Coverage Problem Research Articles

Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.

Quantum Annealing with Inequality Constraints: The Set Cover Problem

AbstractQuantum annealing is a promising method for solving hard optimization problems by transforming them into quadratic unconstrained binary optimization (QUBO) problems. However, when constraints are involved, particularly multiple inequality constraints, incorporating them into the objective function poses challenges. In this paper, the authors present two novel approaches for solving problems with multiple inequality constraints on a quantum annealer and apply them to the set cover problem (SCP). The first approach uses the augmented Lagrangian method to represent the constraints, while the second approach employs a higher‐order binary optimization (HUBO) formulation. The experiments show that both approaches outperform the standard approach for solving the SCP on the D‐Wave Advantage quantum annealer. The HUBO formulation performs slightly better than the augmented Lagrangian method in solving the SCP, but its scalability in terms of embeddability in the quantum chip is worse. The results demonstrate that the proposed augmented Lagrangian and HUBO methods can successfully implement a large number of inequality constraints, making them applicable to a broad range of constrained problems beyond the SCP.

Q-Overlaps in the Random Exact Cover Problem

q-Overlaps in the Random Exact Cover Problem

Sewer networks monitoring through a topological backtracking

The interest in wastewater monitoring is always growing, with applications mainly aimed at detection of pollutants and at the environmental epidemiological surveillance. However, it often happens that the strategies proposed to manage these problems are inapplicable due to the lack of information on the hydraulics of the systems. To overcome this problem, the present paper develops and proposes a topological backtracking strategy for the optimal monitoring of sewer networks, which acts by subrogating the hydraulic information with the geometric ones, e.g., diameter and slope, thus not requiring any hydraulic simulation. The topological backtracking approach aims at evaluating an impact coefficient for each node of the network used to face with the problems of sensor location and network coverage for purposes related to the spread of contaminants and pathogens. Finally, the positioning of the sensors for each monitoring scheme is addressed by a priority rank, based on the efficiency of each sensor in terms of network coverage with respect to a specific weight (e.g., length, flow). The main goal is to design a monitoring scheme that provide the required coverage of the network by minimizing the number of sensors with respect to specific measurement threshold value.The results show the effectiveness of the strategy in supporting the optimal design with the topological-based backtracking approach without the necessity of performing hydraulic simulations, with great advantage in terms of required data and computational time.

A novel model for representing a plane target and finding the worst-case coverage in wireless sensor network based on Clifford algebra

Wireless ad hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential and ability to transform our lives and pose many new system building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Most of researches in wireless sensor networks are focused in obtaining better target coverage in order to reduce energy and cost of the network. The problem of planar target analysis is one of the crucial problems that should be considered while studying coverage problem of sensor networks. By combining computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, this paper introduces a novel sensor network coverage model that deals with plane target problem based on Clifford algebra which is a powerful tool that is coordinate free. Also, the calculations of the node coverage rate for the plane target in the sensor network using Clifford algebra are presented. Then, the maximum clearance path (worst-case coverage) of the sensor network for a plane target is proposed. The optimality and reliability of the proposed algorithm have been proved using simulation. Also, a comparison between the breach weight of the point target and the plane target is provided.

Distributed Coverage Control with Mobile Robots: A Potential Game Approach

The use of mobile robots in industrial applications has led to a demand for autonomous multi-robot systems with robust and distributed algorithms. A critical objective in such systems is coverage control, where a team of mobile robots need to respond to spatiotemporal events in a bounded region. Here, we address a specific coverage problem, where a group of mobile robots are tasked with responding to events by covering specific locations on two sides of a linear workstation. We formulate the problem as a game played by the mobile robots with well-designed player strategies, and we demonstrate that the resulting framework is a potential game based on equally shared utilities among the robots. The proposed framework is distributed and decentralized, allowing for anonymous identities and constrained sensing capabilities in the robots. A set of simulation studies verify our approach.

TIVC: An Efficient Local Search Algorithm for Minimum Vertex Cover in Large Graphs.

