Network Coverage Rate Research Articles

Efficient Deployment Approach in WSNs Using Heuristic Technique

Several researchers have paid attention to designing deployment algorithms in WSNs. In fact, there are many different ways to deploy sensors in sensors' fields. Selecting one of them mainly is based on the application for which WSN design. However, two main factors should be considered when designing a deployment approach in WSN: coverage and connectivity. In this paper, we present a genetic algorithm (GA) to enhance the sensor deployment in WSNs while concurrently improving the coverage and connectivity rate. The most popular deployment approach is to deploy sensor nodes randomly in the field. Although this approach is simple and easy, it may not achieve good results. In the proposed GA algorithm, the metaheuristic algorithm is used to deploy sensors. Simulations demonstrate that GA achieves a good deployment result compared to other research papers by ensuring maximum network coverage and connectivity rate by achieving efficient coverage and connectivity.

Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm

In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introduced to obtain the optimal deployment algorithm of wireless sensor network nodes. The experimental results show that the algorithm can reduce the number of active nodes efficiently, improve coverage uniformity, reduce network energy consumption and prolong network lifetime under the premise that the coverage rate is greater than 90%, and compared with an algorithm called coverage configuration protocol, an algorithm called finding the minimum working sets in wireless sensor networks, and an algorithm called binary particle swarm optimization-g in literature, the number of active nodes in this algorithm is reduced by about 36%, 30% and 23% respectively.

The Impact of Cyberviolence on Individuals

With the wider network coverage and higher penetration rate, many social problems emerge, such as cyberbullying. This paper explores how cyberbullying influences the short-term and long-term development of individuals through domestic and foreign literature reviews as well as interviews. The most apparent impact is that teenagers who have suffered from cyberbullying are prone to depression, sleep deprivation, and bad behaviors. For adults, in addition to negative emotions, cyberbullying also harms their jobs and future opportunities. Meanwhile, for adults and minors, cyberbullying has an impact on their interpersonal relationships and future development. To better solve cyberbullying and protect potential victims, this paper argues that the government is responsible for subdividing the degree and types of cyberbullying from the legal perspective, and then taking different punishments. Schools can implement medienbuildng, from which teenagers can foster their media literacy, acquire media knowledge, and master the skills of media. Besides, media content can be identified to judge the consequences of media behavior and control media behavior. All major platforms are obliged to recover the comments and reports of cyberbullying in time, and popularize the basic knowledge of network usage for every user before registering media accounts, such as in the form of videos or questions and answers.

Does High-Speed Railway Promote the Quality of Urbanization? From a Dynamic Network Perspective

Applying the improved social-network analysis method and the idea of rolling-window regression, this paper explores the impact of an HSR network on the quality of urbanization and its dynamics. Based on a sample of 273 cities in China over the period 2009–2019, we find that the high-speed railway network has an increasingly positive effect on the quality of urbanization, which proves the existence of a network effect. The empirical results further show that this effect is closely related to the coverage rate of the high-speed rail network. In addition, heterogeneity analysis reveals that urban agglomeration cities are the main beneficiaries. Academically, our study provides a plausible explanation and evidence from network size differences for the two conflicting views of the HSR effect. Practically, we also propose some important policy implications for countries in different high-speed-rail-network construction stages.

Coverage Enhancement Strategy for WSNs Based on Multiobjective Ant Lion Optimizer

