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.

A Mobile Coverage Strategy for Sparse Overlay Network

2015 ◽  
Vol 738-739 ◽  
pp. 1119-1122
Author(s):  
Xia Ling Zeng
Keyword(s):  
Sensor Network ◽  
Overlay Network ◽  
Tree Structure ◽  
Sensor Nodes ◽  
Regional Strategy ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Quad Tree ◽  
Mobile Sensor Nodes ◽  
Monitoring Area

For sparse overlay network built by static sensor nodes, this paper proposes the introduction of heterogeneous mobile sensor nodes, and designs a mobile coverage strategy to optimize the coverage performance of sensor network. At first, the monitoring area is divided into several sub-regions by iterative quartering and the sub-regions are represented by a quad-tree structure. Secondly, the mobile coverage strategy is planned based on the idea of divide and conquers and combing the overall regional strategy with local regional strategy. The overall regional mobile strategy is planned by go through all sub-regions according to breadth-first traversal of the quad-tree. The local regional mobile strategy in each sub-region is designed based on the distribution of static nodes after the sub-region meshing. The results show that the mobile coverage strategy can achieve good network coverage, and it is very suitable for multiple mobile nodes.

Coverage Control Strategy for Mobile Sensor Network Based on Voronoi Division

2014 ◽  
Vol 602-605 ◽  
pp. 3278-3281
Author(s):  
Xia Ling Zeng
Keyword(s):  
Sensor Network ◽  
Control Policy ◽  
Sensor Nodes ◽  
Mobile Nodes ◽  
Coverage Ratio ◽  
Coverage Optimization ◽  
Optimization Control ◽  
Low Energy Consumption ◽  
Coverage Hole ◽  
Coverage Holes

According to the multilayer sensor network structure of mobile nodes, proposing a coverage optimization control policy based on voronoi division. Based on the mobile policy of Sink nodes by the voronoi division, the essay proposes a coverage optimization control algorithm (COCA) based on coverage hole calculating to optimize the initial random deployment of sensor nodes, and then to eliminate coverage holes and improve the coverage performance of network. Simulation results show that, compared with the random deployed network, the COCA algorithm only requires fewer nodes to achieve a higher coverage ratio and to maintain in low energy consumption, which embodies optimum performance of the network deployment.

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.

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.

Maximizing the Lifetime of Sliding Wireless Sensors in Barrier Coverage

2012 ◽  
Vol 157-158 ◽  
pp. 503-506 ◽  
Author(s):  
Tao Yang ◽  
Pan Guo Fan ◽  
De Jun Mu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Wireless Sensors ◽  
Specific Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Barrier Coverage ◽  
Coverage Ratio ◽  
Simulation Results

Wireless sensor network is always deployed in specific area for intrusion detection and environmental monitoring. The sensor nodes suffer mostly from their limited battery capacity.Maximizing the lifetime of the entire networks is mainly necessary considered in the design. Sliding the sensors in different barriers under the optimal barrier construction is a good solution for both maximizing network lifetime and providing predetermined coverage ratio. The simulation results demonstrate that the scheme can effectively reduce the energy consumption of the wireless sensor network and increase the network lifetime.

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.

scholarly journals Priority Based Congestion Control Dynamic Clustering Protocol in Mobile Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
R. Beulah Jayakumari ◽  
V. Jawahar Senthilkumar
Keyword(s):  
Wireless Sensor Network ◽  
Congestion Control ◽  
Sensor Network ◽  
Significant Loss ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Dynamic Clustering ◽  
Mobile Nodes ◽  
Control Dynamic

