Abstract
Underwater wireless sensor networks nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. If no underwater wireless sensor node is available in the monitoring area of underwater wireless sensor networks due to used up energy or any other reasons, the monitoring area where is not detected by any underwater wireless sensor node forms coverage holes. In order to improve the coverage of the underwater wireless sensor networks and prolong the lifetime of the underwater wireless sensor networks, based on the perception model, establish nodes detection model, combining with the data fusion. Because the underwater wireless sensor networks nodes coverage holes appear when the initial randomly deployment, a nodes deployment algorithm based on perception model of underwater wireless sensor networks is designed in this article. The simulation results show that this algorithm can effectively reduce the number of deployment underwater wireless sensor networks nodes, improve the efficiency of underwater wireless sensor networks coverage, reduce the underwater wireless sensor networks nodes energy consumption, prolong the lifetime of the underwater wireless sensor networks.
1 Introduction
Because the wireless sensor network nodes coverage holes appear when the initial randomly deployment, a nodes deployment algorithm based on perception model of wireless sensor network is designed in this article [1,2,3,4,5]. In order to improve the coverage of the wireless sensor network and prolong the lifetime of the wireless sensor network, based on the perception model, establish nodes detection model, combining with the data fusion [6,7,8,9,10]. This algorithm can effectively reduce the number of deployment wireless sensor network nodes, improve the efficiency of wireless sensor network coverage, reduce the wireless sensor network nodes energy consumption, and prolong the lifetime of the wireless sensor network [11,12,13,14,15].
2 Assumption
To simplify the calculation, randomly deploy the quantity \( N_{k} \) of the k-th type mobile nodes in the monitoring region and mobile wireless sensor node \( s_{j} \) owns wireless sensor network ID number j.
The k-th type wireless sensor nodes in the network own the same sensing radius \( R_{sk} \), the same communication radius \( R_{ck} \), and \( R_{ck} = 2R_{sk} \).
The wireless sensor nodes can obtain the location information of itself and its neighbor nodes.
The k-th type mobile node owns \( E_{k} \) energy and is sufficient to support the completion of the mobile node position migration process.
The k-th type mobile node sending 1 byte data consumes \( E_{sk} \) energy and receiving 1 byte data consumes \( E_{rk} \) energy.
The k-th type mobile node migration 1 m consumes \( E_{mk} \) energy.
3 Coverage Model
The monitored area owns A × B × C pixels which means that the size of each pixel is the ⊿x × ⊿y × ⊿z.
The perceived probability of the i-th pixel is perceived by the wireless sensor network is \( P(p_{i} ) \), when \( P\left( {p_{i} } \right) \ge P_{th} \) (\( P_{th} \) is the minimum allowable perceived probability for the wireless sensor network), the pixels can be regarded as perceived by the wireless sensor network.
The i-th pixel is whether perceived by the wireless sensor node perceived to be used \( P_{\text{cov}} (P_{i} ) \) to measure, i.e.
The coverage rate is the perceived area and the sum of monitoring area ratio is defined in this article, i.e.
Among them, \( P_{area} \) is the perceived area while \( S_{area} \) is the sum of monitoring area.
4 Perception Model
The event \( r_{ij} \) is defined that the i-th pixel \( p_{i} \) which is perceived by the ID number j wireless sensor nodes, the probability of occurrence of the event is \( P\left( {r_{ij} } \right) \) which is the perceived probability \( P(p_{i} ,s_{j} ) \) that the pixel \( p_{i} \) is perceived by wireless sensor node \( s_{j} \), i.e.
Among them, the \( d\left( {p_{i} ,s_{j} } \right) \) is the distance between the i-th pixel \( p_{i} \) and the j-th wireless sensor node \( s_{j} \), the sensing radius of the k-th type wireless sensor node is \( R_{sk} \), the perceived error range of the k-th type wireless sensor node is \( R_{ek} \).
