Study of 3D Wireless Sensor Network Based on Overlap Method

Wireless sensor network as a new information acquisition technology has profound impact on people's work and life style, has the very high research value. Energy issues important factor restricting the development of the deep WSN is node, sensor nodes for processing data collected information and communication between nodes will speed up the energy consumption of nodes. Cover the deployment strategy is directly related to the optimal distribution of target area monitoring the degree of perception and limited resources of wireless sensor network, determines the service quality of the wireless sensor network to improve the. How to design an efficient coverage algorithm directly affects the coverage and network lifetime, because the actual environment of 3D wireless sensor network is more close to people, so the 3D WSN. Covering research has more realistic significance. At present, about the research of wireless sensor network many 3D covering literature, according to the general configuration of nodes is divided into deterministic coverage and random covering two aspects. This paper presents a wireless sensor network node for 3D scene coverage model and its deployment method, based on analyzing the common regular polyhedron models used in 3D space coverage, proposed a model based on covering the structure, on the basis of this theory to derive a quantitative relationship between coverage model and node sensing radius, more based on the quantitative relationship between the further calculation of network area remains fully covering the minimum number of nodes are required, the network regional 3D mesh finite mesh node coverage model in accordance with the deployment

Sensor Coverage Strategy in Underwater Wireless Sensor Networks

This paper mainly describes studies hydrophone placement strategy in a complex underwater environment model to compute a set of "good" locations where data sampling will be most effective. Throughout this paper it is assumed that a 3-D underwater topographic map of a workspace is given as input.Since the negative gradient direction is the fastest descent direction, we fit a complex underwater terrain to a differentiable function and find the minimum value of the function to determine the low-lying area of the underwater terrain.The hydrophone placement strategy relies on gradient direction algorithm that solves a problem of maximize underwater coverage: Find the maximize coverage set of hydrophone inside a 3-D workspace. After finding the maximize underwater coverage set, to better take into account the optimal solution to the problem of data sampling, the finite VC-dimension algorithm computes a set of hydrophone that satisfies hydroacoustic signal energy loss constraints. We use the principle of the maximize splitting subset of the coverage set and the ”dual” set of the coverage covering set, so as to find the hitting set, and finally find the suboptimal set (i.e., the sensor suboptimal coverage set).Compared with the random deployment algorithm, although the computed set of hydrophone is not guaranteed to have minimum size, the algorithm does compute with high network coverage quality

SPARCO: Stochastic Performance Analysis with Reliability and Cooperation for Underwater Wireless Sensor Networks

Reliability is a key factor for application-oriented Underwater Sensor Networks (UWSNs) which are utilized for gaining certain objectives and a demand always exists for efficient data routing mechanisms. Cooperative routing is a promising technique which utilizes the broadcast feature of wireless medium and forwards data with cooperation using sensor nodes as relays. Here, we present a cooperation-based routing protocol for underwater networks to enhance their performance called Stochastic Performance Analysis with Reliability and Cooperation (SPARCO). Cooperative communication is explored in order to design an energy-efficient routing scheme for UWSNs. Each node of the network is assumed to be consisting of a single omnidirectional antenna and multiple nodes cooperatively forward their transmissions taking advantage of spatial diversity to reduce energy consumption. Both multihop and single-hop schemes are exploited which contribute to lowering of path-losses present in the channels connecting nodes and forwarding of data. Simulations demonstrate that SPARCO protocol functions better regarding end-to-end delay, network lifetime, and energy consumption comparative to noncooperative routing protocol—improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (iAMCTD). The performance is also compared with three cooperation-based routing protocols for UWSN: Cognitive Cooperation (Cog-Coop), Cooperative Depth-Based Routing (CoDBR), and Cooperative Partner Node Selection Criteria for Cooperative Routing (Coop Re and dth)

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Part 1: acceptance test and administration of a farm of servers. Part 2: improving TCP performance in underwater wireless sensor networks