In order to improve the quality of service and prolong the network lifetime of wireless sensor networks (WSNs), optimizing the network coverage rate and the sensor nodes moving distance in the secondary deployment process has become a crucial research object. Aiming at these two goals, this paper improves the classic multi-objective ant lion optimization algorithm, and proposes an improved multi-objective ant lion optimization algorithm based on fast non-dominated sorting (NSIMOALO). Firstly, we use the idea of fast non-dominated sorting algorithm and elite strategy of NSGA-II, which avoids the algorithm from falling into the local optimal solution and improves the solution accuracy of the algorithm. Secondly, a more reasonable congestion calculation equation is proposed, which greatly increases the diversity of the population. Finally, we introduce Lévy flight to update the ant position by dynamically changing the weight coefficients among the antlion, elite antlion and Lévy flight, which improves the global optimization ability of the algorithm. The standard function simulation results show that NSIMOALO algorithm has higher convergence and coverage. The algorithm is applied to WSNs sensor nodes deployment, and 80 sensor nodes are deployed in a monitoring area of 200m×300m. Compared with MOALO algorithm, NSGA-II and NSMOFPA, the coverage rate of NSIMOALO algorithm is increased by 12.753%, 12.413% and 4.492% respectively, and the sensor nodes average moving distance is decreased by 2.551m, 2.316m and 4.457m respectively.

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

IMF 2 O 2 : A Fully Connected Sensor Deployment Algorithm for Underwater Sensor Networks

To address the problems of node deployment schemes in existing underwater sensor networks that lack consideration of network connectivity and high deployment costs, this article constructs an optimization model that maximizes network coverage and minimizes deployment costs while ensuring full connectivity. For the NP-hard property of this optimization model, an improved moth flame optimization node deployment algorithm based on fuzzy operators (IMF 2 O 2 ) is proposed. First, comprehensively considering the two performance metrics of network coverage and network connectivity, a multi-objective selection mechanism based on fuzzy operators is proposed to improve network coverage while ensuring full connectivity. Second, a fixed number of nodes are used to monitor the target event points, transforming the node deployment of sensors into an optimal problem and proposing an improved moth flame optimization algorithm to solve this problem. Finally, the two metrics of coverage and deployment cost are measured and the fuzzy operator is used to select the optimal number of nodes to be deployed. Numerical results showed that the proposed algorithm improved network coverage rate by 10%, 22%, and 25%, and improved network connectivity rate by 12%, 20%, and 8% as compared to PSSD, RAWS, and VODA, respectively, while ensuring full connectivity.

ECAH: A New Energy-Aware Coverage Method for Wireless Sensor Networks using Artificial Bee Colony and Harmony Search

Wireless Sensor Networks (WSNs) offer diverse applications in the research and commercial fields, such as military applications, medical science, waste management, home automation, habitat monitoring, and environmental observation. WSNs are generally composed of a large number of low-cost, low-power, and multifunctional sensor nodes that sense, process, and communicate data. These nodes are connected by a wireless medium, allowing them to collect and share data with each other. To achieve network coverage in a WSN, a few to thousands of tiny and low-power sensor nodes should be placed in an interconnected manner. Over the last decade, deploying sensor nodes in a WSN to cover a large area has received much attention. Coverage, regarded as an NP-hard problem, is an essential parameter for WSNs that determines how the deployed sensor nodes handle each point of interest. Various algorithms have been proposed to tackle this problem. However, they often come with a trade-off between energy efficiency and coverage rate. Moreover, the scalability of the algorithms needs to be considered for large-scale networks. This paper proposes a novel energy-aware method combining Artificial Bee Colony (ABC) and Harmony Search (HS) algorithms to address the coverage problem in WSN, called ECAH. The proposed ECAH algorithm has been tested with various network scenarios and compared with other existing algorithms. The results show that ECAH outperforms the existing methods in terms of network lifetime, coverage rate, and energy consumption. Additionally, the proposed algorithm is also more robust and efficient as it can adjust to dynamic network environment changes, making it suitable for various network scenarios.

