Coverage Optimization Algorithm Research Articles

Overlay Optimization Algorithm for Directed Sensor Networks with Virtual Force and Particle Swarm Optimization Synergy

In this study, a novel algorithm for optimizing the coverage of directed sensor networks is proposed. The deployment of sensor networks is typically random, leading to the potential issues of extensive coverage overlaps and blind areas. To address this challenge and enhance the effectiveness of network coverage, a directional sensor network coverage optimization algorithm is developed based on the principles of virtual force and particle swarm optimization. Firstly, the article introduces the concept of a segmented virtual negative centroid model. This model revolutionizes the configuration of the virtual negative centroid, thereby enabling a more efficient adjustment of the gravitational forces exerted by the coverage blind areas on the sensor nodes. Therefore, the influence of these blind areas on the improvement of network coverage is significantly amplified. Secondly, taking into account the characteristics of global optimization and the inherent randomness of particle swarm optimization, the algorithm synergistically combines the principles of virtual force and particle swarm optimization. This integration effectively fine-tunes the sensing direction of the sensor nodes, thereby optimizing their overall performance. The algorithm in this study incorporates an adjusted inertia weight strategy and introduces Gaussian disturbance in the local optimization enhancement phase to prevent local optimization, accelerate particle convergence, and facilitate the sensor network’s attainment of an optimal distribution for coverage optimization. Simulation experiments were conducted to verify the algorithm’s effectiveness. The initial sensor network coverage was 31.04%. After applying the algorithm, the average coverage increased to 80.16%, with a maximum coverage of 84.2%. These results verify the effectiveness of the algorithm.

A Deployment and Coverage Optimization Algorithm for Self-Powered Wireless Sensor Networks Based on Hybrid Swarm Intelligence

A Deployment and Coverage Optimization Algorithm for Self-Powered Wireless Sensor Networks Based on Hybrid Swarm Intelligence

Research on video sensor networks coverage optimization method in obstacle scene

Aiming at the presence of obstacles when monitoring wild animals in the forest, an obstacle avoidance method is introduced, and a video sensor networks coverage optimization algorithm for virtual force affected by obstacles fused to an immune clone particle swarm is proposed. The algorithm adjusts locally through virtual forces to reduce the overlapping area and then uses the immune cloning particle swarm algorithm to optimize the global nodes to improve the coverage of nodes in the obstacle scene. The simulation results show that the algorithm proposed in this paper can improve the coverage of nodes.

A Coverage Optimization Algorithm for Underwater Acoustic Sensor Networks based on Dijkstra Method

A Coverage Optimization Algorithm for Underwater Acoustic Sensor Networks based on Dijkstra Method

Trans-population graph-based coverage optimization of allogeneic cellular therapy.

Pre-clinical development and in-human trials of 'off-the-shelf' immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduce the risk of GvHD or to improve allo-IEC persistence, as warranted by different clinical indications, disease kinetics, on-target/off-tumor effects, and therapeutic cell type (T cell subtype, NK, etc.). The optimal choice of candidate donors to maximize target patient population coverage and minimize cost and redundant effort in creating off-the-shelf IECT product banks is still an open problem. We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing. We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). We then compared the efficiency of different optimization algorithms - a greedy solution, a linear programming (LP) solution, and integer linear programming (ILP) -- as well as random donor selection (average of 5 random trials) to show that an optimization can be performed at the entire population level. The average additional population coverage per donor decrease with the number of donors, and varies with the scenario. The Greedy, LP and ILP algorithms consistently achieve the optimal coverage with far fewer donors than the random choice. In all cases, the number of randomly-selected donors required to achieve a desired coverage increases with increasing population. However, when optimal donors are selected, the number of donors required may counter-intuitively decrease with increasing population size. When comparing recruiting more donors vs gene editing, the latter was generally more expensive. When choosing donors and patients from different populations, the number of random donors required drastically increases, while the number of optimal donors does not change. Random donors fail to cover populations different from their original populations, while a small number of optimal donors from one population can cover a different population. Graph-based coverage optimization algorithms can flexibly handle various HLA matching criteria and accommodate additional information such as KIR genotype, when such information becomes routinely available. These algorithms offer a more efficient way to develop off-the-shelf IECT product banks compared to random donor selection and offer some possibility of improved transparency and standardization in product design.

