Network Navigation Research Articles

Integrated Sensing, Navigation, and Communication for Secure UAV Networks with a Mobile Eavesdropper

Integrated Sensing, Navigation, and Communication for Secure UAV Networks with a Mobile Eavesdropper

Retracted: Key Technologies of Real-Time Location Service in Satellite Navigation and Positioning Network Based on Internet of Things.

[This retracts the article DOI: 10.1155/2022/5191871.].

Indoor Navigation Network Model Construction Method Based on Building Information Model

Indoor Navigation Network Model Construction Method Based on Building Information Model

Evaluation of Indoor Paths Based On Indoor Navigation Network Models and Space Syntax Measures

Evaluation of Indoor Paths Based On Indoor Navigation Network Models and Space Syntax Measures

Development of a Model for Trust Management in the Social Internet of Things

The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades of computer science. It is becoming increasingly fashionable for the IoT to be rebranded as the “Social Internet of Things” (SIoT), and this is drawing the attention of the scientific community. Smart items in the Internet of Things (IoT) ecosystem can locate relevant services based on the social ties between neighbors. As a result, SIoT displays the interplay between various items as a problem in the context of the social IoT ecosystem. Navigating a network can be difficult because of the number of friends and the complexity of social ties. By identifying difficulties with standard SIoT devices’ interaction with social objects, truthful friend computing (TFC) is a new paradigm for tracing such difficulties by utilising a relationship management component to improve network navigability. The concept of trust management can be useful as a strategy during collaborations among social IoT nodes. As a result, the trustor can use a variety of measures to evaluate a smart object’s trustworthiness. Hence, this article demonstrates the need for the trustor to evaluate the extent to which a given metric has contributed to the overall trust score and illustrates profitability when engaging in a transaction with other nodes. With the help of the SIoT, this paper used a unified fuzzy-based computational technique and a multiple-criteria decision-making approach to evaluate the trust weights. The statistical findings show that the computing of “truthful friends” is the biggest challenge for successful SIoT implementation at the initial level.

Low Energy, Non-Cortical, Graphene Nanoribbon-Based STDP Plastic Synapses

The realization of energy efficient, low area, and fast processing neuron and synapse circuits is of prime importance for unleashing neuromorphic computing full potential. In this paper, we introduce a graphene-based synapse, which can emulate Spike Timing Dependent Plasticity (STDP) and Short/Long Term Plasticity (STP/LTP) with variable signal amplitude and temporal dynamics. The synapse operation is validated by means of SPICE simulations, and its synaptic modulation ability is showcased through reinforcement learning within a Spiking Neural Network for robotic navigation with obstacles avoidance. Besides its functional versatility, the proposed graphene-based synapse can potentially occupy low active area ($ \approx 170{\kern 1pt} {\mathrm{n}}{{\mathrm{m}}^2}$≈170nm2) and operate at low voltage ($200{\kern 1pt} {\mathrm{mV}}$200 mV ). When compared with a biological brain synapse, its energy consumption per spike for a weight update operation ($0.5{\kern 1pt} {\mathrm{fJ}}$0.5 fJ ) is $20 \times $20× lower, while the processing speed is increased by six orders of magnitude. Such properties are essential desiderata for the realization of large scale neuromorphic systems, making the proposed graphene-based synapse an outstanding candidate for this purpose.

Hybrid TOA/AOA Virtual Station Localization Based on Scattering Signal Identification for GNSS-Denied Urban or Indoor NLOS Environments

