Reliable Path Research Articles

A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0

Industry 5.0 requires intelligent self-organi- zed, self-managed, and self-monitoring applications with ability to analyze and predict the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for Internet of Things applications in Industry 5.0. Knowledge-defined networks (KDN) bridge this gap by extending software-defined networking architecture with knowledge plane, which learns the network dynamics to avoid suboptimal decisions. Cognitive routing leverages the sixth-generation (6G) self-organized networks with self-learning feature. This article presents a self-organized cognitive routing framework for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most reliable path with minimal probability of route-flapping. The proposed framework precalculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry-stranded link-state and distance-vector routing algorithms SPF and DUAL, respectively.

GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks. Specifically, the RSP problem that we are targeting at is to minimize the (weighted) linear combination of mean and standard deviation of the path’s travel time. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose a Gaussian process path planning (GP3) algorithm to calculate the a priori optimal path as the RSP solution. With a series of equivalent RSP problem transformations, we are able to reach a polynomial time complexity algorithm with guaranteed solution accuracy. Extensive experimental results over various sizes of realistic transportation networks demonstrate the superior performance of GP3 over the state-of-the-art algorithms.

CTD: Cascaded Temporal Difference Learning for the Mean-Standard Deviation Shortest Path Problem

This paper investigates the reliable shortest path (RSP) planning problem from the reinforcement learning perspective. Different from canonical path planning methods, which require at least the first- order statistic (mean) and second-order statistic (variance) information of travel time distribution, we target at the RSP planning problem without the assumption of knowing any travel time distribution characteristic beforehand, and propose a cascaded temporal difference learning (CTD) method, which simultaneously estimates the mean and variance of the executing path and thereby gradually makes improvements through the generalized policy iteration (GPI) scheme, as the ego vehicle interacts with the environment. Extensive simulation results demonstrate the applicability of the proposed method for RSP learning in various transportation networks.

Determination and application of path duration of seismic ground motions based on the K-NET data in Sagami Bay, Japan

Duration models are one of the important parameters in ground-motion simulations. This model varies in different study areas, and plays a critical role in nonlinear structural response analysis. Currently, available empirical models are being globally used in ground-motion simulations, with limited research focusing on path duration in specific regions. In this study, we collected 6,486 sets of three-component strong-motion records from 29 K-NET stations in the Sagami Bay, Japan, and its surrounding areas between January 2000 to October 2018. We extracted the effective duration of 386 pieces of ground-motion records by manually picking up the S-wave arrival time and calculating the significant duration. We then obtained the path duration model of the study area based on the empirical equation of dynamic corner frequency and source duration of [7]. Compared with the results of the available empirical models, the Fourier spectrum of the simulated ground motion from our effective duration model showed higher accuracy in the long-term range, with less fitting residuals. This path duration model was then applied to simulate two earthquakes of MW5.4 and MW6.2, respectively, in the region using the stochastic finite-fault method with a set of reliable source, path, and site parameters determined for the study area. The simulation results of most stations fit well with observation records in the 0–30 Hz frequency band. For the MW5.4 earthquake, the simulated ground motions at KNG005/KNG010/SZO008 stations were relatively weak in the mid to high frequency band (1–30 Hz) because the quality factor and geometric diffusion model used in the simulation were the averages of the entire Sagami Bay region, causing a bias in the results of a few stations owing to local crustal velocity anomalies and topographic effects. For the MW6.2 earthquake, the simulated ground motions were relatively weak at all SZO and TKY stations, mainly because of the close distance from these stations to the epicenter and the complex seismic-wave propagation paths. The analysis suggests that the differences between the simulation results of the two earthquakes were mainly related to complex geological conditions and seismic-wave propagation paths.

Three-Dimensional Path Planning for Unmanned Helicopter Using Memory-Enhanced Dueling Deep Q Network

Unmanned helicopter (UH) is often utilized for raid missions because it can evade radar detection by flying at ultra-low altitudes. Path planning is the key technology to realizing the autonomous action of UH. On the one hand, the dynamically changing radar coverage area and the mountains in the low airspace environment will seriously affect the flight safety of UH. On the other hand, the huge state space of the three-dimensional (3D) environment will also make traditional algorithms difficult to converge. To address the above problems, a memory-enhanced dueling deep Q-network (ME-dueling DQN) algorithm was proposed. First, a comprehensive reward function was designed, which can guide the algorithm to converge quickly and effectively improve the sparse reward problem. Then, we introduced a dual memory pool structure and proposed a memory-enhanced mechanism, which can reduce invalid exploration, further improve the learning efficiency of the algorithm, and make the algorithm more stable. Finally, the path planning ability of the proposed algorithm in multiple experimental environments was verified. Experiments showed that the proposed algorithm has good environmental adaptability and can help UH to accurately identify dangerous areas and plan a safe and reliable flight path.

