228 research outputs found

    Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

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    Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.Comment: Accepted at NeurIPS 202

    An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Travelling Salesman Problems

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    Recent years have witnessed a surge in research on machine learning for combinatorial optimization since learning-based approaches can outperform traditional heuristics and approximate exact solvers at a lower computation cost. However, most existing work on supervised neural combinatorial optimization focuses on TSP instances with a fixed number of cities and requires large amounts of training samples to achieve a good performance, making them less practical to be applied to realistic optimization scenarios. This work aims to develop a data-driven graph representation learning method for solving travelling salesman problems (TSPs) with various numbers of cities. To this end, we propose an edge-aware graph autoencoder (EdgeGAE) model that can learn to solve TSPs after being trained on solution data of various sizes with an imbalanced distribution. We formulate the TSP as a link prediction task on sparse connected graphs. A residual gated encoder is trained to learn latent edge embeddings, followed by an edge-centered decoder to output link predictions in an end-to-end manner. To improve the model's generalization capability of solving large-scale problems, we introduce an active sampling strategy into the training process. In addition, we generate a benchmark dataset containing 50,000 TSP instances with a size from 50 to 500 cities, following an extremely scale-imbalanced distribution, making it ideal for investigating the model's performance for practical applications. We conduct experiments using different amounts of training data with various scales, and the experimental results demonstrate that the proposed data-driven approach achieves a highly competitive performance among state-of-the-art learning-based methods for solving TSPs.Comment: 35 pages, 7 figure

    Multi-Service Group Key Management for High Speed Wireless Mobile Multicast Networks

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    YesRecently there is a high demand from the Internet Service Providers to transmit multimedia services over high speed wireless networks. These networks are characterized by high mobility receivers which perform frequent handoffs across homogenous and heterogeneous access networks while maintaining seamless connectivity to the multimedia services. In order to ensure secure delivery of multimedia services to legitimate group members, the conventional cluster based group key management (GKM) schemes for securing group communication over wireless mobile multicast networks have been proposed. However, they lack efficiency in rekeying the group key in the presence of high mobility users which concurrently subscribe to multiple multicast services that co-exist in the same network. This paper proposes an efficient multi-service group key management scheme (SMGKM) suitable for high mobility users which perform frequent handoffs while participating seamlessly in multiple multicast services. The users are expected to drop subscriptions after multiple cluster visits hence inducing huge key management overhead due to rekeying the previously visited cluster keys. The already proposed multi-service SMGKM system with completely decentralised authentication and key management functions is adopted to meet the demands for high mobility environment with the same level of security. Through comparisons with existing GKM schemes and simulations, SMGKM shows resource economy in terms of reduced communication and less storage overheads in a high speed environment with multiple visits

    Combined Hybridizable Discontinuous Galerkin (HDG) and Runge-Kutta Discontinuous Galerkin (RK-DG) formulations for Green-Naghdi equations on unstructured meshes

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    In this paper, we introduce some new high-order discrete formulations on general unstructured meshes, especially designed for the study of irrotational free surface flows based on partial differential equations belonging to the family of fully nonlinear and weakly dispersive shallow water equations. Working with a recent family of optimized asymptotically equivalent equations, we benefit from the simplified analytical structure of the linear dispersive operators to conveniently reformulate the models as the classical nonlin-ear shallow water equations supplemented with several algebraic source terms, which globally account for the non-hydrostatic effects through the introduction of auxiliary coupling variables. High-order discrete approximations of the main flow variables are obtained with a RK-DG method, while the trace of the auxiliary variables are approximated on the mesh skeleton through the resolution of second-order linear elliptic sub-problems with high-order HDG formulations. The combined use of hybrid unknowns and local post-processing significantly helps to reduce the number of globally coupled unknowns in comparison with previous approaches. The proposed formulation is then extended to a more complex family of three parameters enhanced Green-Naghdi equations. The resulting numerical models are validated through several benchmarks involving nonlinear waves transformations and propagation over varying topographies, showing good convergence properties and very good agreements with several sets of experimental data

