An analysis of the lifetime of OLSR networks

The Optimized Link State Routing (OLSR) protocol is a well-known route discovery protocol for ad-hoc networks. OLSR optimizes the flooding of link state information through the network using multipoint relays (MPRs). Only nodes selected as MPRs are responsible for forwarding control traffic. Many research papers aim to optimize the selection of MPRs with a specific purpose in mind: e.g., to minimize their number, to keep paths with high Quality of Service or to maximize the network lifetime (the time until the first node runs out of energy). In such analyzes often the effects of the network structure on the MPR selection are not taken into account. In this paper we show that the structure of the network can have a large impact on the MPR selection. In highly regular structures (such as grids) there is even no variation in the MPR sets that result from various MPR selection mechanisms. Furthermore, we study the influence of the network structure on the network lifetime problem in a setting where at regular intervals messages are broadcasted using MPRs. We introduce the ’maximum forcedness ratio’, as a key parameter of the network to describe how much variation there is in the lifetime results of various MPR selection heuristics. Although we focus our attention to OLSR, being a widely implemented protocol, on a more abstract level our results describe the structure of connected sets dominating the 2-hop neighborhood of a node

Energy-aware self stabilization in mobile ad hoc networks : A multicasting case study

Dynamic networks, e.g. Mobile Ad Hoc Networks (MANETs), call for adaptive protocols that can tolerate topological changes due to nodes ’ mobility and depletion of battery power. Also proactivity in these protocols is essential to ensure low latency. Self-stabilization techniques for distributed systems provide both adaptivity and proactivity to make it suitable for the MANETs. However, energyefficiency- a prime concern in MANETs with batterypowered nodes- is not guaranteed by self-stabilization. In this paper, we propose a node-based energy metric that minimizes the energy consumption of the multicast tree by taking into account the overhearing cost. We apply the metric to Self-Stabilizing Shortest Path Spanning Tree (SS-SPST) protocol to obtain energy-aware SS-SPST (SS-SPST-E). Using simulations, we study the energy-latency tradeoff by comparing SS-SPST-E with SS-SPST and other MANET multicast protocols, such as ODMRP and MAODV.

Minimizing energy and maximizing network lifetime multicasting in wireless ad hoc networks

Abstract-Most mobile nodes in a wireless ad hoc network are powered by energy limited batteries, the limited battery lifetime imposes a constraint on the network performance. Therefore, energy efficiency is paramount of importance in the design of routing protocols for the applications in such a network, and efficient operations are critical to enhance the network lifetime. In this paper we consider energy-efficient routing for the minimizing energy and maximizing network lifetime multicast problem in ad hoc networks. We aim to construct a multicast tree rooted at the source and spanning the destination nodes such that the minimum residual battery energy (also referred to the network lifetime) among the nodes in the network is maximized and the total transmission energy consumption is minimized. Due to the NP-hardness of the concerned problem, all previously proposed algorithms for it are heuristic algorithms, and there is little known about the analytical performance of these algorithms in terms of approximation ratios. We here focus on devising approximation algorithms for the problem with provably guaranteed approximation ratios. Specifically, we present an approximation algorithm for finding a multicast tree such that the total transmission energy consumption is no more than γ times of the optimum, under the constraint that the network lifetime is no less than β times of the optimum, where γ is either 4 ln K or O(K ), depending on whether the network is symmetric or not, and β are constants with 0 < , β ≤ 1, and K is the number of destination nodes in a multicast session

Distributed energy efficient channel allocation in underlay multicast D2D communications

In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one.Ministerio de Ciencia, Innovación y Universidades | Ref. GO2EDGERED2018-102563-TAgencia Estatal de Investigación | Ref. TEC2017-85587-RAgencia Estatal de Investigación | Ref. RED2018-102563-

