Coverage Algorithm Research Articles

Reconstruction of soft tissue defects of hand: A systematic approach.

A thorough insight into the management of hand injuries can shape the approach of a surgeon in order to achieve optimal outcomes for the patients. The aim of this study was to share our experience in reconstruction of the hand and establishing an algorithm for classification and treatment of hand injuries. This is a descriptive cross sectional study and was conducted from January 2020 to August 2022 at Burns and Plastic Surgery center, Peshawar. Data was collected from medical records about the patient demographics, mechanism of injury and type of procedures done. Defect size was classified into small (<5cm), medium (5cm to 10 cm) and large (>10cm). The defect site and size was cross tabulated against the method of soft tissue reconstruction in order to make the algorithm for reconstruction of hand injuries. Data was analyzed using SPSS. The study population included 41 (75.9%) male and 13 (24.1%) female patients, mean age 31.56±14.1. Machine injuries (33.3%) and electric burns (24.1%) were the most common cause of hand soft tissue defects. The most commonly performed flap was Posterior introsseous artery (PIA) flap, followed by First dorsal metacarpal artery (FDMA) flap. Flap necrosis was observed in three cases (5.6%). This treatment algorithm for coverage of soft tissue defects in hand injuries will help with the decision making process of hand reconstruction and has didactic value for novice surgeons. It will also form the foundation for further work on this aspect of hand injuries.

A 5G Coverage Calculation Optimization Algorithm Based on Multifrequency Collaboration

The fifth–generation (5G) network is developing rapidly. The network coverage directly influences the quality of service (QoS) of vertical industries. Coverage capability is a crucial and indispensable indicator when evaluating the performance of a network. However, the results of the current algorithm fall short in terms of accuracy. To restore the current status of 5G network coverage more realistically, in this study, we design a new optimization algorithm for coverage calculation based on the traditional coverage algorithm combined with fourth–generation (4G) coverage reference signal receiving power (RSRP) in management report (MR) and adopt a multisystem collaborative analysis method. The algorithm corrects the coverage results and restores the true value of 5G coverage. Based on this, we provide a practical analysis of the largest standalone (SA) commercial network in the world, which confirms the viability of the algorithm. Both theoretical and practical analyses show that the algorithm can effectively detect hidden weak coverage areas, providing a further reference for future 5G construction and improving the 5G user experience. The proposed approach can be broadly generalized and applied to multifrequency, multioperator, or even sixth–generation (6G) networks.

MAES: a ROS 2-compatible simulation tool for exploration and coverage algorithms

With the aim of allowing the efficient and realistic simulation of swarm algorithms for exploration and coverage, we present the tool Multi-Agent Exploration Simulator (MAES), which is an open-source physics-based discrete step multi-robot simulator. MAES features movement in a continuous 2D space, realistic physics based on the Unity framework, advanced visualization techniques such as heatmaps, custom wireless signal degradation, both randomly generated and custom user-provided maps, and a ROS (Robot Operating System) interface. This latter characteristic could allow to port the simulated algorithms to real-world robots. We present performance tests, conducted with rather modest hardware, showing that MAES is able to simulate up to 5 robots in ROSMode (using the ROS integration) and up to 120 robots in UnityMode (development performed directly into the C# Unity Editor). A usability test was conducted which hinted that the target audience of robotics researchers and developers is able to quickly install, setup, and use MAES for implementing simple robot logic.

Optimal Deployment for Hybrid Sensor Networks Based on Efficient Node Configuration

Hybrid sensor networks, which contain mobile nodes and stationary nodes, are being used more and more widely. The second deployment of mobile nodes is a key problem to be solved, and the deployment performance of the network directly affects the monitoring effect of the network. Optimizing the configuration ratio of the two nodes can effectively reduce the network cost. In this paper, under the premise of knowing the coverage of the required monitoring area, the impact of sensor devices on node configuration is studied through parameter analysis, and the number and types of sensors that should be deployed in the hybrid sensor network are deduced, which can be conveniently and accurately used to design the actual hybrid sensor network. At the same time, for the secondary deployment of mobile nodes, this paper proposes a new mobile coverage method BS-CCP (box search and concentric circle positioning) to improve the coverage of the hybrid sensor network and maximize the coverage of the target area with the specified sensor types and numbers. Compared with existing work, the method in this paper reduces the number of iterations and reduces the number of required nodes. Comparing BS-CCP with the existing network mobile coverage algorithm, the experimental results show that the coverage obtained by this method is larger and more efficient.