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem aiming to find the smallest set of vertices such that every edge has at least one endpoint in the set. This problem has extensive applications in cybersecurity, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain "good" vertex coverage. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: a 3-improvements (TI) framework with a tiny perturbation and edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with three state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.

Design of Sentiment Analysis Framework of Digital Media Short Text Based on Multi-pattern Sentiment Lexicon

Along the continuous advancement of the network and the rise of digital media, the amount of data produced by the exponential explosion. And how to use these data to provide personalized services for users is one of the current research focuses. To address the issue of insufficient coverage in the current sentiment lexicon and the difficulty of constructing sentiment lexicon in specific fields, this study proposes a multi-modal emotional thesaurus. Semi-supervised learning is used to solve the problem of insufficient coverage of emotional thesaurus, and a semi-supervised classification algorithm is realized by using a large number of unlabeled sample data combined with a small number of labeled sample data. Optimized learning is used to solve the problem of difficult construction of emotional thesaurus in specific fields, the corresponding specific emotional thesaurus is constructed by adaptive adjustment of emotional word score, and finally the improved emotional thesaurus is used to build a digital media short text sentiment analysis framework. For testing, the NLPCC dataset was used in this study, Experiments show that the framework constructed in this study requires 87 iterations, a Recall value of 0.912, a F1 value of 0.753, and an average accuracy of 83.39%, all of which are better than the sentiment analysis framework without the use of multi-pattern sentiment lexicon. In the simulation experiment, the recognition accuracy reached 85.88%, which was 16.85%, 11.57% and 6.72% higher than the test scenarios using a single emotion thesaurus selected in this study. The above results show that the digital media short-text sentiment analysis framework built in this research based on multi-pattern sentiment lexicon can carry out short-text sentiment analysis more accurately and efficiently, so as to accurately analyze users’ needs and provide customized services precisely.

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City.

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead.

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem.

In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

Universal Quantum Optimization with Cold Atoms in an Optical Cavity.

Cold atoms in an optical cavity have been widely used for quantum simulations of many-body physics, where the quantum control capability has been advancing rapidly in recent years. Here, we show the atom cavity system is universal for quantum optimization with arbitrary connectivity. We consider a single-mode cavity and develop a Raman coupling scheme by which the engineered quantum Hamiltonian for atoms directly encodes number partition problems. The programmability is introduced by placing the atoms at different positions in the cavity with optical tweezers. The number partition problem solution is encoded in the ground state of atomic qubits coupled through a photonic cavity mode, which can be reached by adiabatic quantum computing. We construct an explicit mapping for the 3-SAT and vertex cover problems to be efficiently encoded by the cavity system, which costs linear overhead in the number of atomic qubits. The atom cavity encoding is further extended to quadratic unconstrained binary optimization problems. The encoding protocol is optimal in the cost of atom number scaling with the number of binary degrees of freedom of the computation problem. Our theory implies the atom cavity system is a promising quantum optimization platform searching for practical quantum advantage.

Design of Automatic Disconnection Device for Non-power Outage Operation of High-voltage Line

A set of remote electric control disconnection device for high-voltage line with strong adaptability, easy operation, and high safety has been designed to address the problems of multiple spans, wide coverage, and difficulty in coordinating power failure with construction crossing operation. The design process aimed to reduce the weight and improve the reliability of the mechanical structure, and conducted computation and simulation of the traction force and safety load of the electrified tight cable. Based on the designed live disconnection device, experiments were conducted for different spans and cable diameters, while meeting the scope of infrastructure crossing sections. The experiments proved that the designed disconnection device has good effects in practical applications, and can select any point in the straight span to break the cable, in order to achieve safe and convenient uninterrupted operations in power recovery service.

