scholarly journals Coverage and connectivity in three-dimensional underwater sensor networks

2008 ◽  
Vol 8 (8) ◽  
pp. 995-1009 ◽  
Author(s):  
S. M. Nazrul Alam ◽  
Zygmunt J. Haas
Keyword(s):  
Sensor Networks ◽  
Three Dimensional ◽  
Underwater Sensor Networks ◽  
Coverage And Connectivity

scholarly journals ProLo: Localization via Projection for Three-Dimensional Mobile Underwater Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1414 ◽  
Author(s):  
Feng Zhou ◽  
Yushi Li ◽  
Hejun Wu ◽  
Zhimin Ding ◽  
Xiying Li
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Three Dimensional ◽  
Mobile Sensor Networks ◽  
Sensor Nodes ◽  
Underwater Sensor Networks ◽  
Water Currents ◽  
Localization Scheme ◽  
Moving Direction ◽  
Gps Devices

We study the problem of three-dimensional localization of the underwater mobile sensor networks using only range measurements without GPS devices. This problem is challenging because sensor nodes often drift with unknown water currents. Consequently, the moving direction and speed of a sensor node cannot be predicted. Moreover, the motion devices of the sensor nodes are not accurate in underwater environments. Therefore, we propose an adaptive localization scheme, ProLo, taking these uncertainties into consideration. This scheme applies the rigidity theory and maintains a virtual rigid structure through projection. We have proved the correctness of this three-dimensional localization scheme and also validated it using simulation. The results demonstrate that ProLo is promising for real mobile underwater sensor networks with various noises and errors.

A LOW-COST RANGE-FREE LOCALIZATION SCHEME FOR THREE-DIMENSIONAL UNDERWATER SENSOR NETWORKS

2013 ◽  
Vol 05 (01) ◽  
pp. 1350005
Author(s):  
XIANLING LU ◽  
DEYING LI ◽  
YI HONG ◽  
WENPING CHEN
Keyword(s):  
Sensor Networks ◽  
Low Cost ◽  
Three Dimensional ◽  
Underwater Sensor Networks ◽  
Transmission Power ◽  
Localization Accuracy ◽  
Localization Scheme ◽  
Variable Transmission ◽  
Anchor Nodes ◽  
Range Free

Localization is one of the fundamental tasks for underwater sensors networks (USNs). It is required for data tagging, target detection, route protocols, and so on. In this paper, we propose an efficient low-cost range-free localization scheme for 3D underwater sensor networks (3D-LRLS) without any additional hardware infrastructure. In our scheme, each anchor node has variable transmission power levels. At first, the power levels of each anchor are decided by the Delaunay triangulation for the network space. Then, ordinary sensors listen to the beacons sent from the anchor nodes. Based on the beacon messages, each node calculates its location individually by a low computational complexity method. The extensive simulation results demonstrate that 3D-LRLS is efficient in terms of both localization ratio and localization accuracy.

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 Time Evolution of Underwater Sensor Networks Coverage and Connectivity Using Physically Based Mobility Model

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Fatma Bouabdallah
Keyword(s):  
Sensor Networks ◽  
Time Evolution ◽  
Communication Protocols ◽  
Mobility Model ◽  
Sensor Nodes ◽  
Underwater Sensor Networks ◽  
Medium Access ◽  
Buoyant Force ◽  
Coverage And Connectivity ◽  
Networking Protocols

Underwater mobile acoustic sensor networks (UW-ASNs) require the design of new networking protocols due to fundamental differences with terrestrial wireless sensor networks. The performance of these protocols is highly impacted by the mobility of sensors, especially when they are freely floating. In such mobile UW-ASNs, nodes move with the water currents but are constrained by the gravitational weight of the sensor along with the water resistance and the buoyant force. A realistic mobility model that can reflect the physical movement of randomly scattered and freely floating sensor nodes under ocean currents provides clearer understanding of the communication challenges and hence helps conceiving efficient communication protocols. In this paper, we first propose an exhaustive physically inspired mobility model which meticulously captures the dynamics of underwater environments. We, then, study the resulting time evolution of network coverage and connectivity. Our objective is to provide the underwater network research community with a realistic mobility model that could be exploited in conceiving networking communication protocols such as routing, localization, and medium access. Namely, we show that the network mobility effect on coverage and connectivity is more significant in intermediately dense UW-ASNs. Less effect is recorded on the coverage and connectivity for low- and high-density UW-ASNs.

scholarly journals Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5961
Author(s):  
Xuechen Chen ◽  
Wenjun Xiong ◽  
Sheng Chu
Keyword(s):  
Sensor Networks ◽  
Compressive Sensing ◽  
Fitness Function ◽  
Cluster Head ◽  
Three Dimensional ◽  
Life Time ◽  
Reconstruction Error ◽  
Underwater Sensor Networks ◽  
Data Routing ◽  
Reconstructed Signal

Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute i-th effective routing path decide which positions in the i-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches.

Sign in / Sign up

Export Citation Format

Share Document