Reliable Path Research Articles

Real-time unmanned aerial vehicle flight path prediction using a bi-directional long short-term memory network with error compensation

Abstract In recent years, unmanned aerial vehicle (UAV) autonomous flight technology has been applied in many fields. However, in the process of autonomous operation, the UAV may deviate from the set flight path due to various disturbance factors, which results in mission failure. In order to find the abnormal situation in time and take corresponding measures, it is necessary to monitor the operation state of the UAV. Predicting the UAV flight path is the main monitoring method at present; however, the accuracy and real-time of the existing prediction methods are limited. Therefore, this paper proposes an error compensation Bessel bidirectional long short-term memory real-time path prediction model deployed in ground stations. First, because of inconsistency of the units in all directions of the original positioning information provided by global positioning system, the Bessel geodetic coordinate transformation is introduced to unify the units of three-dimensional coordinate data, so as to improve the prediction accuracy. Second, considering the problems of poor data quality and data missing in the operation process, the least square fitting method is used to supplement and correct the positioning coordinate data to obtain more reliable and accurate path observation values as the model input. Finally, a deep learning path prediction model based on bi-directional long short-term memory (BiLSTM) network is constructed, and the appropriate network parameters are determined with the prediction accuracy and time as evaluation indicators. In order to further improve the prediction accuracy, a compensator based on proportional integral differential error control theory is designed according to the output characteristics of the BiLSTM network, which is used for providing compensation values for the prediction results of the model. The training and testing results using the actual flight data of UAV operation show that, under the experimental environment built, the model proposed in this paper can complete the UAV flight path prediction with root mean square error < 1 meter within 0.1 second, and has better performance and higher prediction accuracy than other neural network models.

Development of FriendLink Similarity Metric for Link Prediction in Weighted Multiplex Networks

Link prediction methods can help to reconstruct systems from incomplete datasets, to understand the structure dynamic complex networks, and to predict future interactions in evolving networks. Link prediction in multiplex networks is the problem of finding missing links between users based on information from other layers. Although the link prediction problem has been widely studied in multiplex networks, most methods do not consider both interlayer and intralayer features at the same time. Similarity metrics are one of the most common approaches to solving the link prediction problem. However, the development of similarity metrics over multiple layers with the definition of multiplex networks has become an important challenge that has caught the attention of many researchers. In this article, a novel similarity metric is introduced by considering reliable paths between users for link prediction in multiplex networks. The proposed method maps the network to a weighted network by extracting different interlayer and intralayer features from multiplex networks. Finally, the proposed similarity metric develops the FriendLink metric by considering the weight of links and reliable paths. The proposed method is evaluated in comparison with classical similarity metrics and equivalence algorithms such as SEM-Path, LPIS, and SOIDP. Two real datasets based on multiplex networks including Twitter–Foursquare and Twitter–Instagram have been used to evaluate. Experiments prove the superiority of link prediction in the proposed method over other algorithms.

CoSn3 Intermetallic Nanoparticles for Electronic Packaging.

At present, composite solder pastes are getting a lot of attention, especially composite Sn based solders reinforced by nanoparticles. Indeed, CoSn3 is a strong nucleating agent of Sn crystal, which has potential application value in the field of electronic packaging. However, there is no reliable synthetic path for CoSn3 nanoparticles at present. In this article, a chemical synthesis method for CoSn3 nanoparticles is developed. Here, CoCl2 and SnCl2 are reduced by NaHB4 in triethylene glycol (TEG), dispersed by ultrasonics, and heated to 350 °C in a tube furnace for growth. The CoSn3 nanoparticles with a diameter of about 150 nm are obtained by heating at 350 °C for 10 min. The CoSn3 nanoparticles undergo a step reaction in the process of synthesis and go through different stages of merging and annexation during their growth. The crystal growth behavior and the process of orientation change during the nucleation and growth of CoSn3 nanoparticles are studied, especially the two growth mechanisms, namely OU (orientation unified) and OA (orientation attached). By mixing CoSn3 nanoparticles with SAC305, we obtain a kind of strengthened composite soldering paste. There are obvious six-fold cyclic twins in the joints made by this soldering paste.

