W. -P. NWADIUGWU AND D. -S. KIM, "COMPRESSED TIME-FREQUENCY CHANNEL BEAMFORMING USING EMPIRICAL MIMO-UWB RFS FOR INDOOR JOBSHOP," IN IEEE SENSORS JOURNAL, VOL. 22, NO. 6, PP. 5457-5469, 15 MARCH15, 2022, DOI: 10.1109/JSEN.2021.3117339.
Abstract : Most collaborative analog-digital beamforming precoder architectures are commonly deployed for ground to-underground (G2U) layer system sensing. The architectures are designed based on the target system’s signal channel links in order to leverage its array and multiplexing millimeter wave (mmWave) gains. Such gains includes link quality, energy consumption, delay and packet accuracy. But recent designs have mostly targeted narrowband-based channels and fewer wideband mmWave domains. In this paper, novel sparse-formulated in time-frequency and compressed process resource block (PRB) beamforming sensing, to decode-and-forward (DF) packets by joint-relaying it over radio frequency (RF) mmWave is proposed. The model is deployed into an indoor jobshop with remote sensing capability. The routing path’s transmission energy is minimized using an optimized three-way next-hop node selection over empirically characterized MIMO-ultrawideband RFs of dissimilar soil’s volumetric water content (VWC) and burial depths. The novel collaborative time-frequency PRB-based approach is further exploited for estimating the wideband mmWave channels. Results are corroborated by calibrating the system’s minimum transmission energy (MTE), packet-reception-ratio (PRR), link quality indicator (LQI), and end-to-end delay profiles using the latest vector network analyzer (VNA) 8722ES device.
Publication Date: MARCH 15, 2022
Print ISSN: 1530-437X
Online ISSN: 1558-1748
SIMEON OKECHUKWU AJAKWE, VIVIAN UKAMAKA IHEKORONYE, DONG-SEONG KIM, JAE MIN LEE, DRONET: MULTI-TASKING FRAMEWORK FOR REAL-TIME INDUSTRIAL FACILITY AERIAL SURVEILLANCE AND SAFETY, DRONES, VOL. 6, NO. 2, 46; HTTPS://DOI.ORG/10.3390/DRONES6020046
The security of key and critical infrastructures is crucial for uninterrupted industrial process flow needed in strategic management as these facilities are major targets of invaders. The emergence of non-military use of drones especially for logistics comes with the challenge of redefining the anti-drone approach in determining a drone’s harmful status in the airspace based on certain metrics before countering it. In this work, a vision-based multi-tasking anti-drone model is proposed to detect drones, identifies the airborne objects, determines its harmful status through perceived threat analysis, and checks its proximity in real-time prior to taking an action. The model is validated using manually generated 5460 drone samples from six (6) drone models under sunny, cloudy, and evening scenarios and 1709 airborne objects samples of seven (7) classes under different environments, scenarios (blur, scales, low illumination), and heights. The proposed model was compared with seven (7) other object detection models in terms of accuracy, sensitivity, F1-score, latency, throughput, reliability, and efficiency. The simulation result reveals that overall, the proposed model achieved superior multi-drone detection accuracy of 99.6%, attached object identification of sensitivity of 99.80%, and F1-score of 99.69%, with minimal error, low latency, and less computational complexity needed for effective industrial facility aerial surveillance. A benchmark dataset is also provided for subsequent performance evaluation of other object detection models.
W. -P. NWADIUGWU AND D. -S. KIM, "RECONFIGURABLE PHYSICAL RESOURCE BLOCK USING NOVEL GM–C BEAMFORMING FILTER CIRCUIT FOR LTE-BASED CELL-EDGE TERMINALS," IN IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, DOI: 10.1109/TCAD.2022.3146847. 26-JAN-2022. (ACCEPT)
This paper presents a novel reconfigurable physical resource block (PRB), where long term evolution (LTE) and mobile cell-edge terminals are interfaced using low-power tunable second order GmC filter beamforming circuit, hence, achieving an uplink-downlink (UL-DL) cellular system. A total of ten (10) wireless multipoint (MP) terminals were structured and investigated. The BS-to-UAV enabled terminals are propagated by line-of-sight (LoS) and non-LoS beamforming. The subordinate terminals are handled using the derived Rayleigh signal distribution expressions. To guarantee higher frequency shift, the embedded control logic (CL) circuit is modeled upon the NMOS low dropout (LDO) regulator configurations with center frequency of 9.2 MHz and pass band of 1.4 MHz. The quality factors for both the first and second poles remain stable at 4.1 and 6.5 respectively. Stochastic analysis of the anticipated signal impacts with further estimation of its inherent variation challenges within the confined spectrum-sharing zone were corroborated. This is validated in an introduced direct and relay network density variations. The model’s performance evaluation compliment a 45.3 dB gain, an optimal transmission power and robust network sum-rates in both LoS/NLoS conditions.