Improved Ant Lion Optimizer for Coverage Optimization in Wireless Sensor Networks

Coverage optimization is an important research topic in wireless sensor networks (WSNs). By studying the coverage optimization problem, the coverage rate of the network can be improved, and the number of redundant sensors can be reduced. In order to improve the coverage performance of wireless sensor networks, we propose an improved ant lion optimizer (IALO) to solve the coverage optimization problem in a WSN. Firstly, we give a network coverage optimization model with the objective of maximizing network coverage rate. Secondly, we alternately execute Cuckoo Search (CS) and Cauchy mutation to update the positions of the ants to enhance population diversity and accelerate convergence speed. Then, we introduce differential evolution (DE) to mutate the population of antlions to improve the convergence accuracy of our algorithm. We compare IALO with the original ant lion optimizer (ALO) and other algorithms on 9 benchmark functions to verify its effectiveness. Finally, IALO is applied to the coverage optimization in wireless sensor networks. Simulation results show that, compared with previous works, IALO provides higher coverage rate, makes the sensor distribution more uniform, and effectively reduces the deployment cost.

Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm

In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices. Users monitor their data in the form of fog computing (mobile mass monitoring). Service providers are required to pay user rewards without increasing platform costs. One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs (reward) is the Cooperative Based Method for Smart Sensing Tasks (CMST). This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm. The proposed method is implemented with MATLAB. The average findings show that the network coverage rate is 31% and the monitoring cost is 11% optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms. And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.

Optimization Method for Conventional Bus Stop Placement and the Bus Line Network Based on the Voronoi Diagram

With the rapid development of the economy, the existing conventional bus transit system finds it difficult to meet people’s increasing travel needs. In addition, with the emergence and rapid development of urban rail transit, it is also necessary to integrate the existing conventional bus transit system with the rail transit system to realize the optimization of the whole public transport system. This study introduces the concept of the Voronoi diagram and uses it to divide the service area of bus stops. Taking the average walking time of regional passengers to the station as the main index, the convenience of passengers in the service area was evaluated, and a set of candidate station sites is established. Against the background of urban rail transit, a complete optimization model for a conventional bus station layout and line network was proposed. Finally, taking Wuhan East Lake High-tech Development Zone as an example, two optimization schemes for the public transport system were obtained. Compared with the status quo, the optimized scheme had obvious improvement effects on the repetition coefficient of bus lines, per capita transfer time, bus line network coverage and station service rate. This has been recognized by the local authorities, which proves the practicality and scientificity of the optimization method of this study.

Stress Point Monitoring Algorithm for Structure of Steel Cylinder Concrete Pipes in Large Buildings

The internal structure of steel cylinder concrete pipe in large buildings is complex. Traditionally, the safety monitoring method is unable to accurately monitor the situation of every stress point in the structure. Therefore, the wireless sensor network with practical value in the field of building safety monitoring was introduced. A monitoring algorithm for stress points in pipeline structure was put forward. The distribution law of circumferential prestress produced by prestress steel wire on the pipe core concrete was analyzed. According to the influencing factors, the mechanical performance of tube concrete pipe structure was discussed, and the method of calculating prestress of pipe structure was constructed. Combined with sensor network nodes, a series of basic hypothesis information was set. Moreover, the force between node and stress point was analyzed by virtual potential field. Based on the force analysis for the centroid of the sensing area, the monitoring of the stress point in pipeline structures was completed. After that, a rectangular area to be monitored was selected and the force points were established randomly. According to the relationship between network coverage rate and monitoring efficiency, we found that the proposed algorithm had good network immunity. According to the different number of nodes, sensing radii and perception angles, the influence of index on the monitoring accuracy was discussed. Experimental results show that the accuracy of the proposed algorithm is sensitive to the change of node parameter. When the number of nodes, sensing radius and sensing angle change, the maximum fluctuation range of monitoring accuracy is 0.08–0.99. From the application effect of the algorithm, we can see that the detection effect of the algorithm has obvious advantage.

An Improved Intrusion Weed Optimization Algorithm for Node Location in Wireless Sensor Networks

The distribution optimization of WSN nodes is one of the key issues in WSN research, and also is a research hotspot in the field of communication. Aiming at the distribution optimization of WSN nodes, the distribution optimization scheme of nodes based on improved invasive weed optimization algorithm(IIWO) is proposed. IIWO improves the update strategy of the initial position of weeds by using cubic mapping chaotic operator, and uses the Gauss mutation operator to increase the diversity of the population. The simulation results show that the algorithm proposed in this paper has a higher solution quality and faster convergence speed than IWO and CPSO. In distribution optimization example of WSN nodes, the optimal network coverage rate obtained by IIWO is respectively improved by 1.82% and 0.93% than the IWO and CPSO. Under the condition of obtaining the same network coverage rate, the number of nodes required by IIWO is fewer.

