Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6873133 | Future Generation Computer Systems | 2018 | 9 Pages |
Abstract
Delay tolerant network (namely DTN) is regarded as a promising technology for communications in challenging environments for its resilience and adaptability. However, efficient and reliable routing algorithm design is still one of many difficulties it faces in achieving broad and fruitful applications. Due to reasons like electromagnetic inference, lack of infrastructure and constantly moving, communication among vehicles in battlefield environments is difficult and makes a prospective field for DTN to bring effective improvements. In this paper, a timetable-aware opportunistic DTN routing algorithm combining limited message copies and biased message spray is proposed for this scenario. Firstly, a mobility model named timetable based aggregation and spread (ASMM) is abstracted, which captures both the integrity and individuality in vehicles' typical moving behavior on military missions. Then, from the time and coordination information contained in the preset timetables, the meeting chances for different node groups can be predicted. This meeting opportunity is used as a utility for message transmission evaluation in our proposed routing algorithm called GenericSpray. GenericSpray is able to make more accurate and motivated message transferring decisions by exploiting contact opportunity prediction in choosing among possible message carriers and deciding appropriate number of copies to spray. To investigate its performance, both ASMM and GenericSpray are implemented on The ONE, a specialized DTN simulation platform. Results of simulation experiments indicate that GenericSpray outperforms all other classical DTN routing algorithms chosen as contrast, in terms of delivery ratio, delivery delay and transmission overhead.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Haitao Wang, Lihua Song, Guomin Zhang, Hui Chen,