it is
often beneficial to carry out as much processing as possible on the node itself.
Therefore, a distributed reasoning platform (Section 2) was utilized. Rule-
based reasoning on the sensors allows for conclusions concerning measured vari-
ables to be drawn locally. A back-end ontology-based reasoning mechanism,
which has a complete overview of the senor data being produced, can influence
the behavior of the WSN nodes. Section 3 describes the ontology, which is used
to model and reason on the sensor knowledge to reduce energy consumption.
The use of proven standard reasoning mechanisms in WSNs is still premature.
However, the reduction in energy consumption by a reduced transmission rate,
compared to the extra power needed for such processes, should result in a positive
balance. Moreover, using standard reasoning algorithms, instead of proprietary
ones, makes the approach more generic and facilitates reusability. A prototype
was developed to demonstrate these advantages (Section 4).
Sensor gateway
Router
(Local) Rule-based sensor reasoning Back-end
(Global) Ontology-based overall reasoning server User requests
Wireless Sensor D2R
Network (WSN)
MySQL Database
Fig. 1. Reasoning Architecture of the WSN
2 Reasoning architecture
As shown in Figure 1, information requested by the users is gathered from the
sensors, which measure environmental parameters and pre-process them by per-
forming rule-based reasoning. As such, less data is transmitted to the back-end.
The complexity of the local reasoning can be adapted to the sensor’s capabilities,
e.g., battery, to optimize energy consumption. Moreover, the local reasoning is
able to monitor the sensor’s inner workings, e.g., CPU usage, in order to detect
problems that influence energy consumption.
The pre-processed data is forwarded to the back-end via a gateway, optionally
multi-hopping over routers. The sinks perform local, rule-based reasoning, e.g.,
to avoid retransmissions and preserve energy, the network load is monitored.
The back-end maintains an ontology to model the knowledge about the WSN
and its observations. Static information, e.g., sensor specifications, is gathered
from a database using D2R3 . The received sensor data is integrated into this
ontology to answer user requests and optimize the overall energy consumption.
Using an ontology ensures reusability and adaptability. Should new types
of sensors be deployed, their semantic description and measurements can be
mapped on the existing ontology. Moreover, by making the ontology publicly
available as well as the data and conclusions corresponding to the run-time
situation of the WSN, new applications can be created by anyone persuing a
new usage and easy integration of this information.
The ontology can also be used to define the local, rule-based reasoning al-
gorithms. The developed Reasoning Sensor App Generator generates sensor ap-
plication code based on an XML-based application description. This description
specifies a rule set and a template. The first contains the reasoning logic, which
is executed each time the sensor wakes up. The second contains the code needed
to run the reasoning logic on hardware.
3 A SSN Ontology extension modeling energy usage
The W3C Semantic Sensor Network Incubator group has developed the SSN On-
tology4 for modeling sensor devices and their capabilities, systems and processes.
Based on brainstorms with GreenWeCan partners, i.e., OneAccess and Bausch
Datacom, the requirements for the modeling concepts were defined. These were
mapped on the SSN Ontology and some extensions were made. The relations
3
http://d2rq.org/d2r-server
4
http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
Solution hasSolution Fault hasFault Symptom Time Observation qualityOfObservation Quality hasValue
observationResult
DUL:PhysicalPlace hasSymptom SamplingTime SensorOutput
hasValue
ObservationValue
hasLocation Observation
DUL:SpaceRegion is a is a
Observed DataProperty
Deployment DUL:PhysicalObject madeObservation Property
Platform is a
deployed equivalent LitterBinOpened ParkingSpaceOccupied …
deployedOnPlatform System is a
hasSubSystem Sensor observes Property InternalProperty
LinksSensor hasDeployment System
hasSub hasMeasurement is a CPUActivity Uptime SNRatio PacketsSent RadioActivity …
equivalent Magnetic is a
System Capability SurvivalProperty BufferOverflow Packets
Sensor forProperty
Link Received
ROM Battery Measurement BatteryLifetime MinimumBatteryLifetimeOccurence
Radio RFSensor is a Capability is a
Harvester AvailableBatteryLifetime Failed
Network
is a CPU inCondition Condition Transmissions
Role
is a SolarPanel string string string string string string string string string string
is a
PotentialConfiguration hasCommuni- hasTrans- has has hasFre- hasFirm- hasHard- Has has has
Router Sink Worker CurrentConfiguration cationChannel mission Sensi- Fre- quency ware ware CPUFre- Accu- Cali-
hasLinkQuality hasConfiguration Power tivity quency Band Revision Revision quency racy bration
Configuration
string
Fig. 2. The SSN Ontology extended (indicated in blue) to model energy consumption
between the sensor configurations, networks and applications and the influence
on the energy consumption were also derived.
