JOURNAL ARTICLE

A Flooding Mechanism of Stateless Anycast Routing in Mobile Wireless Ad Hoc Networks.

  • Published In: Adhoc & Sensor Wireless Networks, 2023, v. 55, n. 1/2. P. 123 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: ÖZ, GÜRCÜ 3 of 3

Abstract

A detailed simulation study, together with real-world experiments, a stateless flooding mechanism for anycast routing in a mobile wireless ad hoc network is studied with a developed model. The model covers reliability aspects of wireless communication in such networks with a routing mechanism using a scheme of orientation-dependent inter-node communication links. Using the mechanism, the paper addresses another issue of locating the nearest server from a group of replicated servers in the network. A class of extended Petri nets is used in the development of the simulation model to explicitly represent parallelism of events and processes in the network. An application layer prototype system is implemented based on the proposed routing model without any changes at lower layers of the network protocol stack. In simulation and real-world experiments, the behavior of five fundamental performance metrics - response ratio, relative traffic, average response time, average number of hops, and duplicate ratio - were investigated by changing distance of transmission, motion pattern and combining with different model parameters. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Adhoc & Sensor Wireless Networks. 2023/01, Vol. 55, Issue 1/2, p123
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2023
  • ISSN:15519899
  • DOI:10.32908/ahswn.v55.9759
  • Accession Number:162865731
  • Copyright Statement:Copyright of Adhoc & Sensor Wireless Networks is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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