Improved Dynamic K-Coverage Algorithms in Mobile Sensor Networks

In this paper, four PSO based distributed algorithms are presented to attain k-coverage in the target filed. In the first algorithm named K-Coverage Particle Swarm Optimization (KPSO), each static sensor which discovers an event in its sensing range, implements Particle Swarm Optimization (PSO) algorithm in a distributed manner on its mobile sensors. The calculation time is considered as a big bottleneck in PSO, so a second algorithm named K-Coverage Virtual Force directed Particle Swarm Optimization (KVFPSO) is presented, comprised of Virtual Force and KPSO algorithms. In the first and second proposed algorithms, the best experiences of the particles were used to determine their speed. It is possible that these responses might not be the final result and cause extra movements. Another algorithm named KVFPSO-Learning Automata (KVFPSO-LA) is introduced based on which the speed of particles is corrected by using the existing knowledge and the feedback from the actual implementation of the algorithm. To improve performance of the algorithm, Improved KVFPSO-LA is introduced, in which static sensors are equipped with learning automata. Simulation results show that the proposed protocols perform well with respect to balanced energy consumption among nodes, thus maximizing network life-time.

Scheduling optimization for UAV communication coverage using virtual force-based PSO model

When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication services.Therefore,it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption.In this paper,we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization(AVF-PSO)strategy.In particular,we first introduce the gravity model into the traditional Particle Swarm Optimization(PSO)algorithm so as to increase the probability of full coverage.Then,the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced.Finally,in order to reduce the communication interference between UAVs,we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs.Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches.For example,in the target area of any size,the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%,and 2.0–56.1%,respectively.Moreover,the proposed scheduling algorithm is high adaptable to diverse execution environments.©2022 Published by Elsevier Ltd.

Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Cuckoo search algorithm-based optimal deployment method of heterogeneous multistatic radar for barrier coverage

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

Robot coverage algorithm under rectangular decomposition environment

The environment modeling algorithm named rectangular decomposition, which is composed of cellular nodes and interleaving networks, is proposed. The principle of environment modeling is to divide the environment into individual square sub-areas. Each sub-area is orientated by the central point of the sub-areas called a node. The rectangular map based on the square map can enlarge the square area side size to increase the coverage efficiency in the case of there being an adjacent obstacle. Based on this algorithm, a new coverage algorithm, which includes global path planning and local path planning, is introduced. In the global path planning, uncovered subspaces are found by using a special rule. A one-dimensional array P, which is used to obtain the searching priority of node in every direction, is defined as the search rule. The array P includes the condition of coverage towards the adjacent cells, the condition of connectivity and the priorities defined by the user in all eight directions. In the local path planning, every sub-area is covered by using template models according to the shape of the environment. The simulation experiments show that the coverage algorithm is simple, efficient and adapted for complex two- dimensional environments.

An ergodic algorithm for long-term coverage of elliptical orbits

This paper deals with the coverage analysis problem of elliptical orbits. An algorithm based on ergodic theory, for long-term coverage of elliptical orbits, is proposed. The differential form of the invariant measure is constructed via the perturbation on mean orbital elements resulted from the J2 term of non-spherical shape of the earth. A rigorous proof for this is then given. Different from the case of circular orbits, here the flow and its space of the dynamical system are defined on a physical space, and the real-value function is defined as the characteristic function on station mask. Therefore, the long-term coverage is reduced to a double integral via Birkhoff-Khinchin theorem. The numerical implementation indicates that the ergodic algorithm developed is available for a wide range of eccentricities.

An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks

A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Complete Coverage Path Planning Based on Improved Area Division

It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .

A New 3D Wireless Directional Sensing Model and Coverage Enhancement Algorithm

Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this problem,a new 3D directional sensor model and coverage enhancement algorithm is proposed. We can adjust the pitch angle and deviation angle to enhance the coverage rate. And the coverage enhancement algorithm is based on an improved gravitational search algorithm. In this paper the two improved strategies of GSA are directional mutation strategy and individual evolution strategy. A set of simulations show that our coverage enhancement algorithm has a good performance to improve the coverage rate of the wireless directional sensor network on different number of nodes,different virtual angles and different sensing radius.

Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization

In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.

IQABC-Based Hybrid Deployment Algorithm for Mobile Robotic Agents Providing Network Coverage

Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target area.Herein,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground devices.In this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage range.Then,we propose a hybrid deployment algorithm based on the improved quick artificial bee colony.The algorithm is composed of a centralized deployment algorithm and a distributed one.The proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically distributed.Simulation results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

Competitive location problem of multi-level pickup point considering cooperative coverage

To occupy a greater market share in terminal distribution, companies are urged to make full use of cooperative coverage formed with brand effect and information sharing in the layout of pickup points. Based on the diversity of pickup points, the piecewise function, signal intensity function and probability function are introduced. Meanwhile, considering the effect of distance satisfaction and cooperation coverage on customer behavior, the location model of the pickup point under competitive environments is established. The genetic algorithm is used to solve the problem, and the effectiveness of the model and algorithm is verified by a case. The results show that the sensitivity of weighted demand coverages to budget decreases gradually. The maximum weighted demand coverage increases at first and then decreases with the increase of the signal threshold, and there is a positive correlation with the change of the actual demand coverage to the senior customers, but it is negatively related to the intermediate and primary customers. When the number of high-level pickup points in a competitive enterprise is small, the advantage of the target enterprise is more significant. Through comparison, the cooperative coverage model is better than the non-cooperative coverage model, in terms of the weighted demand coverage, the construction cost and the attention paid to the important customers.