ESMOT ARA TULI, MOHTASIN GOLAM, DONG-SEONG KIM, AND JAE-MIN LEE, "PERFORMANCE ENHANCEMENT OF OPTIMIZED LINK STATE ROUTING PROTOCOL BY PARAMETER CONFIGURATION FOR UANET", DRONES 2022, VOL.6, ISSUE 1, DOI: 10.3390/DRONES6010022
The growing need for wireless communication has resulted in the widespread usage of unmanned aerial vehicles (UAVs) in a variety of applications. Designing a routing protocol for UAVs is paramount as well as challenging due to its dynamic attributes. The difficulty stems from features other than mobile ad-hoc networks (MANET), such as aerial mobility in 3D space and frequently changing topology. This paper analyses the performance of four topology-based routing protocols: geographic routing protocol (GRP), dynamic source routing (DSR), ad-hoc on-demand distance vector (AODV), and optimized link state routing (OLSR), using practical simulation software OPNET 14.5. We compared and analyzed the performance using performance metrics like throughput, delay, and data drop rate. Moreover, we proposed an optimized enhanced OLSR routing protocol by reducing holding time, named E-OLSR. The optimized OLSR settings provide better performance than the conventional request for comments (RFC 3626) in the experiment, making it suitable for use in UAV ad-hoc network (UANET) environments. Simulation results indicate the proposed E-OLSR outperforms the existing OLSR and achieves supremacy over other protocols mentioned in this paper.
The Physical Internet (PI, or π ) paradigm has been developed for a global logistics system that aims to move, handle, store, and transport logistics products in a sustainable and efficient way. To achieve this goal, the PI requires a higher level of interconnectivity and interoperability in terms of physical, informational, and operational aspects, which, by following the principle of the digital Internet (DI), is enabled by an interconnected network of intermodal hubs, collaborative protocols, and standardized, modular, and smart containers. Meanwhile, digital transformation (DT) has become mainstream in Industry 4.0 to innovate many industries, including logistics and supply chains, through the use of breakthrough digital technologies in the fields of information, communication, connectivity, analytics, and computing, such as the next generation of communication and networking (i.e., 5G), the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), big data analytics (BDA), and cloud computing (CC). In this context, the introduction of DT in the PI vision has many implications for the development and realization of an efficient and sustainable global logistics system. This study investigated the perspectives of PI under the impact of DT. The major challenges and associated open research regarding the adoption of DT in PI have been thoroughly investigated.
LOVE ALLEN CHIJIOKE AHAKONYE, COSMAS IFEANYI NWAKANMA, JAE-MIN LEE, AND DONG-SEONG KIM, "EFFICIENT CLASSIFICATION OF ENCIPHERED SCADA NETWORK TRAFFIC IN SMART FACTORY USING DECISION TREE ALGORITHM", IEEE ACCESS VOL.9, PP. 154892-154901, NOVEMBER 13, 2021, I.F:3.367, DOI : 10.1109/ACCESS.2021.3127560
Vulnerability detection in Supervisory Control and Data Acquisition (SCADA) network of a Smart Factory (SF) is a high-priority research area in the cyber-security domain. Choosing an efficient Machine Learning (ML) algorithm for intrusion detection is a huge challenge. This study performed an investigative analysis into the classification ability of various ML models leveraging public cyber-security datasets to determine the best model. Based on the performance evaluation, all adaptions of Decision Tree (DT) and KNN in terms of accuracy, training time, MCE, and prediction speed are the most suitable ML for resolving security issues in the SCADA system.
This paper presents a convolution neural network (CNN)-based direction of arrival (DOA) estimation method for radio frequency (RF) signals acquired by a nonuniform linear antenna array (NULA) in unmanned aerial vehicle (UAV) localization systems. The proposed deep CNN, namely RFDOA-Net, is designed with three primary processing modules, such as collective feature extraction, multi-scaling feature processing, and complexity-accuracy trade-off, to learn the multi-scale intrinsic characteristics for multi-class angle classification. In several specific modules, the regular convolutional and grouped convolutional layers are leveraged with different filter sizes to enrich diversified features and reduce network complexity besides adopting residual connection to prevent vanishing gradient. For performance evaluation, we generate a synthetic signal dataset for DOA estimation under the multipath propagation channel with the presence of additive noise, propagation attenuation and delay. In simulations, the effectiveness of RFDOA-Net is investigated comprehensively with various processing modules and antenna configurations. Compared with several state-of-the-art deep learning-based models, RFDOA-Net shows the superiority in terms of accuracy with over 94% accuracy at 5 dB signal-to-noise ratio (SNR) with cost-efficiency.