Cristina Perfecto

Millimeter-Wave V2V Communications: Distributed Association and Beam Alignment

Recently, millimeter-wave (mmWave) bands have been postulated as a means to accommodate the foreseen extreme bandwidth demands in vehicular communications, which result from the dissemination of sensory data to nearby vehicles for enhanced environmental awareness and improved safety level. However, the literature is particularly scarce in regards to principled resource allocation schemes that deal with the challenging radio conditions posed by the high mobility of vehicular scenarios. In this paper, we propose a novel framework that blends together matching theory and swarm intelligence to dynamically and efficiently pair vehicles and optimize both transmission and reception beamwidths. This is done by jointly considering channel state information and queue state information when establishing vehicle-to-vehicle (V2V) links. To validate the proposed framework, simulation results are presented and discussed, where the throughput performance as well as the latency/reliability tradeoffs of the proposed approach are assessed and compared with several baseline approaches recently proposed in the literature. The results obtained in this paper show performance gains of 25% in reliability and delay for ultra-dense vehicular scenarios with 50% more active V2V links than the baselines. These results shed light on the operational limits and practical feasibility of mmWave bands, as a viable radio access solution for future high-rate V2V communications.

Network Traffic Sensor for Multiprocessor Architectures: Design Improvement Proposals

This document describes several design proposals to enhance net-work sensor performance on multiprocessor architectures. Our main contributions are related to the design of an autonomous sensor and to the idea of per-forming some parallelization of the analysis. These proposals can be implemented in network sensors such as intrusion detection systems, network antivirus appliances, QoS monitors and any other device based on network traffic analysing. Taking a certain model of traffic analysis as our starting point, we look deeply into some design proposals to address the difficulties involved in the parallelization. In this work, we propose a series of resources that can help us to solve these difficulties. Later, we study the prototypes developed in order to test different design alternatives and, finally, present selected case studies. We finish by quantitatively analysing the results to validate our design proposals.

Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems

HighlightsWe present a new iterative feature construction approach for supervised learning model based on the meta-heuristic Harmony Search (HS) algorithm and Cartesian Genetic Programming.We propose a novel method to incorporate soft information about the relevance of the constructed features in the HS algorithm so as to enhance its convergence.The performance of the proposed scheme is assessed over datasets from the literature, with promising results that support its suitability to deal with legacy datasets. The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics.

DRED: An evolutionary diversity generation method for concept drift adaptation in online learning environments

Abstract Nowadays fast-arriving information flows lay the basis of many data mining applications. Such data streams are usually affected by non-stationary events that eventually change their distribution (concept drift), causing that predictive models trained over these data become obsolete and do not adapt suitably to the new distribution. Specially in online learning scenarios, there is a pressing need for new algorithms that adapt to this change as fast as possible, while maintaining good performance scores. Recent studies have revealed that a good strategy is to construct highly diverse ensembles towards utilizing them shortly after the drift (independently from the type of drift) to obtain good performance scores. However, the existence of the so-called trade-off between stability (performance over stable data concepts) and plasticity (recovery and adaptation after drift events) implies that the construction of the ensemble model should account simultaneously for these two conflicting objectives. In this regard, this work presents a new approach to artificially generate an optimal diversity level when building prediction ensembles once shortly after a drift occurs. The approach uses a Kernel Density Estimation (KDE) method to generate synthetic data, which are subsequently labeled by means a multi-objective optimization method that allows training each model of the ensemble with a different subset of synthetic samples. Computational experiments reveal that the proposed approach can be hybridized with other traditional diversity generation approaches, yielding optimized levels of diversity that render an enhanced recovery from drifts.

A Harmony Search Approach for the Selective Pick-Up and Delivery Problem with Delayed Drop-Off

In the last years freight transportation has undergone a sharp increase in the scales of its underlying processes and protocols mainly due to the ever-growing community of users and the increasing number of on-line shopping stores. Furthermore, when dealing with the last stage of the shipping chain an additional component of complexity enters the picture as a result of the fixed availability of the destination of the good to be delivered. As such, business opening hours and daily work schedules often clash with the delivery times programmed by couriers along their routes. In case of conflict, the courier must come to an arrangement with the destination of the package to be delivered or, alternatively, drop it off at a local depot to let the destination pick it up at his/her time convenience. In this context this paper will formulate a variant of the so-called courier problem under economic profitability criteria including the cost penalty derived from the delayed drop-off. In this context, if the courier delivers the package to its intended destination before its associated deadline, he is paid a reward. However, if he misses to deliver in time, the courier may still deliver it at the destination depending on its availability or, alternatively, drop it off at the local depot assuming a certain cost. The manuscript will formulate the mathematical optimization problem that models this logistics process and solve it efficiently by means of the Harmony Search algorithm. A simulation benchmark will be discussed to validate the solutions provided by this meta-heuristic solver and to compare its performance to other algorithmic counterparts.

