Andrej Bogdanov

Power-aware base station positioning for sensor networks

We consider the problem of positioning data collecting base stations in a sensor network. We show that in general, the choice of positions has a marked influence on the data rate, or equivalently, the power efficiency, of the network. In our model, which is partly motivated by an experimental environmental monitoring system, the optimum data rate for a fixed layout of base stations can be found by a maximum flow algorithm. Finding the optimum layout of base stations, however, turns out to be an NP-complete problem, even in the special case of homogeneous networks. Our analysis of the optimum layout for the special case of the regular grid shows that all layouts that meet certain constraints are equally good. We also consider two classes of random graphs, chosen to model networks that might be realistically encountered, and empirically evaluate the performance of several base station positioning algorithms on instances of these classes. In comparison to manually choosing positions along the periphery of the network or randomly choosing them within the network, the algorithms tested find positions, which significantly improve the data rate and power efficiency of the network.

A lower bound for testing 3-colorability in bounded-degree graphs

We consider the problem of testing 3-colorability in the bounded-degree model. We show that, for small enough /spl epsiv/, every tester for 3-colorability must have query complexity /spl Omega/(n). This is the first linear lower bound for testing a natural graph property in the bounded-degree model. An /spl Omega/(/spl radic/n) lower bound was previously known. For one-sided error testers, we also show an /spl Omega/(n) lower bound for testers that distinguish 3-colorable graphs from graphs that are (1/3 - /spl alpha/)-far from 3-colorable, for arbitrarily small /spl alpha/. In contrast, a polynomial time algorithm by Frieze and Jerrum (1997) distinguishes 3-colorable graphs from graphs that are 1/5-far from 3-colorable. As a by-product of our techniques, we obtain tight unconditional lower bounds on the approximation ratios achievable by sublinear time algorithms for Max E3SAT, Max E3LIN-2 and other problems.

On worst-case to average-case reductions for NP problems

We show that if an NP-complete problem has a non-adaptive self-corrector with respect to a distribution that can be sampled then coNP is contained in AM/poly and the polynomial hierarchy collapses to the third level. Feigenbaum and Fortnow show the same conclusion under the stronger assumption that an NP-complete problem has a non-adaptive random self-reduction. Our result shows it is impossible (using non-adaptive reductions) to base the average-case hardness of a problem in NP or the security of a one-way function on the worst-case complexity of an NP-complete problem (unless the polynomial hierarchy collapses).

Pseudorandom Bits for Polynomials

We present a new approach to constructing pseudorandom generators that fool low-degree polynomials over finite fields, based on the Gowers norm. Using this approach, we obtain the following main constructions of explicitly computable generators

On Worst-Case to Average-Case Reductions for NP Problems

G:\mathbb{F}^s\to\mathbb{F}^n

More on noncommutative polynomial identity testing

that fool polynomials over a finite field

Pseudorandomness for Read-Once Formulas

\mathbb{F}

Lower bounds for testing bipartiteness in dense graphs

: We stress that the results in (1) and (2) are unconditional, i.e., do not rely on any unproven assumption. Moreover, the results in (3) rely on a special case of the conjecture which may be easier to prove. Our generator for degree-

A Dichotomy for Local Small-Bias Generators

d

Pseudorandomness for Width 2 Branching Programs

polynomials is the componentwise sum of