186 research outputs found

    Differential Evolution-based 3D Directional Wireless Sensor Network Deployment Optimization

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    Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3D directional wireless sensors (DWSs) are constantly employed, thus, research on the real-time deployment optimization problem of 3D directional wireless sensor networks (DWSNs) based on terrain big data has more practical significance. Based on this, we study the deployment optimization problem of DWSNs in the 3D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers and reliability of DWSNs. We propose a modified differential evolution (DE) algorithm by adopting CR-sort and polynomial-based mutation on the basis of the cooperative coevolutionary (CC) framework, and apply it to address deployment problem of 3D DWSNs. In addition, to reduce computation time, we realize implementation of message passing interface (MPI) parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves satisfying performance with respect to either optimization results or operation time

    3D Multi-Objective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

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    Effective monitoring marine environment has become a vital problem in the marine applications. Traditionally, marine application mostly utilizes oceanographic research vessel methods to monitor the environment and human parameters. But these methods are usually expensive and time-consuming, also limited resolution in time and space. Due to easy deployment and cost-effective, WSNs have recently been considered as a promising alternative for next generation IMGs. This paper focuses on solving the issue of 3D WSN deployment in a 3D engine room space of a very large crude-oil carrier (VLCC), in which many power devices are also considered. To address this 3D WSN deployment problem for maritime applications, a 3D uncertain coverage model is proposed with a new 3D sensing model and an uncertain fusion operator, is presented. The deployment problem is converted into a multi-objective problems (MOP) in which three objectives are simultaneously considered: Coverage, Lifetime and Reliability. Our aim is to achieve extensive Coverage, long Lifetime and high Reliability. We also propose a distributed parallel cooperative co-evolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications. In the simulation experiments, the effectiveness of this algorithm is verified in comparing with five state-of-the-art algorithms. The numerical outputs demonstrate that the proposed method performs the best with respect to both optimization performance and computation time

    Distributed Parallel Cooperative Coevolutionary Multi-Objective Large-Scale Immune Algorithm for Deployment of Wireless Sensor Networks

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    Using immune algorithms is generally a time-intensive process especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large number of variables are divided into several groups. Finally, individual evaluations are allocated across many core processing units, and calculations are performed in parallel. Consequently, the computation time is greatly reduced. The proposed algorithm integrates the idea of immune algorithms, which tend to explore sparse areas in the objective space and use simulated binary crossover for mutation. The proposed algorithm is employed to optimize the 3D terrain deployment of a wireless sensor network, which is a self-organization network. In experiments, compared with several state-of-the-art multi-objective evolutionary algorithms the Cooperative Coevolutionary Generalized Differential Evolution 3, the Cooperative Multi-objective Differential Evolution and the Nondominated Sorting Genetic Algorithm III, the proposed algorithm addresses the deployment optimization problem efficiently and effectively

    Modelling and planning reliable wireless sensor networks based on multi-objective optimization genetic algorithm with changeable length

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    Wireless sensor networks (WSN) have shown their potentials in various applications, which bring a lot of benefits to users from different working areas. However, due to the diversity of the deployed environments and resource constraints, it is difficult to predict the performance of a topology. Besides the connectivity, coverage, cost, network longevity and service quality should all be considered during the planning procedure. Therefore, efficiently planning a reliable WSN is a challenging task, which requires designers coping with comprehensive and interdisciplinary knowledge. A WSN planning method is proposed in this work to tackle the above mentioned challenges and efficiently deploying reliable WSNs. First of all, the above mentioned metrics are modeled more comprehensively and practically compared with other works. Especially 3D ray tracing method is used to model the radio link and sensing signal, which are sensitive to the obstruction of obstacles; network routing is constructed by using AODV protocol; the network longevity, packet delay and packet drop rate are obtained via simulating practical events in WSNet simulator, which to the best of our knowledge, is the first time that network simulator is involved in a planning algorithm. Moreover, a multi-objective optimization algorithm is developed to cater for the characteristics of WSNs. Network size is changeable during evolution, meanwhile the crossovers and mutations are limited by certain constraints to eliminate invalid modifications and improve the computation efficiency. The capability of providing multiple optimized solutions simultaneously allows users making their own decisions, and the results are more comprehensive optimized compared with other state-of-the-art algorithms. Practical WSN deployments are also realized for both indoor and outdoor environments and the measurements coincident well with the generated optimized topologies, which prove the efficiency and reliability of the proposed algorithm

    Big Data Management for Cloud-Enabled Geological Information Services

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    Energy Optimization of Smart Water Systems using UAV Enabled Zero-Power Wireless Communication Networks

