A simple information-theoretic analysis of clustered sensor networks with decentralized detection

In this letter, we present a simple information-theoretic framework to analyze clustered sensor networks with hierarchical multi-level majority-like fusion and decentralized detection. The sensor nodes observe a binary phenomenon and transmit their own data to an access point (AP), possibly through intermediate fusion centers (FCs). We investigate the impact of uniform and non-uniform clustering on the system performance, evaluated in terms of mutual information between the true phenomenon status and its estimate at the AP. Being the overall system binary-input binary-output (BIBO), it will be shown that the probability of decision error (P
_e
) is a specific function of the input-output mutual information (I). In other words, the network operational point lies over a specific P
_e
ߝ I curve and depends on the network characteristics (e.g., topology, observation and communication noise levels, etc.).