sensor 2
DESCRIPTION
wsnTRANSCRIPT
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Research Areas in
Wireless Sensor Networks
A gateway to outreach the world while studying in Vietnam
LIM lab/JAIST meeting, Oct.03 2012
By
Quan Le-Trung, Dr.techn.
http://sites.google.com/site/quanletrung/
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Contents Re-configuration and Re-Adaptation in Wireless Sensor
Networks Analysis
In-Progress Simulating in TinyOS/Contiki
Development of WSNs Applications over Existing Protocol Stacks & Standards Examining existing protocols
Information Reduction, Dissemination, Storage, Routing in
A gateway to outreach the world while studying in Vietnam
Information Reduction, Dissemination, Storage, Routing in Wireless Sensor Networks
Examining existing standards uIP/uIPv6/6LoWPAN/IEEE802.15.4, IPSO Alliance, IETF
ROLL/6lowApp, ISA SP100.11a, etc
Re-use (vs. new one) for specific applications
Re-Design and Re-Implement Low Cost Sensor Devices Low Power Wireless PAN Applications
Presented at 13.30-14.00
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Re-configuration and Re-
Adaptation in Wireless Sensor
Networks
A gateway to outreach the world while studying in Vietnam
In association with projects:1. Middleware for Sensor as a Service (SeaS), INRIA
Associate Team [20102013]2. Dependability and Adaptation in Systems-of-Systems,
funded by EGIDE PHC Aurora, France, [2009-2010]3. SWISNET: Scalable Wireless Sensor Networks, funded by
Norway Research Council [2008-2010]
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Contents Overview of WSNs Planes of Functionalities
Data plane Data dissemination, aggregation, storage Examining existing protocols [NEXT SECTION]
Control plane Routing, network architecture and topology Routing, network architecture and topology
[NEXT SECTION] Re-configuration, re-programming
In-progess [OSs: tinyOS/Contiki, middlewares, etc] Management plane [to be presented NOW]
Context management for adaptation & reconfiguration WSNs
Code distribution Internetworking WSNs with external networks
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Overview of WSNs Objectives [ARCH./Net.-Topo. Design]
Scalability of large-scale, heterogeneous WSNs Dynamic environments
Adaptation/re-configuration of WSNs Re-programming WSNs
ARCH. can be applicable to numerous WSN applications Internetworking with external networks
Broaden richness of WSN apps. into other domains Easy tasks of control and management of WSNs under
dynamic changes of environment
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Q. Le-Trung, A. Taherkordi, F. Eliassen, P. E. Engelstad, T. Skeie, and H. N. Pham, (2009), "DCM-Arch: An Architecture for Data,Control, and Management in Wireless Sensor Networks," IEEE AINA'09, May.26-29 2009, Bradford, UK, in press
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Q. Le-Trung, A. Taherkordi, F. Eliassen, P. E. Engelstad, T. Skeie, and H. N. Pham, (2009), "DCM-Arch: An Architecture for Data,Control, and Management in Wireless Sensor Networks," IEEE AINA'09, May.26-29 2009, Bradford, UK, in press
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Management Plane Functionalities
Context (energy) management for adaptation & re-configuration WSNs-WHEN & WHERE
Code distribution-HOW Internetworking WSNs with external networks-HOW
Considerations Heterogeneous WSNs Effects of data/context information flows on performance of
control plane, i.e., re-programming part/whole WSNs Traffic bottleneck, load-balance
Tools/utilities for shortening implementation & deployment time of WSN applications
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management in SWISNET Context management [Dimensions Flexible Apps. dependent]
computing context [CIA] network connectivity, bandwidth, energy, nearby resources
user context [CIA] users profile, application policy, location, behavior preference
physical context [sensor readings DATA] lighting, noise, temperature... lighting, noise, temperature...
temporal context [CIA] time, delay, duration
space context [CIA] memory, processor usage
functional context [CODE] coding version, size,
Context information for adaptation & reconfiguration [CIA]
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management in SWISNET Context management
Necessary parts providing logic reasoning services to process context
information New:
collaboration & optimization
AdaptationReasoning
Optimal # target sensors
Optimal # target clusters
Rule/Role-basedPolicy-based
Context Parser
Context Provider
Abstracting useful contexts from heterogeneous
sources, and convert them to certain representations.