Accurate localization is the premise of many technologies and applications, such as navigation, emergency assistance and wireless sensor network. For Global Navigation Satellite System (GNSS)-denied urban or indoor environments, various localization technologies based on mobile communication networks or other wireless technologies have been designed and developed. The main challenge for these localization technologies is the presence of a non-line-of-sight (NLOS) propagation environment due to dense obstacles or buildings. The virtual station method is a promising high-accuracy target localization technique in NLOS environments, and the localization of the scatterer is key to the virtual station method. Once one-bounce scattering signals from the same scatterer are identified, the localization of the scatterer can be achieved easily with the existing localization algorithm of line-of-sight (LOS) scenario, and then the localization of NLOS scenarios is converted into a problem of LOS easily. In this paper, a hybrid time of arrival (TOA)/angle of arrival (AOA) virtual station localization algorithm based on scattering signal identification is proposed. Firstly, one-bounce scattering signals from the same scatterer are identified based on TOA/AOA measurements. Next, scatterers are located based on one-bounce scattering signals with the LOS localization algorithm, and then scatterers are regarded as virtual stations and used for mobile station (MS) localization. Compared with the existing research on the virtual station method, the proposed algorithm relies only on TOA/AOA measurements and does not require any assumption or prior knowledge about the scatterer, base station (BS) or MS, which provides a solid foundation for feasible target localization. Simulation results demonstrate, as far as we know, the proposed algorithm outperforms the state-of-the-art hybrid TOA/AOA algorithm in localization accuracy.

BGSD: A SBERT and GAT-based Service Discovery Framework for Heterogeneous Distributed IoT

Collaboration between heterogeneous IoT (Internet of Things) devices can enrich the capabilities of the system. In scenarios where network infrastructure or cloud platforms are unavailable, enabling devices to collaborate autonomously in a distributed manner can improve the scalability and invulnerability of the system. A distributed service architecture with semantic services can enable more flexible collaboration among IoT devices, while the efficiency and accuracy of current methods on devices with poor computing and storage capacity need to be improved. Therefore, this paper proposes a SBERT (Sentence Bidirectional Encoder Representations from Transformers) and GAT (Graph Attention Networks) based Service Discovery method, called BGSD. SBERT is used to extract deep semantic features of services during offline to improve the discriminability of semantic features and reduce performance requirements for IoT devices, and GAT is used to perceive the local topology environment to improve the network navigability of service search. To evaluate the method, we generate a heterogeneous IoT topology dataset based on OWLS-TC4. The simulation results of service discovery show that BGSD has advantages in search efficiency, matching accuracy and computational cost, and can support the collaboration of IoT devices in heterogeneous distributed scenarios, which verifies the rationality of this method.

Dissociation of categorical and coordinate spatial relations on dynamic network organization states.

Humans can flexibly represent both categorical and coordinate spatial relations. Previous research has mainly focused on hemisphere lateralization in representing these two types of spatial relations, but little is known about how distinct network organization states support representations of the two. Here we used dynamic resting-state functional connectivity (FC) to explore this question. To do this, we separated a meta-identified navigation network into a ventral and two other subnetworks. We revealed a Weak State and a Strong State within the ventral subnetwork and a Negative State and a Positive State between the ventral and other subnetworks. Further, we found the Weak State (i.e., weak but positive FC) within the ventral subnetwork was related to the ability of categorical relation recognition, suggesting that the representation of categorical spatial relations was related to weak integration among focal regions in the navigation network. In contrast, the Negative State (i.e., negative FC) between the ventral and other subnetworks was associated with the ability of coordinate relation processing, suggesting that the representation of coordinate spatial relations may require competitive interactions among widely distributed regions. In sum, our study provides the first empirical evidence revealing different focal and distributed organizations of the navigation network in representing different types of spatial information.

A high‐resolution AUV navigation framework with integrated communication and tracking for under‐ice deployments

