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Articles 1 - 18 of 18
Full-Text Articles in Systems and Communications
Z-Axis Meandering Patch Antenna And Fabrication Thereof, Eduardo Antonio Rojas, Carlos R. Mejias-Morillo
Z-Axis Meandering Patch Antenna And Fabrication Thereof, Eduardo Antonio Rojas, Carlos R. Mejias-Morillo
Publications
Apparatus and techniques described herein can include antenna configurations and related fabrication. For example, a Z-axis meandering antenna configuration can be fabricated, such as by forming a dielectric substrate extending in two dimensions and defining an undulating region extending out of a plane defined by the two dimensions; and forming at least one conductive region following a contour of the dielectric substrate including at least a portion of the undulating region. The at least one conductive region can follow the contour of the dielectric substrate, such as including a first conductive region on a first layer, and a second conductive …
Improvement On Pdp Evaluation Performance Based On Neural Networks And Sgdk-Means Algorithm, Fan Deng, Houbing Song, Zhenhua Yu, Liyong Zhang, Xi Song, Min Zhang, Zhenyu Zhang, Yu Mei
Improvement On Pdp Evaluation Performance Based On Neural Networks And Sgdk-Means Algorithm, Fan Deng, Houbing Song, Zhenhua Yu, Liyong Zhang, Xi Song, Min Zhang, Zhenyu Zhang, Yu Mei
Publications
With the purpose of improving the PDP (policy decision point) evaluation performance, a novel and efficient evaluation engine, namely XDNNEngine, based on neural networks and an SGDK-means (stochastic gradient descent K-means) algorithm is proposed. We divide a policy set into different clusters, distinguish different rules based on their own features and label them for the training of neural networks by using the K-means algorithm and an asynchronous SGDK-means algorithm. Then, we utilize neural networks to search for the applicable rule. A quantitative neural network is introduced to reduce a server’s computational cost. By simulating the arrival of requests, XDNNEngine is …
Zero-Bias Deep Neural Network For Quickest Rf Signal Surveillance, Yongxin Liu, Jian Wang, Dahai Liu, Houbing Song, Yingjie Chen, Shuteng Niu
Zero-Bias Deep Neural Network For Quickest Rf Signal Surveillance, Yongxin Liu, Jian Wang, Dahai Liu, Houbing Song, Yingjie Chen, Shuteng Niu
Publications
The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open nature also brings obstacles to surveillance. For alleviation, a surveillance oracle, or a cognitive communication entity needs to identify and confirm the appearance of known or unknown signal sources in real-time. In this paper, we provide a deep learning framework for RF signal surveillance. Specifically, we jointly integrate the Deep Neural Networks (DNNs) and Quickest Detection (QD) to form a sequential signal surveillance scheme. We first analyze the latent space characteristic …
Federated Variational Learning For Anomaly Detection In Multivariate Time Series, Kai Zhang, Houbing Song, Yushan Jiang, Lee Seversky, Chengtao M. Xu, Dahai Liu
Federated Variational Learning For Anomaly Detection In Multivariate Time Series, Kai Zhang, Houbing Song, Yushan Jiang, Lee Seversky, Chengtao M. Xu, Dahai Liu
Publications
Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework …
Learning To Detect: A Data-Driven Approach For Network Intrusion Detection, Zachary Tauscher, Yushan Jiang, Kai Zhang, Jian Wang, Houbing Song
Learning To Detect: A Data-Driven Approach For Network Intrusion Detection, Zachary Tauscher, Yushan Jiang, Kai Zhang, Jian Wang, Houbing Song
Publications
With massive data being generated daily and the ever-increasing interconnectivity of the world’s Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of …
Multimedia Networks And Communications, Houbing Song
Multimedia Networks And Communications, Houbing Song
Publications
Sight and sound is a type of correspondence that joins distinctive substance structures like content, sound, pictures, liveliness, or video into a solitary show, rather than customary broad communications, like written word or sound chronicles. Famous instances of interactive media incorporate video web recordings, sound slideshows and animated videos. Multimedia can be recorded for playback on PCs, workstations, cell phones, and other electronic gadgets, either on request or progressively (streaming). In the early long stretches of sight and sound, the expression "rich media" was inseparable from intuitive mixed media. Over the long run. Improved degrees of intuitiveness are made conceivable …
Learning-To-Dispatch: Reinforcement Learning Based Flight Planning Under Emergency, Kai Zhang, Yupeng Yang, Chengtao Xu, Dahai Liu, Houbing Song
Learning-To-Dispatch: Reinforcement Learning Based Flight Planning Under Emergency, Kai Zhang, Yupeng Yang, Chengtao Xu, Dahai Liu, Houbing Song
Publications
The effectiveness of resource allocation under emergencies especially hurricane disasters is crucial. However, most researchers focus on emergency resource allocation in a ground transportation system. In this paper, we propose Learning-to- Dispatch (L2D), a reinforcement learning (RL) based air route dispatching system, that aims to add additional flights for hurricane evacuation while minimizing the airspace’s complexity and air traffic controller’s workload. Given a bipartite graph with weights that are learned from the historical flight data using RL in consideration of short- and long-term gains, we formulate the flight dispatch as an online maximum weight matching problem. Different from the conventional …
