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DISTIN: Distributed Inference and Optimization in WSNs A Message-Passing PerspectivePowerPoint Presentation

DISTIN: Distributed Inference and Optimization in WSNs A Message-Passing Perspective

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### DISTIN: Distributed Inference and Optimization in WSNs A Message-Passing Perspective

SCOM Team

Sensor Selection (Clustering), Correlated Data Aggregation, Event Detection, State Estimation (Localization and Tracking, Activity Recognition).

Monitoring

Distributed Inference?

Issues: resource-constrained sensor nodes, in the presence of packet losses and link failures.

Data Aggregation

Sensor Selection (Clustering), Correlated Data Aggregation, Event Detection, State Estimation (Localization and Tracking, Activity Recognition).

(1324,1245)

Goal:

Developing efficient, scalable, robustmessage-passing algorithms for distributed optimization & inference in large-scale sensor-actuator networks.

Distributed Inference?

Target detection

Data fusion (if different sensors)

Target localization

Target classification (if multiple targets)

Target tracking

Transfer to sink & next leader

Localization & Tracking

- Jointlyoptimizing inference, networking & comm.
- Hierarchical network architecture,
- cross-layer optimization

Ad hoc Gossiping

Hybrid

Hierarchical Structure

Fusion center Decentralized

Hierarchical network is proved to be more scalable and energy-efficient

- Leveraging probabilistic graphical models and message passing for representation & inference

Problem Formulation

(Global Maximization/Marginalization)

Graphical Modeling

(Factor Graph, MRF, etc)

Message-Passing Rules

(Min-Sum, Sum-Product, etc)

Current heuristics are not efficient for WSN

(slow convergence, not scale well, computation & comm. costs increase exponentially with network size)

Distributed Algorithms

(Robust, Energy-Efficient, Scalable)

Probabilistic Graphical Models & Message-Passing Algorithms

- Computing Graph meets Communications Graph
- Capture the structure of a sensor network (for modeling statistical dependencies and/or communication links).
- Parallel nature of message-passing operations (flexible message scheduling)

- Recently significant progress in PGM & MEPA
- Junction Tree: exact solution, provable convergence,
but exponential cost & not parallel

- LoopyBP: approximate solutions,
sufficient conditions of convergence,

- Message-representation, message-censoring/damping.

- Junction Tree: exact solution, provable convergence,
- In-network processing & actuation
- Each node of the network obtains the posterior
distribution/optimal values of its variables.

- Each node of the network obtains the posterior

F. R. Kschischang et. al. “Factor Graph and the Sum-Product Algorithm”, IEEE Trans. Info. Theo. ‘01

Results: Min-Sum Clustering Algo

Min-Cost Hierarchical Architecture for Correlated Data Aggregation

- Convergence rate
- Approximation
- Scalability
- Robustness
- Efficiency

Results: Communication Cost

- Average communication cost of MCDAis approximately ½ of MEGA’s

MCDA1: trade-off between node residual energy and transmission cost with re-clustering after a constant number of rounds

MCDA2: minimize transmission cost by exploiting data correlation without considering node residual energy (no re-clustering)

MEGA: Minimum Energy Gathering Algorithm

Results: Network Lifetime

- Network lifetime is computed as the number of rounds until the first node dies.
- Network lifetime using MCDA is higher than MEGA because MCDA can balance between the transmission cost and node residual energy, resulted in lower re-clustering rate, which is an energy wasted process.

MCDA1: trade-off between node residual energy and transmission cost with re-clustering after a constant number of rounds

MCDA2: minimize transmission cost by exploiting data correlation without considering node residual energy (no re-clustering)

MEGA: Minimum Energy Gathering Algorithm

Software Architecture for DISTIN

APIs:

Inputs:

+Graphical Models & Priors

+Scheduling Rules

Outputs:

+Marginal Distribution, MAP, Avg., etc.

MW Core Components:

Message-Passing Inference Engine (pluggable):

+Update outgoing messages using new sensor readings

+Update marginal distribution using incoming messages

Message-Censoring & Buffering

+If message is not “new”=> do not send (censoring)

+If message lost => update using old (buffered message)

+Marshalling/unmarshalling compact message

Networking layer

+Neighbor Discovery & Maintenance

+Message Broadcasting & Receiving

Research Focus

Sensor Selection (Clustering), Correlated Data Aggregation, Event Detection, State Estimation (Localization and Tracking, Activity Recognition).

Developing efficient, scalable, robustmessage-passing algorithms for distributed optimization & inference in large-scale sensor-actuator networks.

Challenges

- NP-hard global optimization tasks, performed on resource-constrained sensor nodes, in the presence of packet losses and link failures.
- Current heuristics are not efficient for WSN (slow convergence, not scale well, computation & comm. costs increase exponentially with network size)

Contributions

- MEPA: Robust message-passing algorithms for efficient sensor clustering & correlated data aggregation: simple, fast, good approximation & highly localized
- Robust algorithms for planning & learning in collaborative multi-agent settings (e.g. state estimation, activity recognition, localization and tracking in WSN) - ongoing

Broader Impacts

- WSN: MEPA as a macro-programming language for novel applications (structural health monitoring, precision agriculture, etc.)
- Graphical Models: novel mess. passing algorithms under comm. constraints (message representation, censoring, and scheduling)
- Advance the theory & practice in these fields

Methodology

- Jointlyoptimizing inference, networking & comm.
- Leveraging probabilistic graphical models and message passing for representation & inference

Ubiquitous Computing LAB

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