An Efficient Path Planning Algorithm for 2D Ground Area Coverage Using Multi-UAV

An Efficient Path Planning Algorithm for 2D Ground Area Coverage Using Multi-UAV

Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis.

The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of the affected individuals. To address this challenge, IoT big data analytics and unmanned aerial vehicle (UAV) technologies have emerged as key elements in the solution. By analyzing large-scale sensor data, user behavior, and communication traffic, IoT big data analytics can provide real-time communication demand prediction and network optimization strategies, offering decision support for post-disaster communication reconstruction. Given the unique characteristics of post-disaster scenarios, this paper proposes a UAV-assisted communication coverage strategy based on IoT big data analytics. This strategy employs UAVs in a cruising manner to assist in communication by partitioning the target area into multiple cells, each satisfying the minimum data requirements for user communication. Depending on the distribution characteristics of users, flight-communication or hover-communication protocols are selectively employed to support communication. By optimizing the UAV's flight speed and considering the coverage index, fairness index, and average energy efficiency of the mission's target area, the Inner Spiral Cruise Communication Coverage (IS-CCC) algorithm is proposed to plan the UAV's cruising trajectory and achieve UAV-based communication coverage. Simulation results demonstrate that this strategy can achieve energy-efficient cruising communication coverage in regions with complex user distributions, thereby reducing energy consumption in UAV-based communication.

A SwarmBased Flocking Control Algorithm for Exploration and Coverage of Unknown Environments

The exploration of unknown environments can be beneficial for a variety of applications, such as inspection of industrial equipment, environmental monitoring, or search and rescue missions. In order to tackle this problem, swarm robotics has emerged as a promising approach due to its ability to leverage the collective behavior of a group of robots to explore an area efficiently. This paper proposes a swarmbased control algorithm for exploration and coverage of unknown environments. The algorithm utilizes shortrange distributed communication and sensing among agents, with no central unit, to coordinate the swarm’s navigation and search tasks. This sensing is prioritized in the outermost agents of the swarm to reduce processing and energy costs, and these positions can be rotated with other agents in the swarm. The formation rules that keep the system cohesive are simple and independent of the individual robot characteristics, enabling the use of heterogeneous agents. The performance of the proposed strategy is demonstrated through experiments in coverage and search tasks, and compared with other swarm strategies. The results show the effectiveness of the proposed algorithm for exploration and coverage of unknown environments. The research presented in this paper has the potential to contribute to the development of more efficient and effective swarmbased exploration and coverage strategies.

Full-Coverage Path Planning and Stable Interaction Control for Automated Robotic Breast Ultrasound Scanning

Ultrasound examination is widely used for diagnosis of breast cancer, which requires a full-coverage scan of the whole organ and stable probe–breast interaction for high-quality image acquisition. In this study, a robotic system for automated breast ultrasound scanning is proposed. To avoid occlusion, point cloud of the breast is obtained from multiple angles and registered together for accurate tissue shape reconstruction, then scan path planning is performed with isometric 3-D point cloud searching algorithm for full and uniform coverage. To reduce the adverse influence of large tissue deformation during ultrasound scanning, a contact force–strain regression model is built for tissue deformation estimation and used as a feedforward to correct the planned scanning path, and a probe–tissue interaction model with scanning resistance is built for normal probe orientation adjustment. To maintain normal probe orientation and stable contact force during robotic ultrasound scanning, a hybrid force–velocity control framework is proposed based on admittance control algorithm. Experiment results show that the robotic system can successfully maintain a constant contact force and keep the probe's posture along the normal orientation of probe–breast interaction both in in-plane direction and out-of-plane direction.

Global Path Planning Method for Parks based on Complete Coverage Cleaning Task

In this paper, based on a small driving sanitation platform of an enterprise, the global path planning method in the unmanned decision system is studied and designed for the characteristics of the park environment and the task requirements. The operation space of the sanitation platform is divided into rectangular square cleaning area and lane cleaning area based on the task demand of full coverage of the park. For the sweeping task of the lane area, multiple parallel sweeping reference paths are generated by raster erosion of the lane map; for the sweeping task of the rectangular square, this paper proposes an improved bow-shaped full coverage algorithm combining forward and reverse based on the steering capacity constraint of the sanitation vehicle. Finally, the path articulation algorithm is used to connect the sweeping paths of suboperating areas to form a global reference path for the sweeping operation of the park.

