Binary Data Gathering With a Helper in Internet of Things
This paper focuses on one-helper assisted binary data gathering networks, for example, such as in Internet of Things, where a destination makes estimates of binary data relying on a number of agents and one helper. Due to the noise, corrupting errors already exist in the agent observations. To analyze the performance of this system, we formulate this system as a binary chief executive officer (CEO) problem with a helper. Initially, we use a successive decoding scheme to decompose the binary CEO problem with a helper into the multiterminal source coding and final decision problems. Then, we present an outer bound on the rate-distortion region for multiterminal source coding with binary sources and a helper. After solving a convex optimization problem formulated from the derived outer bound, we obtain the final distortion by substituting the minimized distortions of observation into the distortion propagating function, which is derived to bridge the relationship between the joint decoding results and final decision. Finally, we analyze the trade-off of rate-distortion through theoretical calculation and simulations. Both the theoretical and simulation results demonstrate that a helper can obviously reduce the signal-to-noise ratio threshold. We also have an in-depth discussion on the differences of system performance improvement between locating a helper and including an additional agent.