Abstract
Consider a wireless sensor network in which each sensor has a bit of information. Suppose all sensors with the bit 1 broadcast this fact to a basestation. If zero or one sensors broadcast, the basestation can detect this fact. If two or more sensors broadcast, the basestation can only detect that there is a ”collision.” Although collisions may seem to be a nuisance, they can in some cases help the basestation compute an aggregate function of the sensors’ data.
Motivated by this scenario, we study a new model of computation for boolean functions: the 2 + decision tree. This model is an augmentation of the standard decision tree model: now each internal node queries an arbitrary set of literals and branches on whether 0, 1, or at least 2 of the literals are true. This model was suggested in a work of Ben-Asher and Newman but does not seem to have been studied previously.
Our main result shows that 2 + decision trees can ”count” rather effectively. Specifically, we show that zero-error 2 + decision trees can compute the threshold-of-t symmetric function with O(t) expected queries (and that Ω(t) is a lower bound even for two-sided error 2 + decision trees). Interestingly, this feature is not shared by 1 + decision trees. Our result implies that the natural generalization to k + decision trees does not give much more power than 2 + decision trees. We also prove a lower bound of \(\tilde{\Omega}(t) \cdot \log(n/t)\) for the deterministic 2 + complexity of the threshold-of-t function, demonstrating that the randomized 2 + complexity can in some cases be unboundedly better than deterministic 2 + complexity.
Finally, we generalize the above results to arbitrary symmetric functions, and we discuss the relationship between k + decision trees and other complexity notions such as decision tree rank and communication complexity.
James Aspnes is supported in part by NSF grant CCF-0916389. Murat Demirbas’ work was partially supported by NSF Career award #0747209. Ryan O’Donnell is supported in part by NSF grants CCF-0747250 and CCF-0915893, a Sloan fellowship, and an Okawa fellowship. Atri Rudra and Steve Uurtamo are supported in part by NSF CAREER grant CCF-0844796.
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Aspnes, J., Blais, E., Demirbas, M., O’Donnell, R., Rudra, A., Uurtamo, S. (2010). k + Decision Trees. In: Scheideler, C. (eds) Algorithms for Sensor Systems. ALGOSENSORS 2010. Lecture Notes in Computer Science, vol 6451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16988-5_7
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