The need for prioritized multi-tenant quality of service (QoS) management in emerging mobile edge systems is particularly critical for high-throughput next generation networks. Current traffic engineering tools rely on network administrator driven, complex functions embedded in closed, proprietary infrastructures, which significantly restrict design flexibility, scalability, and adaptability. This study addresses these challenges by proposing a software-defined networking (SDN) based dynamic QoS provisioning scheme, powered by a digital twin (DT) of networks. By separating the control and data planes, the scheme enables automated traffic management through SDN programmability and data-driven decision-making. It incorporates few-shot learning to dynamically identify and prioritize multi-tenant network traffic utilizing flow statistics from SDN. The proposed QoS provisioning mechanism allocates sufficient bandwidth to high-priority flows while optimizing the remaining bandwidth for lower-priority traffic. Performance evaluations show that the model achieves up to 98% accuracy in identifying the priority of previously unseen traffic flows. Hardware-in-the-loop (HiL) simulations further validate the scheme's effectiveness in meeting multi-tenant QoS requirements, offering a robust and scalable solution for traffic prioritization in SDN based edge networks.

The Unscented Kalman Filter (UKF) stands out as a versatile and dynamic algorithm, celebrated for its prowess in estimating the states of nonlinear dynamical systems within uncertain environments. However, the accuracy of UKF state estimations hinges significantly on the thoughtful selection of pivotal hyperparameters, $\alpha, \beta$, and $\kappa$, which are integral in shaping the distribution of sigma points around the current state estimate. Prevailing methods for tuning these parameters encompass heuristic approaches such as arbitrarily fix $\alpha$ at 0.001, $\kappa$ at 0, and $\beta$ at 2 for Gaussian noise, though the efficacy of such rules heavily hinges on the intricacies of the specific problem. Alternatively, the grid search technique seeks to optimize these hyperparameters, but it can become computationally burdensome, particularly when the search space is extensive and intricate. To navigate these hurdles, this paper introduces the RNN-UKF algorithm-a pioneering strategy that leverages recurrent neural networks (RNNs) to autonomously fine-tune UKF hyperparameters. The inherent adaptability of RNNs is harnessed to dynamically adjust the $\alpha, \beta$, and $\kappa$ parameters of the unscented transformation during each state estimation step, all aimed at minimizing the root mean squared error (RMSE). Demonstrated through numerical simulations, we provide compelling evidence that the RNN-UKF approach outperforms both heuristic rule of thumb and grid search techniques in terms of RMSE performance. Moreover, the RNN-UKF methodology showcases its superiority over the conventional extended Kalman filter (EKF) approach, particularly in scenarios characterized by substantial system noise.

This paper presents an eXplainable Deep Reinforcement Learning (XDRL) based strategy for solving the proposed problem of fleet-level aircraft maintenance scheduling (AMS) optimization. The XDRL-AMS considers various factors such as the aircraft’s initial status, mission requirements, maintenance resource capacity, and operational constraints to create a maintenance schedule for a specified period. The schedule aims to balance both mission readiness and cost reduction. We developed an RL environment, called AMS-Gym, using the OpenAI Gym toolkit specifically designed for this problem. AMS-Gym is highly flexible, allowing for easy extension to more complex scenarios and incorporating additional explanatory capabilities. The explainable RL capability was achieved by utilizing a decomposed reward Deep Q-Network (drDQN) algorithm. In the context of the AMS scenario, the drDQN consists of two parts: (i) a DQN that aims to maximize the mission accomplishment objective, and (ii) a DQN that aims to minimize the maintenance cost objective. As a result, the proposed drDQN strategy can generate real-time aircraft maintenance decisions, explain why those decisions were selected, and present the tradeoffs between the chosen action and non-selected alternatives. Experiment results show that the proposed drDQN performs well, providing an approximate solution to the vanilla DQN with a simpler structure while offering the ability to explain its decisions. In addition, a web-based prototype with an intuitive textual and visual user interface was developed to demonstrate the feasibility of the drDQN approach.

