Jointly compressing and caching data in wireless sensor networks

We propose a novel policy for data compression and caching in a wireless sensor network (WSN) that provably optimizes utility and cost jointly, providing a theoretical basis to understand the compression-caching tradeoff for data analytics in a WSN. Our optimization framework provides analytical answers to how much compression should be performed at each sensors, and where the data should be cached in the network. We propose a distributed algorithm to implement the optimal policy and adapt to the changes (e.g., cache size and request processes) in the network. We evaluate our approach through extensive simulations on WSNs.