scholarly journals Spark-Based Parallel Genetic Algorithm for Simulating a Solution of Optimal Deployment of an Underwater Sensor Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2717 ◽  
Author(s):  
Peng Liu ◽  
Shuai Ye ◽  
Can Wang ◽  
Zongwei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Sensor Network ◽  
Large Scale ◽  
Working Environment ◽  
Underwater Sensor Networks ◽  
Single Node ◽  
Running Time ◽  
Node Deployment ◽  
Underwater Sensor Network ◽  
Optimal Deployment

Underwater sensor networks have wide application prospects, but the large-scale sensing node deployment is severely hindered by problems like energy constraints, long delays, local disconnections, and heavy energy consumption. These problems can be solved effectively by optimizing sensing node deployment with a genetic algorithm. However, the genetic algorithm (GA) needs many iterations in solving the best location of underwater sensor deployment, which results in long running time delays and limited practical application when dealing with large-scale data. The classical parallel framework Hadoop can improve the GA running efficiency to some extent while the state-of-the-art parallel framework Spark can release much more parallel potential of GA by realizing parallel crossover, mutation, and other operations on each computing node. Giving full allowance for the working environment of the underwater sensor network and the characteristics of sensors, this paper proposes a Spark-based parallel GA to calculate the extremum of the Shubert multi-peak function, through which the optimal deployment of the underwater sensor network can be obtained. Experimental results show that while faced with a large-scale underwater sensor network, compared with single node and Hadoop framework, the Spark-based implementation not only significantly reduces the running time but also effectively avoids the problem of premature convergence because of its powerful randomness.

Efficient Node Deployment Algorithm for Enhancing Connectivity and Coverage in Underwater Sensor Networks

2019 ◽  
Vol 17 (12) ◽  
pp. 947-954
Author(s):  
Kamal Kumar Gola ◽  
Bhumika Gupta
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Water Surface ◽  
Area Coverage ◽  
Underwater Sensor Networks ◽  
Node Deployment ◽  
Underwater Sensor Network ◽  
Surface Station ◽  
Coverage And Connectivity ◽  
2D Network

As deployment process is one of the major tasks in underwater sensor network due to its constraint like: acoustic communication, energy, processing speed, cost and memory and dynamic nature of water. As many researchers have proposed many algorithms for the deployment of nodes in underwater sensor network. It was always a great issue in WSN as well as underwater sensor networks. This work proposes a node deployment technique based on depth. This work consists the following major components: (i) sensor nodes to sense the phenomena in underwater sensor networks, (ii) multiple surface station on the water surface. Use of multiple surface station provides better area coverage and connectivity in the networks. This work is divided into three phase like: initialization where nodes are randomly deployed at water surface and from 2D network topology, second phase is depth calculation for all the nodes and third is to distribute the depth to each node and send them to their designated depth to expand the 2D network into the 3D network. The proposed technique is simulated on Matlab for the analysis of area coverage and connectivity. Simulation results show better performance in terms of area coverage and connectivity as compared to ADAN-BC.

scholarly journals A Cluster Sleep-Wake Scheduling Algorithm Based on 3D Topology Control in Underwater Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 156 ◽  
Author(s):  
Wenbo Zhang ◽  
Jing Wang ◽  
Guangjie Han ◽  
Xinyue Zhang ◽  
Yongxin Feng
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Topology Control ◽  
Scheduling Algorithm ◽  
Basic Unit ◽  
Underwater Sensor Networks ◽  
Efficient Operation ◽  
Underwater Sensor Network ◽  
Temporary Control ◽  
3D Topology

3D topology control in underwater sensor networks is of great significance to ensuring reliable and efficient operation of the network. In this paper, by analyzing the characteristics of an underwater sensor network, we take the cube as the basic unit to perform 3D partition of the monitoring area, define the 3D partition unit and basic cluster structure of the underwater sensor network, and arrange rotating temporary control nodes in the cluster. Then, a cluster sleep-wake scheduling algorithm is proposed that compares the remaining node energy. It selects the node with the largest remaining energy as the working node, and the remaining nodes complete the transition of dormancy and waiting states as long as they reach the preset dormancy time. The node state settings of this phase are completed by the temporary control node. Temporary control nodes selecting and sleep-wake scheduling are used in the algorithm through 3D topology control, which reduces energy consumption and guarantees maximum sensing coverage of the entire network and the connection rate of active nodes. Simulation results further verify the effectiveness of the proposed algorithm.

