{'fraction_non_alphanumeric': 0.01935483870967742, 'fraction_numerical': 0.0027649769585253456, 'mean_word_length': 6.006451612903226, '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}}
Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem

We extend prior results on a single decision maker opportunistic sensing problem to a distributed, multidecision maker setting. The original formulation of the problem considers how to opportunistically use “in-flight” sensors to maximize target coverage. In that paper, the authors show that this problem is NP-hard with a strong polynomial heuristic for a single decision maker. This paper extends this by considering a distributed decision making scenario in which multiple independent parties attempt to simultaneously engage in opportunistic sensor assignment while managing interassignment conflict. Specifically, we develop an algorithm that: 1) produces a Pareto optimal opportunistic sensor allocation; 2) requires fewer bits of communicated information than a completely centralized deconfliction approach; and 3) runs in distributed polynomial time once the individual decision makers identify their preferred (optimal) sensor allocations. We validate these claims using appropriate simulations.