This work proposes a collaborative sensor caching and data reconstruction method based on the sequential compressed sensing framework. Here, multiple caches are assumed to exist in the wireless sensor network to store the most recent data gathered from sensors within their respective coverage areas. To reduce the cache size and the data-acquisition overhead, each cache accesses measurements only from a small subset of sensors. This work proposes a collaborative sparse-signal reconstruction method that exploits the presence of sensors simultaneously accessible by multiple caches as anchor nodes to introduce dependency in the reconstruction. The reconstruction is based on the use of the alternating direction method of multipliers (ADMM), which enables distributed implementation of the algorithm. Simulations are provided to demonstrate the effectiveness of the proposed scheme.