Context interpreter
Context Database
Storing current and past contexts for a particular sub-domain. Each domain has one logic context database.
New: TYPES of STORAGE1. DATA [Sensor readings]
2. CODE Repository3. CIA Context information for adaptation/Reconfiguration
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management: Collaboration/Optimization Adaptation
middleware Big changes
[add/replace/remove components]
Cluster Head/Sink makes local makes local decision
Goal-oriented, Utility-based
Small changes [re-configure components]
Sensors locally makes their own decisions
Situation-action
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management Network modeling directions [Add/Update/Replace Comp.]
[FIRST] Pro-active or Code allocation Triggered by Cluster Head(s)/Sink Based on CIA information collected from sensors Goal
Determining no. optimal target sensor nodes for code updates or code replacementscode replacements
[SECOND] Re-active or Code acquisition Triggered by sensor nodes, optimized by Cluster
Head(s)/Sink Based on both local CIA at each sensor node, and collected
CIA at Cluster Head(s)/Sink Can require %Error rate
User profile, or application policy Determining by additional algorithms, e.g., selection a sub-set!
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management [FIRST] Utility
Speedup the total time [min latency/delay] to finish a code update event in a cluster
Cost Minimum total energy usage of sensors/cluster head
{ }...
.minmin
++++=
++=
iwaiting
ilinking
iswap
isafe
i
Vi
iiPTXT
ttttt
txTT
Minimum total energy usage of sensors/cluster head
Constraints PiEi (residual energy) % error rate [user-context] miMi (memory, i.e., space context)
Variables xi{0,1} (joined or not in update) yi{0,1} (safed state or not in update)
+Vi
iiTX PxP .min
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management [FIRST] Requirements
Transmission/Propagation model Energy consumption model Different network modeling for Cluster vs. Global network
Solutions for numerical data With [constant-scale ti, only min{T} in goal/objective]
Integer programming with x{0,1} Integer programming with xi{0,1} With [variable-scale ti , both min{T} and min{P}]
Multi-objective problem Micro-genetic algorithm xi,yi{0,1}, together other set of variables depending context dimensions
Solutions are called [Optimal/Theoretical] centralized algorithm E.g., cluster head/sink gets 100% network knowledge [CIA] Real cases: X%, X100% WHEN? Convergence Time?
Distributed algorithms with heuristics
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management [SECOND] Given
Goal
( )
%Err]on [based V set,sensor .CIA]on [based set,sensor
: function, )(,, ,/
OP VVSelectedNoVVSatisfied
REcweightedecEVGTreeGraph
SA
SA
+
iecingDeter )(min i.e., ,V min OP
Solution Rooted k-minimum spanning
tree [k-MST] problem NP-Hard problem Different approximation
algorithms, currently O(2+)
OPVi TREEeiOP
Quan Le-Trung, (2011), "Algorithms for Optimized Code Distribution in Wireless Sensor Networks," Journal of Science and Technology, ISSN: 0866-708X, Vol. 49, No. 4A, pp.369-375, 2011
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management in [SECOND] Motivation Example Algorithm TREE(G(V,E,c(e)),VSA,)
BeginVSA=; numLoop=0;If Event_Triggered() {
START:numLoop=numLoop+1;Notification(V);While (NOT(expired(T ))) {While (NOT(expired(Twait))) {
If Received( ) AND Satisfied(S) {; Updated(c(S)); }
}If (|VSA|*|V|)
Output(G(V,E,c(e)),VSA,); // to k-MST, See Figure 4
Else If (numLoop
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Context Management in [SECOND]|| VN =( ) ( ) ( )2
22
.
