Coverage Algorithm Research Articles

Multi-UAV Relay Connectivity Optimization for Heterogeneous Users Based on Load Balancing and Throughput Maximization

By relaying messages between users, unmanned aerial vehicles (UAVs), which are frequently utilized in the communication industry, can be used as airborne communication relays to resolve connectivity problems. In order to effectively connect a large number of heterogeneous users dispersed on the ground, we deploy a large number of UAVs for reliable relay coverage and message transmission between users. We initially put forth a relay coverage algorithm that continuously optimizes the position of UAVs and the cell division of terrestrial users in order to provide users with full coverage. Second, we study several relay message forwarding methods between UAVs while taking into account the features of heterogeneous users. A relay selection method, which takes the relay link throughput as the optimization goal, is proposed to ensure full connectivity between UAVs. Finally, we contrast and analyze how various relay forwarding methods affect the effectiveness of the relay network. The experimental results demonstrate that the proposed relay coverage algorithm can balance the UAV relay load better and reduce transmission delay, while the suggested relay selection strategy can minimize the number of isolated UAVs and maximize the throughput of UAV relay links.

Study on Node preserving routing coverage algorithm for sensor networks based on compressed HMAC.

Study on Node preserving routing coverage algorithm for sensor networks based on compressed HMAC.

An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

This study deals with a new strategy of the re-assignment for multi-robot seamless coverage tasks using the concept of propagation in a multi-robot surveillance system (MRSS). In the context of MRSSs, multi-robot coverage tasks play a critical role. These tasks require generating paths for two or more robots to cover an entire area, with the objective of minimizing the time needed to complete the task. However, over time, robots may need to be excluded from coverage missions due to issues such as battery charging or malfunctions. It is important to handle these situations efficiently in order to maintain the completeness and balance of the coverage mission. Typically, it can be resolved by either recomputing the coverage algorithm for the remaining robots or redistributing the coverage task of the excluded robot to its neighbors. However, in the proposed method, the amount of coverage area of the excluded robot is equally and efficiently assigned to the remaining robots. First off, a relational graph between robots and a tree based on the excluded robot are sequentially constructed to necessarily know how the robots are geometrically arranged in the given area centered on the excluded robot. The excluded robot becomes the root of the tree, and the depth of the tree indicates the proximity of the coverage areas. Subsequently, the amount of the original coverage area of the excluded robot can be differently assigned to its nearest neighbor robots according to the size of the subtree. Then, the coverage area of the robots corresponding to the second level of the tree are added from the partial coverage area of their parent robot to keep their coverage area balanced, respectively. The similar process is continuously performed, such as propagation, until the re-assignment of the coverage area over the leaf nodes is complete. Finally, balanced coverage area is re-assigned to the remaining robots, which is time-efficiently computed. Simulations were performed on two occupancy grid maps that were acquired from a simultaneous localization and mapping method. The proposed method was evaluated against conventional methods on three factors such as the balanced re-assignment of the coverage area (Balancing), the variation of the individual coverage area before and after the re-assignment process (Seamless Coverage), and the total computational efficiency over time (Time-efficiency). The coverage area was uniformly re-allocated after the proposed method was applied. In addition, the proposed method had a short calculation time and enables seamless coverage even after re-allocation. In the future, probabilistic maps related to the importance rate, accident rate, and crowds in the coverage area will also be taken into consideration.

An Improved Spanning Tree-Based Algorithm for Coverage of Large Areas Using Multi-UAV Systems

In this work, we propose an improved artificially weighted spanning tree coverage (IAWSTC) algorithm for distributed coverage path planning of multiple flying robots. The proposed approach is suitable for environment exploration in cluttered regions, where unexpected obstacles can appear. In addition, we present an online re-planner smoothing algorithm with unexpected detected obstacles. To validate our approach, we performed simulations and real robot experiments. The results showed that our proposed approach produces sub-regions with less redundancy than its previous version.

Multi-UAV trajectory planning for 3D visual inspection of complex structures

The application of autonomous UAVs to infrastructure inspection tasks provides benefits in terms of operation time reduction, safety, and cost-effectiveness. This paper presents trajectory planning for three-dimensional autonomous multi-UAV volume coverage and visual inspection of infrastructure based on the Heat Equation Driven Area Coverage (HEDAC) algorithm. The method generates trajectories using a potential field and implements distance fields to prevent collisions and to determine UAVs’ camera orientation. It successfully achieves coverage during the visual inspection of complex structures such as a wind turbine and a bridge, outperforming a state-of-the-art method by allowing more surface area to be inspected under the same conditions. The presented trajectory planning method offers flexibility in various setup parameters and is applicable to real-world inspection tasks. Conclusively, the proposed methodology could potentially be applied to different autonomous UAV tasks, or even utilized as a UAV motion control method if its computational efficiency is improved.

