An Improved Greedy Algorithm for Coverage in Directional Sensor Networks

2013 ◽  
Vol 711 ◽  
pp. 440-445
Author(s):  
Xiang Fu ◽  
Chun Ping Lu ◽  
Hao Li
Keyword(s):  
Greedy Algorithm ◽  
Sensor Nodes ◽  
Network Coverage ◽  
Simulation Experiments ◽  
Directional Sensor Networks ◽  
Coverage Area ◽  
Priority Level ◽  
Coverage Ratio ◽  
Processing Order ◽  
Directional Sensor

DGreedy (distributed greedy) algorithm evaluates the priority level in view of remaining energy of terminals, and the relationships between neighbor nodes are not considered. At the same time, the adjustable sensing orientations of sensors are limited. Therefore, the network coverage ratio of DGreedy is affected usually by the processing order of sensor nodes. In this paper, an improved Greedy algorithm for the coverage in directional sensor network is proposed based on the principle of global greedy. The single coverage area of nodes is considered as priority. The direction of node with maximum single coverage area is deployed firstly. Thereby it reduces the sensing overlapping regions and accomplishes coverage enhancement of the networks. Meanwhile, in order to improve the network coverage ratio, the sensing orientations of sensors are adjustable continuously, so the best sensing orientation of node can be selected by considering the deployment of neighbor nodes. Simulation experiments show that the proposed algorithm can improve the coverage area effectively.

Optimization of the Virtual Potential Field Based on Coverage-Enhancing Algorithm for Directional Sensor Networks

2014 ◽  
Vol 651-653 ◽  
pp. 1882-1887
Author(s):  
Jun Zhu ◽  
Chen Shi ◽  
Shan Shan Zhu ◽  
Jun Zhang
Keyword(s):  
Sensor Networks ◽  
Potential Field ◽  
Boundary Region ◽  
Sensor Nodes ◽  
Target Area ◽  
Virtual Node ◽  
Directional Sensor Networks ◽  
Coverage Ratio ◽  
Coverage Holes ◽  
Directional Sensor

After directional sensor nodes are randomly thrown into target area, coverage ratio often less than the anticipant value, in order to improve the coverage, sensor nodes should turn from overlapping regions to coverage holes by a much faster way. In this paper, we improved the existing potential field based coverage-enhancing algorithm (PFCEA), presented a optimization of the virtual potential field based on coverage-enhancing algorithm for directional sensor networks (OPFCEA), we introducing a new-style virtual node to enhance the coverage of boundary region and a new-style control for the rotation angle. By these ways, we can improve network’s performance. This algorithm enhanced the coverage ratio of the network, the simulation results show the effectiveness of the algorithm.

A New Sensing Direction Rotation Approach to Area Coverage Optimization in Directional Sensor Network

2020 ◽  
Vol 24 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Song Peng ◽  
◽  
Yonghua Xiong
Keyword(s):  
Area Coverage ◽  
Sensor Nodes ◽  
Target Area ◽  
Coverage Problem ◽  
Directional Sensor Networks ◽  
High Coverage ◽  
Coverage Ratio ◽  
Verification Algorithm ◽  
Grid Points ◽  
Directional Sensor

Coverage is a crucial issue in directional sensor networks (DSNs), and a high coverage ratio ensures a good quality of service (QoS). However, a DSN encounters various problems because they use directional sensor nodes, which are characterized by directionality and a definite sensing angle. To address the area coverage problem of DSNs, this paper proposes a new sensing direction rotation approach to optimize coverage. First, we conduct grid partitioning in the target area and propose a coverage verification algorithm to justify the coverage situation of the grid points. Then, we utilize particle swarm optimization (PSO) to find an optimal sensing direction group of the directional sensor nodes to maximize the coverage ratio. Extensive simulation experiments were conducted to prove the effectiveness and reliability of our proposed approach. The results show that the approach improves the area coverage ratio of DSNs in various scenarios.

Deployment Strategy in Distributed Underwater Sensor Networks

2014 ◽  
Vol 602-605 ◽  
pp. 3643-3647
Author(s):  
Li Yan Liu ◽  
Rong Fu ◽  
Yi He ◽  
Ying Qian Zhang
Keyword(s):  
Network Performance ◽  
Heuristic Method ◽  
Grid Method ◽  
Sensor Nodes ◽  
Triangular Grid ◽  
Underwater Sensor Networks ◽  
Network Coverage ◽  
Coverage Ratio ◽  
Initial Node ◽  
Deployment Strategy

Distributed underwater sensor network coverage is divided into two main categories: deterministic coverage and stochastic coverage. A strategy is put forward to deploy determinate area by using a triangular-grid method. When a coverage ratio is known, the distance between nodes can be adjusted to meet the coverage ratio in the monitored area, and the least number of sensor nodes can be calculated. Also a heuristic method is proposed for stochastic area deployment strategy. It is under the premise that the initial node location randomly deployed is given, using Voronoi diagram, the not easiest monitored path is searched, and the network coverage performance is improved by configuring the new nodes in the path. Finally it is proved that network performance is more improved by the simulation experiments, when one to four nodes are configured in the easiest breach path.

