Symbol-level reliable broadcasting of sensitive data in error-prone wireless networks

Reliable packet transmission over error-prone wireless networks has received a lot of attention from the research community. In this paper, instead of using simple packet retransmissions to provide reliability, we consider a novel retransmission approach, which is based on the importance of bits (symbols). We study the problem of maximizing the total gain in the case of partial data delivery in error-prone wireless networks, in which each set of bits (called symbols) has a different weight. We first address the case of one-hop single packet transmission, and prove that the optimal solution that maximizes the total gain has a round-robin symbol transmission pattern. Then, we extend our solution to the case of multiple packets. We also enhance the expected gain using random linear network coding. Our simulation results show that our proposed multiple packets transmission mechanism can increase the gain up to 60%, compared to that of a simple retransmission. Moreover, our network coding scheme enhances the expected total gain up to 15%, compared to our non-coding mechanism.     

Real-time adaptive fluid simulation with complex boundaries

In this paper, we present a new adaptive model for real-time fluid simulation with complex boundaries based on Smoothed Particle Hydrodynamics (SPH) framework. Firstly, we introduce an adaptive SPH framework that is based on our character field function composed of four factors: geometrical complexity, boundary condition, physical complexity, and complementary condition in terms of the neighboring particle number. Meanwhile, the rule for particle adaptation is presented. We also present a two-step method to fast detect collision with complex boundary. The first step is voxelization on the complex scene. In the second step, based on the result of voxelization, we propose a three-phase method to fast detect collisions between complex boundaries and particles. By using this method, we avoid most of the useless intersection detection computation and greatly enhance the computation efficiency. In addition, a subdivision of boundary is precomputed before the collision interaction method so that fluid in a scene with complex boundary can still be simulated at relatively high speed and system stability risk is reduced greatly. To further accelerate the simulation, a highly parallel fluid algorithm is presented and implemented using GPU so that we can simulate dynamic fluid with mutual interaction between fluid and complex boundary at a considerably fast speed without compromising realism.     

A Hybrid Approach to Multiple Fluid Simulation using Volume Fractions

This paper presents a hybrid approach to multiple fluid simulation that can handle miscible and immiscible fluids, simultaneously. We combine distance functions and volume fractions to capture not only the discontinuous interface between immiscible fluids but also the smooth transition between miscible fluids. Our approach consists of four steps: velocity field computation, volume fraction advection, miscible fluid diffusion, and visualization. By providing a combining scheme between volume fractions and level set functions, we are able to take advantages of both representation schemes of fluids. From the system point of view, our work is the first approach to Eulerian grid‐based multiple fluid simulation including both miscible and immiscible fluids. From the technical point of view, our approach addresses the issues arising from variable density and viscosity together with material diffusion. We show that the effectiveness of our approach to handle multiple miscible and immiscible fluids through experiments.     

Formal analysis for robust anti-SPIT protection using model checking

Anti-SPIT policies counter the SPam over Internet Telephony (SPIT) by distinguishing bots launching unsolicited bulks of VoIP calls from human beings. We propose an Anti-SPIT Policy Management mechanism (aSPM) that detects spam calls and prevents VoIP session establishment by the Session Initiation Protocol (SIP). The SPIN model checker is used to formally model and analyze the robustness of the aSPM mechanism in execution scenarios with parallel SIP sessions. In case of a possible design flaw, the model checker provides a trace of the caught unexpected behavior (counterexample), that can be used for the revision of the mechanism’s design. Our SPIN model is parameterized, based on measurements from experiments with VoIP users. Non-determinism plays a key role in representing all possible anti-SPIT policy decisions, in terms of the SIP messages that may be exchanged. The model checking results provide evidence for the timeliness of the parallel SIP sessions, the absence of deadlocks or livelocks, and the fairness for the VoIP service users. These findings ensure robust anti-SPIT protection, meaning that the aSPM mechanism operates as expected, despite the occurrence of random SPIT calls and communication error messages. To the best of our knowledge, this is the first analysis for exhaustively searching security policy flaws, due to complex interactions between anti-SPIT measures and the SIP protocol services.     

