Reliable data gathering by Mobile Sink for wireless sensor networks

Author(s):  
P. Madhumathy ◽  
D. Sivakumar
2020 ◽  
Vol 11 (1) ◽  
pp. 36-48
Author(s):  
Amiya Bhusan Bagjadab ◽  
Sushree Bibhuprada B. Priyadarshini

Wireless sensor networks are commonly used to monitor certain regions and to collect data for several application domains. Generally, in wireless sensor networks, data are routed in a multi-hop fashion towards a static sink. In this scenario, the nodes closer to the sink become heavily involved in packet forwarding, and their battery power is exhausted rapidly. This article proposes that a special node (i.e., mobile sink) will move in the specified region and collect the data from the sensors and transmit it to the base station such that the communication distance of the sensors will be reduced. The aim is to provide a track for the sink such that it covers maximum sensor nodes. Here, the authors compared two tracks theoretically and in the future will try to simulate the two tracks for the sink movement so as to identify the better one.


2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Mariam Akbar ◽  
Nadeem Javaid ◽  
Wadood Abdul ◽  
Sanaa Ghouzali ◽  
Abid Khan ◽  
...  

Mobile Sink (MS) based routing strategies have been widely investigated to prolong the lifetime of Wireless Sensor Networks (WSNs). In this paper, we propose two schemes for data gathering in WSNs: (i) MS moves on random paths in the network (RMS) and (ii) the trajectory of MS is defined (DMS). In both the schemes, the network field is logically divided into small squares. The center point of each partitioned area is the sojourn location of the MS. We present three linear programming based models: (i) to maximize network lifetime, (ii) to minimize path loss, and (iii) to minimize end to end delay. Moreover, a geometric model is proposed to avoid redundancy while collecting information from the network nodes. Simulation results show that our proposed schemes perform better than the selected existing schemes in terms of the selected performance metrics.


2014 ◽  
Vol 21 (1) ◽  
pp. 227-249 ◽  
Author(s):  
Charalampos Konstantopoulos ◽  
Basilis Mamalis ◽  
Grammati Pantziou ◽  
Vasileios Thanasias

2017 ◽  
Vol 30 (12) ◽  
pp. e3264 ◽  
Author(s):  
Farzad Tashtarian ◽  
Khosrow Sohraby ◽  
Amir Varasteh

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