A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends

This paper examines the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state-of-the-art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. We also introduce the family of various jamming attacks and their counter-measures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.Comment: 36 pages. Accepted to Appear in Proceedings of the IEEE, 201

Massively Parallel Algorithms for the Stochastic Block Model

Learning the community structure of a large-scale graph is a fundamental problem in machine learning, computer science and statistics. Among others, the Stochastic Block Model (SBM) serves a canonical model for community detection and clustering, and the Massively Parallel Computation (MPC) model is a mathematical abstraction of real-world parallel computing systems, which provides a powerful computational framework for handling large-scale datasets. We study the problem of exactly recovering the communities in a graph generated from the SBM in the MPC model. Specifically, given kn vertices that are partitioned into k equal-sized clusters (i.e., each has size n), a graph on these kn vertices is randomly generated such that each pair of vertices is connected with probability p if they are in the same cluster and with probability q if not, where p > q > 0. We give MPC algorithms for the SBM in the (very general) s-space MPC model, where each machine is guaranteed to have memory s = ?(log n). Under the condition that (p-q)/?p ? ??(k^{1/2} n^{-1/2+1/(2(r-1))}) for any integer r ? [3,O(log n)], our first algorithm exactly recovers all the k clusters in O(kr log_s n) rounds using O?(m) total space, or in O(rlog_s n) rounds using O?(km) total space. If (p-q)/?p ? ??(k^{3/4} n^{-1/4}), our second algorithm achieves O(log_s n) rounds and O?(m) total space complexity. Both algorithms significantly improve upon a recent result of Cohen-Addad et al. [PODC\u2722], who gave algorithms that only work in the sublinear space MPC model, where each machine has local memory s = O(n^?) for some constant ? > 0, with a much stronger condition on p,q,k. Our algorithms are based on collecting the r-step neighborhood of each vertex and comparing the difference of some statistical information generated from the local neighborhoods for each pair of vertices. To implement the clustering algorithms in parallel, we present efficient approaches for implementing some basic graph operations in the s-space MPC model

Massively Parallel Algorithms for the Stochastic Block Model

Learning the community structure of a large-scale graph is a fundamental problem in machine learning, computer science and statistics. We study the problem of exactly recovering the communities in a graph generated from the Stochastic Block Model (SBM) in the Massively Parallel Computation (MPC) model. Specifically, given $kn$ vertices that are partitioned into $k$ equal-sized clusters (i.e., each has size $n$), a graph on these $kn$ vertices is randomly generated such that each pair of vertices is connected with probability~$p$ if they are in the same cluster and with probability $q$ if not, where $p > q > 0$. We give MPC algorithms for the SBM in the (very general) \emph{$s$-space MPC model}, where each machine has memory $s=\Omega(\log n)$. Under the condition that $\frac{p-q}{\sqrt{p}}\geq \tilde{\Omega}(k^{\frac12}n^{-\frac12+\frac{1}{2(r-1)}})$ for any integer $r\in [3,O(\log n)]$, our first algorithm exactly recovers all the $k$ clusters in $O(kr\log_s n)$ rounds using $\tilde{O}(m)$ total space, or in $O(r\log_s n)$ rounds using $\tilde{O}(km)$ total space. If $\frac{p-q}{\sqrt{p}}\geq \tilde{\Omega}(k^{\frac34}n^{-\frac14})$, our second algorithm achieves $O(\log_s n)$ rounds and $\tilde{O}(m)$ total space complexity. Both algorithms significantly improve upon a recent result of Cohen-Addad et al. [PODC'22], who gave algorithms that only work in the \emph{sublinear space MPC model}, where each machine has local memory~$s=O(n^{\delta})$ for some constant $\delta>0$, with a much stronger condition on $p,q,k$. Our algorithms are based on collecting the $r$-step neighborhood of each vertex and comparing the difference of some statistical information generated from the local neighborhoods for each pair of vertices. To implement the clustering algorithms in parallel, we present efficient approaches for implementing some basic graph operations in the $s$-space MPC model

