scholarly journals Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2105 ◽  
Author(s):  
Wenyu Cai ◽  
Meiyan Zhang
Keyword(s):  
Sensor Networks ◽  
Data Gathering ◽  
Underwater Sensor Networks ◽  
Mobile Data ◽  
Mobile Data Gathering ◽  
Dubins Curves

scholarly journals Mobile Data Gathering With Hop-Constrained Clustering in Underwater Sensor Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 21118-21132 ◽  
Author(s):  
Seyed Mohammad Ghoreyshi ◽  
Alireza Shahrabi ◽  
Tuleen Boutaleb ◽  
Mohsen Khalily
Keyword(s):  
Sensor Networks ◽  
Data Gathering ◽  
Underwater Sensor Networks ◽  
Constrained Clustering ◽  
Mobile Data ◽  
Mobile Data Gathering

scholarly journals An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4813
Author(s):  
Faisal Abdulaziz Alfouzan ◽  
Seyed Mohammad Ghoreyshi ◽  
Alireza Shahrabi ◽  
Mahsa Sadeghi Ghahroudi
Keyword(s):  
Sensor Networks ◽  
Network Performance ◽  
Data Gathering ◽  
Mac Protocols ◽  
Cross Layer ◽  
Delivery Ratio ◽  
Underwater Sensor Networks ◽  
Mobile Data ◽  
Packet Delivery ◽  
Mobile Data Gathering

Underwater sensor networks (UWSNs) have recently attracted much attention due to their ability to discover and monitor the aquatic environment. However, acoustic communication has posed some significant challenges, such as high propagation delay, low available bandwidth, and high bit error rate. Therefore, proposing a cross-layer protocol is of high importance to the field to integrate different communication functionalities (i.e, an interaction between data link layer and network layer) to interact in a more reliable and flexible manner to overcome the consequences of applying acoustic signals. In this paper, a novel Cross-Layer Mobile Data gathering (CLMD) scheme for Underwater Sensor Networks (UWSNs) is presented to improve the performance by providing the interaction between the MAC and routing layers. In CLMD, an Autonomous Underwater Vehicle (AUV) is used to periodically visit a group of clusters which are responsible for data collection from members. The communications are managed by using a distributed cross-layer solution to enhance network performance in terms of packet delivery and energy saving. The cluster heads are replaced with other candidate members at the end of each operational phase to prolong the network lifetime. The effectiveness of CLMD is verified through an extensive simulation study which reveals the performance improvement in the energy-saving, network lifetime, and packet delivery ratio with varying number of nodes. The effects of MAC protocols are also studied by studying the network performance under various MAC protocols in terms of packet delivery ratio, goodput, and energy consumption with varying density of nodes.

scholarly journals Predictive Trajectory-Based Mobile Data Gathering Scheme for Wireless Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Fan Chao ◽  
Zhiqin He ◽  
Renkuan Feng ◽  
Xiao Wang ◽  
Xiangping Chen ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Center ◽  
Mobile Data ◽  
Tour Planning ◽  
Mobile Data Gathering

Tradition wireless sensor networks (WSNs) transmit data by single or multiple hops. However, some sensor nodes (SNs) close to a static base station forward data more frequently than others, which results in the problem of energy holes and makes networks fragile. One promising solution is to use a mobile node as a mobile sink (MS), which is especially useful in energy-constrained networks. In these applications, the tour planning of MS is a key to guarantee the network performance. In this paper, a novel strategy is proposed to reduce the latency of mobile data gathering in a WSN while the routing strategies and tour planning of MS are jointly optimized. First, the issue of network coverage is discussed before the appropriate number of clusters being calculated. A dynamic clustering scheme is then developed where a virtual cluster center is defined as the MS sojourn for data collection. Afterwards, a tour planning of MS based on prediction is proposed subject to minimizing the traveling distance to collect data. The proposed method is simulated in a MATLAB platform to show the overall performance of the developed system. Furthermore, the physical tests on a test rig are also carried out where a small WSN based on an unmanned aerial vehicle (UAV) is developed in our laboratory. The test results validate the feasibility and effectiveness of the method proposed.

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