Design of Moving Coverage Algorithm of Ecological Monitoring Network for Curved Surface

Micro-structured sensors that can perceive and communicate at the same time have emerged as a result of the quick growth of microelectronics technology, wireless communication technology, and sensor technology. This little gadget has the ability to sense many types of environmental data, gather it at the sink node, and then send it to the data centre. In the civic, industrial, agricultural, military, and other domains, wireless sensor networks are frequently employed. A virtual force model of curved surface ecological monitoring network for moving coverage is presented, and a moving coverage algorithm for curved surface ecological monitoring network is given, according to the actual needs of curved surface ecological monitoring, such as grasslands, wetlands, deserts, and coastal beaches. The moving coverage algorithm of curved surface ecology monitoring network pushes the sensor nodes to the uncovered area on the monitored surface and fixes the monitoring blind zone on the monitored surface using a virtual force between sensor nodes in the ecological monitoring network. The moving coverage process of the moving coverage algorithm of the ecological monitoring network is simulated in order to verify the efficiency of the moving coverage algorithm of curved surface ecological monitoring network. The simulation results demonstrate that the moving coverage algorithm suggested in this paper can successfully increase the coverage of the ecological monitoring network on the monitoring surface by precisely locating the monitoring blind zone of the ecological monitoring network and pushing the sensor nodes to the monitoring blind zone for coverage. The final coverage ratio is greater than 95%, and the node deployment phase’s coverage ratio can reach 85% to 90%.

An improved algorithm for delay constrained relay node deployment in wireless sensor networks

Given that Delay Constrained Relay Node Placement (DCRNP) problem is getting more and more attention in time-critical domains, this paper proposes an Improved Greedy-based Selection Algorithm (IGSA) to tackle such problem under delay and budget constraints. IGSA first employs communication cost matrix and Dijkstra’s algorithm to detect whether the problem is solvable, then selects the amount and locations of deployed relay nodes (RNs) layer by layer by continuously calling the greedy coverage algorithm until all sensors have a feasible path to the sink under a preset delay constraint, and finally removes redundant RNs to reach the approximate optimal solution. Extensive simulations show that our approach can save 15%–30% of deployed RNs than the Two-phase Set-Covering-based Algorithm (TSCA), which is the state-of-the-art algorithm for the DCRNP problem, only with a slight sacrifice of solving time. Meanwhile, Ad Hoc On-Demand Distance Vector (AODV) routing protocol is applied to further examine the performance of the deployment results solved by TSCA and IGSA, respectively. Results show that every RN deployed by IGSA is involved in data forwarding, while IGSA deploys leisure RNs which do not participate in the data forwarding. Besides, compared with TSCA, the network formed by IGSA has lower network delay in the data transmission process, and the energy consumption of the central node and throughput of the two topologies are almost at the same level.

PGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19–89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.

Dynamic game coverage control algorithms for multiple mobile agents through virtual repulsive force

To improve the coverage efficiency of mobile agents, the related information of the agents is generally required partially or completely during the coverage control process, which may lead to a dramatic control cost and energy consumption increase. How to balance the improvement in coverage rate and the reduction of control cost is an important issue in the coverage control area of mobile agents. This paper addresses the dynamic cooperative game coverage control algorithm design problem of a multi-agent system under an incomplete information condition. First, by separating the moving multi-agent system into several subgroups, where each agent can only obtain the position information of the neighbor agents, the coverage control problem of the moving multi-agent system is transformed into a dynamic cooperative game coverage problem of moving multi-groups under incomplete information conditions. Then, the cooperative game rules of the subgroups are presented, and a virtual repulsive force-based dynamic coverage game decision strategy of the agent system is developed to compute the new candidate positions of the agents. Third, the moving multi-agent system is considered a rigid virtual structure, and the coverage control problem of the system is turned into a new one with the virtual structure as a reference frame. Thus, the displacement path planning design of the coverage control for a multi-agent system is simplified. Simulation results reveal that the dynamic cooperative game coverage algorithm based on the virtual repulsive force can effectively realize the coverage control requirement of the moving agent system under the incomplete information condition and can remarkably reduce the cost of coverage control. In addition, compared with several existing coverage algorithms, using the dynamic cooperative game coverage algorithm proposed in this paper requires fewer moving times of agents while obtaining a higher coverage rate.