Improved Tradeoffs for Leader Election

We consider leader election in clique networks, where $n$ nodes are connected by point-to-point communication links. For the synchronous clique under simultaneous wake-up, i.e., where all nodes start executing the algorithm in round $1$, we show a tradeoff between the number of messages and the amount of time. More specifically, we show that any deterministic algorithm with a message complexity of $n f(n)$ requires $\Omega\left(\frac{\log n}{\log f(n)+1}\right)$ rounds, for $f(n) = \Omega(\log n)$. Our result holds even if the node IDs are chosen from a relatively small set of size $\Theta(n\log n)$, as we are able to avoid using Ramsey's theorem. We also give an upper bound that improves over the previously-best tradeoff. Our second contribution for the synchronous clique under simultaneous wake-up is to show that $\Omega(n\log n)$ is in fact a lower bound on the message complexity that holds for any deterministic algorithm with a termination time $T(n)$. We complement this result by giving a simple deterministic algorithm that achieves leader election in sublinear time while sending only $o(n\log n)$ messages, if the ID space is of at most linear size. We also show that Las Vegas algorithms (that never fail) require $\Theta(n)$ messages. For the synchronous clique under adversarial wake-up, we show that $\Omega(n^{3/2})$ is a tight lower bound for randomized $2$-round algorithms. Finally, we turn our attention to the asynchronous clique: Assuming adversarial wake-up, we give a randomized algorithm that achieves a message complexity of $O(n^{1 + 1/k})$ and an asynchronous time complexity of $k+8$. For simultaneous wake-up, we translate the deterministic tradeoff algorithm of Afek and Gafni to the asynchronous model, thus partially answering an open problem they pose

Equivalent stiffness and dynamic response of new mechanical elastic wheel

To investigate the stiffness characteristics of the new mechanical elastic wheel (MEW), the elastic foundation closed circle curved beam model of MEW was established by curved beam theory. With the Laplace transformation and boundary conditions of the governing differential equations, the analytical relations among the radial deformation, bending stiffness of elastic wheel, the elastic foundation stiffness of hinges, elastic wheel laminated structure parameters and excitation frequency were analyzed. The correctness of the curved beam model was validated by the finite element method. Curved beam model validation and the application of the nonlinear finite element model show that the influence of elastic wheel laminated structure and deformation on dynamic response is equal to the equivalent stiffness. The results indicate that the equivalent stiffness and dynamic response of MEW become increased nonlinearly with component content of elastic bead ring, moreover, the equivalent stiffness and dynamic response of MEW increase nonlinearly with the deformation amount of MEW, and the dynamic response significantly decreases with the increase of excitation frequency, under this circumstance that the laminated structure of elastic wheel has been unchanged

Equivalent stiffness and dynamic response of new mechanical elastic wheel

To investigate the stiffness characteristics of the new mechanical elastic wheel (MEW), the elastic foundation closed circle curved beam model of MEW was established by curved beam theory. With the Laplace transformation and boundary conditions of the governing differential equations, the analytical relations among the radial deformation, bending stiffness of elastic wheel, the elastic foundation stiffness of hinges, elastic wheel laminated structure parameters and excitation frequency were analyzed. The correctness of the curved beam model was validated by the finite element method. Curved beam model validation and the application of the nonlinear finite element model show that the influence of elastic wheel laminated structure and deformation on dynamic response is equal to the equivalent stiffness. The results indicate that the equivalent stiffness and dynamic response of MEW become increased nonlinearly with component content of elastic bead ring, moreover, the equivalent stiffness and dynamic response of MEW increase nonlinearly with the deformation amount of MEW, and the dynamic response significantly decreases with the increase of excitation frequency, under this circumstance that the laminated structure of elastic wheel has been unchanged