Coverage Problem Research Articles

Constraint Composite Graph-Based Weighted CSP Solvers: An Empirical Study

The Weighted Constraint Satisfaction Problem (WCSP) is a very expressive framework for optimization problems. The Constraint Composite Graph (CCG) is a graphical representation of a given (Boolean) WCSP that facilitates its reduction to a Minimum Weighted Vertex Cover (MWVC) problem by introducing intelligently chosen auxiliary variables. It also enables kernelization: a maxflow procedure used to fix the optimal values of a subset of the variables before initiating search. In this paper, we present some CCG-based WCSP solvers and compare their performance against toulbar2, a state-of-the-art WCSP solver, on a variety of benchmark instances. We also study the effectiveness of kernelization.

Online Multiset Submodular Cover

We study the Online Multiset Submodular Cover problem (OMSC), where we are given a universe U of elements and a collection of subsets S⊆2U\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {S}\\subseteq 2^U$$\\end{document}. Each element uj∈U\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_j \\in U$$\\end{document} is associated with a nonnegative, nondecreasing, submodular polynomially computable set function fj\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f_j$$\\end{document}. Initially, the elements are uncovered, and therefore we pay a penalty per each unit of uncovered element. Subsets with various coverage and cost arrive online. Upon arrival of a new subset, the online algorithm must decide how many copies of the arriving subset to add to the solution. This decision is irrevocable, in the sense that the algorithm will not be able to add more copies of this subset in the future. On the other hand, the algorithm can drop copies of a subset, but such copies cannot be retrieved later. The goal is to minimize the total cost of subsets taken plus penalties for uncovered elements. We present an O(ρmax)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\sqrt{\\rho _{\\max }})$$\\end{document}-competitive algorithm for OMSC that does not dismiss subset copies that were taken into the solution, but relies on prior knowledge of the value of ρmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho _{\\max }$$\\end{document}, where ρmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho _{\\max }$$\\end{document} is the maximum ratio, over all subsets, between the penalties covered by a subset and its cost. We provide an Olog(ρmax)ρmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O\\left( \\log (\\rho _{\\max }) \\sqrt{\\rho _{\\max }} \\right) $$\\end{document}-competitive algorithm for OMSC that does not rely on advance knowledge of ρmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho _{\\max }$$\\end{document} but uses dismissals of previously taken subsets. Finally, for the capacitated versions of the Online Multiset Multicover problem, we obtain an O(ρmax′)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\sqrt{\\rho _{\\max }'})$$\\end{document}-competitive algorithm when ρmax′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho _{\\max }'$$\\end{document} is known and an Olog(ρmax′)ρmax′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O\\left( \\log (\\rho _{\\max }') \\sqrt{\\rho _{\\max }'} \\right) $$\\end{document}-competitive algorithm when ρmax′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho _{\\max }'$$\\end{document} is unknown, where ρmax′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho _{\\max }'$$\\end{document} is the maximum ratio over all subset incarnations between the penalties covered by this incarnation and its cost.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Incremental Concolic Testing of Register-Transfer Level Designs

Concolic testing is a scalable solution for automated generation of directed tests for validation of hardware designs. Unfortunately, concolic testing fails to cover complex corner cases such as hard-to-activate branches. In this article, we propose an incremental concolic testing technique to cover hard-to-activate branches in register-transfer level (RTL) models. We show that a complex branch condition can be viewed as a sequence of easy-to-activate events. We map the branch coverage problem to the coverage of a sequence of events. We propose an efficient algorithm to cover the sequence of events using concolic testing. Specifically, the test generated to activate the current event is used as the starting point to activate the next event in the sequence. Experimental results demonstrate that our approach can be used to generate directed tests to cover complex corner cases in RTL models while state-of-the-art methods fail to activate them.

