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Privacy-preserving data aggregation (DA) in ad hoc networks is a challenging problem, considering the distributed communication and control requirement, dynamic network topology, unreliable communication links, etc. Different from the widely used cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. We first propose a secure consensus-based DA algorithm that guarantees an accurate sum aggregation while preserving the privacy of sensitive data. Then, we prove that the proposed algorithm converges accurately and is $(\epsilon, \sigma)$ -data privacy, and the mathematical relationship between $\epsilon$ and $\sigma$ is provided. Extensive simulations have shown that the proposed algorithm has high accuracy and low complexity, and they are robust against network dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel continuous-time protocol that solves the consensus problem on the median value, i.e., it provides distributed agreement in networked multi-agent systems where the quantity of interest is the median value of the agents' initial values. In contrast to the average value, the median value is a statistical measure inherently robust to the presence of outliers, which is a significant robustness issue in large-scale sensor and multi-agent networks. The proposed protocol requires only binary information regarding the relative state differences among the neighboring agents and achieves consensus on the median value in finite time by exploiting a suitable ad-hoc discontinuous local interaction rule. In addition, we characterize certain resiliency properties of the proposed protocol against the presence of uncooperative agents which do not implement the underlying local interaction rule whereas they interact with their neighbors thus influencing the network. In particular, we prove that despite the persistent influence of (at most) a certain number of uncooperative agents, the cooperative agents achieve finite time consensus on a value lying inside the convex hull of the cooperative agents' initial conditions, provided that the special class of so-called “$k$-safe” network topology is considered. Capabilities of the proposed consensus protocol and its effectiveness are supported by numerical studies. [ABSTRACT FROM AUTHOR]

The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks. Based on the nodal channel quality, the game can work out a channel gain threshold, which decides the candidates for taking part in the concurrent transmission. The utility formula is made for maximizing the overall throughput based on channel quality variation. For an achievable Bayesian Nash equilibrium (BNE) solution, this paper further price the selfish players in utility functions for attempting to improve the channel gain one-sidedly. Accordingly, this game allows each node to distributed decide whether to transmit concurrently with others depending on the Nash equilibrium (NE). Besides, to make the proposed game practical, this paper next presents an efficient particle swarm optimization (PSO) model to fasten the otherwise very slow convergence procedure due to the large computational complexity. Numerical results show the proposed approach is feasible to increase concurrent transmission opportunities for active nodes and the convergence can be swiftly obtained with a few of iteration times by the proposed PSO algorithm. [ABSTRACT FROM AUTHOR]

Interference mitigation is one of the main challenges in wireless communication, especially in ad hoc networks. In such context, the Multiple Access Interference (MAI) is known to be of an impulsive nature. Therefore, the conventional Gaussian assumption is inadequate to model this type of interference. Nevertheless, it can be accurately modeled by stable distributions. In fact, it was shown in literature that the α -stable distribution is a useful tool to model impulsive data. In this paper, we tackle the problem of noise compensation in ad hoc networks. More precisely, this issue is addressed within an Orthogonal Frequency Division Multiplexing (OFDM) transmission link assuming a symmetric α -stable model for the signal distortion due to MAI. Based on Bayesian estimation, the proposed approach estimates the transmitted OFDM symbols in the time domain using the Sequential Monte Carlo (SMC) methods. Unlike existing schemes, we consider the more realistic case where the impulsive noise parameters are assumed to be unknown at the receiver. Consequently, our approach deals also with the difficult task of noise parameters estimation which can be very useful for other purposes such as target tracking in wireless sensor networks or channel estimation. Simulations results, provided in terms of Mean Square Error (MSE) and Bit Error Rate (BER), illustrate the efficiency and the robustness of this scheme. [ABSTRACT FROM AUTHOR]