Path reliability-based graph attention networks

Self-attention mechanism has been successfully introduced in Graph Neural Networks (GNNs) for graph representation learning and achieved state-of-the-art performances in tasks such as node classification and node attacks. In most existing attention-based GNNs, attention score is only computed between two directly connected nodes with their representation at a single layer. However, this attention score computation method cannot account for its multi-hop neighbors, which supply graph structure information and have influence on many tasks such as link prediction, knowledge graph completion, and adversarial attack as well. In order to address this problem, in this paper, we propose Path Reliability-based Graph Attention Networks (PRGATs), a novel method to incorporate multi-hop neighboring context into attention score computation, enabling to capture longer-range dependencies and large-scale structural information within a single layer. Moreover, path reliability-based attention layer, a core layer of PRGATs, uses a resource-constrain allocation algorithm to compute the reliable path and its attention scores from neighboring nodes to non-neighboring nodes, increasing the receptive field for every message-passing layer. Experimental results on real-world datasets show that, as compared with baselines, our model outperforms existing methods up to 3% on standard node classification and 12% on graph universal adversarial attack.

Study on micro crack propagation mechanism of ferrite–pearlite gas transmission pipeline steel with lamellar structure

The deformation and failure characteristics of pipeline steel depend on its atomic structure and microstructure. Based on the serial multi-scale analysis technology, the ferrite/cementite (α-Fe/Fe3C) lamellar atomic structure with Bagaryatskii orientation relationship is established. In order to obtain the experimental sample of the lowest energy state, The step-by-step relaxation method of conjugate gradient energy minimization and constant temperature and constant pressure relaxation under NPT conditions is carried out, and the energy state and atomic structure of the relaxed samples are analyzed. For the models of different cementite terminal plane structures, the tension displacement curves on the propagation path of mode I central through crack are extracted respectively, combined with the bilinear cohesion zone model, The cohesion parameters at the atomic scale are successfully transferred from bottom to top to the macro and micro scales. By simulating the reaction force and displacement response law at the loading point, the critical fracture toughness of each terminal interface of ferrite–pearlite pipeline steel at different scales is calculated, which provides a reliable path for exploring the micro mechanism of macro cracking behavior of pipeline steel.

A Survey on the autonomous exploration of confined subterranean spaces: Perspectives from real-word and industrial robotic deployments

Confined and subterranean areas are common in many civilian and industrial sites, although they are hazardous for humans given the presence of noxious gases, extreme temperatures, narrow spaces, unhealthy oxygen levels, flooding, and collapsing structures. Therefore, exploration, routine inspections, and surveillance tasks can benefit from using autonomous mobile robots to improve safety by reducing the presence of humans in those scenarios. However, despite advances in the field, there are still challenges to overcome for confined and subterranean robot operation. Real word robotic exploration requires robust and reliable map generation, precise localization, safe navigation, and efficient path planning. These requirements make exploration in complex 3D environments with rugged terrain difficult. The challenge is increased when considering multi-robot teams, as there is no guarantee of a functional network infrastructure. Despite consistent increasing interest in the area, there is a lack of research summarizing the results and best practices for exploring such environments. Therefore, in this paper, we provide a review and discuss state-of-the-art robotic exploration techniques, including single and cooperative approaches with homogeneous and heterogeneous teams, with a focus on complex subterranean and confined 3D scenarios. We also present a comprehensive list of insights on open challenges and possible directions for future investigation in the topic.

Rescue vehicle allocation problem based on optimal reliable path under uncertainty

Rescue vehicle allocation problem based on optimal reliable path under uncertainty

Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Wireless Sensor Networks (WSNs) is the innovative technology that covers wide range of application that possesses high potential merits such as long-term operation, unmonitored network access, data transmission, and low implementation cost. In this context, Internet of Things (IoT) have evolved as an exciting paradigm with the rapid advancement of cellular mobile networks, near field communications and cloud computing. WSNs potentially interacts with the IoT devices based on the sensing features of web devices and communication technologies in sensors. At this juncture, IoT need to facilitate huge amount of data aggregation with security and disseminate it to the reliable path to make it reach the required base station. In this paper, Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication Mechanism (UAIPCA-BSO) is proposed for Securing data and estimate the optimal path through which it can be forwarded in the IoT environment. In specific, Unity Attractors Inspired Programmable Cellular Automata is adopted for guaranteeing security during the data transmission process. It also aids in determining the optimal path of data transmission based on the merits of Barnacles Swarm Optimization Algorithm (BSOA), such that data is made to reach the base station at the required destination in time. The simulation results of UAIPCA-BSO confirmed minimized end-to-end delay , accuracy and time taken for malicious node detection, compared to the baseline approaches used for comparison.