AbstractWe developed an environmentally adaptive under‐ice navigation framework that was deployed in the Arctic Beaufort Sea during the United States Navy Ice Exercise in March 2020 (ICEX20). This navigation framework contained two subsystems developed from the ground up: (1) an on‐board hydrodynamic model‐aided navigation (HydroMAN) engine, and (2) an environmentally and acoustically adaptive integrated communication and navigation network (ICNN) that provided acoustic navigation aiding to the former. The HydroMAN synthesized measurements from an inertial navigation system (INS), ice‐tracking Doppler velocity log (DVL), ICNN and pressure sensor into its self‐calibrating vehicle flight dynamic model to compute the navigation solution. The ICNN system, which consisted of four ice buoys outfitted with acoustic modems, trilaterated the vehicle position using the one‐way‐travel‐times (OWTT) of acoustic datagrams transmitted by the autonomous underwater vehicle (AUV) and received by the ice buoy network. The ICNN digested salinity and temperature information to provide model‐assisted real‐time OWTT range conversion to deliver accurate acoustic navigation updates to the HydroMAN. To decouple the contributions from the HydroMAN and ICNN subsystems towards a stable navigation solution, this article evaluates them separately: (1) HydroMAN was compared against DVL bottom‐track aided INS during pre‐ICEX20 engineering trials where both systems provided similar accuracy; (2) ICNN was evaluated by conducting a static experiment in the Arctic where the ICNN navigation updates were compared against GPS with ICNN error within low tens of meters. The joint HydroMAN‐ICNN framework was tested during ICEX20, which provided a nondiverging high‐resolution navigation solution—with the majority of error below 15 m—that facilitated a successful AUV recovery through a small ice hole after an 11 km untethered run in the upper and mid‐water column.

Through the Gate: Mapping the Transfer Landscape and Understanding What Students Say Builds Their Capacity for Transfer

ABSTRACT Educational practitioners, researchers, and policymakers have focused on increasing community college transfer rates as an equity strategy for improving social and economic mobility among historically underserved populations. While there is a vast amount of research on students who have successfully transferred, there is limited evidence base on the experience of community college students who are on the verge of this transition. This study employed a mixed-methods design to map California’s transfer landscape and understand what factors facilitate or hinder students’ capacity to transfer to a university successfully. Findings reveal a missed opportunity: between 2010 and 2015, over one-third (N = 292,556) of California community college students did not make it “through the gate” to a university within six years – even though they had met most or all of their transfer requirements. Subsequent student surveys and interviews revealed four critical factors for building students’ transfer capacity and helping them reach their goals of a baccalaureate degree and economic and social mobility for themselves and their families: university affordability, school-life balance, pathway navigation, and support network. This article discusses implications and future research considerations for leveraging these findings to support equity and transfer success efforts.

A Combined Measurement Method for the Seafloor Positioning, Navigation, and Timing Network

The idea of constructing the “GNSS-like” seafloor geodetic network for underwater positioning, navigation, and timing (PNT) has been proposed by many countries. Based on this idea, this paper introduces the principle of the seafloor PNT network and provides a combined measurement method, including the absolute positioning and the relative positioning. Experimental results show that the positioning difference between the proposed method and circle-sailing positioning is approximately 10 cm, and the observation efficiency is higher than the existing measurement in the seafloor PNT network. In addition, a model is derived to determine the optimal configuration of the unit network (the basic component of the seafloor PNT network), considering the ranging capability of acoustic beacons, which is helpful to balance the side length of the unit network and the number of observation sessions in the relative positioning. Finally, a chain coordinate transfer strategy in the whole seafloor PNT network is proposed, and the positions where the absolute positioning should be carried out in the whole network are derived based on the conditional adjustment model. Based on this strategy, the design schemes of a seafloor PNT network with centimeter, decimeter, and meter positioning accuracy when the acoustic velocity measuring accuracy is 0.02 m/s and the time measuring accuracy is 10−5 s, are given in the experimental section.

Deep Neural Network for Navigation of a Single-Element Transducer During Transcranial Focused Ultrasound Therapy: Proof of Concept.

Transcranial focused ultrasound (tFUS) has gained attention in the field of brain stimulation owing to its non-invasive neurotherapeutic potentials. However, complex interactions between acoustic waves and the cranium may introduce misalignment of the acoustic focus from a geometric target location, thus, necessitate on-site feedback of real-time navigational information of the transducer (spatial coordinates and angular orientation) for the operators to accurately place the acoustic focus to the desired brain area. In this study, we propose a deep-learning-based network model that can provide spatial navigational information of a single-element FUS transducer with respect to the targeted brain region. The training dataset were acquired through forward simulations that reflect the different tFUS transmissions for each skull structure using cranial computed tomography (CT) image data. The performance of the network was evaluated through three ex vivo calvaria. As a result show that the presented neural network-based method can an accurately navigate the FUS transducer with the conformity of ∼99.59% in placement of the transducer and ∼74.49% in the focal volume and an average difference of ∼0.96 mm in the focal point, all capable of real-time operation (∼10 ms).