A framework for identity privacy in SIP

Secure multimedia delivery in modern and future networks is one of the most challenging problems towards the system integration of fourth generation (4G) networks. This integration means that different service and network providers will have to interoperate in order to offer their services to end users. This multidomain environment poses serious threats to the end user who has contract with, and trusts only a limited number of operators and service providers. One such threat is end users’ privacy on which we will focus in this paper. Probably the most promising protocol for multimedia session management is the Session Initiation Protocol (SIP), which is an application layer protocol and thus can operate on top of different lower layer technologies. SIP is quite popular and a lot of research has been conducted; however, it still has some security issues, one of which is related to privacy and more particularly the protection of user identities (IDs). In this paper we comment on the ID privacy issue of SIP and propose a framework called PrivaSIP that can protect either the caller's ID or both the caller's and the callee's IDs in multidomain environments. We present different implementations of our framework based on asymmetric and symmetric cryptography analyzing the pros and cons of each one of them. Furthermore, we provide performance measurements in order to estimate the performance penalty of our framework over standard SIP. The most significant advantage of our method is that it can assure user ID protection even when SIP messages are transmitted through untrusted SIP domains, while our results show that this can be achieved with no perceived delay by the end user.     

一种新的基于IMS的SIP重传机制

目前,IP多媒体子系统（IMS）中基于会话初始化协议（SIP）的会话建立时间受到无线信道约束带宽、帧错误率（FER）值的影响,交换的消息数量、消息长度以及重传机制对于会话建立的时延有很大的影响,延长了会话建立的时间。将基于SIP的会话建立与无线信道的性能结合,提出一种新的SIP自适应重传机制。实验结果表明,该重传机制缩短了会话建立的时间,对IMS端到端的服务质量（QoS）性能有明显的改善。     

Buffer Coding for Reliable Transmissions over Wireless Networks

In-network caching is a useful technique for reducing latency and retransmission overhead of lost packets for reliable data delivery in wireless networks. However, in-network caching is challenging to implement in memory constrained devices such as RFIDs and sensors, and also in Wireless LAN (WLAN) gateways for large-scale deployments. In this paper we propose a novel technique for management of in-network caches using XOR coding for optimizing the use of limited buffer space in presence of random and burst packet losses. We identify two critical parameters, coding degree and coding distance for the coding scheme. As a case-study we implement our approach over Snoop and evaluate its performance for WLANs. We further propose a self-adaptive algorithm that tunes coding degree on the fly based on the measured coding behavior and packet loss probability. Using simulations in ns-2, we observe that in our simulation settings, when the size of the retransmission buffer in the gateway is less than 16 packets per TCP flow, the throughput can be enhanced by up to 30% for random losses and up to 20% for burst losses.     

Metamodels and emergent behaviour in models of conflict

In this paper, we develop a simplified mathematical model (a metamodel) of a simulation model of conflict, based on ideas drawn from the analysis of more general physical systems, such as found in fluid dynamics modelling. We show that there is evidence from the analysis of historical conflicts to support the kind of emergent behaviour implied by this approach. We then apply this approach to the development of a metamodel of a particular complexity based simulation model of conflict (ISAAC), developed for the US Marine Corps.The approach we have illustrated here is very generic, and is applicable to any simulation model which has complex interactions similar to those found in fluid dynamic modelling, or in simulating the emergent behaviour of large numbers of simple systems which interact with each other locally.     

Boundary Handling at Cloth–Fluid Contact

We present a robust and efficient method for the two‐way coupling between particle‐based fluid simulations and infinitesimally thin solids represented by triangular meshes. Our approach is based on a hybrid method that combines a repulsion force approach with a continuous intersection handling to guarantee that no penetration occurs. Moreover, boundary conditions for the tangential component of the fluid's velocity are implemented to model the different slip conditions. The proposed method is particularly useful for dynamic surfaces, like cloth and thin shells. In addition, we demonstrate how standard fluid surface reconstruction algorithms can be modified to prevent the calculated surface from intersecting close objects. For both the two‐way coupling and the surface reconstruction, we take into account that the fluid can wet the cloth. We have implemented our approach for the bidirectional interaction between liquid simulations based on Smoothed Particle Hydrodynamics (SPH) and standard mesh‐based cloth simulation systems.     