Coverage Optimization for Directional Sensor Networks: A Novel Sensor Redeployment Scheme

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

A Coverage Optimization Algorithm for the Wireless Sensor Network with Random Deployment by Using an Improved Flower Pollination Algorithm

Due to complex terrain and harsh environments, sensor nodes are often randomly scattered in the monitoring area, which may cause coverage holes or network disconnection. Current works move some sensor nodes to certain places to address this problem. However, these works cannot guarantee the coverage and connectivity simultaneously and have larger moving cost in energy. In this paper, we propose a coverage optimization strategy based on the flower pollination algorithm (FPA). First, to solve the shortcomings of the classical FPA in convergence and accuracy, an improved FPA is proposed. Then, the network deployment optimization problem is modeled as a multi-objective optimization problem that guarantees the coverage of target points and the connectivity of the network while minimizing the energy consumption of sensor nodes’ moving. The sensor nodes are selected and moved to the proper position by utilizing the improved FPA to minimize the energy consumed by the sensors’ motion and guarantee the coverage and connectivity. Test results show that the improved FPA has good convergence speed and accuracy compared with other evolutionary algorithms. Simulation results demonstrate that the proposed algorithm can guarantee network connectivity and satisfy the coverage requirement while minimizing the energy consumption of the sensor movement. Consequently, more energy of the sensor node can be used to collect and transmit sensed data. These results indicate that our algorithm can prolong network lifetime and improve monitoring quality in fields such as forest monitoring.

A Design Approach for Simultaneous Cooperative Interception Based on Area Coverage Optimization

In this paper, a design approach for simultaneous cooperative interception is presented for a scenario where the successful handover cannot be guaranteed by a single interceptor due to the target maneuver and movement information errors at the handover moment. Firstly, the concepts of the reachable interception area and predicted interception area are introduced, a performance index function is constructed, and the probability of a successful handover is described by considering the coverage of the predicted interception area. Taking the probability of successful handover as a constraint, the simultaneous cooperative interception design problem is formulated based on area coverage. Then, an area coverage optimization algorithm is presented to design the spatial distributions of the interceptors. In order to enhance the handover probability, a simultaneous cooperative interception design approach is proposed to obtain the number of interceptors and the corresponding spatial distributions. Finally, simulation experiments are carried out to validate the effectiveness of the proposed approach.

Continuous Use Intention of Mobile Social Network Information Service Based on User Behavior Perception.

In order to improve the willingness of continuous use of mobile social network information services, this study combines user behavior perception to analyze the continuous use of mobile social network information services and proposes a data coverage optimization strategy based on service quality perception. Furthermore, this study measures participants' regional preferences based on the duration of participants in the perceptual region and the number of historical perceptual tasks completed on the perceptual region. In addition, this study designs a perceptual data coverage optimization algorithm to optimize the perceptual data coverage and ensure the real-time validity of the perceptual data. Through algorithm research and systematic evaluation, it can be seen that the continuous use willingness system of mobile social network information service based on user behavior perception can basically meet the actual needs.

