Frequency hopping (FH) has been the most popularly considered approach for alleviating the effects of jamming attacks. We re-examine, the efficacy of FH based on both experimentation and analysis. Briefly, the limitations of FH are: (a) the energy spill over between adjacent channels that are considered to be orthogonal, and (b) the small number of available orthogonal bands. In a nutshell, the main contributions of our work are: (a) Construction of a measurement-driven game theoretic framework which models the interactions between a jammer and a communication link employing FH. Our model accounts for the above limiting factors and provides bounds on the performance of proactive FH in coping with jamming. (b) Extensive experimentation to quantify the impact of a jammer on 802.11a/g/n networks. Interestingly, we find that 802.11n devices can be more vulnerable to jamming as compared with legacy devices. We carefully analyze the reasons behind this observation. (c) Application of our framework to quantify the efficacy of proactive FH and validation of our analytical bounds across various 802.11 network configurations. (d) Formal derivation of the optimal strategies for both the link and the jammer in 802.11 networks. Our results demonstrate that FH seems to be inadequate in coping with jamming attacks in current 802.11 networks.

The paper presents distributed and parallel δ-approximation algorithms for covering problems, where δ is the maximum number of variables on which any constraint depends (for example, δ = 2 for VERTEX COVER).

Frequency hopping has been the most popularly considered approach for alleviating the effects of jamming attacks. In this paper, we provide a novel, measurement-driven, game theoretic framework that captures the interactions between a communication link and an adversarial jammer, possibly with multiple jamming devices, in a wireless network employing frequency hopping (FH). The framework can be used to quantify the efficacy of FH as a jamming countermeasure. Our model accounts for two important factors that affect the aforementioned interactions: (a) the number of orthogonal channels available for use and (b) the frequency separation between these orthogonal bands. If the latter is small, then the energy spill over between two adjacent channels (considered orthogonal) is high; as a result a jammer on an orthogonal band that is adjacent to that used by a legitimate communication, can be extremely effective. We account for both these factors and using our framework we provide bounds on the performance of proactive frequency hopping in alleviating the impact of a jammer. The main contributions of our work are: (a) Construction of a measurement driven game theoretic framework which models the interactions between a jammer and a communication link that employ FH. (b) Extensive experimentation on our indoor testbed in order to quantify the impact of a jammer in a 802.11a/g network. (c) Application of our framework to quantify the efficacy of proactive FH across a variety of 802.11 network configurations. (d) Formal derivation of the optimal strategies for both the link and the jammer in 802.11 networks. Our results demonstrate that frequency hopping is largely inadequate in coping with jamming attacks in current 802.11 networks. In particular, we show that if current systems were to support hundreds of additional channels, FH would form a robust jamming countermeasure.

We give an approximation algorithm for packing and covering linear programs (linear programs with non-negative coefficients). Given a constraint matrix with n non-zeros, r rows, and c columns, the algorithm (with high probability) computes feasible primal and dual solutions whose costs are within a factor of I +epsiv of OPT l+ epsiv of OPT (the optimal cost) in time O(n + (r +c) log(n) / epsiv2). For dense problems (with r,c = O(-radicn)) the time is Omega (n log(n) / epsiv2)-linear even as epsiv rarr 0. In comparison, previous Lagrangian-relaxation algorithms generally take at least Omega(n log(n)/epsiv2) time, while (for small epsiv) the Simplex algorithm typically takes at least Omega(n min(r, c)) time.