A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling

As a counterpoint to classical stochastic particle methods for linear diffusion equations, such as Langevin dynamics for the Fokker-Planck equation, we develop a deterministic particle method for the weighted porous medium equation and prove its convergence on bounded time intervals. This generalizes related work on blob methods for unweighted porous medium equations. From a numerical analysis perspective, our method has several advantages: it is meshfree, preserves the gradient flow structure of the underlying PDE, converges in arbitrary dimension, and captures the correct asymptotic behavior in simulations. The fact that our method succeeds in capturing the long time behavior of the weighted porous medium equation is significant from the perspective of related problems in quantization. Just as the Fokker-Planck equation provides a way to quantize a probability measure ρ ¯ \bar {\rho } by evolving an empirical measure ρ N ( t ) = 1 N ∑ i = 1 N δ X i ( t ) \rho ^N(t) = \frac {1}{N} \sum _{i=1}^N \delta _{X^i(t)} according to stochastic Langevin dynamics so that ρ N ( t ) \rho ^N(t) flows toward ρ ¯ \bar {\rho } , our particle method provides a way to quantize ρ ¯ \bar {\rho } according to deterministic particle dynamics approximating the weighted porous medium equation. In this way, our method has natural applications to multi-agent coverage algorithms and sampling probability measures. A specific case of our method corresponds to confined mean-field dynamics of training a two-layer neural network for a radial basis activation function. From this perspective, our convergence result shows that, in the overparametrized regime and as the variance of the radial basis functions goes to zero, the continuum limit is given by the weighted porous medium equation. This generalizes previous results, which considered the case of a uniform data distribution, to the more general inhomogeneous setting. As a consequence of our convergence result, we identify conditions on the target function and data distribution for which convexity of the energy landscape emerges in the continuum limit.

An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm.

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node's mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network.

Dynamic collaborative optimization coverage algorithm of UAV based on trajectory prediction in Internet of Vehicles

This research presents a dynamic pre-deployment strategy based on vehicle trajectory prediction information to address the issues of coverage gaps of base stations and local traffic congestion in urban vehicle networking. Under the architecture of distributed federated learning and blockchain, numerous UAVs equipped with edge computing servers eliminate the central aggregation node and use an enhanced Raft method to train a unified Seq2Seq-GRU trajectory prediction model. In the training cycle, the scheme selects the nodes to perform parameter aggregation and model update based on the quantity of data given. Second, based on the model’s prediction findings, this study offers an enhanced virtual force guide deployment method that directs the UAV to dynamically deploy across each virtual force in order to increase the vehicle’s access rate and communication quality.

An Efficient Switch Migration Scheme for Load Balancing in Software Defined Networking

Software-defined networking (SDN) provides increased flexibility to network management through distributed SDN control, and it has been a great breakthrough in network innovation. Switch migration is extensively used for workload balancing among distributed controllers. The time-sharing switch migration (TSSM) scheme proposes a strategy in which more than one controller is allowed to share the workload of a switch via time sharing during overloaded conditions, resulting in the mitigation of ping-pong controller difficulty, a reduced number of overload occurrences, and better controller efficiency. However, it has increased migration costs and higher controller resource consumption during the TSSM operation period because it requires more than one controller to perform. Therefore, we have proposed a strategy that optimizes the controller selection during the TSSM period based on flow characteristics through a greedy set coverage algorithm. The improved TSSM scheme provides reduced migration costs and lower controller resource consumption, as well as TSSM benefits. For its feasibility, the implementation of the proposed scheme is accomplished through an open network operating system. The experimental results show that the proposed improved TSSM scheme reduces the migration cost and lowers the controller resource consumption by about 36% and 34%, respectively, as compared with the conventional TSSM scheme.

Improved Optimization Strategy Based on Region Division for Collaborative Multi-Agent Coverage Path Planning

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more "cohesive", thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase.

Optimal algorithm for min-max line barrier coverage with mobile sensors on 2-dimensional plane

Emerging IoT applications impose line barrier coverage (LBC) tasks with min–max movement objective due to requirements of energy balance, fairness, etc. In LBC, we are given a line barrier and a set of n sensors distributed on the plane. The aim is to move the sensors to fully cover the given barrier, such that the maximum movement of the mobile sensors is minimized and hence the energy consumption of the sensors are balanced. This paper proposes an exact algorithm to optimally solve LBC, which deserves a runtime On2 compared favorably to the previous state-of-art runtime On2logn. The key idea of the improvement is acceleration-via-approximation: devise a novel approximation algorithm and then use it to accelerate the calculation of optimum solutions. Extensive numerical experiments were carried out to evaluate the practical performance of our algorithm against other baselines, demonstrating its performance gain over the previous state-of-art algorithms.

