Differential Game for Distributed Power Control in Device-to-Device Communications Underlaying Cellular Networks

Wireless Personal Communications - In the formulating of power control for wireless networks, the radio channel is commonly formulated using static models of optimization or game theory. In these...     

Key-dependent 3D model hashing for authentication using heat kernel signature

Multimedia-based hashing is considered an important technique for achieving authentication and copy detection in digital contents. However, 3D model hashing has not been as widely used as image or video hashing. In this study, we develop a robust 3D mesh-model hashing scheme based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications. We further discuss the robustness, uniqueness, security, and spaciousness of the method for 3D model hashing. In the proposed hashing scheme, we calculate the local and global HKS coefficients of vertices through time scales and 2D cell coefficients by clustering HKS coefficients with variable bin sizes based on an estimated L² risk function, and generate the binary hash through binarization of the intermediate hash values by combining the cell values and the random values. In addition, we use two parameters, bin center points and cell amplitudes, which are obtained through an iterative refinement process, to improve the robustness, uniqueness, security, and spaciousness further, and combine them in a hash with a key. By evaluating the robustness, uniqueness, and spaciousness experimentally, and through a security analysis based on the differential entropy, we verify that our hashing scheme outperforms conventional hashing schemes.     

Reversible DNA data hiding using multiple difference expansions for DNA authentication and storage

Multimedia Tools and Applications - Recently the data hiding techniques on DNA sequence have attracted interest for DNA authentication and high-capacity DNA storage. However, since DNA sequence...     

Advanced Primary–Backup Platform with Container-Based Automatic Deployment for Fault-Tolerant Systems

Within mission-critical systems, the primary–backup scheme is a desirable approach for improving reliability and fault tolerance. It can be used to ensure a high mission success rate despite unexpected errors. However, it must cope with the need to maintain consistency between a primary and a backup whenever the primary encounters unexpected errors. We overcome this issue by introducing a platform that uses container-based light virtualization and an automatic build system to isolate an application so that it may then be deployed on different devices without manual intervention. We believe an advanced deployment procedure can retain the consistency of the primary–backup systems with low implementation complexity. Integrated with a cloud application, it can also manage mission-critical systems effectively, communicate with the redundant systems, and detect unexpected errors by using sophisticated fault-detection technologies. We demonstrate that the platform can improve the reliability of mission-critical systems through realistic experiment using a model electronic vehicle and can reduce hardware dependencies.     

Polyline curvatures based robust vector data hashing

The growth in applications for vector data such as CAD design drawings and GIS digital maps has increased the requirements for authentication, copy detection, and retrieval of vector data. Vector data hashing is one of the main techniques for meeting these requirements. Its design must be robust, secure, and unique, which is similar to image or video hashing. This paper presents a vector data hashing method based on the polyline curvature for design drawings and digital maps. Our hashing method extracts the feature values by projecting the polyline curvatures, which are obtained from groups of vector data using GMM clustering, onto random values, before generating the final binary hash by binarization. A robustness evaluation showed that our hashing method had a very low false detection probability during geometrical modifications, rearrangements, and similar transformations of objects and layers. A security evaluation based on differential entropy showed that the level of uncertainty was very high with our hashing method. Furthermore, a uniqueness evaluation showed that the Hamming distances between hashes were very low.     

Cognitive Heterogeneous Networks with Unreliable Backhaul Connections

To enhance the spectrum scarcity of cooperative heterogeneous networks (HetNets) with unreliable backhaul connections, we examine the impact of cognitive spectrum sharing over multiple small-cell transmitters in Nakagami-m fading channels. In this system, the secondary transmitters are connected to macro-cell via wireless backhaul links and communicate with the secondary receiver by sharing the same spectrum with the primary user. Integrating cognitive radio (CR) network into the system, we address the combined power constraints: 1) the peak interference power at the primary user and 2) the maximal transmit power at the secondary transmitters. In addition, to exclude the signaling overhead for exchanging channel-state-information (CSI) at the transmitters, the selection combining (SC) protocol is assumed to employ at the receivers. To evaluate the performance, we first derive the closed-form statistics of the end-to-end signal-to-noise (SNR) ratio, from which the exact outage probability, ergodic capacity and symbol error rate expressions are derived. To reveal further insights into the effective unreliable backhaul links and the diversity of fading parameters, the asymptotic expressions are also attained. The two interesting non-cooperative and Rayleigh fading scenarios are also investigated. Numerical results are conducted to verify the performance of the considered system via Monte-Carlo simulations.     

A Rate Allocation Framework for Multi-Class Services in Software-Defined Networks

Software defined networking (SDN) is a network architecture with a programmable control plane (e.g., controllers) and simple data plane (e.g., forwarders). One of the popular SDN protocols/standards is OpenFlow, for which researchers have recently proposed some quality-of-service (QoS) supports. However, the proposals for rate allocation have some limitations in network scalability and multi-class services’ supports. In the literature, rate allocation formulations are commonly based on the framework of network utility maximization (NUM). Nevertheless, multi-class services are rarely considered in that framework since they make the formulated NUM become nonconvex and prevent its subgradient-based algorithm from converging. In this paper, we propose a scalable QoS rate allocation framework for OpenFlow in which multi-class services are considered. The convergence issue in the algorithm of our NUM-based framework is resolved by an admission control scheme. The network scalability is improved by our decentralized algorithms that can run on multiple parallel controllers. Extensive simulation and emulation results are provided to evaluate the performance of our method.