Layout optimization of fiber Bragg grating strain sensor network based on modified artificial fish swarm algorithm

The effectiveness of sensor networks depends largely on the coverage provided by sensor deployment schemes. In order to improve the coverage rate of sensor network, it is necessary to optimize the sensor layout. In this paper, an optimized layout method of fiber Bragg grating (FBG) sensor network based on modified artificial fish swarm algorithm (MAFSA) is proposed. Firstly, the elliptical sensing model of FBG is established. According to the transfer characteristics of FBG, the exponential attenuation model between the sensor node and signal source is constructed. Secondly, taking the network coverage rate as the objective function, the movement of nodes is analogized to the behavior of artificial fish such as swarming, following and preying. In the process of status updating for artificial fish, aiming at the defects of the standard artificial fish swarm algorithm (AFSA) in optimization, three adaptive step methods are proposed. The convergence accuracy and speed of the algorithm are improved. Finally, the performance of the MAFSA is evaluated with particle swarm optimization algorithm and genetic algorithm. The results indicate that the MASFA has stronger search performance and can make the sensor node layout more reasonable, which is suitable for solving the problem of the sensor layout optimization.

WSN Node Optimal Deployment Algorithm Based on Adaptive Binary Particle Swarm Optimization

In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introduced to obtain the optimal deployment algorithm of wireless sensor network nodes. The experimental results show that the algorithm can reduce the number of active nodes efficiently, improve coverage uniformity, reduce network energy consumption and prolong network lifetime under the premise that the coverage rate is greater than 90%, and compared with an algorithm called coverage configuration protocol, an algorithm called finding the minimum working sets in wireless sensor networks, and an algorithm called binary particle swarm optimization-g in literature, the number of active nodes in this algorithm is reduced by about 36%, 30% and 23% respectively.

HEPSO: an efficient sensor node redeployment strategy based on hybrid optimization algorithm in UWASN

In Underwater Acoustic Sensor Network (UWASN), node redeployment strategy is utilized to handle the reliable network coverage. The sensors deployed in the underwater are used to intellect the area and collected information is moved to the sink node. Node redeployment strategy is essential for the nodes which are placed outside the monitoring area in UWASN. In this paper, the node redeployment strategy is performed based on the hybrid Emperor Penguin Optimization (EPO) algorithm with Particle Search Algorithm (PSO) for better underwater acoustic communication and the proposed method is named as HEPSO. This hybridization is performed to reduce node failure rate and network energy consumption rate by optimally place the sensor nodes in underwater acoustic communication. The stability of the network topology is guaranteed by this algorithm and it enhances the node redeployment strategy by calculating the fitness function for each and every node. The implementation of the proposed algorithm is carried out by the MATLAB platform. The performance parameters like network coverage rate, network connectivity rate, network lifetime, number of nodes outside monitored space and total movement distance of nodes are evaluated and related with current methods like NRBSCT (Node Redeployment Based on Stratified Connected Tree) and MRNR (Moving Redundancy Nodes Redeployment) strategy.

