practical belief propagation in wireless sensor networks
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Practical Belief Propagation in Wireless Sensor Networks. Bracha Hod Based on a joint work with: Danny Dolev, Tal Anker and Danny Bickson The Hebrew University of Jerusalem. Outline. Introduction to Wireless Sensor Networks Belief Propagation overview - PowerPoint PPT PresentationTRANSCRIPT
Practical Belief Practical Belief Propagation Propagation
in Wireless Sensor in Wireless Sensor Networks Networks
Bracha HodBracha HodBased on a joint work with: Based on a joint work with:
Danny Dolev, Tal Anker and Danny Danny Dolev, Tal Anker and Danny BicksonBickson
The Hebrew University of The Hebrew University of JerusalemJerusalem
Outline
Introduction to Wireless Sensor Networks
Belief Propagation overview Efficient Belief Propagation
framework for Wireless Sensor Networks
Experimental evaluation Summary
Israeli Networking Seminar May 29, 2008
Wireless Sensor Networks Technology is pushed by
breakthroughs in MEMS, wireless communication, battery power, etc.
Wireless Sensor Networks (WSNs) Wireless network consisting of
spatially distributed autonomous devices using sensors
The sensors cooperatively monitor physical or environmental conditions
Israeli Networking Seminar May 29, 2008
WSN Applications
WSN Characteristics Limited power sources and restricted
computational capacities Wireless medium which imposes
constraints, such as collisions and errors
Topology changes due to interference, poor link quality, sleep states, death, etc.
Special network dynamic resulting from the self-organization property
Scaling problems because the network has a large number of nodes
Israeli Networking Seminar May 29, 2008
History 1994 - DARPA funded research on ‘Low
Power Wireless Integrated Microsensor’ 1998 - WSN technology has been nurtured
in its early stages at UC-Berkeley and UCLA It is estimated that in the US over $100 million
in government funding has been invested in university WSN research projects since then
2003 - Technology Review from MIT, listed WSN on the top, among 10 emerging technologies, that would impact our future
2008 – About ten years of academic work has been done in this area but still a long way to go
Israeli Networking Seminar May 29, 2008
Current Status Research
Many protocols for routing, synchronization, fault tolerant, localization, collaborative information processing, data aggregation, etc.
Development Dedicated Operating System and Database
system, programming languages and test deployments
Standardization IEEE 802.15.4, Zigbee, 6lowpan
Still a lot to do Deployment, integration with other
networks, security and scalability
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Inference in WSNs Data fusion and processing are the
core information gathering activities performed in the sensor nodes
Consequently, inference methods become an increasing research interest in the field of WSNs
In this model, an undirected graph G = (V,E) is a set of nodes V and arcs E, which represent dependencies among random variables
A complex system is viewed as a combination of many simpler pieces connected by probability theory
The idea: instead of calculating 8-sumswe can calculate4-sumsand 2-sums
Graphical Model
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A
D
C
B
Belief Propagation (BP) BP is an iterative algorithm for
computing maximum or marginal posterior probabilities by a local message passing
BP is associated with rapid convergence, accurate results and good performance in asynchronous environment
When performed on trees, BP converges to the correct values in a finite number of iterations
Israeli Networking Seminar May 29, 2008
The Min-Sum Variation The goal is to minimize the overall cost
in the network, based on the local cost functions and the constraints between the nodes
Each node transmits to its neighbors a message with its local and joint costs. Each neighbor updates its own belief accordingly and transmits the new belief
Gradually the information is propagated through the network until the nodes converge to a common belief
Israeli Networking Seminar May 29, 2008
Practical Considerations
The unique constraints and requirements in WSN demand changes in traditional algorithms
Several issues to address Mapping WSN to graphical model Robustness against failures Scalability
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Mapping WSN to Graphical Model
Loopy BP Operates on a cyclic network Usually works well because the cycles are
large Junction Tree
Creates a tree based on the cliques in the graph
Scales exponentially with the number of nodes
Involves high overhead
X1
X5
X4X3
X2
m12m21
m45m54
m24m32
m23
m42
Israeli Networking Seminar May 29, 2008
Robustness against Failures Broadcast communication
The message update rule is not an atomic operation which may result in erroneous calculation
Synchronization problems Asynchronous messages may harm the
accuracy Topology changes
A link break in the middle of the message-passing may badly affect the convergence
Israeli Networking Seminar May 29, 2008
Scalability
The original BP algorithm is based on
a local message passing, but it is not
scalable The process is performed in the entire
network
The convergence depends on the size
of the network, and as a result, time
and message complexity are not
constantIsraeli Networking Seminar May 29, 2008
Our Solution Adoption of two WSNs’ approaches
Localized Algorithms Data-centric
Resulting in
Approximation by a set of local optimums instead of a single global optimum
Energy-efficient, fully distributed, asynchronous, robust and scale
scheme
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Efficient BP Framework Construction of multiple trees according to
the routing tree properties and the information that the nodes hold
Every tree is created on-the-fly using a special message, without any maintenance
A “round” field in each message helps in dealing with the asynchronous process
Part of the errors may be detected and overcome
Scalability is achieved by operating in a restricted region, with limited number of rounds
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Empirical Evaluation Case study:
clustering The challenge is to
efficiently form a connected disjointed group of nodes in a local and distributed manner. Each group contains a single leader and several ordinary nodes
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Simulation Framework Simulation in TOSSIM, TinyOS simulator 5 different time slots were used to
validate the behavior on different network topologies
The localized algorithm vicinity was set to 2 with constant number of rounds equal to 8
In the simulation, the average density is 14 which means that the optimal number of clusters for 50 nodes is about 4
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Simulation Results
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Network topology
Message-passing trees
Clustered network
Simulation Results
Number of clusters per 50 nodes
Percent of nodes who have a cluster head
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Simulation Results
Percent of nodes who reach a full convergence
Average loss messages in a message-passing tree
Israeli Networking Seminar May 29, 2008
Summary WSNs are envisioned to become an
integral part of our lives, in applications such as environmental monitoring, smart spaces, medical monitoring, etc.
Two leading approaches: localized algorithms and data-centric, are essential for the design of practical and robust algorithms in WSNs
BP is a promising approach to solve inference tasks in WSNs, when combined with these two approaches.
Israeli Networking Seminar May 29, 2008
Thank You!Thank You!
Israeli Networking Seminar May 29, 2008