Quantum alternating operator ansatz for solving the minimum exact cover problem

Quantum alternating operator ansatz for solving the minimum exact cover problem

Strong Structural Controllability of Boolean Networks: Polynomial-Time Criteria, Minimal Node Control, and Distributed Pinning Strategies

In this paper, we initiate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strong structural controllability</i> of Boolean networks (BNs), coping with the trouble in identifying full nodal dynamics in practice, and utilize it to build up some existing results about BNs. The derived criteria for the strong structural controllability of BNs are polynomial-time checkable. As an interesting feature, controllability is shown to be equivalent to fixed-time controllability in this regard. We further consider the minimal strong structural controllability problem of BNs which, reduced from the minimal vertex cover problem of graphs, is turned out to be NP-hard. Proceeding forward, strong structural controllability provides useful insights to exclude the deficiencies of existing approaches. First, the network aggregation subject to the controllability of BNs is built by showing that the predecessor aggregated blocks can be dropped in turn if they are strongly structurally controllable. Second, the distributed pinning controllers are carried out to render arbitrary BN controllable through node-to-node message exchange, while traditional results only check the controllability with the preassigned controllers. Notably, the time complexity to design such controllers is only exponential with the maximal in-degree of pinned nodes rather than the node number of BNs. Third, in scenarios of BNs encountering probability disturbances, a basic theorem is put forward to elaborate the equivalence between several types of asymptotic stability. Together with this theorem, the criteria of strong structural controllability also facilitate the design of distributed controllers to asymptotically stabilize probabilistic BNs in probability.

Infrared Dim and Small Target Sequence Dataset Generation Method Based on Generative Adversarial Networks

With the development of infrared technology, infrared dim and small target detection plays a vital role in precision guidance applications. To address the problems of insufficient dataset coverage and huge actual shooting costs in infrared dim and small target detection methods, this paper proposes a method for generating infrared dim and small target sequence datasets based on generative adversarial networks (GANs). Specifically, first, the improved deep convolutional generative adversarial network (DCGAN) model is used to generate clear images of the infrared sky background. Then, target–background sequence images are constructed using multi-scale feature extraction and improved conditional generative adversarial networks. This method fully considers the infrared characteristics of the target and the background, which can achieve effective expansion of the image data and provide a test set for the infrared small target detection and recognition algorithm. In addition, the classifier’s performance can be improved by expanding the training set, which enhances the accuracy and effect of infrared dim and small target detection based on deep learning. After experimental evaluation, the dataset generated by this method is similar to the real infrared dataset, and the model detection accuracy can be improved after training with the latest deep learning model.

Health and Healthcare Access for Essential, Nonessential, and Nonworkers During the COVID-19 Pandemic.

The aim of the study is to describe sociodemographic characteristics, healthcare access, and health status of low-income essential, nonessential, and nonworkers during the COVID-19 pandemic. Using survey data (2020-2021) from Wisconsin Medicaid enrollees ( N = 2528), we compared sociodemographics, healthcare access, and health status between essential, nonessential, and nonworkers. Essential workers had less consistent health insurance coverage and more problems paying medical bills than nonessential and nonworkers. They reported better health than nonessential and nonworkers. They reported fewer work-limiting conditions and less outpatient healthcare utilization than nonworkers but similar rates as nonessential workers. Essential workers reported masking less frequently than nonworkers but similar frequency to nonessential workers, and lower COVID-19 vaccine willingness than nonessential and nonworkers. Essential workers report better health, fewer protective behaviors, and more healthcare barriers than nonessential and nonworkers. Findings indicate essential worker status may be a social determinant of health.