Frontier-led swarming: Robust multi-robot coverage of unknown environments

This paper proposes a novel swarm-based control algorithm for exploration and coverage of unknown environments, while maintaining a formation that permits short-range communication. The algorithm combines two elements: swarm rules for maintaining a close-knit formation and frontier search for driving exploration and coverage. Inspired by natural systems in which large numbers of simple agents (e.g., schooling fish, flocking birds, swarming insects) perform complicated collective behaviors for efficiency and safety, the first element uses three simple rules to maintain a swarm formation. The second element provides a means to select promising regions to explore (and cover) by minimising a cost function involving robots’ relative distance to frontier cells and the frontier’s size. We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments. We measure both coverage performance and swarm formation statistics as indicators of the robots’ ability to explore effectively while maintaining a formation conducive to short-range communication. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recently presented map coverage methodologies and conventional swarming methods. • A novel, distributed algorithm leveraging the strengths of frontier-based area coverage and swarm behaviors is proposed for large-scale multi-robot systems. • Each robot can directly exchange the individual coverage and obstacle information with a set of neighbors without passing through a central station. • An improved artificial potential field obstacle avoidance method is designed based on Lidar information. • System robustness and fault tolerance of the proposed strategy is validated and analysed through a series of real experiments. • This paper demonstrates our space coverage controller’s relative merits with respect to the frontierbased map coverage strategy without swarming, and the three different map coverage control laws through a rigorous comparative study. • Algorithms are examined on both homogeneous and heterogeneous robot teams within a shortranged communication. Hence, the proposed system can normally work in unknown and uncertain environments.

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot.

The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot.

Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks

This paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequency (RF) signals, and a mobile unmanned vehicle (MUV)-loaded laser transmitter travels on a fixed path to charge the on-board energy-limited UAV when it arrives just below the UAV. Based on the system, we investigate the trajectory optimization of laser-charged UAVs for charging WRSNs (TOLC problem), which aims to optimize the flight trajectories of a UAV and the travel plans of an MUV cooperatively to minimize the total working time of the UAV so that the energy of every sensor is greater than or equal to the threshold. Then, we prove that the problem is NP-hard. To solve the TOLC problem, we first propose the weighted centered minimum coverage (WCMC) algorithm to cluster the sensors and compute the weighted center of each cluster. Based on the WCMC algorithm, we propose the TOLC algorithm (TOLCA) to design the detailed flight trajectory of a UAV and the travel plans of an MUV, which consists of the flight trajectory of a UAV, the hovering points of a UAV with the corresponding hovering times used for the charging sensors, the hovering points of a UAV with the corresponding hovering times used for replenishing energy itself, and the hovering times of a UAV waiting for an MUV. Numerical results are provided to verify that the suggested strategy provides an effective method for supplying wireless rechargeable sensor networks with sustainable energy.

Approximation algorithm for MinSum linear barrier coverage with sink-based mobile sensors on the plane

Emerging wireless and mobile applications, such as border intrusion detection with station-based drones, brought a new barrier coverage problem of using sink-based mobile sensors to cover a given line barrier with minimum energy consumption. In this paper, we focus on the uniform sink-based line barrier coverage (SLBC) problem, in which we are given a line barrier and k sink stations distributed on the plane which can emit an infinite number of sensors with an identical sensing radius. The problem aims to find their final positions on the barrier for the sensors emitted by the stations, such that the total moving distance of the sensors is minimized and each point of the barrier is within the sensing area of at least one sensor. We first observe the geometric structure of an optimal solution that any optimal solution can be considered as a set of intersecting tangent (disk) segments, where a tangent (disk) segment is a sequence of tangent disks. Then, we devise an algorithm to calculate all possible tangent (disk) segments and another algorithm to calculate the near-optimal positions for each of such segments. After computing all tangent (disk) segments and their near-optimal positions, an algorithm is proposed to transform uniform SLBC into an instance of the shortest path problem. It is shown the whole algorithm deserves a runtime O(k2log⁡krε) and consumes at most ε more movement than an optimal solution, where ε is any given positive real number, and r and k are the sensor radius and the number of sink stations, respectively.

Monitoring Area Coverage Based on Adjusting Node Spacing in Mixed Underwater Mobile Wireless Sensor Networks

Wireless sensor nodes have the characteristics of small size, light weight, simple structure, and limited energy. They are random during deployment in the monitoring area, and the location of nodes is uncertain after deployment. It is easy to have uneven node distribution, resulting in dense nodes in some areas and sparse nodes in some areas. In the area with dense nodes, the monitoring area is covered repeatedly due to the distance between nodes which is too close. In the area with sparse nodes, the problem of covering blind areas appears due to the distance between nodes which is too far. Aiming at the complex structure of underwater wireless sensor networks, a coverage algorithm based on adjusting the nodes spacing is proposed. The algorithm calculates the reasonable distance between adjacent nodes before the wireless sensor node moves. The distance between wireless sensor nodes increases gradually. The simulation results show that the algorithm can make the clustered wireless sensor nodes disperse gradually by reasonably adjusting the distance between wireless sensor nodes, improve the coverage effect of wireless sensor networks, and reduce the energy consumption of wireless sensor nodes.

