| 1. |
- Wen, Fuxi, 1982, et al.
(author)
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A survey on 5G massive MIMO Localization
- 2019
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In: Digital Signal Processing: A Review Journal. - : Elsevier BV. - 1051-2004 .- 1095-4333. ; 94:November 2019, s. 21-28
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Journal article (peer-reviewed)abstract
- Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field.
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| 2. |
- Bhutto, Adil B., et al.
(author)
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Reinforced Transformer Learning for VSI-DDoS Detection in Edge Clouds
- 2022
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In: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 94677-94690
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Journal article (peer-reviewed)abstract
- Edge-driven software applications often deployed as online services in the cloud-to-edge continuum lack significant protection for services and infrastructures against emerging cyberattacks. Very-Short Intermittent Distributed Denial of Service (VSI-DDoS) attack is one of the biggest factor for diminishing the Quality of Services (QoS) and Quality of Experiences (QoE) for users on edge. Unlike conventional DDoS attacks, these attacks live for a very short time (on the order of a few milliseconds) in the traffic to deceive users with a legitimate service experience. To provide protection, we propose a novel and efficient approach for detecting VSI-DDoS attacks using reinforced transformer learning that mitigates the tail latency and service availability problems in edge clouds. In the presence of attacks, the users’ demand for availing ultra-low latency and high throughput services deployed on the edge, can never be met. Moreover, these attacks send very-short intermittent requests towards the target services that enforce longer delays in users’ responses. The assimilation of transformer with deep reinforcement learning accelerates detection performance under adverse conditions by adapting the dynamic and the most discernible patterns of attacks (e.g., multiplicative temporal dependency, attack dynamism). The extensive experiments with testbed and benchmark datasets demonstrate that the proposed approach is suitable, effective, and efficient for detecting VSI-DDoS attacks in edge clouds. The results outperform state-of-the-art methods with 0.9%-3.2% higher accuracy in both datasets.
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| 3. |
- Rabiee, Ramtin, et al.
(author)
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LaIF: A Lane-Level Self-Positioning Scheme for Vehicles in GNSS-Denied Environments
- 2019
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In: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 20:8, s. 2944-2961
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Journal article (peer-reviewed)abstract
- Vehicle self-positioning is of significant importance for intelligent transportation applications. However, accurate positioning (e.g., with lane-level accuracy) is very difficult to obtain due to the lack of measurements with high confidence, especially in an environment without full access to a global navigation satellite system (GNSS). In this paper, a novel information fusion algorithm based on a particle filter is proposed to achieve lane-level tracking accuracy under a GNSS-denied environment. We consider the use of both coarse-scale and fine-scale signal measurements for positioning. Time-of-arrival measurements using the radio frequency signals from known transmitters or roadside units, and acceleration or gyroscope measurements from an inertial measurement unit (IMU) allow us to form a coarse estimate of the vehicle position using an extended Kalman filter. Subsequently, fine-scale measurements, including lane-change detection, radar ranging from the known obstacles (e.g., guardrails), and information from a high-resolution digital map, are incorporated to refine the position estimates. A probabilistic model is introduced to characterize the lane changing behaviors, and a multi-hypothesis model is formulated for the radar range measurements to robustly weigh the particles and refine the tracking results. Moreover, a decision fusion mechanism is proposed to achieve a higher reliability in the lane-change detection as compared to each individual detector using IMU and visual (if available) information. The posterior Cramér-Rao lower bound is also derived to provide a theoretical performance guideline. The performance of the proposed tracking framework is verified by simulations and real measured IMU data in a four-lane highway.
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| 4. |
- Rabiee, Ramtin, et al.
(author)
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Vehicle localization in GNSS-denied environments
- 2019
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In: Cooperative localization and navigation. - : CRC Press. - 9781138580619 - 9780429507229 ; , s. 199-222
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Book chapter (other academic/artistic)abstract
- This chapter discusses possible information sources and methods to fuse the available information to achieve a certain level of tracking precision. It explains the plausible information sources that may aid in vehicular localization. The chapter considers on-board sources, and examines external sources including from the wireless infrastructure as well as from other vehicles. Radar provides distances from objects in their field of view. The likelihood of range measurements from side-scan radars is given by considering the fact that the measured range might be from a known guardrail or from the vehicles in possible adjacent lanes. The assumption of a global navigation satellite system -denied scenario is situational in most cases. Pseudo-range measurements from sharing collected global navigation satellite system (GNSS) data in GNSS-denied environments. The wireless communication between vehicles, and between vehicles and related wireless networks' base stations/infrastructure can be a source of information for the purpose of localization.
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| 5. |
- Song, Yang, et al.
(author)
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Anchor-free multi-level self-localization in ad-hoc networks
- 2021
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In: 2021 IEEE Wireless Communications and Networking Conference (WCNC). - : IEEE. - 9781728195056 ; , s. 1-6
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Conference paper (peer-reviewed)abstract
- In this paper, we propose a multi-level localization algorithm that breaks a centralized localization problem into a cluster-level distributed localization problem, where each cluster is a centralized unit. In contrast to fully distributed localization, the cluster-level distributed scheme results in reduction in contention, communication overheads, convergence time and energy consumption because cluster heads are responsible for the intracluster positioning on behalf of the whole cluster. To generate a global map, the cluster heads communicate with their direct neighbors to carry out inter-cluster ranging and positioning. The proposed method is suitable for large ad-hoc networks where most agents are low-cost, low-power RF transceivers used for ranging only while some agents are integrated with microcomputers such as Raspberry Pis capable of running intra and inter-cluster localization algorithms. The proposed system can work without anchor nodes and thus it can be deployed in the environments such as urban canyon, inside multi-story buildings, airports, and underground shopping malls where access to anchors or Global Navigation Satellite System (GNSS) is limited or prohibitive. We exploit a hybrid of two well-known methods: multidimensional scaling (MDS) and extended Kalman filtering (EKF) to effectively construct local and global position maps, even in the absence of GNSS information, anchors, or a complete ranging matrix.
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| 6. |
- Yan, Yongsheng, et al.
(author)
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A tightly coupled integration approach for cooperative positioning enhancement in DSRC vehicular networks
- 2022
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In: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 23:12, s. 23278-23294
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Journal article (peer-reviewed)abstract
- Intelligent transportation system significantly relies on accurate positioning information of land vehicles for both safety and non-safety related applications, such as hard-braking ahead warning and red-light violation warning. However, existing Global Navigation Satellite System (GNSS) based solutions suffer from positioning performance degradation in challenging environments, such as urban canyons and tunnels. In this paper, we focus on the positioning performance enhancement of land vehicles via cooperative positioning under a partial GNSS environment in a Vehicular Ad-hoc NETwork (VANET). The availability of Time-of-Flight (ToF) based inter-vehicle or vehicle-to-infrastructure ranges is verified via 5.9 GHz Dedicated Short-Range Communication (DSRC) vehicle-to-everything communication with RTS/CTS unicast mechanism. An inertial navigation sensor aided, tightly coupled integration approach for land vehicle cooperative positioning using DSRC ToF ranges and carrier frequency offset range-rates is proposed, where a digital map is used to constrain the position estimates. If available, the GNSS pseudorange and Doppler shift under partial GNSS environment can also be incorporated. A Rao–Blackwellized particle filter is utilized to estimate the unknown variables allowing for reduced computational complexity in comparison with the conventional particle filter. The posterior Cramer–Rao lower bound is also derived to give a theoretical performance guideline. Both simulation and experimental results show the validity of our proposed approach.
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