Fixed set search matheuristic applied to the knapsack problem with forfeits

This paper focuses on solving the knapsack problem with forfeits (KPF). This variation of the knapsack problem includes soft conflicts or forfeits, where forfeit pairs consist of two items and an associated penalty, which must be subtracted from the total profit if both items in the pair are chosen to be jointly in the knapsack. The proposed method combines the fixed set search (FSS) metaheuristic’s learning mechanism with integer programming to solve subproblems. A new ground set of elements for the KPF is introduced to augment the information provided by the fixed set, and the method for creating fixed sets is adjusted to increase the diversity of solutions. The conducted computational experiments show that the proposed method significantly outperforms current state-of-the-art methods and finds a large number of best-known solutions for the standard test instances. Moreover, the method performs well across a wide range of parameter values. The proposed approach does not significantly exploit any specific properties of the KPF and could potentially be applied to other 0–1 problems, such as the minimum vertex cover problem or the facility location problem, without significant modifications. Additionally, the method’s simplicity makes it suitable for hybridization with other metaheuristic approaches or the incorporation of specific KPF properties to improve its performance.

A Strong Combinatorial Relaxation for the Problem of Min-Time Coverage in Constricted Environments

A Strong Combinatorial Relaxation for the Problem of Min-Time Coverage in Constricted Environments

We Are Not Speaking the Same Language: Current Procedural Terminology Coding and Provision of Care in Lymphatic Reconstruction.

Current Procedural Terminology (CPT) codes provide a uniform language for medical billing, but specific codes have not been assigned for lymphatic reconstruction techniques. The authors hypothesized that inadequate codes would contribute to heterogeneous coding practices and reimbursement challenges, ultimately limiting surgeons' ability to treat patients. A 22-item virtual questionnaire was offered to 959 members of the American Society of Reconstructive Microsurgeons to assess the volume of lymphatic reconstruction procedures performed, CPT codes used for each procedure, and challenges related to coding and providing care. The survey was completed by 66 board-certified/board-eligible plastic surgeons (6.9%), who unanimously agreed that lymphatic surgery is integral to cancer care, with 86.4% indicating that immediate lymphatic reconstruction should be offered after lymphadenectomy. Most performed lymphovenous bypass, immediate lymphatic reconstruction, liposuction, and vascularized lymph node transfer.Respondents reported that available CPT codes failed to reflect procedural scope. A wide variety of CPT codes was used to report each type of procedure. Insurance coverage problems led to 69.7% of respondents forgoing operations and 32% reducing treatment offerings. Insurance coverage and CPT codes were identified as significant barriers to care by 98.5% and 95.5% of respondents, respectively. Respondents unanimously agreed on the importance of lymphatic reconstruction in cancer care, and most identified inadequate CPT codes as causing billing issues, which hindered their ability to offer surgical treatment. Appropriate and specific CPT codes are necessary to ensure accuracy and consistency of reporting and ultimately to improve patient access to care.

Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.

Smart Water Quality Monitoring with IoT Wireless Sensor Networks.

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Elderly care facility location in the face of the climate crisis: A case study in Canada

The climate crisis poses a dual threat to the environment and human health, with older adults being particularly vulnerable. This study aims to establish a network of elderly care facilities designed to respond to climate-induced disasters—an essential element in creating resilient cities that address the challenges of elderly care within the context of the climate crisis. First, we formulate an equation grounded in expert opinions to estimate demand in each region based on factors such as the elderly population, average age, frailty, and proximity to healthcare service nodes. We then introduce an Integrated Coverage and Distance Facility Location problem (ICDFL), a novel approach tailored to address the unique needs of seniors during disasters while considering governmental budget constraints. The ICDFL, therefore, simultaneously pursues two key objectives: (i) maximizing coverage while respecting a maximum allowable coverage radius, and (ii) minimizing overall travel distance. The first objective ensures the resilience of elderly health, guiding healthcare policymakers, while the second considers transportation and service costs faced by government agencies. To address dual-objective optimization, we utilize the epsilon-constrained optimization technique, which enables precise management of both coverage and distance goals through an iterative problem-solving approach. We apply the proposed model to a real-world case study in southern Ontario, Canada, to validate its effectiveness. This is an important aspect of the study, as Canada’s warming trend is happening twice as fast as the rest of the world and its elderly population is expanding at an alarming rate. The results of our model provide guidance to policy makers and healthcare planners in improving emergency preparedness, thus creating a sustainable community for our growing elderly population and improving the well-being of older people during climate-induced disasters.