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    Real-time energy consumption is a crucial consideration when assessing the effectiveness and efficiency of communication using energy hungry devices. Utilizing new technologies such as UAV-enabled wireless powered communication networks (WPCN) and 3D beamforming, and then a combination of static and dynamic optimization methodologies are combined to improve energy usage in water distribution systems (WDS). A proposed static optimization technique termed the Dome packing method and dynamic optimization methods such as extremum seeking are employed to generate optimum placement and trajectories of the UAV with respect to the ground nodes (GN) in a WDS. In this thesis, a wireless communication network powered by a UAV serves as a hybrid access point to manage many GNsin WDS. The GNs are water quality sensors that collect radio frequency (RF) energy from the RF signals delivered by the UAV and utilise this energy to relay information via an uplink. Optimum strategies are demonstrated to efficiently handle this process as part of a zero-power system: removing the need for manual battery charging of devices, while at the same time optimizing energy and data transfer over WPCN. Since static optimization does not account for the UAV's dynamics, dynamic optimization techniques are also necessary. By developing an efficient trajectory, the suggested technique also reduces the overall flying duration and, therefore, the UAV's energy consumption. This combination of techniques also drastically reduces the complexity and calculation overhead of purely high order static optimizations. To test and validate the efficacy of the extremum seeking implementation, comparison with the optimal sliding mode technique is also undertaken. These approaches are applied to ten distinct case studies by randomly relocating the GNs to various positions. The findings from a random sample of four of these is presented, which reveal that the proposed strategy reduces the UAV's energy usage significantly by about 16 percent compared to existing methods. The (hybrid) static and dynamic zero-power optimization strategies demonstrated here are readily extendable to the control of water quality and pollution in natural freshwater resources and this will be discussed at the end of this thesis

    Localization Context-Aware Models for Wireless Sensor Network

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    Wireless sensor networks (WSNs) are emerging as the key technology to support the Internet of Things (IoT) and smart objects. Small devices with low energy consumption and limited computing resources have wide use in many applications and different fields. Nodes are deployed randomly without a priori knowledge of their location. However, location context is a fundamental feature necessary to provide a context-aware framework to information gathered from sensors in many services such as intrusion detection, surveillance, geographic routing/forwarding, and coverage area management. Nevertheless, only a little number of nodes called anchors are equipped with localization components, such as Global Positioning System (GPS) chips. Worse still, when sensors are deployed in an indoor environment, GPS serves no purpose. This chapter surveys a variety of state-of-the-art existing localization techniques and compares their characteristics by detailing their applications, strengths, and challenges. The specificities and enhancements of the most popular and effective techniques are as well reported. Besides, current research directions in localization are discussed