The large amounts of movement data collected from various sources such as wide area motion imagery (WAMI) and mobile phone apps call for innovative technologies to infer valuable information from these data. In this paper, we present two such tools to extract pattern of life (PoL) information from vehicle trajectories. The first tool, intersection traffic analysis (ITA) detects abnormal traffic patterns in major street intersections; while the second one, frequent trajectory patterns analysis (FTPA) discerns the most frequent trajectory patterns in a given time-interval in the region of concern. Both tools support comprehensive trajectory-based pattern of life situation awareness. Using both simulated trajectories and the measurements extracted from WAMI imagery demonstrate ITA and FTPA utility.

3D sensors such as LiDAR, stereo cameras, and radar have been used in many applications, for instance, virtual or augmented reality, real-time immersive communications, and autonomous driving systems. The output of 3D sensors is often represented in the form of point clouds. However, the massive amount of point cloud data generated from 3D sensors poses big challenges in data storage and transmission. Therefore, effective compression schemes are needed for reducing the bandwidth of wireless networks or storage space of 3D point cloud data. Several point cloud compression (PCC) algorithms have been proposed using signal processing or neural network techniques. In this study, we investigate four state-of-the-art PCC methods using two different datasets with various configurations. The objective of this study is to provide a comprehensive understanding of various approaches in PCC. The results of this paper will be helpful in developing an adaptive 3D point cloud stream compression benchmark that is efficient and benefited from different PCC techniques.

In this paper, we developed a pattern discovery and anomaly detection system using a knowledge graph constructed by integrating data from heterogeneous sources. Specifically, the knowledge graph is constructed based on data extracted from structured and unstructured sources. Besides the extracted entities and relations, the knowledge graph finds hidden relations via link prediction algorithms. Based on the constructed knowledge graph, the normalcy model for entity, action, and triplets are established. The information of the incoming streaming data is extracted and compared to the normalcy model in order to detect abnormal behaviors. In addition, we apply the lambda framework to enable a computationally scalable algorithm for pattern discovery and anomaly detection in a big data environment. Real time tweets data are used for evaluation and preliminary results show promising performance in detecting abnormal pattern and activities.

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

Abstract For spectrum sensing, energy detection has the advantages of low complexity, rapid analysis, and requires no knowledge of the transmission signal, which makes it suitable for a wide range of applications. However, under low signal‐to‐noise ratio conditions, the required window length (or the time‐bandwidth product) for energy detection to achieve a desired detection performance is large. In addition, conventional energy detection assumes that the detection tests are independent, that is, there is no overlap between individual detection tests. These properties significantly reduce the detection speed when energy detection is used for the continuous monitoring over a communication channel for the detection of signal transmission activities. In this paper, we propose a sliding window detection analysis with overlap among multiple tests. Algorithms for effective performance analysis of the proposed sliding window energy detection are proposed. The impact of window length on distribution of detection time is investigated. Simulation results on the proposed sliding window energy detection are also compared with the theoretically predicted and conventional energy detection performance estimates. Copyright © 2015 John Wiley & Sons, Ltd.

Quantum cryptography is one of the most promising technologies for guaranteeing the absolute security in communications over various advanced networks, including fiber networks and wireless networks. In particular, quantum key distribution is an efficient encryption scheme on implementing secure satellite communications between satellites and ground stations. However, it faces many new challenges such as high attenuation and low polarization-preserving capability or extreme sensitivity to the environment. In order to guarantee the quality of service (QoS) provisioning of quantum communications over 3D satellite networks, we need to focus on the security problem and throughput efficiency through correcting the errors resulted from the objective and adversary influences. To overcome these problems, we model the noisy quantum channel and implement an efficient quantum error correction scheme to ensure the security and increase the quantum throughput efficiency in QKD-based satellite networks. The simulation results obtained show that our proposed efficient QEC scheme for QoS guarantee outperforms the other existing quantum error correction schemes in terms of security and the quantum throughput efficiency.