Recent and Future Node Deployment Strategies in the Underwater Sensor Network (UWSN)

2021 ◽  
pp. 34-44
Author(s):  
Krishna Pandey ◽  
Manish Kumar
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Generalized Model ◽  
Node Deployment ◽  
The Future ◽  
Underwater Sensor Network ◽  
Technical Challenges

The chapter focuses on the recent development in the field of the sensor node deployment in the UWSN (under water wireless sensor network). In the chapter, the technical challenges during the node deployment of the sensor nodes in the UWSN (under water wireless sensor network) are represented with prefacing the background. The chapter focuses on the different methods of node deployment and presents a generalized model for ensure the reliability. A view of analyzing the deployment of sensor nodes is also shown in the example by following the recent researches in the domain. Finally, the future scope and conclusion is represented with the idea of new paradigms in the deployment of sensor nodes in the UWSN.

scholarly journals Investigation of Parameter Effects on Virtual-Spring-Force Algorithm for Wireless-Sensor-Network Applications

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3082
Author(s):  
Zhiyong Yu ◽  
Rongxin Tang ◽  
Kai Yuan ◽  
Hai Lin ◽  
Xin Qian ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Large Scale ◽  
Central Force ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Node Deployment ◽  
Spring Force

Virtual-force algorithms (VFAs) have been widely studied for accurate node deployment in wireless-sensor-network (WSN) applications. Their main purpose is to achieve the maximum coverage area with the minimum number of sensor nodes in the target area. Recently, we reported a new VFA based on virtual spring force (VFA-SF) and discussed in detail the corresponding efficiency via statistical analysis. The optimized strategy by adding an external central force (VFA-SF-OPT) was presented, which effectively eliminates the coverage hole or twisted structure in the final network distribution. In this paper, the parameter effects on VFA-SF and the VFA-SF-OPT were further investigated: (1) Node velocity dramatically affects the convergence rate of the node-deployment process. (2) A suitable external central force improves equilibrium distance and reduces energy consumption. (3) The effects of VFA-SF and VFA-SF-OPT for different types of obstacles are discussed. Generally, by choosing suitable parameters, both VFA-SF and VFA-SF-OPT can effectively improve node deployment and energy consumption for the whole sensor network. The results give important insight in parameter selection and information fusion in the application of a large-scale WSN.

scholarly journals A Novel 3D Node Deployment Inspired by Dusty Plasma Crystallization in UAV-Assisted Wireless Sensor Network Applications

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7576
Author(s):  
Rongxin Tang ◽  
Yuhao Tao ◽  
Jiahao Li ◽  
Zhiming Hu ◽  
Kai Yuan ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Dusty Plasma ◽  
Large Scale ◽  
Debye Length ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Node Deployment ◽  
System Utilization ◽  
Sensing Radius

With the rapid progress of hardware and software, a wireless sensor network has been widely used in many applications in various fields. However, most discussions for the WSN node deployment mainly concentrated on the two-dimensional plane. In such a case, some large scale applications, such as information detection in deep space or deep sea, will require a good three dimensional (3D) sensor deployment scenario and also attract most scientists’ interests. Excellent deployment algorithms enable sensors to be quickly deployed in designated areas with the help of unmanned aerial vehicles (UAVs). In this paper, for the first time, we present a three dimensional network deployment algorithm inspired by physical dusty plasma crystallization theory in large-scale WSN applications. Four kinds of performance evaluation methods in 3D space, such as the moving distance, the spatial distribution diversion, system coverage rate, and the system utilization are introduced and have been carefully tested.Furthermore, in order to improve the performance of the final deployment, we integrated the system coverage rate and the system utilization to analyze the parameter effects of the Debye length and the node sensing radius. This criterion attempts to find the optimal sensing radius with a fixed Debye length to maximize the sensing range of the sensor network while reducing the system redundancy. The results suggest that our 3D algorithm can quickly complete an overall 3D network deployment and then dynamically adjust parameters to achieve a better distribution. In practical applications, engineers may choose appropriate parameters based on the sensor’s hardware capabilities to achieve a better 3D sensor network deployment. It may be significantly used in some large-scale 3D WSN applications in the near future.

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