{'fraction_non_alphanumeric': 0.015742128935532233, 'fraction_numerical': 0.0, 'mean_word_length': 5.5763546798029555, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Impact of Receive Diversity in the Presence of Correlation in Wireless Sensor Networks

We demonstrate the benefits of incorporating receive diversity into wireless sensor network (WSN) applications that require high data fidelity and resolution upon event triggering. Such an approach is particularly applicable to active sensing scenarios, where the distribution of active sensors across the network varies or can be adaptively varied. By integrating spatial correlation in wireless sensor networks with receive diversity, we show that the field estimation accuracy can be significantly improved while ensuring energy and spectrum efficiency. The underlying physical phenomenon is modeled as a spatially correlated joint Gaussian random process where the sensor correlation is a function of the separation or node density. We abstract the overall system as a multiple input multiple output (MIMO) system, since we consider joint (multiple and simultaneous) sensor transmissions and multiple receive antennas at the base station. The optimum estimator for the overall system is derived and the mean square error (MSE) is analyzed as a function of equivalent MIMO system parameters. We investigate the combined effects of the data correlation, node density, and multi-user and receive diversity on the overall estimation accuracy.