A New Approach for Information Dissemination in VANETs Based on Covering Location and Metaheuristics

Masegosa, Antonio D.; Iglesia, Idoia de la; Hernández-Jayo, Unai; Díez, Luis Enrique; Bahillo, Alfonso; Onieva, Enrique

doi:10.1007/978-3-319-62359-7_9

Cited by 5 publications

(12 citation statements)

References 21 publications

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“…NAVI presented many advantages as an appropriate uplink performance, reduced use of fixed infrastructure and better use of individual technologies. More recently, in [20], A.D. Masegosa et al using this VANET architecture, they modeled the selection of vehicles used as VI by means of a CLP whose objective consists on maximizing the covering (vehicles that receive the message), while minimizing the number of vehicles used as VI. They also proposed a Genetic Algorithm to solve the optimization problem, and they show that this new methodology outperformed the VI selection used in [10].…”

Section: Related Work 21 Information Dissemination In Vanetsmentioning

confidence: 99%

“…As mentioned in the introduction, in this paper, we aim at extending the research done in [20]. Therefore, we will use the same VANET architecture based on NAVI that will be further described in Section 3.1.…”

Section: Related Work 21 Information Dissemination In Vanetsmentioning

confidence: 99%

“…Based on the communication architecture for VANETs presented in [10] and called NAVI (Neighbor-Aware Virtual Infrastructure), in a previous work, Masegosa et. al [20] proposed a new approach to address the VI selection process, modelling it as the optimization of a Covering Location optimization Problem (CLP) [11]. Concretely, they modeled those vehicles that must receive the information as demand nodes, and those vehicles that play the role of VI as potential facility locations.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, our objective is to continue deepening the research line proposed in [20] through the development of better optimization methods based on nature-inspired metaheuristics. Specifically, we propose and compare four nature-inspired metaheuristics to improve the performance of this approach for information dissemination in VANETs.…”

Section: Introductionmentioning

confidence: 99%

“…To assess and compare the performance of the new methods and the new modeling approach, we use a real scenario consisting of 45 vehicles moving around in the downtown area of the city of Malaga in Spain. Apart from this, the proposed optimization algorithms are compared against two baseline algorithms, the GA proposed in [20] and the state-of-the-art approach for information dissemination presented in [10].…”

Section: Introductionmentioning

confidence: 99%

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Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

Masegosa

Osaba

Angarita-Zapata

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Connected vehicles are revolutionizing the way in which transport and mobility are conceived. The main technology behind are the socalled Vehicular Ad-Hoc Networks (VANETs), which are communication networks that connect vehicles and facilitate various services. Usually, these services require a centralized architecture where the main server collects and disseminates information from/to vehicles. In this paper, we focus on improving the downlink information dissemination in VANETs with this centralized architecture. With this aim, we model the problem as a Vertex Covering optimization problem and we propose four new nature-inspired methods to solve it: Bat Algorithm (), Firefly Algorithm, Particle Swarm Optimization, and Cuckoo Search. The new methods are tested over data from a real scenario. Results show that these metaheuristics, especially Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization, can be considered as powerful solvers for improving information dissemination in VANETs.

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Section: Related Work 21 Information Dissemination In Vanetsmentioning

confidence: 99%

Section: Related Work 21 Information Dissemination In Vanetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

Masegosa

Osaba

Angarita-Zapata

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Systematic Literature Review of AI/ML Techniques Applied to VANET Routing

Teixeira

Ferreira

Macedo

2022

Lecture Notes in Networks and Systems

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Vehicular ad hoc networks (VANETs) have emerged as a powerful network model for providing road safety, as well as infotainment applications, for vehicle passengers and drivers alike. These networks are characterized by high node mobility, which in turn introduces communication intermittency and unreliability, deteriorating the network's performance. To provide solutions to the concerns of performance of such networks, articles have proposed intelligent approaches to deal with such issues by means of artificial intelligence and machine learning techniques. This study aimed to review the literature regarding intelligent routing protocols in VANETs by focusing on the techniques used and on how the solutions were evaluated based on performance. A systematic literature review of studies was conducted by adopting the snowballing procedure to collect studies that propose novel approaches to solving the VANET routing protocol by means of intelligent algorithms. The 86 included studies reported that heuristics, fuzzy logic systems and reinforcement learning approaches are the most popular and effective methods to improve network performance in the dynamic VANET environment. The findings have also shown that the literature has yet to find a consensus as to how to evaluate routing protocol performance. An evaluation and comparison framework is required as to enable transparent routing protocol design and selection in future vehicle applications.

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Applications of Soft Computing in Intelligent Transportation Systems

Masegosa

Onieva

Osaba

2017

Soft Computing Based Optimization and Decision Models

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Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively proved in many different facets of transport and Soft Computing has played a major role in achieving these successful results. This book chapter aims at gathering and discussing some of the most relevant and recent advances of the application of Soft Computing in four important areas of Intelligent Transportation Systems as autonomous driving, traffic state prediction, vehicle route planning and vehicular ad hoc networks.

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A New Approach for Information Dissemination in VANETs Based on Covering Location and Metaheuristics

Cited by 5 publications

References 21 publications

Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

Systematic Literature Review of AI/ML Techniques Applied to VANET Routing

Applications of Soft Computing in Intelligent Transportation Systems

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