Coverage Algorithm Research Articles

Design of coverage algorithm for mobile sensor networks based on virtual molecular force

General virtual force algorithm, when the density of sensor nodes is large in the monitoring area, there are some shortcomings, such as uneven distribution of nodes, more overlap of coverage area and so on. In view of these shortcomings, based on the basic idea of air molecular theory, a virtual molecular force model of mobile sensor networks is established, and the virtual molecular force algorithm for node deployment and mobile coverage of mobile sensor networks is given. The virtual molecular force algorithm assumes that there is interaction between nodes in mobile sensor networks, and the resultant force of these forces constitutes the resultant force network of sensor nodes in the monitoring area, which drives the sensor nodes to move to the corresponding location to repair the monitoring blind area, so as to maximize the coverage of the network. In order to verify the feasibility and effectiveness of the virtual molecular force algorithm, the virtual molecular force algorithm for mobile sensor network node deployment and mobile coverage is simulated and analysed by using MATLAB simulation tool. The simulation results show that the virtual molecular force algorithm can make the sensor nodes repair the monitoring blind area efficiently and quickly, and maximize the monitoring coverage area of the sensor network. In terms of repairing monitoring blind areas and improving network coverage, the virtual molecular force algorithm is superior to other network coverage algorithms such as general virtual force algorithm.

Dynamic Adjustment Optimisation Algorithm in 3D Directional Sensor Networks Based on Spherical Sector Coverage Models

In directional sensor networks research, target event detection is currently an active research area, with applications in underwater target monitoring, forest fire warnings, border areas, and other important activities. Previous studies have often discussed target coverage in two-dimensional sensor networks, but these studies cannot be extensively applied to three-dimensional networks. Additionally, most of the previous target coverage detection models are based on a circular or omnidirectional sensing model. More importantly, if the directional sensor network does not design a better coverage algorithm in the coverage-monitoring process, its nodes’ energy consumption will increase and the network lifetime will be significantly shortened. With the objective of addressing three-dimensional target coverage in applications, this study proposes a dynamic adjustment optimisation algorithm for three-dimensional directional sensor networks based on a spherical sector coverage model, which improves the lifetime and coverage ratio of the network. First, we redefine the directional nodes’ sensing model and use the three-dimensional Voronoi method to divide the regions where the nodes are located. Then, we introduce a correlation force between the target and the sensor node to optimise the algorithm’s coverage mechanism, so that the sensor node can accurately move to the specified position for target coverage. Finally, by verifying the feasibility and accuracy of the proposed algorithm, the simulation experiments demonstrate that the proposed algorithm can effectively improve the network coverage and node utilisation.

An Adaptive, Discrete Space Oriented Wolf Pack Optimization Algorithm for a Movable Wireless Sensor Network.

Recently, many related algorithms have been proposed to find an efficient wireless sensor network with good sustainability, a stable connection, and a high covering rate. To further improve the coverage rate of movable wireless sensor networks under the condition of guaranteed connectivity, this paper proposes an adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network (DSO-WPOA). Firstly, a strategy of adaptive expansion based on a minimum overlapping full-coverage model is designed to achieve minimum overlap and no-gap coverage for the monitoring area. Moreover, the adaptive shrinking grid search wolf pack optimization algorithm (ASGS-CWOA) is improved to optimize the movable wireless sensor network, which is a discrete space oriented problem. This improvement includes the usage of a target–node probability matrix and the design of an adaptive step size method, both of which work together to enhance the convergence speed and global optimization ability of the algorithm. Theoretical research and experimental results indicate that compared with the coverage algorithm based on particle swarm optimization (PSO-WSN) and classical virtual force algorithm, the newly proposed algorithm possesses the best coverage rate, better stability, acceptable performance in terms of time, advantages in energy savings, and no gaps.

GM-VPC: An Algorithm for Multi-robot Coverage of Known Spaces Using Generalized Voronoi Partition

SUMMARYIn this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2D×2Dgrids, whereDis the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.

Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

Hierarchy Graph Based Barrier Coverage Strategy with a Minimum Number of Sensors for Underwater Sensor Networks.

Underwater sensor networks based barrier coverage is increasingly important for intrusion detection due to the scarcity of underwater sensor resource. To improve UWSNs’ detection performance and prolong their lifetime, an efficient barrier coverage strategy is very important. In this paper, a novel concept: hierarchy graph is proposed. Hierarchy graph can make the network’s topology more clarity. In accordance with the hierarchy graph, 1-barrier coverage algorithm and k-barrier coverage algorithm are presented to construct the barrier with less sensors for higher energy efficiency. Both analytical and simulation studies demonstrate that the proposed algorithms can provide high detection probability and long lifetime for UWSNs.

Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors.

Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling of sensing regions among different BRs, the coverage problem of BR sensor networks is very challenging. Existing works in BR barrier coverage focus mainly on homogeneous BR sensor networks. This paper studies the heterogeneous BR placement problem on a line barrier to achieve optimal coverage. 1) We investigate coverage differences of the basic placement sequences of heterogeneous BRs on the line barrier, and prove the optimal basic placement spacing patterns of heterogeneous BRs. 2) We study the coverage coupling effect among adjacent BRs on the line barrier, and determine that different placement sequences of heterogeneous BR transmitters will affect the barrier’s coverage performance and length. The optimal placement sequence of heterogeneous BR barrier cannot be solved through the greedy algorithm. 3) We propose an optimal BRs placement algorithm on a line barrier when the heterogeneous BR transmitters’ placement sequence is predetermined on the barrier, and prove it to be optimal. Through simulation experiments, we determine that the different placement sequences of heterogeneous BR transmitters have little influence on the barrier’s maximum length. Then, we propose an approximate algorithm to optimize the BR placement spacing sequence on the heterogeneous line barrier. 4) As a heterogeneous barrier case study, a minimum cost coverage algorithm of heterogeneous BR barrier is presented. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

Target coverage in random wireless sensor networks using cover sets

There are numerous coverage algorithms which efficiently monitor targets in sensor networks by dividing the sensor network into cover sets where each cover monitors all the targets. Creating maximum number of cover sets is a NP Complete problem and therefore problems providing subprime solutions have been proposed. In this paper we propose a novel and energy efficient target coverage algorithm that produces disjoint as well as non-disjoint cover sets. Our proposed solution along with generating cover sets for monitoring targets also gives the energy optimized minimum path from sink to the sensor node and from cover set to the sink. Our algorithm keeps a track of the number of targets a sensor is monitoring along with the energy remaining to find a path which is energy optimized to increase the lifetime of the sensor network. Through simulations, we have shown that our proposed algorithm outperforms similar algorithms found in literature. The increased network lifetime provided by energy optimized path and energy efficient cover sets makes our algorithm desirable for a wider range of applications.

Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

Minimum Connected Dominating Set Algorithms for Ad Hoc Sensor Networks.

To achieve effective communication in ad hoc sensor networks, researchers have been working on finding a minimum connected dominating set (MCDS) as a virtual backbone network in practice. Presently, many approximate algorithms have been proposed to construct MCDS, the best among which is adopting the two-stage idea, that is, to construct a maximum independent set (MIS) firstly and then realize the connectivity through the Steiner tree construction algorithm. For the first stage, this paper proposes an improved collaborative coverage algorithm for solving maximum independent set (IC-MIS), which expands the selection of the dominating point from two-hop neighbor to three-hop neighbor. The coverage efficiency has been improved under the condition of complete coverage. For the second stage, this paper respectively proposes an improved Kruskal–Steiner tree construction algorithm (IK–ST) and a maximum leaf nodes Steiner tree construction algorithm (ML-ST), both of which can make the result closer to the optimal solution. Finally, the simulation results show that the algorithm proposed in this paper is a great improvement over the previous algorithm in optimizing the scale of the connected dominating set (CDS).

A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks.

Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system’s accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface.

Approximation Algorithms for Barrier Sweep Coverage

Time-varying coverage, namely sweep coverage is a recent development in the area of wireless sensor networks, where a few mobile sensors sweep or monitor a comparatively large number of locations periodically. In this article, we study barrier sweep coverage with mobile sensors where the barrier is considered as a finite length continuous curve on a plane. The coverage at every point on the curve is time-variant. We propose an optimal solution for sweep coverage of a finite length continuous curve. Usually, energy source of a mobile sensor is a battery with limited power, so energy restricted sweep coverage is a challenging problem for long running applications. We propose an energy-restricted sweep coverage problem where every mobile sensor must visit an energy source frequently to recharge or replace its battery. We propose a [Formula: see text]-approximation algorithm for this problem. The proposed algorithm for multiple curves achieves the best possible approximation factor 2 for a special case. We propose a 5-approximation algorithm for the general problem. As an application of the barrier sweep coverage problem for a set of line segments, we formulate a data gathering problem. In this problem a set of mobile sensors is arbitrarily monitoring the line segments one for each. A set of data mules periodically collects the monitoring data from the set of mobile sensors. We prove that finding the minimum number of data mules to collect data periodically from every mobile sensor is NP-hard and propose a 3-approximation algorithm to solve it.

Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.

Cooperative Game Theory Based Approach for Target Coverage in Wireless Sensor Networks

Wireless sensor networks has the potential of diverse applications ranging from civilian applications, monitoring and recording of environmental attributes to the tedious task of surveillance in battlefield. The sensing tasks of these networks are usually application specific, a major concern for the network performance is lifetime maximization in the constrained environment. Coverage maximization is also a challenging problem in the field of wireless sensor networks. The scheduling approaches aim to achieve these objectives. In this paper we propose a cooperative game theory based approach to enhance the performance of the network in terms of network throughput, lifetime and energy conservation. The results show that the proposed approach improves the network efficiency in terms of number of set covers and network lifetime by a factor of 86% and 86% respectively as compared to energy efficient coverage algorithm (EECP).

Target Detection Coverage Algorithm Based on 3D-Voronoi Partition for Three-Dimensional Wireless Sensor Networks

The detection of target events is an important research area in the field of wireless sensor networks (WSNs). In recent years, many researchers have discussed the problem of WSN target coverage in a two-dimensional (2D) coordinate system. However, the target detection problem in a 3D coordinate system has not been investigated extensively, and it is difficult to improve the network coverage ratio while ensuring reliable performance of WSN. In addition, sensor nodes that are initially deployed randomly cannot achieve accurate target coverage in practice. Moreover, it is necessary to consider the energy consumption factor owing to the limited energy of the sensor node itself. Hence, with the objective of addressing the target event coverage problem of WSNs in 3D space applications, this paper proposes a target detection coverage algorithm based on 3D-Voronoi partitioning for WSNs (3D-VPCA) in order to ensure reliable performance of the entire network. First, we extend Voronoi division based on the 2D plane, which allows 3D-Voronoi partitioning of sensor nodes in 3D regions. Then, it is optimized according to the 3D-Voronoi neighbouring node partitioning characteristics and combined with the improved algorithm. Next, we set the priority coverage mechanism and introduce the correlation force between the target point and the sensor node in the algorithm, so that the sensor node can move to the target position for accurate coverage. Finally, we carry out related simulation experiments to evaluate the performance and accuracy of the proposed algorithm. The results show that the proposed algorithm can effectively improve the coverage performance of the network while ensuring a high overall coverage ratio.

Striving toward team-based continuity: provision of same-day access and continuity in academic primary care clinics

BackgroundAn important goal of the patient-centered medical home is increasing timely access for urgent needs, while maintaining continuity. In academic primary care clinics, meeting this goal, along with training medical residents and associated professionals, is challenging.MethodsThe aim of this study was to understand how academic primary care clinics provide continuity to patients requesting same-day access and identify factors that may affect site-level success. We conducted qualitative interviews from December 2013–October 2014 with primary care leadership involved with residency programs at 19 Veterans Health Administration academically-affiliated medical centers. Interview recordings were transcribed verbatim. To analyze the data, we created comprehensive, structured transcript summaries for each site. Site summaries were then entered into NVivo 10 software and coded by main categories to facilitate within-case and cross-case analyses. Themes and patterns across sites were identified using matrix analysis.ResultsInterviewees found it challenging to provide continuity for same-day in-person visits. Most sites took a team-based approach to ensure continuity and provide coverage for same-day access, notably using NPs, PAs, and RNs in their coverage algorithms. Further, they reported several adaptations that increased multiple types of continuity for walk-in patients, urgent care between in-person visits, and follow-up care. While this study focused on longitudinal continuity, both by individual PCPs or by a team of professionals, informational continuity and continuity of supervision, as well as, to a lesser extent, relational and management continuity, were also addressed in our interviews. Finally, most interviewees reported clinic intention to provide patient-centered, team-based care and a robust educational experience for trainees, and endeavored to structure their clinics in ways that align these two missions.ConclusionsIn contending with the tension between providing continuity and educating new clinicians, clinics have re-conceptualized continuity as team-based, creating alternative strategies to same-day visits with a usual provider, coupled with communication strategies. Understanding the effect of these strategies on different types of continuity as well as patient experience and outcomes are key next steps in the further development and dissemination of effective models for improving continuity and the transition to team-based care in the academic clinic setting.