Implementation of remote MRI scanning

The present shortage of MRI technologists continues to interfere with the delivery of care of patients. In addition, there have been limited solutions to cover disability, sickness, vacation, and personal days of that limited workforce. Our practice decided that the best way to address these coverage problems was to implement a virtual remote scanning solution. We explain the IT infrastructure, training and policies needed to implement this innovative solution safely and successfully to the MRI technologist shortage.

Joint modeling of causal phrases-sentiments-aspects using Hierarchical Pitman Yor Process

Traditional sentiment-aware topic models assume that topic or sentiment transition occurs from either a sentence to the next sentence or from a word to the next word. Such models cannot capture a topic or sentiment transition at phrase boundaries. Further, most of the models adopt a sentiment lexicon to initialize sentiment priors and this approach induces coverage problems. To overcome the above-cited limitations, we have proposed a topic model that extracts aspects, sentiments, and causal phrases simultaneously by leveraging Hierarchical Pitman Yor Process (HPYP) that is modified using a sentiment component, a word-tagger to guide the causal phrase generation and a sentiment prior initialized through a sequential model to address coverage problems. We have evaluated our model on six datasets and found that the proposed model outperforms the baselines in terms of perplexity by 14%, topical coherence by 20%, topic diversity by 5%, sentiment classification task’s accuracy by 4% and, precision, recall and F1 score by 2%. Ablation studies assert that sequence model based sentiment prior initialization results in increasing the accuracy of sentiment classification by 2%.

Collision Avoiding Max-Sum for Mobile Sensor Teams

Recent advances in technology have large teams of robots with limited computation skills work together in order to achieve a common goal. Their personal actions need to contribute to the joint effort, however, they also must assure that they do not harm the efforts of the other members of the team, e.g., as a result of collisions. We focus on the distributed target coverage problem, in which the team must cooperate in order to maximize utility from sensed targets, while avoiding collisions with other agents. State of the art solutions focus on the distributed optimization of the coverage task in the team level, while neglecting to consider collision avoidance, which could have far reaching consequences on the overall performance. Therefore, we propose CAMS: a collision-avoiding version of the Max-sum algorithm, for solving problems including mobile sensors. In CAMS, a factor-graph that includes two types of constraints (represented by function-nodes) is being iteratively generated and solved. The first type represents the task-related requirements, and the second represents collision avoidance constraints. We prove that consistent beliefs are sent by target representing function-nodes during the run of the algorithm, and identify factor-graph structures on which CAMS is guaranteed to converge to an optimal (collision-free) solution. We present an experimental evaluation in extensive simulations, showing that CAMS produces high quality collision-free coverage also in large and complex scenarios. We further present evidence from experiments in a real multi-robot system that CAMS outperforms the state of the art in terms of convergence time.

DQN based coverage control for multi‐agent system in line intersection region

AbstractGenerally, the coverage control is studied in a convex region, in which the agent kinematics and the coverage environment both have strong limitations. It is difficult to directly apply these results to practical scenarios, such as the road environment or indoor environment. In this study, the multi‐agent coverage control problems in a line intersection region is investigated, where the agents can only move along the given lines. To present the agents motion in this line intersection region, the moving directions and velocities of the agents are analyzed in the first part. Then, the coverage control model for the multi‐agent system in line intersection region is presented, in which the cost function is provided based on the agent's minimum moving distance and the agent motions are used as the constraints. To solve this constrained coverage problem, the deep Q‐learning network (DQN) is employed to find the optimal positions for each agent in the line intersection region. In final, numerical simulations are presented to validate the feasibility and effectiveness of proposed approaches.

