Sensor 2

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/

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


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

Re-configuration and Re-

Adaptation in Wireless Sensor

Networks


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]

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


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


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


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


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]


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


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


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!


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


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


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


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


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


Code Distribution Code size


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]


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)

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


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

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].

Routing & Information Reduction,

Dissemination, Storage in Wireless

Sensor Networks


Sensor Networks

Outline

Motivations

Information Reduction Techniques

Logical Network Infrastructures

Information Storage and Processing


Information Storage and Processing

Routing and Physical Network Infrastructures

Motivations

Examining existing protocols and organized

into a layer architecture

Re-use in specific applications


Motivations Example: An Integration

View of Wireless Sensor Networks


Motivations Example: An Integration ARCH.

for Wireless Sensor Networks


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]


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

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


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


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


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

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]


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

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


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


Avg, Max, Min

Tributary-DeltaMulti-path routing

Tree-based

Combining Tree &

Multi-path routingN/A

DATA

QUERYFixed


Avg, Medium, Uniform,

Max, Min, Quantiles

Sweep

One-to-many

downstream

neighbors, local

backtracking

Wavefront CSMA MACDATA

QUERYFixed


Avg, Max, Min

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]


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

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


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]

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,


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

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


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

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]


Greedy Perimeteer Stateless Routing [GPSR]

Multi-Path Routing

Mobile-Sink

Mobile-Relay

Sensor/Actuator

Anycast

Hybrid Energy-Efficient Distributed Clustering [HEED]

Updating ...

Routing Protocols in WSNs


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]

Wireless Sensor & (Actuator)

Networks (WSANs) towards Reliable

Automation Systems:

Development of WSNs Applications over Existing Protocol Stacks and Standards


Automation Systems: Adaptation & Management

[A Case Study]

Another case study

In-progress

ZigBee applicationsMANETs over WSNs: will be presented at 11.30-12.30

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


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

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


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

http://www.zigbee.org/

Outline Stack Architecture in ZigBee Specification

Detailed ZigBee Software Architecture in Jennic

http://www.jennic.com/

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


Ho Chi Minh city, Vietnam

E-mail:

[email protected], [email protected]

Home page:

http://sites.google.com/site/quanletrung/

Sensor 2

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