Cost-efficient Selective Network Caching in Large-Area Vehicular Networks using Multi-objective Heuristics

In the last decade the interest around network caching tech- niques has augmented notably for alleviating the ever-growing demand of resources by end users in mobile networks. This gained momentum stems from the fact that even though the overall volume of traffic re- trieved from Internet has increased at an exponential pace over the last years, several studies have unveiled that a large fraction of this traffic is usually accessed by multiple end users at nearby locations, i.e. content demands are often local and redundant across terminals close to each other, even in mobility. In this context this manuscript explores the ap- plication of multi-objective heuristics to optimally allocate cache profiles over urban scenarios with mobile receivers (e.g. vehicles). To this end we formulate two conflicting objectives: the utility of the cache allocation strategy, which roughly depends on the traffic offloaded from the net- work and the number of users demanding contents; and its cost, given by an cost per unit of stored data and the rate demanded by the cached profile. Simulations are performed and discussed over a realistic vehicu- lar scenario modeled over the city of Cologne (Germany), from which it is concluded that the proposed heuristic solver excels at finding caching solutions differently balancing the aforementioned objectives

An Analysis of Coalition-Competition Pricing Strategies for Multi-Operator Mobile Traffic Offloading using Bi-objective Heuristics

In a competitive market relationships between telecommuni- cations operators serving simultaneously over a certain geographical area are diverse and motivated by very different business strategies and goals. Such relationships ultimately yield distinct pricing portfolios depending on the contractual affiliation of the user being served. Furthermore a key role in the last decade is the concept of tethering (connection sharing) which, when controlled by the operator, may help alleviating the con- sumption of network resources in densely populated scenarios. In this work we investigate the application of bi-objective heuristics for the de- sign of Pareto-optimal network topologies leading to an optimal Pareto between the revenue of the incumbent operators in the scenario and the quality of service degradation experienced by the end users as a result of tethering. Based on computer simulation this work unveils that such a Pareto-optimal set of topologies is strongly determined by the market relationships between such operators

ANALYTICS AND DATA SCIENCE APPLIED TO THE TRAJECTORY OUTLIER DETECTION

Nowadays, logistics for transportation and distribution of merchandise are a key element to increase the competitiveness of companies. However, the election of alternative routes outside the panned routes causes the logistic companies to provide a poor-quality service, with units that endanger the appropriate deliver of merchandise and impacting negatively the way in which the supply chain works. This paper aims to develop a module that allows the processing, analysis and deployment of satellite information oriented to the pattern analysis, to find anomalies in the paths of the operators by implementing the algorithm TODS, to be able to help in the decision making. The experimental results show that the algorithm detects optimally the abnormal routes using historical data as a base

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

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

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)

Microclimatic changes and the indirect loss of ant diversity in a tropical agroecosystem

Recent changes in the coffee agroecosystem of Costa Rica were used to study the mechanism of biodiversity loss in transforming agroecosystems, focusing on the ground-foraging ant community. Coffee farms are being transformed from vegetationally diverse shaded agroforestry systems to unshaded coffee monocultures. We tested the hypothesis that the high-light environment and lack of leaf litter cover in the unshaded system are the determinants of the differences in ground-foraging ant diversity. Four treatments were established within the light gaps of a shaded plantation: shade, leaf litter, shade plus leaf litter, and a control (no shade or leaf litter added). Ants were sampled using tuna fish baits and light and temperature were measured. Shade and leaf litter had a significant effect on the ant fauna but probably for indirect reasons having to do with species interactions. In both shade treatments, Solenopsis geminata , the tropical fire ant, decreased significantly while the other species increased. The possibility that the physical factor changes the nature of competitive interactions between the most abundant species is discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47696/1/442_2004_Article_BF00333736.pd

Pest suppression by ant biodiversity is modified by pest biodiversity

Summary 1. Agroecosystems are often complex ecosystems with diverse food webs. Changes in food web complexity may have important context-dependent consequences for pest control strategies. 2. The success of predator introductions to suppress pests may depend on the diversity of pests. For crops with diverse pest assemblages, it is hypothesized that diverse predator communities are needed to suppress diverse pest assemblages below damaging levels. 3. In this study, we compare the ability of ant predator monocultures and polycultures to suppress single-and diverse-(three species) pest assemblages in a coffee foodweb. We use a factorial experiment that compared treatments of predator and pest diversity to understand the impact of pest diversity on multiple predator effects. 4. We show that predator polycultures enhanced pest risk relative to predator monocultures significantly more in the diverse-pest treatment relative to in the single-pest treatments for two of three pest species. Further, we show that pest diversity significantly reduced pest risk in all predator treatments except for the predator polyculture treatment. 5. These results suggest that pest diversity may reduce the efficiency of single predator species at suppressing pest damage, but do not limit multiple predator species. This in turn leads to stronger effects of predator diversity with greater pest diversity. These results highlight the need to consider foodweb complexity, such as pest diversity, when designing and implementing biology control programs

Hybridizing Cartesian Genetic Programming and Harmony Search for Adaptive Feature Construction in Supervised Learning Problems

The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very di- verse approaches. In this context this work focuses on the automatic con- struction 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 proce- dure. The performance of the proposed ACHS scheme is assessed and com- pared 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

Clusters of ant colonies and robust criticality in a tropical agroecosystem

Although sometimes difficult to measure at large scales, spatial pattern is important in natural biological spaces as a determinant of key ecological properties such as species diversity, stability, resiliency and others(1-6). Here we demonstrate, at a large spatial scale, that a common species of tropical arboreal ant forms clusters of nests through a combination of local satellite colony formation and density- dependent control by natural enemies, mainly a parasitic fly. Cluster sizes fall off as a power law consistent with a so-called robust critical state(7). This endogenous cluster formation at a critical state is a unique example of an insect population forming a non- random pattern at a large spatial scale. Furthermore, because the species is a keystone of a larger network that contributes to the ecosystem function of pest control, this is an example of how spatial dynamics at a large scale can affect ecosystem service at a local level.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62598/1/nature06477.pd

THE MICHIGAN BIG WOODS RESEARCH PLOT AT THE EDWIN S. GEORGE RESERVE, PINCKNEY, MI, USA

The Michigan Big Woods research plot is a 23-ha forest dynamics research area at the Edwin S. George Reserve in Pinckney, MI, USA and is part of the Smithsonian Institution’s ForestGEO network of research stations. The plot’s freestanding woody vegetation (trees and shrubs) were censused three times, in 2003, 2008–2010, and 2014; lianas were censused on 20 ha from 2017 to 2018.http://deepblue.lib.umich.edu/bitstream/2027.42/156251/1/MP 207.pdfDescription of MP 207.pdf : Main ArticleSEL