Hierarchical control system design using approximate simulation

In this paper, we present a new approach for hierarchical control based on the recent notions of approximate simulation and simulation functions, a quantitative version of the simulation relations. Given a complex system that needs to be controlled and a simpler abstraction, we show how the knowledge of a simulation function allows us to synthesize hierarchical control laws by first controlling the abstraction and then lifting the abstract control law to the complex system using an interface. For the class of linear control systems, we give an effective characterization of the simulation functions and of the associated interfaces. This characterization allows us to use algorithmic procedures for their computation. We show how to choose an abstraction for a linear control system such that our hierarchical control approach can be used. Finally, we show the effectiveness of our approach on an example.     

Double auction-inspired meta-scheduling of parallel applications on global grids

Meta-schedulers map jobs to computational resources that are part of a Grid, such as clusters, that in turn have their own local job schedulers. Existing Grid meta-schedulers either target system-centric metrics, such as utilisation and throughput, or prioritise jobs based on utility metrics provided by the users. The system-centric approach gives less importance to users’ individual utility, while the user-centric approach may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel meta-scheduler, based on the well-known double auction mechanism that aims to satisfy users’ service requirements as well as ensuring balanced utilisation of resources across a Grid. We have designed valuation metrics that commodify both the complex resource requirements of users and the capabilities of available computational resources. Through simulation using real traces, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies up to 15% more user requirements than others, but also improves system utilisation through load balancing.     

ARITO: Cyber-attack response system using accurate risk impact tolerance

We propose a novel approach for automated intrusion response systems to assess the value of the loss that could be suffered by a compromised resource. A risk assessment component of the approach measures the risk impact and is tightly integrated with our response system component. When the total risk impact exceeds a certain threshold, the response selection mechanism applies one or more responses. A multi-level response selection mechanism is proposed to gauge the intrusion damage (attack progress) relative to the response impact. This model proposes a feedback mechanism, which measures the response goodness and helps indicate the new risk level following application of the response(s). Not only does our proposed model constitutes a novel online mechanism for response activation and deactivation based on the online risk impact, it also addresses the factors inherent in assessing risk and calculating response effectiveness that are more complex in terms of detail. We have designed a sophisticated multi-step attack to penetrate Web servers, as well as to acquire root privilege. Our simulation results illustrate the efficiency of the proposed model and confirm the feasibility of the approach in real time. At the end of paper, we discuss the various ways in which an attacker might succeed in completely bypassing our response system.     

Vortex visualization for practical engineering applications

In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability.     

A CNN‐based Flow Correction Method for Fast Preview

Eulerian‐based smoke simulations are sensitive to the initial parameters and grid resolutions. Due to the numerical dissipation on different levels of the grid and the nonlinearity of the governing equations, the differences in simulation resolutions will result in different results. This makes it challenging for artists to preview the animation results based on low‐resolution simulations. In this paper, we propose a learning‐based flow correction method for fast previewing based on low‐resolution smoke simulations. The main components of our approach lie in a deep convolutional neural network, a grid‐layer feature vector and a special loss function. We provide a novel matching model to represent the relationship between low‐resolution and high‐resolution smoke simulations and correct the overall shape of a low‐resolution simulation to closely follow the shape of a high‐resolution down‐sampled version. We introduce the grid‐layer concept to effectively represent the 3D fluid shape, which can also reduce the input and output dimensions. We design a special loss function for the fluid divergence‐free constraint in the neural network training process. We have demonstrated the efficacy and the generality of our approach by simulating a diversity of animations deviating from the original training set. In addition, we have integrated our approach into an existing fluid simulation framework to showcase its wide applications.     

