Course Contents

Course Syllabus:  Low-power Personal and Body Area Networks,IEFT RPL & uIP standard, Critical Transmission Power and Asymptotic Connectivity, Sensing Coverage in Convex / non-Convex environments, Deterministic and Probabilistic Sensor Deployment, Synchronization / FSP, Bio-inspired networking methods for dense sensor networks (reaction-diffusion MAC, PCO and firefly synchronization, Immune system based DNRS). Distributed algorithms for acquisition, storage and processing : Consensus and Gossip algorithms, Distributed Data Compression, Network Coding Schemes. Modelling and Learning of Spatio-temporal data : Compressed Sensing, Sparse Representations, Low Rank Matrix Completion. Localization: dead-reckoning, passive, multimodal. Programming principles with Real-time Operating Systems: tinyOS / nesC, protothreads / Contiki OS, Over-the-air-programming

 

 PART I: WSNs - Networking Perspective

 Topic 1: Introduction

  • Examples, Applications, Challenges, Metrics

 

Topic 2: Networking Fundamentals

  • Fundamentals on PHY, Medium Access Control Sublayer for Low-Rate Personal and Body-Area Networks (IEEE 802.15.4 / IEEE 802.15.6)

  • Routing over Low-Rate Networks (RPL) and the uIP IEFT standards

  • Radio Duty Cycle Protocols for WSN

 

Topic 3: WSNs Deployment

  • Connectivity Graphs and Modelling

  • Sensing Coverage in Convex / Non-convex environments

  • Deterministic and Probabilistic WSN Deployment

 

Topic 4: Empirical WSN studies

  •  Radio-link quality estimation

 

PART II: WSNs - Data Perspective

 Topic 5: Data Models & Acquisition

  • Intelligence in WSN

  • Spatio-temporal models

  • Multidimensional time-series

 

Topic 6: Distributed Signal Processing

  • Distributed processing algorithms (Gossip, Consensus)

  • Distributed denoising, estimation & detection

 

Topic 7: Compression and Storage

  • Decentralized data storage & recovery

  • Distributed erasure coding

  • Distributed data compression

 

Topic 8: Localization & Tracking

  • Principles, architectures and infrastructure

  • Dead-reckoning and fingerprinting

  • Distributed tracking

 

Topic 9: Distributed Learning Architectures

  • Data classification & clustering

  • Learning from streams

 

PART III: Programming WSNs

Topic 10: Operating systems & Programming Models

  • Programming paradigms for WSN platforms

  • Simulation and emulation environments for WSN

  • Over-the-air programming

 

 

 

 

SmartWater