3D building interior modelling for navigation in emergency response applications

• Broad perspective review on building interior modelling. • Three-dimensional model representations for navigation in emergency response. • Application requirements on a spatial model: space subdivision, topology representation, semantics and graphic representation. The aim of this paper is to present a broad perspective on applications dealing with 3D building models and indoor navigation during an emergency. This is an immense topic, which has been considerably investigated by researchers from different fields of expertise: construction, cognition, spatial information science, cartography, robotics, mathematics, engineering, etc. It is not possible to provide one, complete approach to the problem, because it largely depends on the application. However, some solutions are prevailing, and they are adopted by others. In this paper there is an attempt made to signalise specific directions and trends related to the topic. They include building model requirements, navigation network generation and representation, and 3D visualisation of models and navigation paths. These issues are important in the process of rapid model generation, which is suitable for decision support and situational awareness systems for first responders. The authors highlight issues that may be investigated by young researchers or may be considered as alternatives by more experienced ones. The paper is therefore the aim of this paper is to summarise the contemporary research conducted in various research teams, as well as to present author’s own observations and experiences.

Monocular vision with deep neural networks for autonomous mobile robots navigation

Enabling mobile robots to explore the formerly unidentified environment is a challenging task. The current paper describes the internal analysis algorithm for mobile robots that combines various convolutional neural network (CNN) layers with the decision-making process in a hierarchical way. The whole system is trained end-to-end on data captured by a low-cost depth camera (RGB-D). The output consists of the proposed expansion model of the robot's critical moving directions to achieve autonomous analysis ability. Training this model through the dataset is created using Hand-Controlled Mobile Robot (HCMR) built for this purpose. The experiments were conducted by moving this robot in natural and diverse environments. The robot was trained using this data and applied for environmental investigation decisions (the control labels) using CNN to enable the robot to automatically sense the navigation without a map in an unknown environment. Furthermore, extensive experiments were conducted indoors and attained an accuracy of 77%. Experiments showed that the proposed model was able to reach equivalent results that are generally obtained enormously from an expensive sensor. In addition, comprehensive comparisons were drawn between the human-controlled robot and a robot trained using a deep learning process to determine decisions to control the robot's movement. The reached results were identical and satisfactory.

Hierarchical Planning with Deep Reinforcement Learning for 3D Navigation of Microrobots in Blood Vessels

Designing intelligent microrobots that can autonomously navigate and perform instructed routines in blood vessels, a crowded environment with complexities including Brownian disturbance, concentrated cells, confinement, different flow patterns, and diverse vascular geometries, can offer enormous opportunities and challenges in biomedical applications. Herein, a biological‐agent mimicking a hierarchical control scheme that enables a microrobot to efficiently navigate and execute customizable routines in simplified blood vessel environments is reported. The control scheme consists of two decoupled components: a high‐level controller decomposing complex navigation tasks into short‐ranged, simpler subtasks and a low‐level deep reinforcement learning (DRL) controller responsible for maneuvering microrobots to accomplish subtasks. The proposed DRL controller utilizes 3D convolutional neural networks and is capable of learning control policies directly from raw 3D sensory data. It is shown that such a control scheme achieves effective and robust decision‐making within unseen, diverse complicated environments and offers flexibility for customizable task routines. This study provides a proof of principle for designing intelligent control systems for autonomous navigation in vascular networks for microrobots.