Detail‐Preserving Explicit Mesh Projection and Topology Matching for Particle‐Based Fluids

We propose a new explicit surface tracking approach for particle‐based fluid simulations. Our goal is to advect and update a highly detailed surface, while only computing a coarse simulation. Current explicit surface methods lose surface details when projecting on the isosurface of an implicit function built from particles. Our approach uses a detail‐preserving projection, based on a signed distance field, to prevent the divergence of the explicit surface without losing its initial details. Furthermore, we introduce a novel topology matching stage that corrects the topology of the explicit surface based on the topology of an implicit function. To that end, we introduce an optimization approach to update our explicit mesh signed distance field before remeshing. Our approach is successfully used to preserve the surface details of melting and highly viscous objects, and shown to be stable by handling complex cases involving multiple topological changes. Compared to the computation of a high‐resolution simulation, using our approach with a coarse fluid simulation significantly reduces the computation time and improves the quality of the resulting surface.     

FluidSim: a tool to simulate fluid models of high-speed networks

In this paper, we present a tool for the simulation of fluid models of high-speed telecommunication networks. The aim of such a simulator is to evaluate measures which cannot be obtained through standard tools in reasonable time or through analytical approaches. We follow an event-driven approach in which events are associated with rate changes in fluid flows. We show that under some loose restrictions on the sources, this suffices to efficiently simulate the evolution in time of fairly complex models. Some examples illustrate the utilization of this approach and the gain that can be observed over standard simulation tools.     

Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow

We propose a method for the data‐driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flow problems, and we propose a novel LSTM‐based approach to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space‐time data sets. We demonstrate for the first time that dense 3D+time functions of physics system can be predicted within the latent spaces of neural networks, and we arrive at a neural‐network based simulation algorithm with significant practical speed‐ups. We highlight the capabilities of our method with a series of complex liquid simulations, and with a set of single‐phase buoyancy simulations. With a set of trained networks, our method is more than two orders of magnitudes faster than a traditional pressure solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.     

Tool integration for flexible simulation of distributed algorithms

Over the last two decades, considerable research has been done in distributed operating systems, which can be attributed to faster processors and better communication technologies. A distributed operating system requires distributed algorithms to provide basic operating system functionality like mutual exclusion, deadlock detection, etc. A number of such algorithms have been proposed in the literature. Traditionally, these distributed algorithms have been presented in a theoretical way, with limited attempts to simulate actual working models. This paper discusses our experience in simulating distributed algorithms with the aid of some existing tools, including OPNET and Xplot. We discuss our efforts to define a basic model‐based framework for rapid simulation and visualization, and illustrate how we used this framework to evaluate some classic algorithms. We have also shown how the performance of different algorithms can be compared based on some collected statistics. To keep the focus of this paper on the approach itself, and our experience with tool integration, we only discuss some relatively simple models. Yet, the approach can be applied to more complex algorithm specifications. Copyright © 2001 John Wiley & Sons, Ltd.     

大规模流体场景的真实感与实时模拟

目的 基于物理的流体动画模拟是计算机图形学领域中的研究热点,针对实际应用中仍难以实现大规模流体场景的真实感与实时模拟,提出了基于shallow water方程的物理模拟方法。方法 首先,给出shallow water方程的稳定欧拉数值求解方法,解决模拟过程中存在的毛刺、陡坡水滴斑点等数值求解的不稳定性问题;其次,提出刚体和粒子系统与流体高度场的稳定耦合模型,实现双向固流耦合和流体表面细节的真实感模拟;最后,设计高度场的多精度网格算法以及粒子的隔点采样方法,加速大规模流体的物理模拟计算。结果 实验结果表明,本文方法解决了传统欧拉方法求解shallow water方程的流体模拟过程中存在的不稳定和计算复杂等问题,在300×300网格分辨率和2.2×10⁴粒子数的规模下,达到了20帧/s的实时模拟速度。结论 本文算法具有良好的高效性和稳定性,适用于电子游戏和视景仿真等实时应用领域中的大规模流体场景的真实感模拟。