Toggle navigation
Home
About
About Journal
Historical Evolution
Indexed In
Awards
Reference Index
Editorial Board
Journal Online
Archive
Project Articles
Most Download Articles
Most Read Articles
Instruction
Contribution Column
Author Guidelines
Template
FAQ
Copyright Agreement
Expenses
Academic Integrity
Contact
Contact Us
Location Map
Subscription
Advertisement
中文
Journals
Publication Years
Keywords
Search within results
(((HE Xiaomin[Author]) AND 1[Journal]) AND year[Order])
AND
OR
NOT
Title
Author
Institution
Keyword
Abstract
PACS
DOI
Please wait a minute...
For Selected:
Download Citations
EndNote
Ris
BibTeX
Toggle Thumbnails
Select
RSSI collaborative location algorithm of selecting preference accuracy for wireless sensor network
WANG Ming, XU Liang, HE Xiaomin
Journal of Computer Applications 2018, 38 (
7
): 1981-1988. DOI:
10.11772/j.issn.1001-9081.2017123050
Abstract
(
380
)
PDF
(1237KB)(
307
)
Knowledge map
Save
Concerning insufficient and blind use of the Received Signal Strength Indicator (RSSI) information among unknown nodes, a new RSSI collaborative location algorithm of selecting preference accuracy for Wireless Sensor Network (WSN) was proposed. Firstly, the nodes with high locating accuracy were selected from coarsely located unknown nodes based on the RSSI thresholds. Secondly, subset judgment method was used to seek out the unknown nodes which were less affected by the environment as the second collaboration backbone nodes. Then, based on the positioning errors of the anchor nodes, anchor node replacement criterion was used to further extract the high-precision node from the secondary selected cooperative nodes as the optimal cooperative backbone nodes. Finally, the collaborative backbone nodes were used as the cooperative objects, and the unknown nodes were modified according to the precision priorities. In the simulation experiments, the average localization accuracy of the proposed algorithm was within 1.127 m in 100 m*100 m grids. In terms of locating accuracy, the average locating accuracy of the proposed algorithm is improved by 15% compared with the improved WSN locating algorithm using RSSI model. In terms of time efficiency, compared with the traditional RSSI collaborative location algorithm, the proposed algorithm improves the time efficiency by 20% under the same condition. It can be seen that the proposed algorithm can effectively enhance the locating accuracy, reduce computational complexity and improve time efficiency.
Reference
|
Related Articles
|
Metrics
Select
Dynamic clustering target tracking based on energy optimization in wireless sensor networks
WEI Mingdong, HE Xiaomin, XU Liang
Journal of Computer Applications 2017, 37 (
6
): 1539-1544. DOI:
10.11772/j.issn.1001-9081.2017.06.1539
Abstract
(
500
)
PDF
(945KB)(
477
)
Knowledge map
Save
Concerning the problem of high energy consumption caused by data collision and cluster selection process in dynamic clustering target tracking of Wireless Sensor Network (WSN), a dynamic clustering method based on energy optimization for WSN was proposed. Firstly, a time division election transmission model was proposed, which avoided data collision actively to reduce energy consumption of nodes in a dynamic cluster. Secondly, based on energy information and tracking quality, the energy-balanced farthest node scheduling strategy was proposed, which optimized custer head node scheduling. Finally, according to the weighted centroid localization algorithm, the target tracking task was completed. Under the environment of random deployment of nodes, the experimental results show that, the average tracking accuracy of the proposed method for non-linear moving objects was 0.65 m, which is equivalent to that of Dynamic Cluster Member Selection method for multi-target tracking (DCMS), and improved by 45.8% compared to Distributed Event Localization and Tracking Algorithm (DELTA). Compared with DCMS and DELTA, the proposed algorithm can effectively reduce energy consumption of the dynamic tracking clusters by 61.1% and prolong the network lifetime.
Reference
|
Related Articles
|
Metrics