Coverage-Optimized Deployment Research for Maximizing the Sensor Network Coverage

2015 ◽  
Vol 713-715 ◽  
pp. 1137-1140
Author(s):  
Xia Ling Zeng
Keyword(s):  
Sensor Network ◽  
Bipartite Graph ◽  
Graph Matching ◽  
Sensor Nodes ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Coverage Ratio ◽  
Total Movement ◽  
Distribution Matrix ◽  
Grid Division

For multilayer mobile sensor network, the issues of improving sensor network coverage by the use of mobile sensors are studied. A coverage-optimized deployment algorithm based on grid-division and bipartite graph matching is proposed. Firstly, the deployment area is divided into many grids and build distribution matrix of sensor nodes. Then construct a bipartite graph G based on the grid-division and solve a matching of maximum cardinality of G. It corresponds to an optimal deployment scheme which maximizes the network coverage and minimizes the total movement cost. Results show that after optimized deployment the network coverage increases, and with the increase in the percentage of mobile nodes it increases accordingly. In the way of distributed calculating the network also can achieve a higher coverage ratio and the movement cost is lower. It is very suitable for deployment of multilayer sensor network.

scholarly journals An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1192 ◽  
Author(s):  
Song Peng ◽  
Yonghua Xiong
Keyword(s):  
Energy Consumption ◽  
Cluster Head ◽  
Area Coverage ◽  
Optimization Approach ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Ratio ◽  
Energy Consumption Optimization ◽  
Set Up ◽  
Directional Sensor

Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xiaochao Dang ◽  
Chenguang Shao ◽  
Zhanjun Hao
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Three Dimensional ◽  
Target Coverage ◽  
Dynamic Adjustment ◽  
Directional Sensor Networks ◽  
Coverage Ratio ◽  
Optimisation Algorithm ◽  
Sensing Model ◽  
Directional Sensor

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.

Optimal Deployment Based on Tree in Multilayer Mobile Sensor Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 1218-1221
Author(s):  
Xia Ling Zeng ◽  
Lin Zhang
Keyword(s):  
Sensor Network ◽  
Sensor Nodes ◽  
Information Collection ◽  
Network Coverage ◽  
Mobile Sensor ◽  
Optimization Scheme ◽  
Mobile Sensor Network ◽  
Coverage Ratio ◽  
Error Ratio ◽  
Optimal Deployment

In sensor networks, a reasonable distribution of sensor nodes is an important role for the improvement of sensor ability, information collection ability and network survival. For multilayer mobile sensor network, a tree-based deployment optimization scheme was proposed and better sensor coverage could be achieved by topology adjustment utilizing mobility of sensor node. Sink nodes complete mobile positioning of S nodes based on rectangle division method and bounding box algorithm, and achieve the distribution optimization of S nodes by establishing extending-tree of Sink node as the center. Results show that the location error ratio reduces with the increase of Sink nodes. Compared with initial random deployment network, the network coverage ratio after optimal deployment significantly enhances, which effectively improve the coverage sensor range of overall network.

scholarly journals MARKOV MODEL OF WIRELESS SENSOR NETWORK AVAILABILITY

2020 ◽  
pp. 491-498
Author(s):  
Maryna Kolisnyk ◽  
Dmytro Kochkar ◽  
Vyacheslav Kharchenko
Keyword(s):  
Sensor Networks ◽  
Forest Fires ◽  
Markov Models ◽  
Fire Detection ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Area ◽  
Coverage Ratio ◽  
Network Availability ◽  
Forest Fire Detection

The use of wireless sensor networks (WSN) in industry and for forest fire detection has recently become increasingly popular. Assessment of the availability of such networks is an important task, since they perform essential functions in critical situations. Sensor networks can be used to prevent and detect forest fires, and they must meet high availability requirements. Various options for organizing the WSN system are considered - with and without recovery. For such systems, the paper evaluates the probability of no-failure operation, as well as the readiness function, taking into account the network coverage ratio. In the paper the Markov WSN model for evaluating its availability function is developed taking into account the network coverage area. The obtained graphical dependencies allow us to evaluate how a change in the failure rate of sensors or system equipment affects the availability function value. The goal of this paper is to obtain metrics to assess the availability of system for monitoring forest by WSN and the availability function of a network using the Markov models. A special metric, so-called coverage availability factor is suggested in this paper taking into account different combinations of sensor failures which influence on completeness of monitoring forest fires.

scholarly journals Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shahzad Ashraf ◽  
Omar Alfandi ◽  
Arshad Ahmad ◽  
Asad Masood Khattak ◽  
Bashir Hayat ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Computation Time ◽  
Fruit Fly ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Network Coverage ◽  
Coverage Area ◽  
Pulse Emission ◽  
Fruit Fly Optimization

Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

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