..
*2
**
wlrN
rwlr
wlsAvgDegn Vspi
+
+==
NnN PC .11 ++=
PC nLoopN += max2
( ) ( )
in theory ,HardNPMSTkO
Node Type Space Computation Max. Comm.Sensor O(n) O(1) Nc1
CH/Sink O(N.n) O(k-MST) + O(TREEVOP) Nc2
( ) ( ) + ionapproximatin ,2O MSTkO
( ) ( )( )
systems blockedin ,T*maxLoop*NOsystems blocked-nonin ,max*
wait
LoopNOTREEO
OPV
Quan Le-Trung, (2011), "Algorithms for Optimized Code Distribution in Wireless Sensor Networks," Journal of Science and Technology, ISSN: 0866-708X, Vol. 49, No. 4A, pp.369-375, 2011
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Code Distribution Code size
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Internetworking WSNs with Internet Objectives
Broaden the richness of WSN apps. into other domains Easy of management & control tasks from Internet
Approaches Inter-working approaches
Proxy Gateway Tunneling/Half-Tunneling Specific Standards/Protocols in WSNs
ZigBee/IEEE802.15.4, ISA SP100.11a ZigBee/IEEE802.15.4, ISA SP100.11a Transport protocols in WSNs
Overlay Network, e.g., DTNs New Applications
Modified TCP/IP Link-Repaired Approach
uIPv6/6LoWPAN/IEEE802.15.4, IPSO Alliance Service approaches
Web Service [SOA] REST-based ARCH Assumption on the network layer interoperability!
Web service: All-IP [sensor networks & enterprise networks/Internet] REST-based ARCH: HTTP [the most widespread Internet standard]
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Wireless Embedded Internet group, Dept. CSE, International UniversityWireless Embedded Internet group, Dept. CSE, International University
Internetworking WSNs with Internet Modified TCP/IP
Initially, Why not TCP/IP for wireless networks? Misinterpretation of packet loss (congestion high BER) Frequent path breaks (long route reconfiguration, slow start,
inefficient use of resource) Effect of path length
(Ref. http://www.cs.tut.fi/~moltchan/adhoc TUT 2004)
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Internetworking WSNs with Internet: Modified TCP
NOW, Modified TCP is dominant 6LoWPAN
The Wireless Embedded Internet, http://6lowpan.net/
uIP now part of Contiki,
http://en.wikipedia.org/wiki/UIP_%28micro_IP%29
uIPv6 http://www.sics.se/taxonomy/term/951
lwIP
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lwIP now developed by a world-wide group of developers
http://en.wikipedia.org/wiki/LwIP
Interconnecting Smart Objects with IP: The Next Internet http://www.thenextinternet.org/
IPSO Alliance http://www.ipso-alliance.org/
Industry ArchRock (now part of Cisco), etc
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1. Quan Le-Trung, (2011), "Algorithms for Optimized Code Distribution in Wireless Sensor Networks," Journal of Science and Technology, ISSN: 0866-708X, Vol. 49, No. 4A, pp.369-375, 2011.