WSN node coverage optimization algorithm based on global and neighborhood difference DE

Wireless sensor networks are widely used in today's fields, such as scientific research, industry and agriculture. However, due to the influence of its geographical location and the problems of low coverage and waste of resources caused by random placement, it is very important to adopt appropriate strategies to improve its coverage. To this end, an improved GND-DE (Global and Neighborhood Difference Guided DE) algorithm is proposed. This algorithm uses both the global topology structure and the neighborhood topology structure, combined with the evaluation of contemporary optimization results, and selects the results from the two topology structures. The value-dominant individual, the individual to be evolved and the two dominant individuals calculate the difference operator corresponding to the two topological structures; a diversity neighborhood topology is proposed for the creation of the neighborhood topology; at the same time, the algorithm step size factor F is adaptively adjusted and the JADE external archive mutation strategy is introduced to eliminate the possibility of algorithm search stagnation. In order to verify the effectiveness of its improved algorithm, compared with other mainstream improved algorithms on the CEC2017 test set, it shows that its optimization efficiency and convergence are better than other comparison algorithms; finally, GND-DE is applied to WSN node coverage optimization, which proves the feasibility of its optimization strategy.

Coverage optimization algorithm based on control nodes position in wireless sensor networks

SummaryCoverage is one of the basic problems that any wireless sensor network application must solve properly to achieve the expected quality of service. The communication distance of wireless sensor network nodes is limited, the initial energy is low, and many optimization objectives need to be considered in the deployment, which makes the network deployment optimization very complicated, and many problems are difficult. In essence, the deployment problem is a multidimensional nonlinear optimization problem with continuous discrete variables. In order to meet the needs of network monitoring, this article establishes a reasonable mathematical model according to the research objectives. By adjusting node position to obtain better network coverage effect, an optimization algorithm of monitoring area coverage based on control node position is proposed. By calculating the new location of nodes, more monitoring areas can be covered by the nodes. The simulation results show that the proposed algorithm can improve the coverage effect rapidly.

Monitoring area coverage optimization algorithm based on nodes perceptual mathematical model in wireless sensor networks

With the rapid development of microelectronics technology, wireless communication technology and sensor technology, micro-structured sensors that can sense and communicate at the same time have emerged. This tiny device can sense all kinds of information in the surrounding environment, collect it to the sink node, and then transmit it to the data center. Wireless sensor networks are widely used in civil, industrial, agricultural, military and other fields. In order to meet the demand of monitoring area coverage effect, this paper firstly analyzes the research status of wireless sensor networks at home and abroad, points out the main problems existing in the current research process, and establishes a reasonable mathematical model according to the research status. Secondly, to solve the problem of poor coverage effect of existing algorithms, a monitoring area coverage optimization algorithm based on perceived probability around nodes is proposed. By designing a reasonable scheme of moving nodes, the algorithm can gradually disperse the nodes and improve the coverage effect in the monitoring area. Finally, the simulation results show that the algorithm has a good performance, which can reduce the moving distance of nodes, improve the coverage of the network and prolong the service time of the whole network.

Coverage optimization of visual sensor networks for observing 3-D objects: survey and comparison

Coverage is a basic and critical issue for design and deployment of visual sensor networks, however, the optimization problem is very challenging especially when considering coverage of three-dimensional (3-D) scenarios. This paper provides a brief survey of mainstream coverage optimization methods for visual sensor networks, including the greedy algorithm, genetic algorithm (GA), particle swarm optimization (PSO), binary integer programming (BIP) and differential evolution algorithm (DE). We provide an efficient open-source C++ implementation of these algorithms and compare their performance on a typical camera deployment problem for coverage of 3-D objects. In order to improve the computational efficiency, a parallel visual occlusion detection approach is proposed and implemented with graphic processing units (GPUs), which are then integrated into all of the aforementioned optimization approaches for a fair comparison. Evaluation results show that (1) the proposed parallel occlusion detection algorithm largely improves the computational efficiency; (2) among the five typical approaches, BIP has the best coverage performance yet with the highest time cost, and greedy algorithm is the fastest approach at the price of coverage performance; GA, PSO, and DE achieve a compromise between the performance and the time cost, while DE has better coverage performance and less time cost than PSO and GA. These results could serve as engineering guidelines and baselines for further improvement of coverage optimization algorithms.

A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization.