Pharmaceutical Portfolio Development: Strategic Rationale, Challenges & Solutions

Seeking revenue growth has always been one of the key strategic priorities of business entities in the biopharmaceutical industry. The path to such growth can take diverse forms shaped by factors such as business context, strategic and operational capabilities, asset profiles, resources, and competition. Developing assets (whether organic or acquired) with a strategy that seeks to build, maintain, optimize and grow a portfolio of assets offers a reliable path to reducing risks presented by organic development or through mechanisms such as partnering, mergers & acquisitions. The benefits of achieving profitable growth with a portfolio strategy span the asset life cycle continuum, from influencing asset development programs, shaping investment decisions, determining commercialization choices, informing launch planning, and driving corporate priorities. This article outlines the rationale for crafting and adopting a portfolio strategy since it improves the design of corporate, development, commercialization and lifecycle management strategies. Through cases based on the author’s engagements, this article highlights common challenges and creative solutions that lead to the development and execution of viable portfolio strategies implemented by firms of diverse sizes and corporate objectives.

Ob-EID: Obstacle aware event information dissemination for SDN enabled vehicular network

With rapid increase in traffic density in urban areas, the accident rate is also becoming a serious problem which results in traffic congestion, inconvenience and sometimes life loss. The rate of accidents can be minimized by sharing real-time event information among vehicles on the road through safety application. To realize the safety applications, it is paramount to disseminate event information in real-time through a reliable path which is extremely challenging due to unique vehicular characteristics. To ensure reliable path, there are majorly two critical issues: (i). It is very difficult to form the stable connections due to varying vehicular density and dynamic topology. (ii) the dynamic obstacles affect the signal intensity and hinder the communication between vehicles. Indeed, majority of dissemination schemes are based on distance and dissemination time only, while neglecting the effect of dynamic obstacles. Also, the localized traffic view is considered in such schemes lack in providing the real time vehicular dynamics and network status information. To overcome these issues, a Software Defined Network (SDN) based vehicular network model has been presented to ensure real time vehicular dynamics awareness in urban environment to share event information. For reliable event information dissemination, a heuristic based reliable path selection (HRPS) algorithm is proposed to disseminate event messages between a source and destination vehicle in real-time that will minimize the hindrance due to dynamic obstacles. The proposed algorithm selects a multi-modal path reliability function that depends on multiple routing metrics (connectivity, link reliability and obstacle impact probability) and selects the reliable path by optimizing the path reliability function. The proposed algorithm is simulated for varying urban traffic conditions to analyze its performance. The proposed algorithm shows packet delivery ratio between 90%–95% for varying vehicular density. The simulation results show the supremacy of proposed algorithm as compared to existing compared routing scheme. The proposed algorithm ensures real time event information dissemination for delay sensitive event information that minimizes the impact of dynamic obstacles.

Towards Practical Path Loss Predictions in Indoor Corridors Considering 5G mmWave Three-Dimensional Measurements

Theoretical and empirical foundations of how radio waves behave in practical wireless channels need to be fully revisited for millimeter-wave (mmWave) frequencies so that fifth-generation (5G) technologies may be successful. Kriging is an outstanding geostatistal interpolation technique that employs variography to understand the spatial variability of known samples at specific locations to predict unmeasured samples, based on the fact that there is an implied connection between the measured value and its location in space. The research we here report is aimed at validating the improvement in predictions when this tool is included for mmWave frequency path loss modeling in a long indoor corridor with break. In order to quantify the accuracy of the proposed methodology, it is compared with a well-established procedure described in the literature. Extensive path-loss measurements were collected through specialized narrowband sounders at 28 and 60 GHz. Spatially averaged power measurements using omnidirectional and directive antennas at different heights provided the empirical basis for the three-dimensional Kriging-aided model. It was found that this method significantly improves the accuracy as it considers all the singularities and site-associated features that are implicit in measured samples. This is important to obtain a reliable path loss model for planning and deployment of mmWave wireless communication systems in indoor scenarios.