Minimising the number of ranging sensors verifying target positioning uncertainty

Indoor positioning applications are increasingly popular due to the availability of effective and low cost ranging sensors. Many solutions have been proposed recently using these type of sensors to estimate the coordinates (position) of a target within a given target uncertainty. In this paper, we consider the problem of deploying a large scale infrastructure that solves the positioning task with guaranteed estimation uncertainty while minimising the number of ranging sensors. To this end, we adopt a two steps procedure. In the first step, we identify a basic cell structure compounded of a small number of sensors deployed in symmetric configurations. We study the problem in general terms and we specifically focus on how to maximise the area covered by this basic structure using the smallest possible number of three ranging sensors. In the second step, we use this basic cell as an elementary tile structure to be used in standard coverage algorithm minimising the portion of space left uncovered. The approach is validated through a large number of simulations and experimental results.

A POIs Based Method for Location Optimization of Urban Fire Station: A Case Study in Zhengzhou City

In order to strengthen urban rescue capabilities, a new method is proposed to optimize the spatial distribution of fire stations in urban areas. Potential fire risk places are simplified into points of interest (POIs). Based on the Minimize Facilities Method and Maximize Coverage Algorithm, the number and location of fire stations are determined. In addition, the POIs assigned to the stations are also determined. Moreover, the service area of the fire station is identified according to topological convex hull theory. After that, the coverage rate of the POIs and the sensitivity of the accessible area to the driving time are discussed. At last, Zhongyuan District of Zhengzhou City is taken as an example to verify the effectiveness of the proposed method. The results reveal that current fire stations are insufficient for protecting fire safety. It is necessary to construct three new stations, which will significantly enhance firefighting capabilities. Thus, the areas capable of being timely reached by fire forces in 4.3 min reach 108.8 km2, covering more than 50% of the target district. About 94 percent of the area composed of the target district can be reached in 10 min. Good accessibility indicates the proposed method is capable of optimizing the location of the urban fire station.

DRL-UTPS: DRL-Based Trajectory Planning for Unmanned Aerial Vehicles for Data Collection in Dynamic IoT Network

Using highly maneuverable Unmanned Aerial Vehicles (UAV) to collect data is a fast and efficient method that is widely studied. In most studies, they assume that the UAVs can obtain the location of the Cluster Head (CH) before take-off, allocate CHs, and optimize the trajectory in advance. However, in many real scenarios, many sensing devices are deployed in areas with no basic communication infrastructure or cannot communicate with the Internet due to emergencies such as disasters. In this kind of sensing network, the surviving devices often change, and the CHs cannot be known and allocated in advance, thus bringing new challenges to the efficient data collection of the networks by using UAVs. In this paper, a UAV path planning scheme for IoT networks based on reinforcement learning is proposed. It plans hover points for UAV by learning the historical location of CHs and maximizes the probability of meeting CHs and plans the shortest UAV path to visit all hover points by using the simulated annealing method. In addition, an algorithm to search for the location of CHs is proposed which is named Cluster-head Searching Algorithm with Autonomous Exploration Pattern (CHSA-AEP). By using CHSA-AEP, our scheme enables the UAV to respond to the position change of the CHs. Finally, we compare our scheme with other algorithms (area coverage algorithms and random algorithm). It is found that our proposed scheme is superior to other methods in energy efficiency and time utilization ratio.

Dynamic Tracking Coverage With Quantity-Adjustment Behavior

In this article, a novel coverage algorithm is designed to realize dynamic tracking coverage of unmanned ground vehicles (UGVs) by unmanned aerial vehicles (UAVs) with quantity-adjustment behavior, which can adjust the quantity of UAVs to cover the moving UGVs. In traditional coverage control, the paths of UAVs are preplanned to achieve regional coverage. Additionally, the quantity of UAVs remains unchanged, resulting in a waste of resources and a lack of flexibility. To overcome these problems, we present a novel coverage algorithm. First, the tracking coverage algorithm is designed, in which the positions of the UGVs are sampled at a certain interval, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -means is used to obtain the optimal expected positions of UAVs. Then, to realize the quantity-adjustment behavior of UAVs, an evaluation mechanism is proposed to determine whether the UAVs should be added or withdrawn. UAVs should reach the expected positions during the sample interval. As a result, a finite-time terminal sliding-mode controller is designed to drive the UAVs to follow expected positions. Furthermore, we design a Lyapunov function to prove the stability of the controller. Finally, three simulation examples are put forward, and the results demonstrate the effectiveness of the proposed algorithm and controller.

An Improved Many-Objective Evolutionary Algorithm for Multi-Satellite Joint Large Regional Coverage

An Improved Many-Objective Evolutionary Algorithm for Multi-Satellite Joint Large Regional Coverage