The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf’s boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

Area Coverage Optimization Using Heterogeneous Robots: Algorithm and Implementation

This paper advances the previous theoretical work of authors by conducting experiments on a generalized coverage optimization algorithm using a team of heterogeneous mobile robots. A scalar measure (herein called the density) of the environment defines a nonuniform coverage metric of the area. Mobile robots are spatially configured such that their asymptotic placements in an area maximize the nonuniform coverage metric. Over the last fifteen years, a large body of research work has been conducted in solving the area coverage optimization problem where the focus was mainly on theoretical results followed by mostly numerical simulations. In some cases, the coverage optimization algorithms were validated with experimental results, but the implementation platforms were suitable to specific homogeneous robot platforms. Here, the emphasis is on the real-time implementation of the authors’ previously published theoretical results on area coverage optimization problems using a team of heterogeneous mobile robots. The robots are heterogeneous in the sense that they have different actuator limits, physical dimensions, and processing capabilities. The modularity of the algorithm stems from the fact that the additional hardware/software architecture is open-source and can be applied to different robots regardless of their internal electromechanical system architectures. The algorithm is implemented using the emerging robot operating system in a multithreaded manner. A commercial robot simulator was used to validate the coverage performance followed by a set of experiments conducted in an indoor environment.

Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks.

In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor’s locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is challenging to analyze. We transform the network area coverage problem into cell coverage problems by exploiting the Voronoi diagram, which only needs to optimize local coverage for each cell in a decentralized way. To address the cell coverage problem, we propose three local coverage optimization algorithms to improve the cell coverage, namely Move Inside Cell Algorithm (MIC), Rotate Working Direction Algorithm (RWD) and Rotation based on boundary (RB), respectively. Extensive simulations are performed to prove the effectiveness of our proposed algorithms in terms of the coverage ratio.

Coverage Optimization Algorithm of Wireless Sensor Network Based on Mobile Nodes

To reduce the blind zone in network coverage, we propose a coverage optimization algorithm of wireless sensor network based on mobile nodes. This algorithm calculates the irregularity of blind zone in network coverage and obtains the minimum approximate numerical solution by utilizing the quantitative relationship between energy consumption of related nodes and the position of the mobile nodes. After determining the optimal relative position of the mobile nodes, the problem of blind zone between the static nodes is addressed. Simulation result shows that the proposed algorithm has high dynamic adaptability and can address the problem of blind zone maximally. Besides increasing the network coverage, the algorithm also reduces the network energy consumption, optimizes network coverage control and exhibits high convergence.

C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty

We introduce a new problem of continuous, coverage-aware trajectory optimization under localization and sensing uncertainty. In this problem, the goal is to plan a path from a start state to a goal state that maximizes the coverage of a user-specified region while minimizing the control costs of the robot and the probability of collision with the environment. We present a principled method for quantifying the coverage sensing uncertainty of the robot. We use this sensing uncertainty along with the uncertainty in robot localization to develop C-OPT, a coverage-optimization algorithm which optimizes trajectories over belief-space to find locally optimal coverage paths. We highlight the applicability of our approach in multiple simulated scenarios inspired by surveillance, UAV crop analysis, and search-and-rescue tasks. We also present a case study on a physical, differential-drive robot. We also provide quantitative and qualitative analysis of the paths generated by our approach.

An area coverage algorithm for non-line-of-sight ultraviolet communication network

The non-line-of-sight ultraviolet (UV) communication can be applied in certain circumstances. In this paper, a graded area coverage optimization algorithm based on genetic algorithm (GACOA) is proposed to reduce the deployment cost of network and enhance the area coverage of network. Firstly, the simulation results show that the GACOA approach can improve the coverage ratio and save the deployment cost. Then, the performance of GACOA with different communication angles and number of nodes is simulated and analyzed. When the GACOA, PFCEA, and CSRCA are applied in the UV network, the performance metrics of GACOA are better than that of PFCEA and CSRCA in the same UV network. Therefore, the deployment of the UV network can be completed efficiently with a low deployment cost.