    Advance in optimal design and deployment of ambient intelligence systems

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    [SPA]Se ha pronosticado un futuro excepcional para los sistemas de Inteligencia Ambiental (AmI). Dichos sistemas comprenden aquellos entornos capaces de anticiparse a las necesidades de la gente, y reaccionar inteligentemente en su ayuda. La inteligencia de estos sistemas proviene de los procesos de toma de decisión, cuyo funcionamiento resulta transparente al usuario. Algunos de estos entornos previstos pertenecen al ámbito de los hogares inteligentes, monitorización de la salud, educación, lugares de trabajo, deportes, soporte en actividades cotidianas, etc. La creciente complejidad de estos entornos hace cada vez más difícil la labor de tomar las decisiones correctas que sirvan de ayuda a los usuarios. Por tanto, la toma de decisiones resulta una parte esencial de estos sistemas. Diversas técnicas pueden utilizarse de forma eficaz en los sistemas AmI para resolver los problemas derivados de la toma de decisiones. Entre ellas están las técnicas de clasificación, y las herramientas matemáticas de programación. En la primera parte de este trabajo presentamos dos entornos AmI donde la toma de decisiones juega un papel fundamental: • Un sistema AmI para el entrenamiento de atletas. Este sistema monitoriza variables ambientales y biométricas de los atletas, tomando decisiones durante la sesión de entrenamiento, que al atleta le ayudan a conseguir un determinado objetivo. Varias técnicas han sido utilizadas para probar diferentes generadores de decisión: interpolación mediante (m, s)-splines, k-Nearest-Neighbors, y programación dinámica mediante Procesos de Decisión de Markov. • Un sistema AmI para detección de caza furtiva. En este caso, el objetivo consiste en localizar el origen de un disparo utilizando, para ello, una red de sensores acústicos. La localización se realiza utilizando el método de multilateración hiperbólica. Además, la calidad de las decisiones generadas está directamente relacionada con la calidad de la información disponible. Por lo tanto, es necesario que los nodos de la infraestructura AmI encargados de la obtención de datos relevantes del usuario y del ambiente, estén en red y situados correctamente. De hecho, el problema de posicionamiento tiene dos partes: los nodos deben ubicarse cerca de los lugares donde ocurren sucesos de interés, y deben estar conectados para que los datos capturados sean transmitidos y tengan utilidad. Adicionalmente, pueden considerarse otras restricciones, tales como el coste de despliegue de red. Por tanto, en el posicionamiento de los nodos es habitual que existan compromisos entre las capacidades de sensorización y de comunicación. Son posibles dos tipos de posicionamiento. Posicionamiento determinista donde puede seleccionarse de forma precisa la posición de cada nodo, y, aleatorio donde debido a la gran cantidad de nodos o a lo inaccesible del terreno de depliegue, sólo resulta posible la distribución aleatoria de los nodos. Esta tesis aborda tres problemas de posicionamiento de red. Los dos primeros problemas se han planteado de forma general, siendo de aplicación a cualquier tipo de escenario AmI. El objetivo es seleccionar las mejores posiciones para los nodos y mantener los nodos de la red conectados. Las opciones estudiadas son un posicionamiento determinista resuelto mediante el metaheurístico Ant Colony Optimization para dominios continuos, y un posicionamiento aleatorio, donde se realiza un despliegue cuasi-controlado mediante varios clusters de red. En cada clúster podemos determinar tanto el punto objetivo de despliegue, como la dispersión de los nodos alrededor de dicho punto. En este caso, el problema planteado tiene naturaleza estocástica y se resuelve descomponiéndolo en fases de despliegue, una por clúster. Finalmente, el tercer escenario de despliegue de red está estrechamente ligado al entorno AmI para la detección de caza furtiva. En este caso, utilizamos el método matemático de descenso sin derivadas. El objetivo consiste en maximizar la cobertura, minimizando a la vez el coste de despliegue. Debido a que los dos objetivos son opuestos, se utiliza un frente Pareto para que el diseñador seleccione un punto de operación. [ENG] A brilliant future is forecasted for Ambient Intelligence (AmI) systems. These comprise sensitive environments able to anticipate people’s actions, and to react intelligently supporting them. AmI relies on decision-making processes, which are usually hidden to the users, giving rise to the so-called smart environments. Some of those envisioned environments include smart homes, health monitoring, education, workspaces, sports, assisted living, and so forth. Moreover, the complexity of these environments is continuously growing, thereby increasing the difficulty of making suitable decisions in support of human activity. Therefore, decision-making is one of the critical parts of these systems. Several techniques can be efficiently combined with AmI environments and may help to alleviate decisionmaking issues. These include classification techniques, as well as mathematical programming tools. In the first part of this work we introduce two AmI environments where decisionmaking plays a primary role: • An AmI system for athletes’ training. This system is in charge of monitoring ambient variables, as well as athletes’ biometry and making decisions during a training session to meet the training goals. Several techniques have been used to test different decision engines: interpolation by means of (m, s)-splines, k-Nearest-Neighbors and dynamic programming based on Markov Decision Processes. • An AmI system for furtive hunting detection. In this case, the aim is to locate gunshots using a network of acoustic sensors. The location is performed by means of a hyperbolic multilateration method. Moreover, the quality of the decisions is directly related to the quality of the information available. Therefore, is necessary that nodes in charge of sensing and networking tasks of the AmI infrastructure must be placed correctly. In fact, the placement problem is twofold: nodes must be near important places, where valuable events occur, and network connectivity is also mandatory. In addition, some other constraints, such as network deployment cost could be considered. Therefore, there are usually tradeoffs between sensing capacity and communication capabilities. Two kinds of placement options are possible. Deterministic placements, where the position for each node can be precisely selected, and random deployments where, due to the large number of nodes, or the inaccessibility of the terrain, the only suitable option for deployment is a random scattering of the nodes. This thesis addresses three problems of network placement. The first two problems are not tied to a particular case, but are applicable to a general AmI scenario. The goal is to select the best positions for the nodes, while connectivity constraints are met. The options examined are a deterministic placement, which is solved by means of an Ant Colony Optimization metaheuristic for continuous domains, and a random placement, where partially controlled deployments of clustered networks take place. For each cluster, both the target point and dispersion can be selected, leading to a stochastic problem, which is solved by decomposing it in several steps, one per cluster. Finally, the third network placement scenario is tightly related to the furtive hunting detection AmI environment. Using a derivate-free descent methodology, the goal is to select the placement with maximal sensing coverage and minimal cost. Since both goals are contrary, the Pareto front is constructed to enable the designer to select the desired operational point.Universidad Politécnica de Cartagen
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