Symbiotic interaction between humans and robot swarms

Comprising of a potentially large team of autonomous cooperative robots locally interacting and communicating with each other, robot swarms provide a natural diversity of parallel and distributed functionalities, high flexibility, potential for redundancy, and fault-tolerance. The use of autonomous mobile robots is expected to increase in the future and swarm robotic systems are envisioned to play important roles in tasks such as: search and rescue (SAR) missions, transportation of objects, surveillance, and reconnaissance operations. To robustly deploy robot swarms on the field with humans, this research addresses the fundamental problems in the relatively new field of human-swarm interaction (HSI). Four groups of core classes of problems have been addressed for proximal interaction between humans and robot swarms: interaction and communication; swarm-level sensing and classification; swarm coordination; swarm-level learning. The primary contribution of this research aims to develop a bidirectional human-swarm communication system for non-verbal interaction between humans and heterogeneous robot swarms. The guiding field of application are SAR missions. The core challenges and issues in HSI include: How can human operators interact and communicate with robot swarms? Which interaction modalities can be used by humans? How can human operators instruct and command robots from a swarm? Which mechanisms can be used by robot swarms to convey feedback to human operators? Which type of feedback can swarms convey to humans? In this research, to start answering these questions, hand gestures have been chosen as the interaction modality for humans, since gestures are simple to use, easily recognized, and possess spatial-addressing properties. To facilitate bidirectional interaction and communication, a dialogue-based interaction system is introduced which consists of: (i) a grammar-based gesture language with a vocabulary of non-verbal commands that allows humans to efficiently provide mission instructions to swarms, and (ii) a swarm coordinated multi-modal feedback language that enables robot swarms to robustly convey swarm-level decisions, status, and intentions to humans using multiple individual and group modalities. The gesture language allows humans to: select and address single and multiple robots from a swarm, provide commands to perform tasks, specify spatial directions and application-specific parameters, and build iconic grammar-based sentences by combining individual gesture commands. Swarms convey different types of multi-modal feedback to humans using on-board lights, sounds, and locally coordinated robot movements. The swarm-to-human feedback: conveys to humans the swarm's understanding of the recognized commands, allows swarms to assess their decisions (i.e., to correct mistakes: made by humans in providing instructions, and errors made by swarms in recognizing commands), and guides humans through the interaction process. The second contribution of this research addresses swarm-level sensing and classification: How can robot swarms collectively sense and recognize hand gestures given as visual signals by humans? Distributed sensing, cooperative recognition, and decision-making mechanisms have been developed to allow robot swarms to collectively recognize visual instructions and commands given by humans in the form of gestures. These mechanisms rely on decentralized data fusion strategies and multi-hop messaging passing algorithms to robustly build swarm-level consensus decisions. Measures have been introduced in the cooperative recognition protocol which provide a trade-off between the accuracy of swarm-level consensus decisions and the time taken to build swarm decisions. The third contribution of this research addresses swarm-level cooperation: How can humans select spatially distributed robots from a swarm and the robots understand that they have been selected? How can robot swarms be spatially deployed for proximal interaction with humans? With the introduction of spatially-addressed instructions (pointing gestures) humans can robustly address and select spatially- situated individuals and groups of robots from a swarm. A cascaded classification scheme is adopted in which, first the robot swarm identifies the selection command (e.g., individual or group selection), and then the robots coordinate with each other to identify if they have been selected. To obtain better views of gestures issued by humans, distributed mobility strategies have been introduced for the coordinated deployment of heterogeneous robot swarms (i.e., ground and flying robots) and to reshape the spatial distribution of swarms. The fourth contribution of this research addresses the notion of collective learning in robot swarms. The questions that are answered include: How can robot swarms learn about the hand gestures given by human operators? How can humans be included in the loop of swarm learning? How can robot swarms cooperatively learn as a team? Online incremental learning algorithms have been developed which allow robot swarms to learn individual gestures and grammar-based gesture sentences supervised by human instructors in real-time. Humans provide different types of feedback (i.e., full or partial feedback) to swarms for improving swarm-level learning. To speed up the learning rate of robot swarms, cooperative learning strategies have been introduced which enable individual robots in a swarm to intelligently select locally sensed information and share (exchange) selected information with other robots in the swarm. The final contribution is a systemic one, it aims on building a complete HSI system towards potential use in real-world applications, by integrating the algorithms, techniques, mechanisms, and strategies discussed in the contributions above. The effectiveness of the global HSI system is demonstrated in the context of a number of interactive scenarios using emulation tests (i.e., performing simulations using gesture images acquired by a heterogeneous robotic swarm) and by performing experiments with real robots using both ground and flying robots

Modelo de predicción de riesgo en recursos hídricos para agricultura de precisión

En los últimos años la agricultura de precisión a tomado mucha relevancia en la búsqueda de optimizar recursos y obtener mejores resultados, dia a dia se trabaja sobre nuevas tecnologías que permitan llegar al productor para obtener dichas mejoras. En los cultivos que requieren una alta demanda de agua, como es el de arroz, se aplican diferentes técnicas de riego para lograr bajar la demanda hídrica y seguir obteniendo altos rindes. No obstante, se siguen presentando dificultades para realizar un monitoreo óptimo y en tiempo real. El presente trabajo se desarrolla en el IDTILAB de la Facultad de Ciencia y Tecnología de UADER (Concepción del Uruguay, Entre Ríos), en conjunto con la seccional de INTA (Instituto Nacional de Tecnología Agropecuaria, Concepción del Uruguay), y presenta un modelo de comportamiento y prototipo innovador para monitorear cultivos de precisión en tiempo real. Fundado en lo más reciente de la minería de datos temporal, emplea una extensión de los conocidos Sistemas Armónicos (HS por sus siglas en inglés) (Lopez de Luise D. 2013) denominada Sistemas Armónicos difusos (Fuzzy Harmonic System, FHS) (Lopez de Luise D. 2013a , 2013b) (Bel W. 2018) que constituye un heurístico simple y liviano capaz de detectar y predecir los eventos críticos de estrés hídrico en los lotes de los cultivos de arroz. Se presenta el prototipo funcional de KRONOS.AgroData y KRONOS.AgroMonitor que implementa el modelo FHS adaptado para la predicción del nivel de riesgo de sequía en los lotes de riego en cultivos de arroz de la zona de San Salvador ubicada en (Entre Ríos) y en la zona de INTA, Concepción del Uruguay (Entre Ríos). Este prototipo está realizado con tecnología Arduino para la adquisición de datos y tecnologías web como React®, NextJS®, NodeJS® y MQTT®. El diseño permite evaluar el rendimiento y eficiencia del modelo propuesto en un entorno real de prueba de campo donde intervienen variables de diverso tipo (climatológicas, variaciones de humedad en suelo, nivel hídrico en suelo, PH, entre otras). De los estudio de campo y los análisis estadísticos que se muestran en este trabajo, se puede afirmar que el modelo derivado permite determinar intervalos de muestreo y riego mucho más adecuados que los tradicionales, y evaluar satisfactoriamente los rindes y condiciones de cultivo.Facultad de Informátic