    A Framework for Secure Group Key Management

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    The need for secure group communication is increasingly evident in a wide variety of governmental, commercial, and Internet communities. Secure group key management is concerned with the methods of issuing and distributing group keys, and the management of those keys over a period of time. To provide perfect secrecy, a central group key manager (GKM) has to perform group rekeying for every join or leave request. Fast rekeying is crucial to an application\u27s performance that has large group size, experiences frequent joins and leaves, or where the GKM is hosted by a group member. Examples of such applications are interactive military simulation, secure video and audio broadcasting, and secure peer-to-peer networks. Traditionally, the rekeying is performed periodically for the batch of requests accumulated during an inter-rekey period. The use of a logical key hierarchy (LKH) by a GKM has been introduced to provide scalable rekeying. If the GKM maintains a LKH of degree d and height h, such that the group size n ≤ dh, and the batch size is R requests, a rekeying requires the GKM to regenerate O(R × h) keys and to perform O(d × R × h) keys encryptions for the new keys distribution. The LKH approach provided a GKM rekeying cost that scales to the logarithm of the group size, however, the number of encryptions increases with increased LKH degree, LKH height, or the batch size. In this dissertation, we introduce a framework for scalable and efficient secure group key management that outperforms the original LKH approach. The framework has six components as follows. First, we present a software model for providing secure group key management that is independent of the application, the security mechanism, and the communication protocol. Second, we focus on a LKH-based GKM and introduce a secure key distribution technique, in which a rekeying requires the GKM to regenerate O( R × h) keys. Instead of encryption, we propose a novel XOR-based key distribution technique, namely XORBP, which performs an XOR operation between keys, and uses random byte patterns (BPs) to distribute the key material in the rekey message to guard against insider attacks. Our experiments show that the XORBP LKH approach substantially reduces a rekeying computation effort by more than 90%. Third, we propose two novel LKH batch rekeying protocols . The first protocol maintains a balanced LKH (B+-LKH) while the other maintains an unbalanced LKH (S-LKH). If a group experiences frequent leaves, keys are deleted form the LKH and maintaining a balanced LKH becomes crucial to the rekeying\u27s process performance. In our experiments, the use of a B+-LKH by a GKM, compared to a S-LKH, is shown to substantially reduce the number of LKH nodes (i.e., storage), and the number of regenerated keys per a rekeying by more than 50%. Moreover, the B +-LKH performance is shown to be bounded with increased group dynamics. Fourth, we introduce a generalized rekey policy that can be used to provide periodic rekeying as well as other versatile rekeying conditions. Fifth, to support distributed group key management, we identify four distributed group-rekeying protocols between a set of peer rekey agents. Finally, we discuss a group member and a GKM\u27s recovery after a short failure time

    Security in Mobile Networks: Communication and Localization

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    Nowadays the mobile networks are everywhere. The world is becoming more dependent on wireless and mobile services, but the rapid growth of these technologies usually underestimates security aspects. As wireless and mobile services grow, weaknesses in network infrastructures become clearer. One of the problems is privacy. Wireless technologies can reduce costs, increase efficiencies, and make important information more readily and widely available. But, there are also risks. Without appropriate safeguards, these data can be read and modified by unauthorized users. There are many solutions, less and more effective, to protect the data from unauthorized users. But, a specific application could distinguish more data flows between authorized users. Protect the privacy of these information between subsets of users is not a trivial problem. Another problem is the reliability of the wireless service. Multi-vehicle systems composed of Autonomous Guided Vehicles (AGVs) are largely used for industrial transportation in manufacturing and logistics systems. These vehicles use a mobile wireless network to exchange information in order to coordinate their tasks and movements. The reliable dissemination of these information is a crucial operation, because the AGVs may achieve an inconsistent view of the system leading to the failure of the coordination task. This has clear safety implications. Going more in deep, even if the communication are confidential and reliable, anyway the positioning information could be corrupted. Usually, vehicles get the positioning information through a secondary wireless network system such as GPS. Nevertheless, the widespread civil GPS is extremely fragile in adversarial scenarios. An insecure distance or position estimation could produce security problems such as unauthorized accesses, denial of service, thefts, integrity disruption with possible safety implications and intentional disasters. In this dissertation, we face these three problems, proposing an original solution for each one

    GPRKEY - A NOVEL GROUP KEY REKEYING TECHNIQUE FOR MANET

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    A Mobile Ad hoc Network (MANET) is a collection of autonomous nodes or mobile devices that can arrange themselves in various ways and work without strict network administration. Ensuring security in mobile ad hoc networks is a challenging issue and most of the applications in mobile ad hoc networks involve group oriented communication. Mostly cryptographic techniques are used to provide the security to MANETs. Cryptographic techniques will not be efficient security mechanism if the key management is weak. The issue of packet loss in MANET that is caused due to multi casting and backward and forward secrecy results in mobility. Hence, we investigate on this issue and propose a method to overcome this scenario. On analysing the situation we find that frequent rekeying leads to huge message overhead and hence increases energy utilization. With the existing key management techniques it causes frequent disconnections and mobility issues. Therefore, an efficient multi casting group key management will help to overcome the above problems. In this paper we propose a novel group key rekeying technique named GPRKEY (Group key with Periodic ReKEYing) deal with scalability issue of rekeying and also analyze the performance of the newly proposed key management method using key trees. In this approach we use the periodic rekeying to enhance the scalability and avoid out of sync problems. We use sub trees and combine them using the merging algorithm and periodic re-keying algorithm. The GPRKEY is evaluated through NS-2 simulation and compared with existing key management techniques OFT (One-way Function Tree) and LKH (Logical Key Hierarchy). The security and performance of rekeying protocols are analyzed through detailed study and simulation
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