Efficient approximation algorithms for maximum coverage with group budget constraints

Given a ground set U with a non-negative weight wi for each i∈U, a positive integer k and a collection of sets S, which is partitioned into a family of disjoint groups G, the goal of the Maximum Coverage problem with Group budget constraints (MCG) is to select k sets from S, such that the total weight of the union of the k sets is maximized and at most one set is selected from each group G∈G. We first present an approximation algorithm with a factor 1−1e and an exponential time via randomized linear programming rounding technique. Then we improve the time complexity of the algorithm to O((m+n)3.5L+k3.5q7L) for |S|=m, |U|=n, and L being the length of the input, by the key idea of modeling the selection of groups as computing a constrained flow in a corresponding auxiliary graph. The algorithm is later shown can be extended to solve two generalizations of MCG. Last but not the least, we present another algorithm with a time complexity O((m+n)3.5L+kδ10.5L) and a slightly increased approximation ratio 1−e1δ−1 mainly based on the idea of partition, where δ≥2 is a parameter tuning which can balance the time complexity and the ratio.

Rendezvous in planar environments with obstacles and unknown initial distance

In the rendezvous search problem, two or more robots at unknown locations should meet somewhere in the environment as quickly as possible. We study the symmetric rendezvous search problem in unknown planar environments with polygonal obstacles. In the symmetric version of the problem, the robots must execute the same rendezvous strategy. We consider the case where the initial distance between the robots is unknown, and the robot is unaware of its and the other robots' locations in the environment. We first design a symmetric rendezvous strategy for two robots and perform its theoretical analysis. We prove that the competitive ratio of our strategy is O(d/D). Here, d is the initial distance between the robots and D is the length of the sides of the square robots. In unknown polygonal environments, robots should explore the environment to achieve rendezvous. Therefore, we propose a coverage algorithm that guarantees the complete coverage of the environment. Next, we extend our symmetric rendezvous strategy to n robots and prove that its competitive ratio is O(d/(nD)). Here, d is the maximal pairwise distance between the robots. Finally, we validate our algorithms in simulations.

Scheduling algorithms for K-barrier coverage to improve transmission efficiency in WSNs

K-barrier coverage optimization problem is concentrating on how to select sensor nodes from the monitoring area of the wireless sensor networks (WSNs) to form the highest quality of k-barrier coverage. Sink-connected barrier coverage optimization problem (SCBCOP) focuses on how to choose the minimum number of forwarding nodes to make each detecting node sink-connected for the security requirements of the belt monitoring region. However, the existing algorithm, such as optimal node selection algorithm(ONSA), can find the optimized k-barrier coverage of sink-connected, but it may form a large number of data packets interference (or collisions) and cannot transfer the information to sink nodes in time because the different detecting nodes can transmit invasive information at the same time. The purpose of this paper is to discuss how to reduce interference among the nodes and select routing path to optimize k-barrier coverage and satisfy sink-connected. In this paper a scheduling algorithm is proposed to build the routing path and maintain sink-connected. The algorithms is present in detail through forwarding routing tree and multi-channel scheduling to further reduce the interference of packet transmission among sensor nodes. Moreover, the comparison between other approaches and our proposal is mentioned through the simulation to show the potential efficiency and better performance of interference of packet transmission among sensor nodes with the lower packet loss rate, the shorter packet delay and the larger network average throughput.

The ( h,k )-Server Problem on Bounded Depth Trees

We study the k -server problem in the resource augmentation setting, i.e., when the performance of the online algorithm with k servers is compared to the offline optimal solution with h ≤ k servers. The problem is very poorly understood beyond uniform metrics. For this special case, the classic k -server algorithms are roughly (1+1/ϵ)-competitive when k =(1+ϵ) h , for any ϵ > 0. Surprisingly, however, no o ( h )-competitive algorithm is known even for HSTs of depth 2 and even when k / h is arbitrarily large. We obtain several new results for the problem. First, we show that the known k -server algorithms do not work even on very simple metrics. In particular, the Double Coverage algorithm has competitive ratio Ω ( h ) irrespective of the value of k , even for depth-2 HSTs. Similarly, the Work Function Algorithm, which is believed to be optimal for all metric spaces when k = h , has competitive ratio Ω ( h ) on depth-3 HSTs even if k =2 h . Our main result is a new algorithm that is O (1)-competitive for constant depth trees, whenever k =(1+ϵ) h for any ϵ > 0. Finally, we give a general lower bound that any deterministic online algorithm has competitive ratio at least 2.4 even for depth-2 HSTs and when k / h is arbitrarily large. This gives a surprising qualitative separation between uniform metrics and depth-2 HSTs for the ( h , k )-server problem.