Approximation algorithms for maximum weighted target cover problem with distance limitations

Approximation algorithms for maximum weighted target cover problem with distance limitations

Pro-active component image placement in Edge computing environments

Edge computing has attracted a lot of attention both from industry and academia in recent years and is considered as a key enabler for addressing the increasingly strict requirements of Next Generation applications. Contrary to Cloud computing, in Edge computing the computation are placed closer to the end-users into the so-called Edge, to facilitate low-latency and high-bandwidth applications and services that would not be feasible using cloud and far remote processing alone. However, the distributed, dynamic and heterogeneous environment in the Edge computing along with the diverse applications’ requirements make service placement in such infrastructure a challenging issue. One important aspect of Edge computing is the management of the placement of the applications in the network system so as to minimize each application’s runtime, given the resources of system’s devices and the capabilities of the system’s network. To this end, we propose an empirical experimental analysis, by comparing the results of different placements strategies and various edge communication networks. In particular, we model the problem of proactive placement of application images as a Minimum Vertex Cover problem. Our results demonstrate that the Greedy implementation seems to offer the best tradeoff in terms of performance, cost function and execution time.

Effect of constraint relaxation on the minimum vertex cover problem in random graphs.

A statistical-mechanical study of the effect of constraint relaxation on the minimum vertex cover problem in Erdős-Rényi random graphs is presented. Using a penalty-method formulation for constraint relaxation, typical properties of solutions, including infeasible solutions that violate the constraints, are analyzed by means of the replica method and cavity method. The problem involves a competition between reducing the number of vertices to be covered and satisfying the edge constraints. The analysis under the replica-symmetric (RS) ansatz clarifies that the competition leads to degeneracies in the vertex and edge states, which determine the quantitative properties of the system, such as the cover and penalty ratios. A precise analysis of these effects improves the accuracy of RS approximation for the minimum cover ratio in the replica symmetry-breaking (RSB) region. Furthermore, the analysis based on the RS cavity method indicates that the RS/RSB boundary of the ground states with respect to the mean degree of the graphs is expanded, and the critical temperature is lowered by constraint relaxation.

Efficient configuration of heterogeneous multistatic sonar networks: A mixed-integer linear programming approach

The work undertaken in this paper pertains to the optimal spatial configuration of a heterogeneous Wireless Sensor Network (WSN) for the Area Coverage (AC) problem. Specifically, this research falls under the heading of Anti-Submarine Warfare (ASW) with an emphasis on active sonar systems and, more pointedly still, on a specific type of sensor: sonobuoys (portmanteau word formed by “sonar” and “buoy”). These buoys are further divided into three main categories: transmitter-only (Tx), receiver-only (Rx) and transmitter–receiver (TxRx). In this paper, we will therefore try to determine the geographical location of the different buoys comprising a Multistatic Sonar Network (MSN), special case of WSN, so as to maximize the overall surface area covered. To do this, we discretize an Area of Interest (AoI) into regular cells using bathymetric and altimetric data, and place a deployment position and a fictitious target at the center of each cell so that we can evaluate the network’s performance. More precisely, we are taking into account a limited number of sensors (buoys) with possible pairwise incompatibilities, variable performances, probabilistic detection models, an adverse masking effect (direct blast) as well as coastlines features. Finally, in order to solve this problem, we have developed several efficient Mixed-Integer Linear Programs (MILPs), all of which have been thoroughly tried-and-tested on a benchmark set of 100 instances derived from real elevation data. This has led us to identify an ideal model, i.e. one that is significantly better than all the others in the statistical sense.

Optimal UAV deployment with star topology in area coverage problems

Optimal UAV deployment with star topology in area coverage problems

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.