Class-Incremental Learning For Wireless Device Identification In Iot, Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, Houbing Song
Class-Incremental Learning For Wireless Device Identification In Iot, Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, Houbing Song
Publications
Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Noncryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not …
Communication Aware Uav Swarm Surveillance Based On Hierarchical Architecture, Chengtao Xu, Kai Zhang, Yushan Jiang, Shuteng Niu, Thomas Yang, Houbing Song
Communication Aware Uav Swarm Surveillance Based On Hierarchical Architecture, Chengtao Xu, Kai Zhang, Yushan Jiang, Shuteng Niu, Thomas Yang, Houbing Song
Publications
Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of …
Zero-Bias Deep Learning Enabled Quick And Reliable Abnormality Detection In Iot, Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, Houbing Song
Zero-Bias Deep Learning Enabled Quick And Reliable Abnormality Detection In Iot, Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, Houbing Song
Publications
Abnormality detection is essential to the performance of safety-critical and latency-constrained systems. However, as systems are becoming increasingly complicated with a large quantity of heterogeneous data, conventional statistical change point detection methods are becoming less effective and efficient. Although Deep Learning (DL) and Deep Neural Networks (DNNs) are increasingly employed to handle heterogeneous data, they still lack theoretic assurable performance and explainability. This paper integrates zero-bias DNN and Quickest Event Detection algorithms to provide a holistic framework for quick and reliable detection of both abnormalities and time-dependent abnormal events in Internet of Things (IoT).We first use the zero bias dense …
Differential Privacy For Industrial Internet Of Things: Opportunities, Applications And Challenges, Bin Jiang, Houbing Song, Jianqiang Li, Guanghui Yue
Differential Privacy For Industrial Internet Of Things: Opportunities, Applications And Challenges, Bin Jiang, Houbing Song, Jianqiang Li, Guanghui Yue
Publications
The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential …
Zero-Bias Deep Learning For Accurate Identification Of Internet Of Things (Iot) Devices, Yongxin Liu, Houbing Song, Thomas Yang, Jian Wang, Jianqiang Li, Shuteng Niu, Zhong Ming
Zero-Bias Deep Learning For Accurate Identification Of Internet Of Things (Iot) Devices, Yongxin Liu, Houbing Song, Thomas Yang, Jian Wang, Jianqiang Li, Shuteng Niu, Zhong Ming
Publications
The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework …
Deal: Differentially Private Auction For Blockchain Based Microgrids Energy Trading, Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen
Deal: Differentially Private Auction For Blockchain Based Microgrids Energy Trading, Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen
Publications
Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more …
Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang
Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang
Publications
Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …
Security Of The Internet Of Things: Vulnerabilities, Attacks And Countermeasures, Ismail Butun, Houbing Song, Patrik Osterberg
Security Of The Internet Of Things: Vulnerabilities, Attacks And Countermeasures, Ismail Butun, Houbing Song, Patrik Osterberg
Publications
Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. Hence, …
Guest Editorial Special Issue On Toward Securing Internet Of Connected Vehicles (Iov) From Virtual Vehicle Hijacking, Yue Cao, Houbing Song, Omprakash Kaiwartya, Sinem Coleri Ergen, Jaime Lloret, Naveed Ahmad
Guest Editorial Special Issue On Toward Securing Internet Of Connected Vehicles (Iov) From Virtual Vehicle Hijacking, Yue Cao, Houbing Song, Omprakash Kaiwartya, Sinem Coleri Ergen, Jaime Lloret, Naveed Ahmad
Publications
Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.
Artificial Intelligence In The Aviation Manufacturing Process For Complex Assemblies And Components, Elena Vishnevskaya, Ian Mcandrew, Michael Johnson
Artificial Intelligence In The Aviation Manufacturing Process For Complex Assemblies And Components, Elena Vishnevskaya, Ian Mcandrew, Michael Johnson
Publications
Aviation manufacturing is at the leading edge of technology with materials, designs and processes where automation is not only integral; but complex systems require more advanced systems to produce and verify processes. Critical Infrastructure theory is now used to protect systems and equipment from external software infections and cybersecurity techniques add an extra layer of protection. In this research, it is argued that Artificial Intelligence can reduce these risks and allow complex processes to be less exposed to the threat of external problems, internal errors or mistakes in operation.
A Three-Dimensional Pattern-Space Representation For Volumetric Arrays, William C. Barott, Paul G. Steffes
A Three-Dimensional Pattern-Space Representation For Volumetric Arrays, William C. Barott, Paul G. Steffes
Publications
A three-dimensional pattern-space representation is presented for volumetric arrays. In this representation, the radiation pattern of an array is formed by the evaluation of the three-dimensional pattern-space on a spherical surface. The scan angle of the array determines the position of this surface within the pattern-space. This pattern-space representation is used in conjunction with a genetic algorithm to minimize the sidelobe levels exhibited by a thinned volumetric array during scanning.