In recent years, quantum-based techniques have attracted significant research attention because of its unique advantages on satellite communications, especially for security problem. Security guarantee is one of the most important requirements in QoS-driven 3D satellite networks. Quantum key distribution (QKD) is a methodology for generating and distributing random encryption keys using the principles of quantum physics, which enables two distant communications parties to securely communicate in a way that cannot be eavesdropped on without being detected. Although the QKD method can ensure the absolute security transmission over 3D satellite networks, it imposes many new implementation challenges due to the various limitations on quantum communication over long distances via 3D free space, including quantum channel attenuation, photon-state disruption and vulnerability to noise/interference, laser-beam widening, and constrained security-key generation rate. These problems get even more challenging when QoS provisioning is required for the applications over the 3D satellite networks. To overcome the aforementioned difficulties, we propose the framework to efficiently implement the QKD for security guarantees over quantum-repeater-based QoS-driven 3D satellite networks. First, we develop the quantum-repeater-based QKD satellite network architecture. Then, we design the quantum repeater including the purification scheduling algorithm and the optimal QoS-based repeating-router selection scheme in quantum-repeater-based QKD satellite networks. Finally, the obtained simulations evaluation validate and evaluate our proposed algorithms and schemes.

A typical method to obtain valuable information is to extract the sentiment or opinion from a message. Machine learning technologies are widely used in sentiment classification because of their ability to “learn” from the training dataset to predict or support decision making with relatively high accuracy. However, when the dataset is large, some algorithms might not scale up well. In this paper, we aim to evaluate the scalability of Naïve Bayes classifier (NBC) in large datasets. Instead of using a standard library (e.g., Mahout), we implemented NBC to achieve fine-grain control of the analysis procedure. A Big Data analyzing system is also design for this study. The result is encouraging in that the accuracy of NBC is improved and approaches 82% when the dataset size increases. We have demonstrated that NBC is able to scale up to analyze the sentiment of millions movie reviews with increasing throughput.

Cooperative communication mechanisms have been proposed as an effective way of exploiting the spatial diversity to improve the quality of wireless transmissions. To the best of our knowledge, a number of research efforts have been paid to study how to employ diversity to the network layer routing design, realizing the minimum energy expenditure in the data transmission. However, there is a lack of a systematic strategy for evaluating the existing schemes. To address this issue, we first develop a taxonomy to summarize the existing energy-efficient cooperative routing algorithms and compare their pros and cons. In particular, we focus on the relay set selection strategies, which have great impact on energy saving. To fairly compare the performance of those schemes, we conduct theoretical analysis and derive three theorems to instruct energy-efficient cooperative routing. Our extensive experiments validate our findings. Our research summarizes the state-of-art research development and lay out future directions in this area.

Network-based crime has been increasing in both extent and severity and network-based forensics encapsulates an essential part of legal surveillance. A key network forensics tool is trace back, which can be used to identify true sources of suspects. Both accuracy and secrecy are essential attributes of a successful forensic trace back. In this paper, we present a class of hopping based spread-spectrum techniques for forensic trace back, which fully use the benefits of the spread spectrum approach and preserves a greater degree of secrecy. Our proposed techniques, including Code Hopping-Direct Sequence Spread Spectrum (CHDSSS), Frequency Hopping-Direct Sequence Spread Spectrum (FH-DSSS), and Time Hopping-Spread Spectrum (TH-DSSS), operate to randomize the effects of marking traffic through both the time and frequency domains. Our simulation study validates these techniques in terms of accuracy and secrecy.

To address the challenges on non-cooperative long-distance human identification and verification, we propose an innovative cost-efficient system for automatic long-range biometric recognition of non-cooperative individuals in 24/7 operations. The system has three cameras. One is a wide field of view (WFOV) CCD video camera with an Infrared (IR) filter and powerful IR illuminators for human scan in a wide area at a long distance. The other two cameras are high resolution video cameras with narrow field of view (NFOV) and an IR filter & illuminators, mounted on a pan-tilt-unit (PTU) to capture the frontal view of human face and iris respectively. Once the frontal views of moving individuals are captured by the NFOV cameras, the face/iris models will be extracted and classified by the state-of-the-art face/iris recognizers. The hardware of the biometric system also includes one FPGA, three DSP processors, and one Zigbee module for fast bio-data analysis and wireless data transmission.