2. Amirhosein Taherkordi, Frederic Loiret, Azadeh Abdolrazaghi, Romain Rouvoy, Quan Le-Trung, and Frank Eliassen, (2010), Programming Sensor Networks Using REMORA Component Model, in 6th IEEE/ACM International Conference on Distributed Computing in Sensor Systems (DCOSS), Santa Barbara, California, USA, June 2010. [abstract]
3. Amirhosein Taherkordi, Romain Rouvoy, Quan Le-Trung, and Frank Eliassen,(2009), Supporting Lightweight Adaptations in Context-aware Wireless Sensor Networks, The 1st International Workshop on Context-Aware Middleware and Services [ACM CAMS'09] in COMSWARE'09, ISBN: 978-1-60558-525-3, Vol. 385, , June 16, 2009, pp.43-48 Dublin, Ireland. [abstract]
4. Amirhosein Taherkordi, Quan Le-Trung, Romain Rouvoy, and Frank Eliassen,(2009), WiSeKit: A Distributed Middleware to Support Application-level Adaptation in Sensor Networks, 9th IFIP international conference on Distributed Applications and Interoperable Systems (DAIS'09) LNCS
References
international conference on Distributed Applications and Interoperable Systems (DAIS'09) LNCS Vol. 5523, ISBN: 978-3-642-02163-3, June 9-12, 2009, Lisbon, Portugal, pp.44-58. [abstract]
5. Quan Le-Trung, Amirhosein Taherkordi, Frank Eliassen, Paal E. Engelstad, Tor Skeie, and Hai N. Pham, (2009), "DCM-Arch: An Architecture for Data, Control, and Management in Wireless Sensor Networks," IEEE AINA'09 ISBN: 978-0-7695-3638-5, May.26-29 2009, Bradford, UK, pp.898-905. [abstract]
6. Amirhosein Taherkordi, Romain Rouvoy, Quan Le-Trung, and Frank Eliassen (2008), A Self-Adaptive Context Processing Framework for Wireless Sensor Networks, ACM/IFIP/USENEX workshop on Middleware for Sensor Networks (MidSens'08) in conjunction with Middleware'08, Dec.2008, Leuven, Belgium, pp.07-12. [abstract]
7. Amirhosein Taherkordi, Frank Eliassen, Romain Rouvoy, and Quan Le-Trung, (2008), "ReWiSe: A New Component Model for Lightweight Software Reconfiguration in Wireless Sensor Networks," Proc. of 7th International Workshop On System/Software Architectures (IWSSA'08) in conjunction with the 10th International Symposium on Distributed Objects, Middleware, and Applications (DOA'08), Monterrey, Mexico, Nov. 10-12, 2008, pp.415-425. [abstract].
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Routing & Information Reduction,
Dissemination, Storage in Wireless
Sensor Networks
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Sensor Networks
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Outline
Motivations
Information Reduction Techniques
Logical Network Infrastructures
Information Storage and Processing
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Information Storage and Processing
Routing and Physical Network Infrastructures
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Motivations
Examining existing protocols and organized
into a layer architecture
Re-use in specific applications
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Motivations Example: An Integration
View of Wireless Sensor Networks
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Motivations Example: An Integration ARCH.
for Wireless Sensor Networks
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Information Reduction Techniques Compression
Coding, Wavelet, Linear Regression [LR], Compressive Wireless Sensing [CWS], Piecewise Constant Approximate [PCA], PartialPreaggregation [PP]
Selection Connected Correlation Dominating Set [CCDS]
Clustered Aggregation [CAG]
Sparse Aggregation [SAG]
Self-based Regression [SBR]
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Self-based Regression [SBR]
Bayesian Inteference [Infer]
Temporal Coherency-Aware in-Network Aggregation [TiNA]
TAG-based Aggregation SUM/COUNT/AVG/MAX/MIN
Q-Digest [MEDIAN], Range Query
Accuracy Measure Accuracy, System Accuracy
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Information Reduction Techniques
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Information Reduction TechniquesIndex Objectives Target Timing Architecture Reduction Technique
Coding Energy-efficiency Correlated data Yes Tree Compression
Wavelet Energy/Storage-efficiency Spatio-temporal query Yes Quad-Tree Compression
CWS Energy-efficiency/Low-latency Structural regularity data Yes Tree Compression
PCA Energy-efficiency Time-series data Yes Tree Compression
