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

Monitoring Data Aggregation Applications Sensor Selection (Clustering), Correlated Data Aggregation, Event Detection, State Estimation (Localization and Tracking, Activity Recognition). Distributed Inference? Issues: resource-constrained sensor nodes, in the presence of packet losses and link failures.

(1324,1245) Localization & Tracking Goal: Developing efficient, scalable, robust message- passing algorithms for distributed optimization & inference in large-scale sensor-actuator networks. Target detection Data fusion (if different sensors) Target localization Target classification (if multiple targets) Target tracking Transfer to sink & next leader Applications Sensor Selection (Clustering), Correlated Data Aggregation, Event Detection, State Estimation (Localization and Tracking, Activity Recognition). Distributed Inference?

Jointly optimizing inference, networking & comm. Hierarchical network architecture, cross-layer optimization Research Methodology Fusion center Decentralized Ad hoc Gossiping Hybrid Hierarchical Structure Hierarchical network is proved to be more scalable and energy-efficient

Research Methodology 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) Distributed Algorithms (Robust, Energy-Efficient, Scalable) Current heuristics are not efficient for WSN (slow convergence, not scale well, computation & comm. costs increase exponentially with network size)

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. In-network processing & actuation – Each node of the network obtains the posterior distribution/optimal values of its variables. F. R. Kschischang et. al. “Factor Graph and the Sum-Product Algorithm”, IEEE Trans. Info. Theo. ‘01

Results: Min-Sum Clustering Algo Convergence rate Approximation Scalability Robustness Efficiency Min-Cost Hierarchical Architecture for Correlated Data Aggregation

Results: Communication Cost Average communication cost of MCDA is approximately ½ of MEGA’s MCDA 1 : trade-off between node residual energy and transmission cost with re-clustering after a constant number of rounds MCDA 2 : 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. MCDA 1 : trade-off between node residual energy and transmission cost with re-clustering after a constant number of rounds MCDA 2 : minimize transmission cost by exploiting data correlation without considering node residual energy (no re-clustering) MEGA: Minimum Energy Gathering Algorithm

Software Architecture for DISTIN 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 APIs: Inputs: +Graphical Models & Priors +Scheduling Rules Outputs: +Marginal Distribution, MAP, Avg., etc.

Ubiquitous Computing LAB 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) Methodology Jointly optimizing inference, networking & comm. Leveraging probabilistic graphical models and message passing for representation & inference 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 Project UCLab, KHU Applications Sensor Selection (Clustering), Correlated Data Aggregation, Event Detection, State Estimation (Localization and Tracking, Activity Recognition). Research Focus Developing efficient, scalable, robust message- passing algorithms for distributed optimization & inference in large-scale sensor-actuator networks.