This article used a number of wireless sensor nodes cooperative sensing monitoring method and the pixel \( p_{i} \) is perceived by all wireless sensor nodes collaborate perceived probability is
5 This Article Algorithm
The position of wireless sensor network node \( S_{i} \) is \( (x_{i} ,y_{i} ,z_{i} ) \), the perception model is in the following:
Among them, \( P(S_{i} ,T) \) is the perception probability of the wireless sensor network node \( S_{i} \) to target point T, \( d(S_{i} ,T) \) is the distance between sensor node \( S_{i} \) and the target point T, \( R_{e} \) is a uncertainty perception measure of wireless sensor node \( S_{i} \), \( E_{ini} \) is the initial energy of the wireless sensor node \( S_{i} \), \( E_{rem} \) is the remaining energy of the wireless sensor node \( S_{i} \), \( \alpha \), \( \beta \), \( \gamma \) are the perception of the wireless sensor node within the scope of monitoring quality coefficient.
6 Simulation Result
MATLAB software is used as simulation in this article. Assume that require p-reliability coverage in the monitoring area, among them p = 0.9, and do not consider the effect of target distribution and other environmental factors. According to the characteristics of the passive sonar and underwater sensor networks node and the related definitions, respectively the simulation random deployment algorithm and based on the “virtual force” deployment algorithm under monitoring area coverage performance and deployment algorithm based on perception model after monitoring area coverage performance and target node test results.
The simulation results are shown in Figs. 1, 2 and 3.
Figure 1 is under the initial randomly deployment, virtual force algorithm and this article algorithm, in the same test area with an increase in the number of nodes. In contrast to initial randomly deployment and virtual force algorithm, this article algorithm has more effective coverage, a single node perception is more efficient, this is a direct result of the node deployment, compared virtual force algorithm and initial randomly deployment reduced the scope of sensor node overlapping sense perception, and because this article algorithm compared virtual force algorithm adopted data fusion algorithm, and reduced the perceived blind area, therefore, this article algorithm under the effective coverage of the sensor network greater than virtual force algorithm, virtual force algorithm is higher than the initial randomly deployment.
Figure 2 is under the initial randomly deployment, virtual force algorithm and this article algorithm, in the same detection area to deploy the same number of sensor node, respectively, with different number of effective detection rate of the target node contrast figure. By the graph, this article algorithm, the wireless sensor network for effective detection of the target node rate than initial randomly deployment and virtual force algorithm, this is because the this article algorithm deployment under the data fusion algorithm is adopted to perception results effective fusion of different sensors, improve the detection probability of the target node, increase the effective coverage.
Figure 3 is under the initial randomly deployment, virtual force algorithm and this article algorithm, at the same detection area under the same number, under the same coverage performance, the perception node residual energy contrast figure of sensor networks. Because the virtual force algorithm and this article algorithm in the initial stage of perceptual mobile node, and this article algorithm under the movement number is greater than the virtual force algorithm, so the energy consumption is larger, and the energy consumption is greater than the virtual force algorithm this article algorithm is presented. However due to both used the redundancy node dormancy mechanism, after complete the deployment, virtual force algorithm and this article algorithm energy per unit time is less than initial randomly deployment, due to the redundancy this article algorithm under network is greater than the virtual force algorithm, therefore, this article algorithm consumes energy is smaller than the virtual force algorithm. Therefore this article algorithm compared with other algorithm own the longer lifetime of the network.
7 Conclusion
This article aims at wireless sensor network nodes deployment algorithm based on perception model, in order to improve the coverage of the wireless sensor network and prolong the lifetime of the wireless sensor network, based on the perception model, establish nodes detection model, combining with the data fusion. This article algorithm can effectively reduce the number of deployment wireless sensor network nodes, improve the efficiency of wireless sensor network coverage, reduce the wireless sensor network nodes energy consumption, and prolong the lifetime of the wireless sensor network.
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Cui, M., Mei, F., Li, Q., Li, Q. (2018). Nodes Deployment Optimization Algorithm Based on Energy Consumption of Underwater Wireless Sensor Networks. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_36
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