PP Energy-efficiency Aggregation query No Tree Compression
TiNA Energy-efficiency Temporal coherency data Yes Tree Compression
LR Energy-efficiency/Accuracy Correlated spatial data No Clustering Compression/Accuracy
SRA Energy-efficiency/Accuracy Feature extraction query Yes Clustering Compression/Accuracy
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SRA Energy-efficiency/Accuracy Feature extraction query Yes Clustering Compression/Accuracy
CCDS Energy-efficiency Correlated data No Flat/Graph Selection
CAG Energy-efficiency Spatio-temporal data Yes Tree Selection
SAP Energy-efficiency Sparse aggregated data Yes Tree Selection
SBR Energy-efficiency/Bandwidth Correlated data Yes Clustering Selection
Infer Energy-efficiency/Accuracy Unobserved data Yes Flat Selection
TAG Energy-efficiency/Low-latency Declarative query Yes Tree Aggregation
q-digest Energy-efficiency/Bandwidth Quantile/Range query No Tree Aggregation
apx-MEDIAN Energy/Storage-efficiency Quantile query No Tree Aggregation
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Information Reduction Techniques
Index Compression Selection TAG-based Aggregation Accuracy
Dense, small/medium-scale WSNs Large-scale WSNs Large-scale WSNs
May require sensor devices with
high processing capacity, memory
Complementary to "Selection"Complementary to
"Compression"
Require all nodes send Only requires a subset Requires all nodes send data,
Considering tradeoff
between energy-
efficiency vs. either:
+latency
+accuracy
Characteristics
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Require all nodes send
collected data
Only requires a subset
nodes sending data
Requires all nodes send data,
but a subset of nodes can be
selected through query
Event-driven monitoring,
data collection with on-demand
data retrieval via queries
Dynamic environments
+accuracy
+bandwidth
Periodic monitoring, data gathering with predefined data
retrieval patterns
Static environments
ALLApplications
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Logical Network Infrastructures Data Dissemination ARCH.
Flat
Hierarchy Cluster-based [Low-Energy Adaptive Clustering Hierachy (LEACH),
Hybrid Energy-Efficient Distributed Clustering (HEED)]
Chain-based [Power-Efficient Gathering in Sensor InformationSystems (PEGASIS)]
Tree-based [EADAT]
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Tree-based [EADAT]
Grid-based [Two-Tier Data Dissemination (TTDD)]
Query/Data Dissemination ARCH. Tiny Aggregation [TAG]-based Tree
Extensions of TAG-based Tree Approximation, Synopsis Diffusion
Tributary-Delta
Sweep
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Logical Network InfrastructuresIndex Fault-tolerance ARCH. Underlying protocol Target Sink Applications
LEACHRotating cluster
head
Clustering
Push
CSMA MAC, different
CDMA codes in each clusterDATA Fixed
Constant monitoring
Periodic data reporting
HEEDRotating cluster
head
Clustering
Push
Ad-hoc routing for inter-
cluster communicationDATA Fixed Environmental monitoring
PEGASISChain
reconstruction
Chain
Push
Location-aware
CDMA/non-CDMADATA Fixed Environmental monitoring
TTDDUpstream info
duplication, timeout
Grid
Push/Pull
Location-aware
Geographic forwarding
DATA
QUERYMobile Event of interest
EADATRotating branch
points
Tree
Push
Transceiver with on/off
radio capabilityDATA Fixed
Monitoring abnormal
events
TAGMulti-path
routing
Tree
Push/PullAd-hoc routing
DATA
QUERYFixed
Monitoring and data
collection tasks
Multi-path
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Synopsis
diffusion
Multi-path
routing, order- &
duplicate insensitive
synopses (ODI)
Ring
Push/Pull
Broadcast wireless
communication
DATA
QUERYFixed
Aggregation: Sum, Count,
Avg, Medium, Uniform,
Max, Min
Approximation
Multi-path
routing, duplicate-
insensitive sketches
Ring
Push/PullN/A
DATA
QUERYFixed
Aggregation: Sum, Count,
Avg, Max, Min
Tributary-DeltaMulti-path routing
Tree-based
Combining Tree &
Multi-path routingN/A
DATA
QUERYFixed
Aggregation: Sum, Count,
Avg, Medium, Uniform,
Max, Min, Quantiles
Sweep
One-to-many
downstream
neighbors, local
backtracking
Wavefront CSMA MACDATA
QUERYFixed
Aggregation: Sum, Count,
Avg, Max, Min
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Information Storage & Processing Data Storage ARCH.