Deep Learning and Artificial Neural Networks for Spacecraft Dynamics, Navigation and Control

The growing interest in Artificial Intelligence is pervading several domains of technology and robotics research. Only recently has the space community started to investigate deep learning methods and artificial neural networks for space systems. This paper aims at introducing the most relevant characteristics of these topics for spacecraft dynamics control, guidance and navigation. The most common artificial neural network architectures and the associated training methods are examined, trying to highlight the advantages and disadvantages of their employment for specific problems. In particular, the applications of artificial neural networks to system identification, control synthesis and optical navigation are reviewed and compared using quantitative and qualitative metrics. This overview presents the end-to-end deep learning frameworks for spacecraft guidance, navigation and control together with the hybrid methods in which the neural techniques are coupled with traditional algorithms to enhance their performance levels.

Footstep planning of humanoid robot in ROS environment using Generative Adversarial Networks (GANs) deep learning

This paper proposes deep learning-based footstep planning using Generative Adversarial Networks (GANs) for the indoor navigation of humanoid robots. The GAN-based architecture presents an efficient and accurate path to plan the footsteps of a humanoid robot on Robot Operating System (ROS) based architecture. During navigation, humanoid robots must understand their surroundings and be able to generate footsteps accurately. Although some algorithms that are based on sampling, such as Rapidly Exploring Random Tree (RRT*) and A*, are widely used for path planning, they fail to perform in narrow paths, especially for the footstep generation of humanoid robots. The widely growing deep learning approaches such as GANs are now producing extremely surprising results in solving real-life problems. The experiments conclude that GAN based approach performs better than conventional Dijkstra’s or A* algorithms. The accuracy of the generated footsteps from the GAN-based path planner comes out to be approximately 93%.

Design and Testing of a Computer Security Layer for the LIN Bus.

Most modern vehicles are connected to the internet via cellular networks for navigation, assistance, etc. via their onboard computer, which can also provide onboard Wi-Fi and Bluetooth services. The main in-vehicle communication buses (CAN, LIN, FlexRay) converge at the vehicle’s onboard computer and offer no computer security features to protect the communication between nodes, thus being highly vulnerable to local and remote cyberattacks which target the onboard computer and/or the vehicle’s electronic control units through the aforementioned buses. To date, several computer security proposals for CAN and FlexRay buses have been published; a formal computer security proposal for the LIN bus communications has not been presented. So, we researched possible security mechanisms suitable for this bus’s particularities, tested those mechanisms in microcontroller and PSoC hardware, and developed a prototype LIN network using PSoC nodes programmed with computer security features. This work presents a novel combination of encryption and a hash-based message authentication code (HMAC) scheme with replay attack rejection for the LIN communications. The obtained results are promising and show the feasibility of the implementation of an LIN network with real-time computer security protection.

The relationship between the high-risk disordered eating and social network navigation among Saudi college females during the COVID pandemic.

BackgroundDisordered eating behaviors (DEBs) are complex health issues that may lead to negative physical and mental health outcomes among college students. More studies should be directed toward the screening of DEBs. This study aimed to determine the prevalence of DEBs among Saudi female university students and their association with social networking site (SNSs) usage and composite lifestyle behaviors during the unprecedented period of COVID-19.MethodsThis cross-sectional study included 445 females recruited using stratified random sampling. The participants self-reported demographic, social, medical, and lifestyle data and completed the validated Arabic version of the Eating Attitudes Test-26, Social Networking Sites (SNSs) Usage Questionnaire, Bergen Social Media Addiction Scale, and Body Shape Questionnaire.ResultsThe prevalence of DEBs was 27.2% among the female students at Taif University. From the pre-pandemic period until the current time, the DEBs-risk group had a significantly higher SNS navigation rate (36.4%) than the normal group (20.4%) (X2 = 30.015, p = 0.001). The regression analysis revealed that females with a significant body image concern, higher number of SNSs friends, and frequent visits to SNSs, and those seeking social-dependent information in relation to weight loss/dieting were more likely to develop DEBs (Overall Model: Chi-Square X2 = 158.071, p < 0.000**).ConclusionsSNSs usage and DEBs were associated during the COVID-pandemic. However, the composite lifestyle score did not demonstrate a significant association with DEBs among the female students at Taif University. Investigating the magnitude of DEBs and understanding the role of SNS are essential for preventing disordered eating among young females.