Routing optimization strategy of IoT awareness layer based on improved cat swarm algorithm

With the rapid development of Internet of Things technology, wireless sensor networks have been widely used in many places. This study mainly discusses the routing optimization strategy of the IoT perceptive layer based on the improved cat swarm algorithm. This study simulates a perceptive network with 100 nodes deployed randomly. As SDWSN for Internet of Things applications, in order to simulate the data transmission requirements of IoT communication and ensure the fairness of experimental comparison, this study uses the pseudo-random mechanism to generate the source address and destination address of data packets. A special SDN controller node is added to the network. The SDN controller node broadcasts information to each sensing node, and the common sensing node sends node information to the SDN controller. The SDN controller can survive the global time graph of the entire network according to the information of the common node. In order to avoid the problem of high energy consumption of cluster heads caused by long-distance data transmission, the cat algorithm protocol adopts multi-hop communication between cluster heads and BS and uses network overhead index to quantify link overhead as the basis for cluster heads to select the next hop node. When the inter-cluster multi-hop route is successfully established, the wireless sensor node begins to collect data and send it to BS node. Six monitoring nodes, two coordinators and one workstation were selected as the test objects. The data volume sent by each node was 2000, and the accuracy rate of test transmission information at different rates and transmission distances was determined. The group network coverage rate of cat swarm algorithm is always above 95%, and the average energy loss of nodes is the highest and less than 36%. The results show that the aggregate of energy consumption of cluster heads and the variance of energy consumption are the lowest in the improved cat cluster algorithm, which ensures the reliable transmission of node data.

A Fish Swarm Inspired Holes Recovery Algorithm for Wireless Sensor Networks

Wireless sensors networks (WSNs) have be applied to a number of fields such as environment monitoring, military surveillance, data collection and etc. However, the coverage holes problem are usually caused by some undesirable reasons, such as random deployment of sensors, energy consumption imbalance, unethical attack and hardware failures. The holes affect capabilities of WSNs greatly, so its recovery is one of the pivotal problems in WSNs. In order to enhance the performance of holes recovery, a robust approach based on an improved artificial fish swarm algorithm is presented in this paper. The movement of mobile nodes is analogized to the motion of artificial fish with the network coverage rate as objective function. Besides the classic artificial fish motion such as prey, follow and swarm, two novel fish motions called as leap and rebirth are also presented to enhance the convergence of this algorithm. An approach of self-adaptive visual range and step length for fish motion are adopted when updating the status of artificial fish. Simulation experiments show the effectiveness and robustness of the algorithm. The holes can be recovered efficiently without location information and holes detection using the least amount of mobile nodes. The network coverage is improved significantly with this proposed algorithm.

Analysis of Distributed Inter-satellite Link Network Coverage Based on STK and Matlab

Jointing STK and Matlab to solve the problem of GNSS system inter-satellite measurement and control network coverage. By using the interface provided by both parties, Matlab can directly control STK for task analysis, and obtain professional orbit data and star directly from STK. The distance information is processed and analyzed by Matlab. The preliminary analysis results of the link coverage of distributed inter-satellite link network routings show that the distributed network coverage is low and the satellite antenna resource utilization is low. It is necessary to set optimization conditions to improve network coverage and on-board antenna utilization rate.

Multi-sink distributed power control algorithm for Cyber-physical-systems in coal mine tunnels

A coal mine tunnel usually has a long-strip shape. Traditional single-sink cyber-physical-systems (CPS) cannot ensure that sensor nodes far from the entrance of a coal mine tunnel promptly and accurately transmit abnormal data from deep in the tunnel to the ground control center via the sink at the tunnel entrance. At the same time, it causes the “hot spot” problem easily around the Sink and limits the lifetime of the whole network. In order to solve these problems, this paper proposes a Multi-sink distributed power control algorithm (MSDPC-SRMS) suitable for a coal mine tunnel. This algorithm uses the Multiple Sink network structure and the idea of non-uniform cluster, combines the Multi-sink network with the clustering Voronoi scoping routing algorithm. It allocates the optimal transmission range and power for each sink, uses each sink as the cluster head and the network clustering is performed. The network topology is optimized on the basis of a good network coverage rate. Simulation results compared with CNP strategy show that the new algorithm exhibits superior connectivity, power consumption validity, clustering interference, and network performance, can effectively reduce the overall power consumption and prolong the lifetime of the network, thus ensure the monitoring data transmitted to the monitoring center rapidly and quickly.