Standard Sink Model [External, Local] TinyDB
Semantic Routing Tree [SRT]
Distributed Indexing and Storage Distributed Data-Centric Storage [DCS]
Greedy Perimeter Stateless Routing [GPSR]
Multi-Resolution Data Storage [MRDS]
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Multi-Resolution Data Storage [MRDS] Distributed Quad-Tree [DQT]
Predictive Storage [PRESTO]
Distributed Attribute Storage
Query Processing Mechanisms Query Styles
Multi-Query Optimization
Distributed Indexing Techniques
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Information Storage & Processing Query Processing Mechanisms
Query Styles Query Execution [Push vs. Pull vs. Hybrid]
Structured [hash/index] vs. Unstructured Query [Blind Search]
Query Plan [Traditional vs. Conditional]
Others Single-Value, Value-Range, Location-Range
Data Aggregation, Data Acquisition
Multi-Query Optimization
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Multi-Query Optimization Multi-Query Optimization [MQO]
Two-Tier Multiple Query Optimization [TTMQO]
Indexing Techniques Geographic Hash Table [GHT], DIMENSIONS, Distributed Index for
Features in Sensor Networks [DIFS], Distributed Index for Multi-Dimensional Data [DIM]
Fractional Cascading [FC], Hierarchical Spatial Gossip [HSG]
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Information Storage ARCH.
Index Query ARCH.Underlying
protocolsIndexing Reduction Platform
Multi-query
optimizationApplications
TinyDB SQL-based Tree SRT N/ATAG-based
aggregationTinyOS
Ordering of
sampling
& predicates,
event query
batching
Monitoring,
collecting data
Many detected
events,
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DCSClearly-
definedFlat/Grid GPSR GHT N/A N/A N/A
events,
not all event
types are
required
MRDS
Unclearly-
defined,
drill-down
query
Multi-
resolution
hierarchy
DQT/GPSR DIMENSIONSCompression
[Wavelet]
Linux/
EmStarN/A
Data-intensive
scientific apps.
PRESTOPats/
Post-factoTree N/A Skip Graphs
Compression
[Wavelet]N/A N/A
Event-driven
apps.,
surveillance
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Indexing TechniquesIndex Load-balance Fault-tolerance Query-style ARCH. Routing Storage-ARCH.
GHTStructured
replication
Home
perimetersingle-value
Flat/
GridGPSR DCS
DIFSStructured
replication
Home
perimeterrange
Multi-
rooted
hierarchy
GHT/
GPSRDCS/TinyDB
DIMSpreading
storage
Local/mirror
replication,
zone
expansion
multi-
dimensional
range
Binary
zone treeGPSR DCS
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expansion
DIMENSIONS
Periodical
hasing,
rotating
cluster head
Data
replication
multi-
dimensionalsi
ngle-value
drill-down
Quad-tree GPSR MRDS
FC
Recursively
partitioning
sensor field
Data
replicationrange Quad-tree
Geographic
forwarding
PRESTO, local
standard sink
storage
HSGIndividual
trees
Individual
trees
multi-
dimensional
range
Quad-treeGeographic
forwardingMRDS
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Routing & Physical Network Infrastructures Flooding
Gossiping
Sensor Protocol for Information via Negotiation [SPIN]
Directed Diffusion
Rumor Routing
Low Energy Adaptive Clustering Hierarchy [LEACH]
Geographical and Energy-Aware Routing [GEAR]
Greedy Perimeteer Stateless Routing [GPSR]
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Greedy Perimeteer Stateless Routing [GPSR]
Multi-Path Routing
Mobile-Sink
Mobile-Relay
Sensor/Actuator
Anycast
Hybrid Energy-Efficient Distributed Clustering [HEED]
Updating ...