Wireless sensor network is widely used to monitor natural phenomena because natural disaster has globally increased which causes significant loss of life, economic setback, and social development. Saving energy in a wireless sensor network (WSN) is a critical factor to be considered. The sensor nodes are deployed to sense, compute, and communicate alerts in a WSN which are used to prevent natural hazards. Generally communication consumes more energy than sensing and computing; hence cluster based protocol is preferred. Even with clustering, multiclass traffic creates congested hotspots in the cluster, thereby causing packet loss and delay. In order to conserve energy and to avoid congestion during multiclass traffic a novel Priority Based Congestion Control Dynamic Clustering (PCCDC) protocol is developed. PCCDC is designed with mobile nodes which are organized dynamically into clusters to provide complete coverage and connectivity. PCCDC computes congestion at intra- and intercluster level using linear and binary feedback method. Each mobile node within the cluster has an appropriate queue model for scheduling prioritized packet during congestion without drop or delay. Simulation results have proven that packet drop, control overhead, and end-to-end delay are much lower in PCCDC which in turn significantly increases packet delivery ratio, network lifetime, and residual energy when compared with PASCC protocol.

scholarly journals Coverage Optimization Algorithm of Wireless Sensor Network Based on Mobile Nodes

2016 ◽  
Vol 12 (08) ◽  
pp. 45 ◽  
Author(s):  
Li Zhu ◽  
Chunxiao Fan ◽  
Huarui Wu ◽  
Zhigang Wen
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Algorithm ◽  
Quantitative Relationship ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Coverage Optimization ◽  
Blind Zone

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">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. </span>

scholarly journals Wireless Sensor Network Coverage Optimization Based on Fruit Fly Algorithm

2018 ◽  
Vol 14 (06) ◽  
pp. 58 ◽  
Author(s):  
Ren Song ◽  
Zhichao Xu ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fruit Fly ◽  
Effective Radius ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Improved Algorithm

<p class="0abstract"><span lang="EN-US">To solve the defect of traditional node deployment strategy, the improved <a name="_Hlk502130691"></a>fruit fly algorithm was combined with wireless sensor network. The optimization of network coverage was implemented. </span><span lang="EN-US">Based on a new type of intelligent algorithm, the change step of fruit fly optimization algorithm (CSFOA)</span><span lang="EN-US">was proposed. At the same time, the mathematical modeling of two network models was carried out respectively. The grid coverage model was used. The network coverage and redundancy were transformed into corresponding mathematical variables by means of grid partition.</span><span lang="EN-US">Among them, the maximum effective radius of sensor nodes was fixed in mobile node wireless sensor network. The location of nodes was randomly cast. The location of sensor nodes was placed in fixed position nodes. The effective radius of nodes can be changed dynamically.</span><span lang="EN-US">Finally, combined with the corresponding network model, the improved algorithm was applied to wireless sensor network.</span><span lang="EN-US">The combination of the optimal solution of the node position and the perceptual radius was found through the algorithm. The maximum network coverage was achieved.</span><span lang="EN-US">The two models were simulated and verified. The results showed that the improved algorithm was effective and superior to the coverage optimization of wireless sensor networks.</span></p>

scholarly journals An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Belal Al-Fuhaidi ◽  
Abdulqader M. Mohsen ◽  
Abdulkhabeer Ghazi ◽  
Walid M. Yousef
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Ratio ◽  
Proposed Model ◽  
Heterogeneous Wireless Sensor Network

Due to the increase of Wireless Sensor Network (WSN) technologies demand, the optimal sensor node deployment is considered as one of the most important factors that directly affect the network coverage. Most researches in WSNs that solved the problem of coverage in homogeneous and heterogeneous cases are suffering from many drawbacks such as consumed energy and high cost. In this paper, we propose an efficient deployment model based on probabilistic sensing model (PSM) and harmony search algorithm (HSA) to achieve the balance between the network coverage performance and the network cost in a heterogeneous wireless sensor network (HEWSN). The HSA is used for deployment optimization of HEWSN nodes which makes a balance between the coverage and financial cost. The PSM is used to solve the overlapping problem among the sensors. The performance of the proposed model is analyzed in terms of coverage ratio and cost evaluations. The simulation results showed the capability of the proposed heterogeneous deployment model to achieve maximum coverage and a minimum number of sensors compared to homogeneous deployment. Furthermore, a comparative study with a meta-heuristic genetic-based algorithm in HEWSN has also been conducted, and its results confirm the superiority of the proposed model.

Sign in / Sign up

Export Citation Format

Share Document