A Mobile Sinks Based Data Collection Scheme for Isolated Wireless Sensor Networks

Author(s):  
Sunil Kumar Singh ◽  
Prabhat Kumar
2010 ◽  
Vol 6 (1) ◽  
pp. 402680 ◽  
Author(s):  
Harshavardhan Sabbineni ◽  
Krishnendu Chakrabarty

We present a two-tier distributed hash table-based scheme for data-collection in event-driven wireless sensor networks. The proposed method leverages mobile sinks to significantly extend the lifetime of the sensor network. We propose localized algorithms using a distributed geographic hash-table mechanism that adds load balancing capabilities to the data-collection process. We address the hotspot problem by rehashing the locations of the mobile sinks periodically. The proposed mobility model moves the sink node only upon the occurrence of an event according to the evolution of current events, so as to minimize the energy consumption incurred by the multihop transmission of the event-data. Data is collected via single-hop routing between the sensor node and the mobile sink. Simulation results demonstrate significant gains in energy savings, while keeping the latency and the communication overhead at low levels for a variety of parameter values.


2017 ◽  
Vol 16 (5) ◽  
pp. 1420-1433 ◽  
Author(s):  
Shusen Yang ◽  
Usman Adeel ◽  
Yad Tahir ◽  
Julie A. McCann

2021 ◽  
Vol 10 (4) ◽  
pp. 69
Author(s):  
Omar Banimelhem ◽  
Eyad Taqieddin ◽  
Ibrahim Shatnawi

Recently, the data collection problem in wireless sensor networks (WSNs) using mobile sinks has received much attention. The main challenge in such problems is constructing the path that the mobile sink (MS) will use to collect the data. In this paper, an efficient path generation algorithm for the mobile sink based on principal component analysis (PCA) is proposed. The proposed approach was evaluated using two data collection modes—direct and multihop—and it was compared with another approach called the mobile-sink-based energy-efficient clustering algorithm for wireless sensor networks (MECA). When compared with MECA, simulation results have shown that the proposed approach improves the performance of WSN in terms of the number of live nodes and average remaining energy.


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