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Opportunistic routing strategy based on local information in mobile sensor networks
DONG Ting
Journal of Computer Applications    2013, 33 (06): 1505-1518.   DOI: 10.3724/SP.J.1087.2013.01505
Abstract788)      PDF (675KB)(805)       Save
An opportunistic routing strategy was proposed based on the analyses of the channel quality, the movement speed and energy cost of a node synthetically. The received signal strength indicator was used to build opportunity probability. And then, the movement speed was used to reflect the node mobility. At last, the energy cost was used to express node residual life. The opportunistic routing was built by them and the preferred number of a candidate node was defined in order to determine its listening forwarding time. Furthermore the problem of package retransmission was solved. The analysis and simulation show that, compared with Extremely Opportunistic Routing (ExOR) and Opportunistic with Backtracking (OB), the new strategy is more suitable for opportunistic network with higher validity and lower energy cost.
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Double security-based routing algorithms for sensor networks
ZHANG Jin,SHEN Ya-min,DONG Ting,ZHU Wang-bin
Journal of Computer Applications    2005, 25 (08): 1722-1725.   DOI: 10.3724/SP.J.1087.2005.01722
Abstract1455)      PDF (197KB)(925)       Save
It is very difficult to get the security in sensor networks because of the limited resources of nodes and the wireless communication between nodes. A double security-based routing algorithms was proposed based on the existed security arrangement and low power consumption multipath.The existed security arrangement can provide security to some certain case and multipath can provide more security even when security arrangement failed. Theoretic analysis and simulation results show that the new algorithm has better performances.
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Timeber transport vehicle detection based on improved faster RCNN
XU Yiliu,HE Peng,REN Dong,WANG Hui,DONG Ting,SHAO Pan
Journal of Computer Applications    DOI: 10.11772/j.issn.1001-9081.2019081431
Accepted: 30 October 2019