This paper compares different defense strategies against various attacks utilizing a dynamic game theoretic data fusion framework for cyber network defense. In our game theoretic framework, Alerts generated by Intrusion Detection Sensors (IDSs) or Intrusion Prevention Sensors (IPSs) are fed into the data refinement (Level 0) and object assessment (L1) data fusion components. High-level situation/threat assessment (L2/L3) data fusion based on Markov game model and Hierarchical Entity Aggregation (HEA) are proposed to refine the primitive prediction generated by adaptive feature/pattern recognition and capture new unknown features. A Markov (Stochastic) game method is used to estimate the belief of each possible cyber attack pattern. Game theory captures the nature of cyber conflicts: determination of the attacking-force strategies is tightly coupled to determination of the defense-force strategies and vice versa. A software tool is developed to demonstrate and compare the performance of different defense strategies used in game theoretic high level information fusion for cyber network defense situations and a simulation example shows the enhanced understating of cyber-network defense.

This paper describes how pedigree is used to support and enhance situation and threat assessment. It is based on the findings of the technology group of the Data Fusion Levels Two and Three Workshop sponsored by the Office of Naval Research held in Arlington, VA from 15-18 Nov. 2005. It identifies areas that need improvement in situation assessment and threat assessment, such as interoperability, automation, pedigree management, system usability, reliability, and uncertainty. The concept of pedigree must include "standard" metadata, lineage, plus a computational model of the quality of the information. The system must automatically propagate changes and update to derived products when source information or source-pedigree information changes. Several other processes must be automated: generate pedigree, identify and auto fill gaps, fuse pedigree, update pedigree, display of information quality and confidence. The paper concludes with suggestions for future research and development.

The strategy of data fusion has been applied in threat prediction and situation awareness and the terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called JDL data fusion model, which currently called DFIG model. Higher levels of the DFIG model call for prediction of future development and awareness of the development of a situation. It is known that Bayesian network is an insightful approach to determine optimal strategies against asymmetric adversarial opponent. However, it lacks the essential adversarial decision processes perspective. In this paper, a highly innovative data-fusion framework for asymmetric-threat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. In particular, asymmetric and adaptive threats are detected and grouped by intelligent agent and hierarchical entity aggregation in level 2 and their intents are predicted by a decentralized Markov (stochastic) game model with deception in level 3. We have verified that our proposed algorithms are scalable, stable, and perform satisfactorily according to the situation awareness performance metric

In this paper, spectral unmixing methods, which are extensively used in hyperspectral imaging area, are proposed for classification and abundance fraction (concentration) estimation of chemical and biological agents that exist in the mixture form. Several government-furnished datasets, which were collected through the infrared spectrum method, were thoroughly analyzed. Two similarity measures-the spectral angle mapper and spectral information divergence-were investigated in order to provide a quantitative comparison basis with respect to the performance of the applied spectral unmixing methods in the existence of similar and distinct agents. The use of the similarity measures provided valuable information about the signature characteristics of the agents, which led to a better understanding about the capabilities of the investigated methods. The orthogonal subspace projection (OSP) method was investigated as the first unmixing, classification, and abundance estimation technique. It was observed that the OSP method provided good results when the number of agents in the database was small and was composed of distinct agents. However, when the number of agents was incremented by adding agents that share similar characteristics, the abundance estimation accuracy gradually degraded in addition to generating negative abundance fraction estimates. The second investigated unmixing method was called nonnegatively constrained least squares (NCLS). The results and analyses indicated that the NCLS method outperformed the OSP approach by providing considerably more accurate fraction estimates while at the same time not generating any negative fraction estimates; thus, the use of the NCLS method was found to be promising in detection and abundance fraction estimation of chemical and biological agents that exist in the form of mixtures. In addition, efficient implementation of NCLS has resulted in much lower computations than the conventional OSP implementation.