Dynamic source routing protocol for robust path reliability and link sustainability aware routing in wireless communication

Routing is the method of determining the most efficient route between transmitter and the receiver which is enhanced by taking into account the node's energy. The Dynamic Source Routing (DSR) Protocol is presented in this work to increase quality-of-service (QoS) by calculating path reliability and link sustainability criteria in which node energy, node centrality, node degree, and link cost are used to choose the relay nodes. Qualified relaying nodes are used for transmission from the source node to the cluster head (CH). One node is utilized to send data packets to its destination and the relay nodes are chosen in the optimum manner. In terms of packet loss ratio, energy consumption and delay, the suggested technique outperforms other existing routing protocols which has the lower packet loss ratio of about 4 % which is 3 % lesser than existing method.

An improved beetle antennae search path planning algorithm for vehicles.

With the development of society, the application of mobile robots in industry and life is increasingly extensive, and the local path planning of mobile robots in unknown environments is a problem that needs to be solved. Aiming at the problem that the traditional beetle antennae search (BAS) algorithm can easily fall into local optimum and the optimization accuracy is low, we propose an improved beetle antennae search. It introduces a map safety threshold, the addition of virtual target points, and the smoothing of the path. Map safety threshold means extra space with obstacles at all times, improving path reliability by avoiding collisions. Adding virtual target points reduces situations where the vehicle gets stuck in local optima. The B-spline smoothing path reduces the original path’s straight turns to improve the path’s robustness. The effectiveness and superiority of the algorithm are verified by comparing and testing the existing path planning algorithms through simulation in different environments.

Adaptive guided salp swarm algorithm with velocity clamping mechanism for solving optimization problems

Abstract Salp swarm algorithm (SSA) is a well-established population-based optimizer that exhibits strong exploration ability, but slow convergence and poor exploitation capability. In this paper, an endeavour is made to enhance the performance of the basic SSA. The new upgraded version of SSA named as ‘adaptive strategy-based SSA (ABSSA) algorithm’ is proposed in this paper. First, the exploratory scope and food source navigating commands of SSA are enriched using the inertia weight and boosted global best-guided mechanism. Next, a novel velocity clamping strategy is designed to efficiently stabilize the balance between the exploration and exploitation operations. In addition, an adaptive conversion parameter tactic is designed to modify the position update equation to effectively intensify the local exploitation competency and solution accuracy. The effectiveness of the proposed ABSSA algorithm is verified by a series of problems, including 23 classical benchmark functions, 29 complex optimization problems from CEC 2017, and five engineering design tasks. The experimental results show that the developed ABSSA approach performs significantly better than the standard SSA and other competitors. Moreover, ABSSA is implemented to handle path planning and obstacle avoidance (PPOA) tasks in autonomous mobile robots and compared with some swarm intelligent approach-based path planners. The experimental results indicate that the ABSSA-based PPOA method is a reliable path planning algorithm.

MEC-Based Dynamic Controller Placement in SD-IoV: A Deep Reinforcement Learning Approach

The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to provide reliable and scalable wireless network services for the emerging applications in the 5 G and beyond era. The software-defined networks (SDN) could feasibly manage and optimize the network according to the network load. Controller placement is a critical problem in SDN to achieve its robustness and flexibility with the changes of network status. Motivated by the advantages of SDN and Mobile-edge computing (MEC), this paper aims at enhancing the performance of IoV with the assistance of these two. Specifically, we consider a three-layer hierarchical SDN control plane for the IoV, where the SDN controllers are placed at the edge of networks. Under this framework, we investigate a multi-objective optimization problem on controller placement problem including delay, load balancing, and path reliability. To efficiently solve the formulated NP-hard problems, we develop an algorithm based on multi-agent deep Q-learning networks (MADQN) because of its advantages for large-scale combinatorial optimization. At last, we use multi-process technology to accelerate the operation of the algorithm, so as to complete the algorithm iteration faster. Numerical results show that the proposed methods achieve better performances than three baselines.