Cyclic testing of through-plate moment connection for beam to concrete filled and unfilled circular column

This research presents a specific configuration for the fully restrained connections between I-shaped beams and concrete filled and unfilled circular columns. In the proposed connection, there is a vertical plate which passes through the aligned slots in the connection zone. The through-plat connection provides a new reliable path to transfer the in-plane forces from the beam to the circular column. The performance of the through-plate connection is experimentally evaluated and, the interior planar connections are tested under lateral cyclic loading. Specimens are consisted of two concrete-filled columns and one unfiled circular column. Herein, the fabrication process and analysis of the proposed connection are presented. The experimental full-scale tests are carried out to understand the mechanisms of the force transferring from the beam to the circular column, and also the shear transferring in the panel zone region. The failure modes, hysteretic performance, strength, initial stiffness, ductility and panel zone behavior of the through-plate connection are evaluated. It is shown that the through-plate connection is a suitable configuration for seismic applications with large deformation demands. The specimens have sustained rotations more than 6% story drift with no damage in the column and the connection components. The results of the experimental program reveal the adequacy of the proposed connection as a special moment connection according to the AISI/AISC341-16 seismic provisions.

Seismic reliability assessment of bridge networks considering travel time and connectivity reliabilities

AbstractTransportation networks play a vital role in transporting goods and casualties immediately after an earthquake. Thus, identifying the most reliable and shortest path is critical for ensuring efficient emergency response and recovery after an earthquake; consequently, it is important to compute the reliability of transportation networks by simultaneously accounting for connectivity and travel time. In this regard, this paper proposes a novel seismic reliability indicator termed as the travel time and connectivity reliability (TTCR), which combines the travel time and connectivity reliabilities. Additionally, a novel framework for computing the TTCR of transportation networks is proposed. The proposed methodology is applied to a bridge network in California. Furthermore, a retrofit plan based on the TTCR is proposed to help risk managers make appropriate and timely decisions for reducing the seismic risk of transportation networks.

RRT*-based Path Planning for Continuum Arms.

Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms. In this paper, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.

Lighting a Reliable Path to 100% Clean Electricity: Evolving Resource Adequacy Practices for a Decarbonizing Grid

It is the year 2045 in the carbon-neutral southwestern United States. While summer heatwaves have increased in frequency and intensity because of climate change, the region’s abundant solar generation produces tremendous amounts of low-cost energy on hot summer days. This energy not only serves daytime loads but it also charges an enormous fleet of batteries that, aided by wind and geothermal power, discharge overnight to effectively eliminate summertime electric reliability concerns.

Winding down the wind power curtailment in China: What made the difference?

The carbon neutrality goal requires significant acceleration of the renewable energy transition. In China, this acceleration is hampered because of the concerns regarding the recent high-rate wind power curtailment. However, post-2016, wind power curtailment shows notable reductions. Therefore, it is vital to understand the fundamental factors driving such improvements, the curtailment reduction patterns across the countries and key regions, the experiences from the regional heterogeneity of curtailment reductions, and the policy implications available to strengthen wind power curtailment mitigation strategies in the short and long term to accelerate China's clean energy transition. This study constructs an evaluation framework based on the logarithmic mean Divisia index approach to investigate these concerns. Furthermore, it reveals that the eminent contributors at the national scale are local power demand, power exports, and power structures. However, the regional factor patterns oscillate significantly. In northwest China, power transmission is vital to reduce wind power curtailment. In north China, thermal power remains dominant because of its importance to national energy security, impeding its curtailment. Northeast China implements the peak-shaving auxiliary service market, promoting the power grid's capability to consume more local wind power. The fundamental settlement of the curtailment issue calls for market-oriented energy structure reforms to ensure sustainable low-carbon development. This study explains the essence of China's renewable energy development and seeks reliable paths to accelerate wind power integration with policy measures and technical transformations to enhance the adaptability of the power grid system.

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes.

A multi‐step method for passive broadband source localisation using a single vector sensor

In the deep ocean, a vector sensor is deployed near the seabed to receive direct waves from the reliable acoustic path (RAP). In the space domain, the vector sensor can estimate the vertical arrival azimuth of the signal, which is sensitive to distance. In the frequency domain, the periodic interference pattern formed by the direct and surface-reflected wave is decided by both the source depth and the vertical arrival azimuth. In this work, a multi-step method for passive broadband source localisation using a single-vector sensor (SVS) is proposed. At first, the depth is estimated by the interference pattern in the frequency domain. Then the ranging problem is solved based on the depth estimated result. The impact of the surface-generated noise is considered. Compared with the scalar vertical line array (VLA) in simulation, the localisation performance of SVS has higher requirements for the signal-to-noise ratio (SNR) but is much smaller in size. In the experiment, Gaussian white noise from 310 to 430 Hz was emitted to simulate the underwater target. Compared with traditional match-field processing (MFP), the multi-step method is much more stable, accurate, and efficient. The computing time is drastically shortened.

Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks

The Internet of Things (IoT) is an evolving concept that has achieved prominence in the modern era. An autonomous sensor-equipped device is the major component of WSN-assisted IoT infrastructure. These devices intelligently sense the environment, automatically collect the data, and deliver the information to paired devices. However, in WSN-assisted IoT networks, energy depletion and hardware faults might result in device failures. Additionally, this might affect data transmission. A reliable route significantly reduces data retransmissions, which can help in congestion reduction and energy conservation. Generally, the sensor devices are typically deployed densely throughout the WSN-assisted IoT networks. A high number of sensor devices covering a monitoring area might result in duplicate data. The clustering method can be used to overcome this problem. The clustering technique reduces network traffic, whereas the multipath technique ensures path reliability. In CRPSH, we used the clustering technique to reduce the duplicate data. Moreover, the multipath approach can increase the reliability of the proposed protocol. CRPSH is intended to minimize the overhead associated with control packets and extend the network’s lifetime. The complete set of simulations is carried out using the Castalia simulator. The proposed protocol is found to reduce energy consumption and increase the lifetime of IoT infrastructure networks.

Reliability-Based Multivehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach

Abstract Identifying a reliable path in uncertain environments is essential for designing reliable off-road autonomous ground vehicles (AGVs) considering postdesign operations. This article presents a novel bio-inspired approach for model-based multivehicle mission planning under uncertainty for off-road AGVs subjected to mobility reliability constraints in dynamic environments. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility (i.e., maximum attainable speed) for any given terrain and soil conditions. Based on physics-based simulations, the vehicle state mobility reliability in operation is then analyzed using an adaptive surrogate modeling method to overcome the computational challenges in mobility reliability analysis by adaptively constructing a surrogate. Subsequently, a bio-inspired approach called Physarum-based algorithm is used in conjunction with a navigation mesh to identify an optimal path satisfying a specific mobility reliability requirement. The developed Physarum-based framework is applied to reliability-based path planning for both a single-vehicle and multiple-vehicle scenarios. A case study is used to demonstrate the efficacy of the proposed methods and algorithms. The results show that the proposed framework can effectively identify optimal paths for both scenarios of single and multiple vehicles. The required computational time is less than the widely used Dijkstra-based method.

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Abstract Path tracking error control is an essential functionality in the development of autonomous vehicles to follow a planned trajectory. Significant path tracking errors could lead to a collision or even out of the control of the vehicle. Model-based control strategies have been developed to minimize the vehicle’s path tracking errors. However, the vehicle model may not truly represent the actual vehicle dynamics. Furthermore, the parameters employed in the vehicle dynamic model may not represent the actual operating conditions of the vehicle under environmental uncertainty. This paper proposes a real-time bias-learning method coupling with the model predictive control (MPC) to improve the fidelity of a baseline vehicle model with the aid of a few experiments (or virtual experiments) so that the path tracking error can be reduced in real-time operation. Gaussian process (GP) regression and recurrent neural network (RNN) are employed for bias-learning and their effectiveness are compared under different scenarios. GP regression learns non-linearity of the model bias through its nonlinear kernel function, whereas the RNN model formulates the bias as a linear combination of hidden nodes which capture the non-linearity of the model bias with a recurrent form. Results reveal that RNN is more effective for real-time learning of the nonlinear model bias than the classical GP regression and the proposed bias-learning model is able to improve the fidelity of a baseline vehicle dynamic model. Consequently, path tracking performance can be greatly improved under environmental uncertainty using the bias-learning-based MPC.

Interference-Resistant Communication framework for Sensor Nodes in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are becoming increasingly important for various aspects of modern communication. However, the lack of reliable and securecommunication between nodes of the WSN often hinders the realization of many of its potential applications. In order to overcome these issues, we propose an interference-resistant Communication Framework for Sensor Nodes in Wireless Sensor Networks (IRCF-WSN) which is based on the idea of combining different types of compatible transmission schemes to establish a secure and reliable communication link between nodes. The proposed framework aims to provide a secure and reliable connection between nodes of the WSN irrespective of the interference environment. In order to accomplish this goal, the framework combines several interference-resistant techniques like frequency hopping, spread spectrum, and beamforming to form multiple channels of connectivity whichare subsequently used for communication. Additionally, the framework also utilizes a novel routing algorithm to ensure that the most secure and reliable path is chosen for the communication. Simulation results indicate that using the proposed framework, the communication performance of the WSN can be improved significantly compared to existing solutions.

Energy-Efficient Data Transmission in Mobility-Aware Wireless Networks

Data transmission scheme is an effective mode to improve the energy-efficiency in packet delivery. In this paper, we investigate the energy-efficient data transmission over reliable links and unreliable links in mobility-aware wireless networks. The network topologies in mobility-aware wireless networks are changed from one time slot to another, and they could be described by a sequence of static graphs. We first model these network topologies in a time period as a virtual space-time graph. On the virtual space-time graph, energy-efficient data transmission problems over reliable links and unreliable links are defined. The aim of the two data transmission problems is to find a spatial-temporal path with the minimum energy cost. Next, we propose an Energy-Efficient Data Transmission algorithm over Reliable Links (EEDT-RL) to find the optimal space-time path. Based on EEDT-RL, we also develop a heuristic data transmission protocol over unreliable links (named EEDT-UL), in which the path reliability is taken into consideration. Simulation results show that our proposed algorithms perform well in terms of energy cost and transmission count compared with some existing algorithms.

A Path Planning Method for Ship Collision Avoidance Considering Spatial–Temporal Interaction Effects

Efficient and reliable path planning is crucial for smart ships when avoiding collisions with static and dynamic obstacles in complex marine environments. This research proposes a novel path planning method based on the fast marching method to specifically assist with safe navigation for autonomous ships. At the very beginning, a unified representation is specially produced to describe the path planning space based on the parametric fast marching speed function. In addition, the spatial–temporal interaction effects of dynamic obstacles are considered and integrated into the construction of planning space. Subsequently, a path optimization strategy is put forward based on the trajectory prediction of dynamic objects. Particularly, the effectiveness of the method has been validated and evaluated through a number of simulations, which proves that such a method is practical in narrow and crowded waterways.

Combination of seismic attributes using graph-based methods to identify the salt dome boundary

Geological structural and target stratigraphic units can be well distinguished and clearly presented in seismic data based on their physical characteristics. However, due to the inherent band limited and noise contaminated and sometime distorted image on seismic data, it is not straight forward to identify, delineate and image those targets in seismic data. In this regard, the seismic attributes are used as a tool for extracting valuable information and characteristics from the seismic data for further appropriate geological interpretation of the target. In large size 3D seismic data, extreme interaction time with costly labor force is required for the manual interpretation of the seismic images. Automation of this procedure besides intelligent analysis of large number of various combination of attributes with different computational algorithms provides a reliable, fast and high productive alternative path. In this study, an automatic object delineation method based on combination of seismic attributes is developed based on the graph theory, mainly to identify the salt dome boundary in seismic data. Results are compared with the MLP and weight of evidence methods. For this purpose, initially a set of attributes with different window lengths are extracted from the data and then by using the principal component analysis method, the most appropriate set of attributes for combining are determined. Then a corresponding graph is derived for each attribute and the distance matrix of each graph is calculated and finally the similarity matrix is defined. Subsequently, the graphs are combined using the distance method. The binary model then is obtained by applying a threshold and image segmentation technique. To evaluate the results, they are compared with the interpreted models from the leading-edge methods, and for quantitative comparison, the accuracy value of the method then is calculated pixel by pixel. Based on the quantitative comparison of results, the accuracy obtained by the proposed method here is acceptable and is higher than the well-known MLP and weight of evidence methods, which shows the efficient application of the graph-based combination method in combining seismic attributes and delineation the salt dome boundary.

A dynamic bidirectional heuristic trust path search algorithm

Online social networks greatly promote peoples' online interaction, where trust plays a crucial role. Trust prediction with trust path search is widely used to help users find the trusted friends and obtain valid information. However, the shortcomings of accuracy and time still exist in some famous algorithms. Therefore, the dynamic bidirectional heuristic search (DBHS) algorithm is proposed in this paper to find the reliable trust path by studying the heuristic search. First, the trust value and path length are comprehensively considered to find the most trusted user. Specially, it constrains the traversal depth based on the ‘small world’ theory and obtains the acceptable path set by using the relaxation coefficient λ to relax the depth of the shortest path. By this way, some longer path with the higher trust can be considered to improve the precision of algorithm. Then, an adjustment factor is designed based on the meet in the middle (MM) algorithm to assign search weights to two directions based on the size of the search tree expanded, so as to improve the problem of no priori when fixed parameters are used. Besides, the complexity of unidirectional trust path search can also be reduced by searching from two directions, which can reduce the depth and improve the efficiency of search. Finally, the predictive trust degree is outputted by the trust propagation function. Two public datasets are used to generate experimental results, which show that DBHS can quickly search and form reliable trust relationship, and it partly improves other algorithms.