Cost-efficient deployment of multi-hop wireless networks over disaster areas using multi-objective meta-heuristics

Abstract Nowadays there is a global concern with the growing frequency and magnitude of natural disasters, many of them associated with climate change at a global scale. When tackled during a stringent economic era, the allocation of resources to efficiently deal with such disaster situations (e.g., brigades, vehicles and other support equipment for fire events) undergoes severe budgetary limitations which, in several proven cases, have lead to personal casualties due to a reduced support equipment. As such, the lack of enough communication resources to cover the disaster area at hand may cause a risky radio isolation of the deployed teams and ultimately fatal implications, as occurred in different recent episodes in Spain and USA during the last decade. This issue becomes even more dramatic when understood jointly with the strong budget cuts lately imposed by national authorities. In this context, this article postulates cost-efficient multi-hop communications as a technological solution to provide extended radio coverage to the deployed teams over disaster areas. Specifically, a Harmony Search (HS) based scheme is proposed to determine the optimal number, position and model of a set of wireless relays that must be deployed over a large-scale disaster area. The approach presented in this paper operates under a Pareto-optimal strategy, so a number of different deployments is then produced by balancing between redundant coverage and economical cost of the deployment. This information can assist authorities in their resource provisioning and/or operation duties. The performance of different heuristic operators to enhance the proposed HS algorithm are assessed and discussed by means of extensive simulations over synthetically generated scenarios, as well as over a more realistic, orography-aware setup constructed with LIDAR (Laser Imaging Detection and Ranging) data captured in the city center of Bilbao (Spain).

Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria

This work focuses on wide-scale freight transportation logistics motivated by the sharp increase of on-line shopping stores and the upsurge of Internet as the most frequently utilized selling channel during the last decade. This huge ecosystem of one-click-away catalogs has ultimately unleashed the need for efficient algorithms aimed at properly scheduling the underlying transportation resources in an efficient fashion, especially over the so-called last mile of the distribution chain. In this context the selective pickup and delivery problem focuses on determining the optimal subset of packets that should be picked from its origin city and delivered to their corresponding destination within a given time frame, often driven by the maximization of the total profit of the courier service company. This manuscript tackles a realistic variant of this problem where the transportation fleet is composed by more than one vehicle, which further complicates the selection of packets due to the subsequent need for coordinating the delivery service from the command center. In particular the addressed problem includes a second optimization metric aimed at reflecting a fair share of the net benefit among the company staff based on their driven distance. To efficiently solve this optimization problem, several nature-inspired metaheuristic solvers are analyzed and statistically compared to each other under different parameters of the problem setup. Finally, results obtained over a realistic scenario over the province of Bizkaia (Spain) using emulated data will be explored so as to shed light on the practical applicability of the analyzed heuristics.

Dandelion-Encoded Harmony Search Heuristics for Opportunistic Traffic Offloading in Synthetically Modeled Mobile Networks

The high data volumes being managed by and transferred through mobile networks in the last few years are the main rationale for the upsurge of research aimed at finding efficient technical means to offload exceeding traffic to alternative communication infrastructures with higher transmission bandwidths. This idea is solidly buttressed by the proliferation of short-range wireless communication technologies (e.g. mobile devices with multiple radio interfaces), which can be conceived as available opportunistic hotspots to which the operator can reroute exceeding network traffic depending on the contractual clauses of the owner at hand. Furthermore, by offloading to such hotspots a higher effective coverage can be attained by those operators providing both mobile and fixed telecommunication services. In this context, the operator must decide if data generated by its users will be sent over conventional 4G+/4G/3G communication links, or if they will instead be offloaded to nearby opportunistic networks assuming a contractual cost penalty. Mathematically speaking, this problem can be formulated as a spanning tree optimization subject to cost-performance criteria and coverage constraints. This paper will elaborate on the efficient solving of this optimization paradigm by means of the Harmony Search meta-heuristic algorithm and the so-called Dandelion solution encoding, the latter allowing for the use of conventional meta-heuristic operators maximally preserving the locality of tree representations. The manuscript will discuss the obtained simulation results over different synthetically modeled setups of the underlying communication scenario and contractual clauses of the users.

Internet quality of service measurement tool for both users and providers

This paper offers an approach to the definition of access speed measurements. An ever growing increase of Internet Service Providers (ISP) and different types of technologies in the Internet access, makes it difficult for users to decide which is the best or most suitable connection to satisfy their needs. From these considerations, the possibility for Internet users of obtaining a real and neutral measurement of their access may help them to decide if it is covering their demands or, on the contrary, the provider is not complying with the terms of the contract. To perform this measurement, an Internet speed test has been defined, as well as the way to obtain a measure which can comply with the objective of giving users a proper idea of how their Internet access is working. This project is included in the Quality of Service (QoS) investigation area in our investigation group.

Joint Topology Optimization, Power Control and Spectrum Allocation for Intra-Vehicular Multi-hop Sensor Networks Using Dandelion-Encoded Heuristics

In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness imposed on critical sensors within the vehicle. As opposed to point-to-point network topologies, multi-hop networking allows for an enhanced communication reliability at the cost of an additional processing overhead. In this context this manuscript poses a novel bi-objective optimization problem aimed at jointly minimizing (1) the average Bit Error Rate (BER) of sensing nodes under a majority fusion rule at the central data collection unit; and (2) the mean delay experienced by packets forwarded by such nodes due to multi-hop networking, frequency channel switching time multiplexing at intermediate nodes. The formulated paradigm is shown to be computationally tractable via a combination of evolutionary meta-heuristic algorithms and Dandelion codes, the latter capable of representing tree-like structures like those modeling the multi-hop routing approach. Simulations are carried out for realistic values of intra-vehicular radio channels and co-channel interference due to nearby IEEE 802.11 signals. The obtained results are promising and pave the way towards assessing the practical performance of the proposed scheme in real setups.