Rechargeable Battery Cabinet Deployment for Public Bike System

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${e}$ </tex-math></inline-formula> lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings.

Coverage Algorithm Based on Perceived Environment Around Nodes in Mobile Wireless Sensor Networks

Coverage Algorithm Based on Perceived Environment Around Nodes in Mobile Wireless Sensor Networks

Correlation Analysis between Exchange Rate Fluctuations and Oil Price Changes Based on Copula Function

In order to explore the relationship between exchange rate fluctuations and oil prices, this paper combines the copula function to study the correlation between exchange rate fluctuations and oil price changes, conducts a more comprehensive study of the copula function, and applies the algorithm to some practical classification problems. Moreover, this paper improves some defects in the algorithm and combines some new learning frameworks in machine learning to generalize the copula function to a variety of learning models. In addition, this paper studies how to use the coverage algorithm to construct classifiers under various problems and proposes corresponding improvement strategies according to the characteristics of various problems. Finally, this paper builds a correlation analysis algorithm model and uses simulation research to verify that there is a relatively obvious correlation between exchange rate fluctuations and oil price changes.

Efficient Algorithms for Maximizing Group Influence in Social Networks

In social network applications, individual opinion is often influenced by groups, and most decisions usually reflect the majority's opinions. This imposes the group influence maximization (GIM) problem that selects <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> initial nodes, where each node belongs to multiple groups for a given social network and each group has a weight, to maximize the weight of the eventually activated groups. The GIM problem is apparently NP-hard, given the NP-hardness of the influence maximization (IM) problem that does not consider groups. Focusing on activating groups rather than individuals, this paper proposes the complementary maximum coverage (CMC) algorithm, which greedily and iteratively removes the node with the approximate least group influence until at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> nodes remain. Although the evaluation of the current group influence against each node is only approximate, it nevertheless ensures the success of activating an approximate maximum number of groups. Moreover, we also propose the improved reverse influence sampling (IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM. Finally, we carry out experiments to evaluate CMC and IRIS, demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade (IC) model.

Teaching Archives Information Management and Quality Diagnosis Method from the Perspective of Collaborative Education Based on Intelligent Computing

With the growth of better education, improving the quality of better education has become a top priority. In order to facilitate the sustainable and healthy growth of better education and continuously improve the quality of education for faculty and staff, the Ministry of Education has established a regular assessment system for undergraduate-level education. This article analyzes the quality diagnosis of teaching archives management from the perspective of collaborative education by using the bionic method and summarizes the three methods. In the precision rate, the user interface is the highest, followed by accuracy, security requirements, hardware environment, system maintenance, and information flow; in the recall rate, the security requirements are the highest, followed by the hardware environment, accuracy, user interface, information flow, and system maintenance; in F1, the user interface is the highest, followed by hardware environment, information flow, accuracy, security requirements, and system maintenance. In the particle swarm optimization coverage algorithm, the security requirements are obtained, the user interface accuracy is relatively high, and the hardware environment is relatively low; the system maintenance and hardware environment are the most covered, and the security requirements are the least; the number of rejected samples is the largest. It is the hardware environment and system maintenance, and the least is the security requirement; the most correct number of rejected samples is the hardware environment and system maintenance. The scientific analysis of the quality diagnosis of teaching archives management from the view of collaborative education is extremely important for improvement of teaching. The archives management system can be effective in the process of the archives hardware environment, security requirements, system maintenance, user interface, information flow, and accuracy. The efficiency of the manual management process is improved, and the process is simplified.

Design and Assessment of Sweep Coverage Algorithms for Multiagent Systems With Online Learning Strategies

Cooperative sweep coverage of multiagent systems (MASs) has found broad applications in various fields. This article proposes a scheme to address the sweep coverage problem of MASs within uncertain environments. In the proposed formulation, the coverage region is divided into multiple stripes, of which each has the workload completed by MASs in sequence. When the workload on the current stripe is completed, all the agents switch to the next together. The temporal dependence between the switching time computation and the sweep coverage operation is taken into account, and an online learning strategy is designed to handle environmental uncertainties and balance the workload among agents on the same stripe. Thereby, the distributed sweep coverage algorithm is developed to guarantee the complete sweep coverage, which consists of three operations, i.e., communication, workload partition, and sweeping. Theoretical analysis is afterward conducted to estimate the upper bound for the error between the actual and optimal coverage time. Finally, numerical simulations are carried out to substantiate the effectiveness and superiority of the proposed scheme.