A novel reliable path planning approach for multimodal networks based on a two-factor bound convergence algorithm

Due to the influence of diverse factors, travel time is highly uncertain. Travelers are eager to find the most reliable path in multimodal networks to reduce the penalty caused by late arrival. However, the research considering the traveler preferences in multimodal transportation networks to solve the reliable path problem with given budgets is limited. Thus, we propose two multimodal reliable path models to find personalized and reliable paths. First, we build a multimodal network based on smart card data to incorporate the multimodal transfers between public and private transportation and solve corresponding parking issues effectively. Next, we build a multimodal time-reliable path model to find time-reliable paths. Further, considering traveler preferences, we design a multimodal utility-reliable path model to find personalized and reliable paths. A novel two-factor reliability bound convergence algorithm is developed to solve the proposed models and proved for its theoretical feasibility. Finally, a real-world case study is used to verify the effectiveness and efficiency of the proposed models and algorithm.

Refined Network Topology for Improved Reliability and Enhanced Dijkstra Algorithm for Optimal Path Selection during Link Failures in Cluster Microgrids

Cluster microgrids are a group of interoperable smart microgrids, connected in a local network to exchange their energy resources and collectively meet their load. A microgrid can import/export energy to the neighboring microgrid in the network based on energy deficit/availability. However, in executing such an operation, a well-established communication network is essential. This network must provide a reliable communication path between microgrids. In addition, the network must provide an optimal path between any two microgrids in the network to optimize immediate energy generation, import requirements, and export possibilities. To meet these requirements, different conventional research approaches have been used to provide reliable communication, such as backup/alternative/Hot Standby Router Protocol (HSRP)-based redundant path concepts, in addition to traditional/renowned Dijkstra algorithms, in order to find the shortest path between microgrids. The HSRP-based mechanism provides an additional path between microgrids, but may not completely solve the reliability issue, especially during multiple link failures and simultaneous failures of the actual path and redundant path. Similarly, Dijkstra algorithms discussed in the literature do not work for finding the shortest path during link failures. Thus, to enhance reliability, this paper proposes a refined network topology that provides more communication paths between microgrids, while retaining the same number of total links needed, as in conventional HSRP-based networks. In addition, this paper proposes an enhanced Dijkstra algorithm to find the optimum path during link failures. Simulations are executed using NetSimTM by implementing test cases such as single-link and multiple-link failures. The results prove that the proposed topology and method are superior to conventional approaches.

An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

Best-effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning-based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win-win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

Energy Aware Stable Multipath Disjoint Routing Based on Accumulated Trust Value in MANETs

Conserving energy and finding the stable path are the two vital issues in Mobile Ad Hoc Networks (MANETs) as the prior increases the network lifetime and the later increases the network throughput. The nodes which are not legitimate in terms of residual energy and packet forwarding history might be a threat to the path thereby making the path unstable. Thus, it results in frequent link failure, reduced throughput, reduced network and path life time. In order to reduce these hazards, authors have proposed an energy efficient, reliable path selecting protocol referred to as Trust Based Energy Aware Multipath Disjoint Routing Protocol (TEA-MDRP) for MANETS in this article. TEA-MDRP finds the optimum route between the source and the destination nodes using two parameters namely, the Accumulated Trust Value (ATV) and the node’s residual energy (N_res). ATV is calculated based on the packet forwarding status of the node which shows how good the node is in terms of packet forwarding point of view. TEA-MDRP allows only the nodes which have a good ATV and sufficient residual energy. A good ATV shows loyalty in forwarding the packets while a sufficiently large residual energy node avoids frequent path breakups and packet drops. Thus, the TEA-MDRP not only increases the network and path lifetime but also increases the throughput of the communication. Further, with the legitimate nodes in the paths, the TEA-MDRP considerably reduces the control packet overhead which might occur because of the frequent route re-discovery process. An extensive simulation is carried out using Network Simulator-2.35 for the quantitative and qualitative analysis of TEA-MDRP. The results obtained are compared with classical AOMDV and the results are satisfactory.

Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs.

Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework’s effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints.