I. INTRODUCTION
A. Overview of the Current Paper
B. Comparison with Related Survey/Review Articles
C. Paper Organization
D. Notation and Abbreviations

II. BACKGROUND
A. CS Basics
B. Applications of CS in Wireless Sensor Networks: Motivation and Challenges

III. CS FOR DISTRIBUTED DATA GATHERING
A. CS Based Data Gathering Exploiting Temporal Sparsity
B. CS Based Data Gathering/Reconstruction Exploiting Spatial Sparsity
C. Data Gathering Exploiting Spatio-Temporal Sparsity

IV. CS FOR DISTRIBUTED INFERENCE
A. Compressive Detection
B. Compressive Classification
C. Compressive Estimation
D. Sparsity Aware Sensor Management

V. PRACTICAL ASPECTS
A. Impact of Fading Channel Impairments
B. Physical Layer Secrecy Constraints
C. Sparse Signal Reconstruction with Quantized CS
D. Other Issues

VI. LESSONS LEARNED AND FUTURE RESEARCH DIRECTIONS
A. Scalable Network Processing Exploiting Sparsity in Multiple Dimensions with Heterogeneous Data
B. Distributed/Decentralized Processing with Quantized CS
C. CS Based Inference with Multi-Modal Data
D. Further Developments Under Practical Considerations
E. Testbed Experiments and Performance Evaluation

VII. CONCLUSION