As shown in Figure 2, the SSN Ontology allows to model sensors, their obser-
vations and measurement capabilities. To avoid error propagation and retrans-
missions, the SNN was extended with concepts to make the quality of the ob-
servations explicit as they can be imprecise, ambiguous or erroneous. Symptoms
define rules, which allow detecting specific phenomena in the observations. Ax-
ioms are defined that reclassify these Symptoms as Faults and Solutions.
The SSN Property concept models the type of metrics that can be ob-
served. The Data- and InternalProperty subclasses are added to group the
application-relevant observations monitored and the hardware-specific proper-
ties internally measured by the sensor. Figure 2 shows some internal properties
to minimize energy consumption, e.g., sleeping schemes and radio settings can
be adjusted to avoid buffer overflows and thus the amount of retransmissions.
The type of a sensor indicates which local reasoning techniques can be adopted.
The Sensor concept in the SSN Ontology is annotated with a reference towards
SensorML5 . This specification can be used to reflect all the sensor’s details.
Battery, Harvester, ROM, CPU and Radio concepts are introduced as these
influence the reasoning complexity that can be used. The SSN Ontology already
defines the Battery LifeTime property. Some other battery properties were
added. The Current- and Potential Configurations of the radio and CPU are
also modelled. The first models the currently used values for the characteristics,
while the second represents the combination of values that can potentially be
used together. Similarly, new sensors can be modeled, as shown in Figure 2 for
the Magnetic Sensor and its settings, e.g. Sensitivity.
The location of the sensors can influence the energy consumption. The SSN
Ontology models the WSN’s deployment. To represent the physical locations the
SSN Ontology aligns with the DOLCE Ultra Lite Ontology6 . These concepts are
preceded by the DUL namespace in Figure 2. Link and NetworkRole concepts
are introduced to represent the network components used to interconnect the
5
http://www.opengeospatial.org/standards/sensorml
6
http://www.loa.istc.cnr.it/ontologies/DUL.owl
nodes and the role each node plays. Characteristics can be attached to the links,
e.g., LinkQuality, which influences packet drops and retransmissions.
Finally, the context in which the WSN operates plays a role. Therefore, the
ontology is linked to existing ones, e.g., the OWL Time ontology and DBPedia7 .
4 Garbage Bin Tampering Monitor Prototype
The GBTM monitors garbage bins in Ghent, which are used for small-scale
litter disposal and are equipped with a sensor to detect the opening and closing
of their cover. The cover can only be removed by a special-purpose key. Any
other manipulations are illegitimate. A web-based interface8 allows personnel to
consult the observations, either as raw data, as an alligned table clustering data
per bin or on a map. Anomalies are highlighted by combining the observations
with external data, e.g., garbage collection timetables. The views also allow
optimizing garbage collection routes and timetables.
Rule-based sensor reasoning The garbage bins are equipped with a magnet-
activated reed switch, which stores a type, i.e., open or close, and timestamp in
the sensor’s ROM when a hardware interrupt occurs. To reduce the number
of transmissions, rule-based reasoning accumulates the sensor readings during a
configurable time interval, after which they are transported in bulk to a database
on the back-end server, which exposes them via a D2R-based RESTful interface.
Ontology-based back-end reasoning The sensors issue their measure-
ments once per time interval to the back-end. The Next-Wake-Up-Time configu-
ration parameter determines the timepoint at which this happens. It is preferably
avoided that sensors wake up at the same time as this increases the amount of
retransmissions, particularly in single-hop topologies, due to collisions. There-
fore, if such a situation is discovered, the back-end reasoner will use the WSN
ontology to recalculcate a dephazed next wake-up time scheme.
Rule-based gateway reasoning When the gateway receives a request, it
first checks if the required up-to-date info is available in its cache. If it is, the
cached data is sent to back-end to reduce the amount of data transmitted and
thus the energy consumption. If not, the measurements are retrieved from the
sensors. Determining the time after which data in the cache should be refreshed
is difficult. Applications preferably use the most recent measurements. However,
they need to comply with legislation concerning how much Radio Frequency
communication is used, e.g., 6 minutes per hour for a 169 MHz radio. Therefore,
the gateway monitors the duty cycle and adapts its caching strategy accordingly.
References
1. M. Wijnants, et al.: An eco-friendly hybrid urban computing network combining
community-based wireless LAN access and wireless sensor networking. In: Proc. of
GreenCom. (2012) 410-417
2. P. De Mil, et al.: Design and implementation of a generic energy-harvesting frame-
work applied to the evaluation of a large-scale electronic shelf-labeling wireless sensor
network. EURASIP JWCN (2010) 12
7
http://www.w3.org/TR/owl-time/ & http://wiki.dbpedia.org
8
http://mediasharing2.edm.uhasselt.be/greenwecan_v3/php/gwc_usecase_
gbtm.php