ASSESSING THE READINESS LEVEL OF RMG SECTOR OF BANGLADESH TOWARDS IMPLEMENTING INDUSTRY 4.0

Industry 4.0 is considered as the fourth industrial revolution (4IR) and involves virtual and physical systems that are interconnected and collaborative in an autonomous way. The ready-made garments (RMG) sectors in Bangladesh have been undergoing a transition that encompasses the digitization of garment production processes. This research work provides the readiness assessment of the RMG sector of Bangladesh regarding industry 4.0 deployment. Industry 4.0 is a relatively advanced theory within the RMG factories, and it promotes the interest on how to make utilization of technologies included in the idea and take benefit from them as well. The study dilemma is that there is a limited amount of readiness assessments available that cover problems of interest. The research question has been created to assess the readiness level of the RMG sector in Bangladesh with regard to the application of Industry 4.0. The data collection and analysis techniques were adopted from the IMPULS model which is a foundation of the German Engineering Federation, VDMA (Verband Deutscher Maschinen- und Anlagenbau). The quantitative method of the IMPULS model was modified to address the study issue accordingly. The findings demonstrate that RMG factories are rated no higher than level two out of six levels (0-5). It indicates that they are in the emerging state of implementing Industry 4.0 and facing significant challenges, particularly in strategy formation and facility infrastructure to fulfill the demands of Industry 4.0. The conclusion is that a great portion of RMG factories of Bangladesh is at the beginner level. The beginner level is comparable to level 1, which indicates a low level of readiness. Lack of knowledge, limited government help, poor infrastructure, cheap labor availability, and high initial investment costs could all be the contributing factors toward a lower level of readiness.

An efficient algorithm for solving the constellation-to-ground coverage problem based on latitude strip division

The issue of constellation-to-ground coverage is a research focus in Earth observation applications. Traditional calculations often rely on grid point methods or their derivatives, but these can be limited in their application, relatively costly, inefficient, and often do not account for potential errors. This paper proposes a novel, efficient method based on latitude strip division for calculating the ground area coverage of satellite constellations, capable of providing the upper and lower bounds of coverage ratio for any ground area. Initially, the ground target area is divided into several latitude strips, and the target area range is utilized to determine the longitude range of each latitude strip. Subsequently, the upper and lower bounds of coverage of each strip are calculated according to the satellite ground coverage range. On this basis, the coverage boundary function is defined and the coverage ratio is derived through comprehensive statistics analysis. Finally, depending on the accuracy of the latitude strip division, the precise coverage area and coverage ratio with upper and lower bounds are determined for instantaneous, continuous, and cumulative coverage problems. Numerical simulation experiments were carried out and compared with the traditional grid point method to validate the effectiveness and computational efficiency of this algorithm in addressing the coverage issue for arbitrarily shaped ground areas. When compared to the longitude strip method, it was confirmed that for ground areas where the longitude exceeds the latitude range, this approach offers superior computational efficiency than the latitude strip method.

A Point-of-Interest Recommender System for Tourist Groups Based on Cooperative Location Set Cover Problem

Trip recommendation for groups of tourists (TRGT) is a challenging task in tourism since many tourists travel in groups, inducing social interaction and bringing various social benefits. However, TRGT must address various real-life constraints such as limited time for touring, cost, etc. TRGT aims to design personalized tours that meet the preferences of all group members by addressing a variety of tourists’ requirements that may sometimes result in conflicts and stress for the group members. TRGT should satisfy that both the preferences of group members need to be achieved as much as possible and the preferences of group members need to be achieved as evenly as possible. In this paper, we present a methodology for tackling the TRGT problem by reducing it to the Cooperative Location Set Cover Problem (CLSCP), formulated as an integer linear program. The CLSCP aims to select a group of facilities that can satisfy, in aggregate, all demand points. To tackle the CLSCP, we present a new method based on detecting frequent patterns. We also demonstrate the efficiency of the proposed methodology by presenting extensive experimental tests.

Sectorial coverage control with load balancing in non-convex hollow environments

It has long been a challenging task for multi-agent systems (MASs) to inexpensively service probable events in non-convex environments. Coverage control provides an efficient framework to address MAS deployment problem for optimizing the cost of tackling unknown events. By means of the divide-and-conquer methodology, this paper proposes a sectorial coverage formulation to configure MASs in non-convex hollow environments while ensuring load balancing among subregions. Thereby, a distributed controller is designed to drive each agent towards a desirable configuration that minimizes the coverage cost by simultaneously adopting sectorial partition mechanism. Theoretical analysis is conducted to ensure the asymptotic stability of closed-loop MASs with exponential convergence of equitable partition. In addition, a circular search algorithm is proposed to identify desirable solutions to such a sectorial coverage problem, which guarantees approximating the optimal deployment of MASs with arbitrarily small tolerance. Finally, both numerical simulations and multi-robot experiments are conducted to substantiate the efficiency of the present sectorial coverage approach.