A NOVEL INTEREST COVERAGE METHOD BASED ON JENSEN-SHANNON DIVERGENCE IN SENSOR NETWORKS

In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.

Analysis of Phased-Mission System Reliability and Importance with Imperfect Coverage

Accounting for static phased-mission systems (PMS) and imperfect coverage (IPC), generalized and integrated algorithm (GPMS-CPR) implemented a synthesis of several approaches into a single methodology whose advantages were in the low computational complexity, broad applicability, and easy implementation. The approach is extended into analysis of each phase in the whole mission. Based on Fussell-Vesely importance measure, a simple and efficient importance measure is presented to analyze component’s importance of phased-mission systems considering imperfect coverage.

Coverage Control for Underwater Sensor Networks Based on Residual Energy Probability

Underwater sensor networks have important application value in the fields of water environment data collection,marine environment monitoring and so on.It has some characteristics such as low available bandwidth,large propagation delays and limited energy,which bring new challenges to the current researches.The research on coverage control of underwater sensor networks is the basis of other related researches.A good sensor node coverage control method can effectively improve the quality of water environment monitoring.Aiming at the problem of high dynamics and uncertainty of monitoring targets,the random events level are divided into serious events and general events.The sensors are set to sense different levels of events and make different responses.Then,an event-driven optimization algorithm for determining sensor target location based on self-organization map is proposed.Aiming at the problem of limited energy of underwater sensor nodes,considering the moving distance,coverage redundancy and residual energy of sensor nodes,an underwater sensor movement control algorithm based on residual energy probability is proposed.The simulation results show that compared with the simple movement algorithm,the proposed algorithm can effectively improve the coverage and life cycle of the sensor networks,and realize real-time monitoring of the water environment.

Development of Global Geographical Coverage Area for Terrestrial Networks Internetworked with Leo Satellite Network

Network planning, analysis and design are an iterative process aimed at ensuring that a new network service meets the needs of subscribers and operators. During the initial start-up phase, coverage is the big issue and coverage in telecommunications systems is related to the service area where a bare minimum access in the wireless network is possible. In order to guarantee visibility of at least one satellite above a certain satellite elevation, more satellites are required in the constellation to provide Global network services. Hence, the aim of this paper is to develop wide area network coverage for sparsely distributed earth stations in the world. A hybrid geometrical topology model using spherical coordinate framework was devised to provide wide area network coverage for sparsely distributed earth stations in the world. This topology model ensures Global satellite continuous network coverage for terrestrial networks. A computation of path lengths between any two satellites put in place to provide network services to selected cities in the world was carried out. A consideration of a suitable routing decision mechanism, routing protocols and algorithms were considered in the work while the shortest paths as well as the alternate paths between located nodes were computed. It was observed that a particular satellite with the central angle of 27°can provide services into the diameter of the instantaneous coverage distance of 4081.3 Km which is typical of wide area network coverage. This implies that link-state database routing scheme can be applied, continuous global geographical coverage with minimum span, minimum traffic pattern and latency are guaranteed. Traffic handover rerouting strategies need further research. Also, traffic engineering resources such as channel capacity and bandwidth utilization schemes need to be investigated. Satellite ATM network architecture will benefit and needs further study.

Heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes

A heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes(MUAVSs)for multi-objective optimization problem(MOP)is proposed,which is based on quantum wolf pack evolution algorithm(QWPEA)and power law entropy(PLE)theory.The method is composed of preset move and autonomous coordination stages for satisfying non-repeated coverage,connectedness,and energy balance of sink layer critical requirements,which is actualized to cover sensors layer in large-scale outside wireless sensor networks(WSNs).Simulation results show that the performance of the proposed technique is better than the existing related coverage technique.

改进群体智能算法的无线传感器网络覆盖优化

为解决无线传感器网络(Wireless Sensor Networks,WSN)节点分布不均和随机部署中的低覆盖率问题,该文提出一种改进群体智能算法的无线传感器网络覆盖优化算法,即改进的黑猩猩优化和哈里斯鹰优化的混合优化算法(Improved Chimp Optimization and Harris Hawk Optimization Algorithm,ICHHO).该算法首先对黑猩猩优化算法(Chimpanzee Optimization Algorithm,ChOA)进行改进,使用Levy Flight来改善其探索阶段,然后设计一个更新的公式来计算猎物逃逸能量,作为开发和探索之间的选择因素.传感器节点随机部署后,将ICHHO在传感器节点上执行,按照改进策略更新个体位置信息,计算相应的适应程度,找到最优传感器位置,并根据传感器概率模型确定网络最优覆盖率.仿真结果验证了ICHHO对于解决WSN覆盖问题的适用性,与其他优化算法的对比结果显示,ICHHO在提高覆盖率方面优于其他算法.

改进流向算法的无线传感器网络覆盖优化

针对标准流向算法易陷入局部最优和收敛精度低等问题,提出一种融合莱维(Levy)飞行和入侵杂草策略的改进流向算法.首先,该算法在选择水流流向时引入Levy飞行机制,使水流沿最优水流位置方向做Levy飞行运动,避免陷入局部最优;其次,利用入侵杂草策略,对每一代水流进行繁殖、空间扩散和竞争操作,增加水流的多样性,扩大搜索范围,提高全局寻优能力.最后,将改进流向算法应用于无线传感器网络覆盖优化中,并与标准流向算法及其他改进算法进行实验对比.仿真结果表明,相比标准流向算法及其他改进算法,所提改进流向算法的覆盖性能有大幅提升,覆盖率可达98.52%,可实现更均匀的节点分布和更低的部署成本.