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Routing Protocols in WSNs
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Routing Protocols in WSNs
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Routing Protocols in WSNs
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Routing Protocols in WSNs
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Routing Protocols in WSNs
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1. Quan Le-Trung, and Minh-Son Nguyen, (2011), "Issues on Information Dissemination and Routing in Wireless Sensor Networks", in the 9th International Conference on Advances in Mobile Computing & Multimedia ACM MoMM'2011, ISBN: 978-1-4503-0785-7, HCM city, Vietnam, pp. 202-205, Dec.05-07, 2011. [abstract]
2. Quan Le-Trung, Paal E. Engelstad, Tor Skeie, Amirhosein Taherkordi, and Hai N. Pham, (2009), Information Storage, Reduction, and Dissemination in Sensor Networks: A Survey, IEEE IRSN2009, Jan.13 2009, Las Vegas, US. [abstract]
3. Quan Le-Trung, Amirhosein Taherkordi, Frank Eliassen, Paal E. Engelstad, Tor Skeie, and Hai N. Pham, (2009), "DCM-Arch: An Architecture for Data, Control, and Management in Wireless Sensor Networks," IEEE AINA'09 ISBN: 978-0-7695-3638-5, May.26-29 2009, Bradford, UK, pp.898-905. [abstract]
References
May.26-29 2009, Bradford, UK, pp.898-905. [abstract]
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Wireless Sensor & (Actuator)
Networks (WSANs) towards Reliable
Automation Systems:
Development of WSNs Applications over Existing Protocol Stacks and Standards
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Automation Systems: Adaptation & Management
[A Case Study]
Another case study
In-progress
ZigBee applicationsMANETs over WSNs: will be presented at 11.30-12.30
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Motivations HART
Connection of field devices in a field bus Cost of network planning and wiring
WirelessHART/ISA100.11a Wireless vision: autonomic communications
No human-in-the-loop
Flexible systems Self-configured, self-adaptive, self-protected
Our Motivations and Goals Toward Intelligent System
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Toward Intelligent System Adaptation
Adaptation Middleware
Reconfiguration of used devices under dynamic changes of systems, environments
Reliablility & Energy-Efficiency
Management Context Management and Automatic Reasoning
Reliable Code Distribution
Heterogeneous WSNs with other wireless technogies and Internet, i.e., Internetworking
Standadization The system should be based on open standards, e.g.,
IEEE 802.15.4/ZigBee, 6LoWPAN, IPv6, ISA SP100.11a
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S inkC luster HeadSens or
Pre m ier Ne tw orkE xte rnal DAT A /
CO DE /C IA Repos itor ie s
W eb S erve r
M anage m ent S tat ion
F irew all/Ro uter /F ilte r /. ..
INT ER NETM o nitor in g
u ser (s ) thro ugh W W W
Sink /G ateway
Loc al D AT A /CO D E /C IA
Repo sito ries
OUR APPROACHES
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Real Platforms for WSNTestbed Sensor Devices: Crossbow, Jennic
Operating Systems: Contiki OS, Jennic, TinyOS
IDEs/SDKs: Jennic SDKs/Eclipse, Instant Contiki, TinyOS/MoteWorks
Network protocol stacks: IEEE 802.15.4, ZigBee, 6LoWPAN/IPv6, JenNet, Rime/uIPv6, XScale/XMesh
Simulation Tools: Instant Contiki, WSIM/WSNET/TOSSIM, ns-2/ns-3
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http://www.zigbee.org/
Outline Stack Architecture in ZigBee Specification
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Detailed ZigBee Software Architecture in Jennic
http://www.jennic.com/
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ContactsQuan Le-Trung, Dr.techn.
Wireless Embedded Internet group
School of CSE, International University-HCM City
6 quarter, Linh Trung ward, Thu Duc district
Ho Chi Minh city, Vietnam
A gateway to outreach the world while studying in Vietnam
Ho Chi Minh city, Vietnam
E-mail:
[email protected], [email protected]
Home page:
http://sites.google.com/site/quanletrung/