Abstract-Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing packets through the network is a challenging task. Therefore, offering an efficient routing strategy is crucial to the deployment of VANETs. This paper deals with the optimal parameter setting of the optimized link state routing (OLSR), which is a well-known mobile ad hoc network routing protocol, by defining an optimization problem. This way, a series of representative metaheuristic algorithms (particle swarm optimization, differential evolution, genetic algorithm, and simulated annealing) are studied in this paper to find automatically optimal configurations of this routing protocol. In addition, a set of realistic VANET scenarios (based in the city of Málaga) have been defined to accurately evaluate the performance of the network under our automatic OLSR. In the experiments, our tuned OLSR configurations result in better quality of service (QoS) than the standard request for comments (RFC 3626), as well as several human experts, making it amenable for utilization in VANET configurations.Index Terms-Metaheuristics, optimization algorithms, optimized link state routing (OLSR), vehicular ad hoc networks (VANET).

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Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems. We hybridize two of these approaches that promote training diversity. One, E-GAN, at each batch, injects mutation diversity by training the (replicated) generator with three independent objective functions then selecting the resulting best performing generator for the next batch. The other, Lipizzaner, injects population diversity by training a two-dimensional grid of GANs with a distributed evolutionary algorithm that includes neighbor exchanges of additional training adversaries, performance based selection and population-based hyper-parameter tuning. We propose to combine mutation and population approaches to diversity improvement. We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner 's grid. Instead, each training round, a loss function is selected with equal probability, from among the three E-GAN uses. Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate that Mustangs provides a statistically faster training method resulting in more accurate networks.

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bahía Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.

This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem model is presented, which accounts for reducing the investment costs, enhance the number of citizens served by the installed bins, and the accessibility to the system. A family of single-and multi-objective heuristics based on the PageRank method and two mutiobjective evolutionary algorithms are proposed. Experimental evaluation performed on real scenarios on the cities of Montevideo (Uruguay) and Bahía Blanca (Argentina) demonstrates the effectiveness of the proposed approaches. The methods allow computing plannings with different trade-off between the problem objectives. The computed results improve over the current planning in Montevideo and provide a reasonable budget cost and quality of service for Bahía Blanca.

Emerging Vehicle-to-Everything (V2X) applications such as Advanced Driver Assistance Systems (ADAS) and Connected and Autonomous Driving (CAD) requires an excessive amount of data by vehicular sensors, collected, processed, and exchanged in real-time. A heterogeneous wireless network is envisioned where multiple Radio Access Technologies (RATs) can coexist to cater for these and other future applications. The primary challenge in such systems is the Radio Resource Management (RRM) strategy and the RAT selection algorithm. In this paper, a Hybrid Vehicular Network (HVN) architecture and protocol stack is proposed, which combines Dedicated Short-Range Communication (DSRC) technologyenabled ad hoc network and infrastructure-based Cellular V2X (C-V2X) technologies. To this end, we address the design and performance evaluation of a distributed RRM entity that manages and coordinates Radio Resources (RR) in both RATs. Central to distributed RRM are adaptive RAT selection and Vertical Handover (VHO) algorithms supported by two procedures. (1) Measurement of Quality of Service (QoS) parameters and associated criteria to select the suitable RAT according to the network conditions. (2) Dynamic communication management (DCM) via implementing RR-QoS negotiation. The simulation results show the effectiveness of the proposed architecture and protocol suite under various parameter settings and performance metrics such as the number of VHOs, packet delivery ratio, and throughput, and latency. INDEX TERMS C-V2X, DSRC, Hybrid Vehicular Networks, IEEE 802.11p, LTE, RAT Selection, Vertical Handover (VHO) I. INTRODUCTION T HE emerging vehicular networking applications and use cases demand stringent Quality of Service (QoS) requirements in terms of latency, data rate, reliability, and communication range. These performance requirements are hard to meet by a single communication technology [1]. Several Radio Access Technologies (RATs) exist for vehicular networking but predominantly include two RATs. (1) The Dedicated Short-Range Communication (DSRC) technology that allows short-range, un-coordinated communication among vehicles and between vehicles and Roadside Units (RSUs), thus establishing Vehicular Ad Hoc Networks (VANETs). (2) The Cellular Vehicle-to-Everything (C-V2X) [2] technology is wildly considered as a feasible alternative for providing vehicular communications because it offers superior performances in terms of throughput and lower latencies. Moreover, simplified network architecture and advanced algorithms for resource management resulted in lower cost and higher performance efficiency. Combing these two competitive standards bring immense opportunities as well as challenges to provide seamless connectivity that could not only enhance existing applications but also spur

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