Abstract:We present a study of using game theory for protecting wireless sensor networks (WSNs) from selfish behavior or malicious nodes. Due to scalability, low complexity and disseminated nature of WSNs, malicious attacks can be modeled effectively using game theory. In this study, we survey the different game-theoretic defense strategies for WSNs. We present a taxonomy of the game theory approaches based on the nature of the attack, whether it is caused by an external attacker or it is the result of an internal node acting selfishly or maliciously. We also present a general trust model using game theory for decision making. We, finally, identify the significant role of evolutionary games for WSNs security against intelligent attacks; then, we list several prospect applications of game theory to enhance the data trustworthiness and node cooperation in different WSNs.

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Min-Sum decoding is widely used for decoding LDPC codes in many modern digital video broadcasting decoding due to its relative low complexity and robustness against quantization error. However, the suboptimal performance of the Min-Sum affects the integrated performance of wireless receivers. In this paper, we present the idea of adapting the scaling factor of the Min-Sum decoder with iterations through a simple approximation. For the ease of implementation the scaling factor can be changed in a staircase fashion. The stair step is designed to optimize the decoder performance and the required storage for its different values. The variable scaling factor proposed algorithm produces a nontrivial improvement of the performance of the Min-Sum decoding as verified by simulation results.

The deployment of femtocells in current and future communication systems promises an effective solution for limited indoor coverage problem and a possible gateway for mobile data offloading. In this paper, we cast a cognitive interference align ment approach (IA) suitable for heterogeneous cellular networks with a mixed macrocell and femtocell deployment Specifically, in our approach a restricted waterfflling (RWF) algorithm is used to maximize the downlink data rate, while reserving some eigenmodes for giving the femtocell basestations the opportunity to do their transmissions even at high signal-to-noise power ratio (SNR) for the macrocell basestation. Additionally, both the cross-tier and co-tier interference is to be aligned at each femtocell user's receiver using an Iterative Reweighted Least Squares(IRLS) algorithm. The simulation results show that the proposed IA approach provides an improved sum rate for the femtocell users, compared to the conventional IA techniques, Uke, the leakage minimization approach and the nuclear norm based rank constraint rank minimization approach.

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