Repositori institucional URV
| URV's Author/s: | Aliagas Castell, Carlos / Millán Marco, Pedro / Molina Clemente, Carlos María |
| Author, as appears in the article.: | Millan, Pere; Aliagas, Carles; Molina, Carlos; Meseguer, Roc; Ochoa, Sergio F; Santos, Rodrigo M |
| Author's mail: | carlos.molina@urv.cat pere.millan@urv.cat carles.aliagas@urv.cat carles.aliagas@urv.cat |
| Author identifier: | 0000-0003-1955-0128 0000-0002-4132-7099 |
| Journal publication year: | 2020 |
| Publication Type: | Journal Publications |
| ISSN: | 14248220 |
| APA: | Millan, Pere; Aliagas, Carles; Molina, Carlos; Meseguer, Roc; Ochoa, Sergio F; Santos, Rodrigo M (2020). Predicting topology propagation messages in mobile ad hoc networks: The value of history. Sensors, 20(1), 24-. DOI: 10.3390/s20010024 |
| Papper original source: | Sensors. 20 (1): 24- |
| Abstract: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols. |
| Article's DOI: | 10.3390/s20010024 |
| Link to the original source: | https://www.mdpi.com/1424-8220/20/1/24 |
| Papper version: | info:eu-repo/semantics/publishedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Zootecnia / recursos pesqueiros Química Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Letras / linguística Interdisciplinar Instruments & instrumentation Instrumentation Information systems Geografía Geociências Farmacia Engineering, electrical & electronic Engenharias iv Engenharias iii Engenharias ii Engenharias i Electrochemistry Electrical and electronic engineering Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Biochemistry Atomic and molecular physics, and optics Astronomia / física Arquitetura, urbanismo e design Analytical chemistry |
| Keywords: | Wireless sensor networks Urban emergencies Routing metrics Network topology prediction messages Mobile ad-hoc networks Mobile ad hoc networks Localization Link quality History-based prediction Challenges Algorithms network topology prediction messages mobile ad hoc networks history-based prediction |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-09-07 |
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