Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches

In Mobile Wireless Sensor Networks there could be scenarios where absolutely all network nodes (including the base station) are mobile, becoming a very hard task to find a communication path between a sensor node and the base station due to many network variables are changing at each moment. In addi...

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Autores principales:	German A. Montoya, Carlos Lozano-Garzon, Yezid Donoso
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Mathematical optimization model for time-varying graphs delays energy consumption prediction algorithm Markov chains deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2889091baf304051ab02f760954c1a55
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spelling	oai:doaj.org-article:2889091baf304051ab02f760954c1a552021-11-24T00:01:04ZEnergy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches2169-353610.1109/ACCESS.2021.3124737https://doaj.org/article/2889091baf304051ab02f760954c1a552021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598816/https://doaj.org/toc/2169-3536In Mobile Wireless Sensor Networks there could be scenarios where absolutely all network nodes (including the base station) are mobile, becoming a very hard task to find a communication path between a sensor node and the base station due to many network variables are changing at each moment. In addition, there are delay-sensitive applications that require establishing communication paths as soon as possible to mitigate low network performance in terms of end-to-end delay, reducing, at the same time, the energy consumption of the network. For this reason, we propose a multiobjective mathematical optimization model for finding the optimal communication path between a source node and a sink (base station) considering hard scenarios where all network nodes are mobile and minimizing end-to-end delay and energy consumption. This mathematical model would offer significant advantages to evaluate new algorithms due to we could know how far or close are the algorithm results from the optimal values given by the mathematical model. In addition, we propose a prediction distributed routing algorithm based on Markov Chains that takes into account the network mobility in order to find as fast as possible a communication path between a source node and a sink with minimal energy consumption. We also propose a deep learning approach to predict future nodes’ distances in a mobile network to determine if future movements of nodes will cause communication disruptions in paths. Significant findings were obtained when the Markov Chains and Deep Learning approaches were compared in terms of predicting nodes mobility and reducing the delay and the energy consumption in the network. The performance of our prediction algorithms (Markov Chains and Deep Learning approaches) is evaluated against the mathematical model to determine how good it is. Finally, to analyze our prediction algorithms considering real online scenarios, we compared it against typical routing algorithms, obtaining promising results in terms of delay and energy consumption in all mobile node scenarios.German A. MontoyaCarlos Lozano-GarzonYezid DonosoIEEEarticleMathematical optimization model for time-varying graphsdelaysenergy consumptionprediction algorithmMarkov chainsdeep learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153382-153400 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematical optimization model for time-varying graphs delays energy consumption prediction algorithm Markov chains deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Mathematical optimization model for time-varying graphs delays energy consumption prediction algorithm Markov chains deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 German A. Montoya Carlos Lozano-Garzon Yezid Donoso Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches
description	In Mobile Wireless Sensor Networks there could be scenarios where absolutely all network nodes (including the base station) are mobile, becoming a very hard task to find a communication path between a sensor node and the base station due to many network variables are changing at each moment. In addition, there are delay-sensitive applications that require establishing communication paths as soon as possible to mitigate low network performance in terms of end-to-end delay, reducing, at the same time, the energy consumption of the network. For this reason, we propose a multiobjective mathematical optimization model for finding the optimal communication path between a source node and a sink (base station) considering hard scenarios where all network nodes are mobile and minimizing end-to-end delay and energy consumption. This mathematical model would offer significant advantages to evaluate new algorithms due to we could know how far or close are the algorithm results from the optimal values given by the mathematical model. In addition, we propose a prediction distributed routing algorithm based on Markov Chains that takes into account the network mobility in order to find as fast as possible a communication path between a source node and a sink with minimal energy consumption. We also propose a deep learning approach to predict future nodes’ distances in a mobile network to determine if future movements of nodes will cause communication disruptions in paths. Significant findings were obtained when the Markov Chains and Deep Learning approaches were compared in terms of predicting nodes mobility and reducing the delay and the energy consumption in the network. The performance of our prediction algorithms (Markov Chains and Deep Learning approaches) is evaluated against the mathematical model to determine how good it is. Finally, to analyze our prediction algorithms considering real online scenarios, we compared it against typical routing algorithms, obtaining promising results in terms of delay and energy consumption in all mobile node scenarios.
format	article
author	German A. Montoya Carlos Lozano-Garzon Yezid Donoso
author_facet	German A. Montoya Carlos Lozano-Garzon Yezid Donoso
author_sort	German A. Montoya
title	Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches
title_short	Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches
title_full	Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches
title_fullStr	Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches
title_full_unstemmed	Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches
title_sort	energy-efficient and delay sensitive routing paths using mobility prediction in mobile wsn: mathematical optimization, markov chains, and deep learning approaches
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/2889091baf304051ab02f760954c1a55
work_keys_str_mv	AT germanamontoya energyefficientanddelaysensitiveroutingpathsusingmobilitypredictioninmobilewsnmathematicaloptimizationmarkovchainsanddeeplearningapproaches AT carloslozanogarzon energyefficientanddelaysensitiveroutingpathsusingmobilitypredictioninmobilewsnmathematicaloptimizationmarkovchainsanddeeplearningapproaches AT yeziddonoso energyefficientanddelaysensitiveroutingpathsusingmobilitypredictioninmobilewsnmathematicaloptimizationmarkovchainsanddeeplearningapproaches
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Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches

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