Roteamento multicast multisessão: modelos e algoritmos

Multicast Technology has been studied over the last two decades and It has shown to be a good approach to save network resources. Many approaches have been considered to solve the multicast routing problem considering only one session and one source to attending session‘s demand, as well, multiple sessions with more than one source per session. In this thesis, the multicast routing problem is explored taking in consideration the models and the algorithms designed to solve it when where multiple sessions and sources. Two new models are proposed with different focuses. First, a mono-objective model optimizing residual capacity, Z, of the network subject to a budget is designed and the objective is to maximize Z. Second, a multi-objective model is designed with three objective functions: cost, Z and hops counting. Both models consider multisession scenario with one source per session. Besides, a third model is examined. This model was designed to optimize Z in a scenario with multiple sessions with support to more than one source per session. An experimental analysis was realized over the models considered. For each model, a set of algorithms were designed. First, an ACO, a Genetic algorithm, a GRASP and an ILS algorithm were designed to solve the mono-objective model – optimizing Z subject to a budget. Second, a set of algorithm were designed to solve the multi-objective model. The classical approaches were used: NSGA2, ssNSGA2, SMS-EMOA, GDE3 and MOEA/D. In addition, a transgenetic algorithm was designed to solve the problem and it was compared against the classical approaches. This algorithm considers the use of subpopulations during the evolution. Each subpopulation is based on a solution construction operator guided by one of the objective functions. Some solutions are considered as elite solutions and they are considered to be improved by a transposon operator. Eight versions of the transgenetic algorithm were evaluated. Third, an algorithm was designed to solve the problem with multiple sessions and multiple sources per sessions. This algorithm is based on Voronoi Diagrams and it is called MMVD. The algorithm designed were evaluated on large experimental analysis. The sample generated by each algorithm on the instances were evaluated based on non-parametric statistical tests. The analysis performed indicates that ILS and Genetic algorithm have outperformed the ACO and GRASP. The comparison between ILS and Genetic has shown that ILS has better processing time performance. In the multi-objective scenario, the version of Transgenetic called cross0 has shown to be statistically better than the other algorithms in most of the instances based on the hypervolume and addictive/multiplicative epsilon quality indicators. Finally, the MMVD algorithm has shown to be better than the algorithm from literature based on the experimental analysis performed for the model with multiple session and multiple sources per session.A tecnologia multicast tem sido amplamente estudada ao longo dos anos e apresenta-se como uma solução para melhor utilização dos recursos da rede. Várias abordagens já foram avaliadas para o problema de roteamento desde o uso de uma sessão com apenas uma fonte a um cenário com múltiplas sessões e múltiplas fontes por sessão. Neste trabalho, é feito um estudo dos modelos matemáticos para o problema com múltiplas sessões e múltiplas fontes. Dois modelos matemáticos foram propostos: uma versão multissessão mono-objetivo que visa a otimização da capacidade residual sujeito a um limite de custo e uma versão multiobjetivo com três funções-objetivo. Ambos os modelos levam em conta o cenário multissessão com uma fonte por sessão. Além disso, um estudo algorítmico foi realizado sobre um modelo da literatura que utiliza múltiplas fontes por sessão. Três conjuntos de algoritmos foram propostos. O primeiro conjunto trata do problema mono-objetivo proposto e considera as abordagens ACO, Genético, GRASP e ILS. O segundo conjunto consiste dos algoritmos propostos para o modelo multiobjetivo. Foram projetados os seguintes algoritmos: NSGA2, ssNSGA2, GDE3, MOEA/D e SMS-EMOA. Além disso, foi projetado um algoritmo transgenético com subpopulações baseadas em operadores de criação de solução direcionados por objetivos do problema. Também foi utilizado o conceito de soluções de elite. No total, 8 versões do algoritmo transgenético foram avaliadas. O terceiro conjunto de algoritmos consiste da heurística MMVD proposta para o modelo da literatura com múltiplas fontes por sessão. Esta heurística é baseada no uso de diagramas de Voronoi. O processo experimental foi realizado com amplo número de instâncias configuradas de modo a avaliar diferentes situações. Os resultados foram comparados utilizando métodos estatísticos não-paramétricos. A análise final indicou que o ILS e o Genético obtiveram resultados muito similares, entretanto o ILS possui melhor tempo de processamento. A versão cross0 do algoritmo transgenético obteve o melhor resultado em praticamente todos os cenários avaliados. A heurística MMVD obteve excelentes resultados sobre algoritmos da literatura