Dissertação de mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2017Abstract 1 During the last decades, companies and organizations have focused on how to provide to the end-users or clients with web services or applications to make them more closer and involved to the activity. Therefore, many enterprises through their direction of the IT service, propose varieties of applications that allow to the stakeholders to perform what they need. The aim of this report is to present what the application integration job is and to report the missions that I have been able to carry out such as application integration, application qualification, and acceptance tests. This represents in total: - 19 qualified applications, - 33 administrated serversResumo 1 Ao longo das últimas décadas, as empresas e as organizações concentraram-se na forma de fornecer aos usuários finais ou clientes, serviços Web ou aplicativos para torná-los mais próximos e envolvidos na actividade. Portanto, muitas empresas através da sua direcção do serviço de Tecnólogia da Informação TI, propõem variedades de aplicativos que permitem às partes interessadas realizar o que necessitam. O objectivo deste relatório é apresentar o que é o trabalho de integração de aplicativos e as missões que fui capaz de executar, como a integração de aplicativos, a qualificação de aplicativos e testes de aceitação. Isto representa no total: - 19 aplicações qualificadas, - 33 servidores administradosAbstract 2 Underwater wireless sensor networks (UWSNs) are becoming popular due to their important role in different applications, such as offshore search and underwater monitoring. However, the data transmission in this underwater environment is impacted by various aspects such as bandwidth usage limitation, surrounding noise and large acoustic propagation delays. Therefore, communication itself is an outstanding challenge. The well-known traditional transmission control protocol (TCP), one of the most used transport protocol on the internet, is not suitable to enable this technology. Even though TCP variants for the wireless network are not foolproof in an underwater environment, their use could probably be more difficult in such a multi-hop communication system. We have chosen Newreno for our study. This variant is a modern implementation that includes the four congestion control algorithms. These algorithms have proved to be effective when it comes to terrestrial networks which could be a basis for our study. In addition, Newreno is known for its algorithm of recovery of several segments lost within the same sending window. In this dissertation, we have conducted a general study of UWSN technology and examined methods to improve TCP performance in a multi-hop UWSN. And then, we propose Underwater-Newreno (U-Newreno) our enhanced version of Newreno to improve TCP performance in UWSN. U-Newreno consists of two major modifications: controlling the maximum size of the congestion window and the adaptation of the round trip time (RTT) timeout. The results of simulations carried out with the Aquasim simulator show improvements of performances in terms of gain of: packets delivery Retransmission ratio of packets delivery.Resumo 2 As redes de sensores sem fio subaquáticos (Underwater Wireless Sensor Networks- UWSN) estão-se a tornar cada vez mais populares devido à sua importância em diferentes aplicações, como a pesquisa offshore e monitoramento subaquático. No entanto, a transmissão de dados neste ambiente subaquático sofre devido a vários factores, como a limitação do uso da largura de banda, o ruído envolvente e grandes atrasos de propagação acústica. Portanto, a comunicação é um desafio problemático. O familiar transmission control protocol (TCP) tradicional, um dos protocolos de transporte mais utilizados na internet, não é adequado para habilitar esta tecnologia. Mesmo que as variantes TCP para a rede sem fio não sejam infalíveis num ambiente subaquático, o seu uso provavelmente pode ser mais difícil num sistema de comunicação de múltiplos saltos. Nós escolhemos o Newreno para o nosso estudo. Esta variante é uma implementação moderna que inclui os quatro algoritmos de controle de congestionamento. Estes algoritmos demonstraram a sua eficácia em redes terrestres que poderiam ser uma base para o nosso estudo. Além disso, Newreno é conhecido pelo seu algoritmo de recuperação de vários segmentos perdidos dentro da mesma janela de envio. Nesta dissertação, realizamos um estudo geral da tecnologia UWSN e examinamos métodos para melhorar o desempenho do TCP num UWSN de vários saltos. E então, propomos a U-Newreno (Underwater-Newreno), a nossa versão melhorada do Newreno para melhorar o desempenho do TCP no UWSN. O U-Newreno consiste em duas modificações principais: controlar o tamanho máximo da janela de congestionamento e a adaptação do tempo limite “Round Trip Time”(RTT). Os resultados das simulações realizadas com o simulador Aquasim mostram melhorias nos desempenhos em termos de ganho de: • entrega de pacotes • Taxa de retransmissão da entrega de pacotes

Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation

El concepto de entorno inteligente concibe un mundo donde los diferentes tipos de dispositivos inteligentes colaboran para conseguir un objetivo común. En este concepto, inteligencia hace referencia a la habilidad de adquirir conocimiento y aplicarlo de forma autónoma para conseguir el objetivo común, mientras que entorno hace referencia al mundo físico que nos rodea. Por tanto, un entorno inteligente se puede definir como aquel que adquiere conocimiento de su entorno y aplicándolo permite mejorar la experiencia de sus habitantes. La computación ubicua o generalizada permitirá que este concepto de entorno inteligente se haga realidad. Normalmente, el término de computación ubicua hace referencia al uso de dispositivos distribuidos por el mundo físico, pequeños y de bajo precio, que pueden comunicarse entre ellos y resolver un problema de forma colaborativa. Cuando esta comunicación se lleva a cabo de forma inalámbrica, estos dispositivos forman una red de sensores inalámbrica o en inglés, Wireless Sensor Network (WSN). Estas redes están atrayendo cada vez más atención debido al amplio espectro de aplicaciones que tienen, des de soluciones para el ámbito militar hasta aplicaciones para el gran consumo. Esta tesis se centra en las redes de sensores inalámbricas y subacuáticas o en inglés, Underwater Wireless Sensor Networks (UWSN). Estas redes, a pesar de compartir los mismos principios que las WSN, tienen un medio de transmisión diferente que cambia su forma de comunicación de ondas de radio a ondas acústicas. Este cambio hace que ambas redes sean diferentes en muchos aspectos como el retardo de propagación, el ancho de banda disponible, el consumo de energía, etc. De hecho, las señales acústicas tienen una velocidad de propagación cinco órdenes de magnitud menor que las señales de radio. Por tanto, muchos algoritmos y protocolos necesitan adaptarse o incluso rediseñarse. Como el despliegue de este tipo de redes puede ser bastante complicado y caro, se debe planificar de forma precisa el hardware y los algoritmos que se necesitan. Con esta finalidad, las simulaciones pueden resultar una forma muy conveniente de probar todas las variables necesarias antes del despliegue de la aplicación. A pesar de eso, un nivel de precisión adecuado que permita extraer resultados y conclusiones confiables, solamente se puede conseguir utilizando modelos precisos y parámetros reales. Esta tesis propone un ecosistema para UWSN basado en herramientas libres y de código abierto. Este ecosistema se compone de un modelo de recolección de energía y unmodelo de unmódemde bajo coste y bajo consumo con un sistema de activación remota que, junto con otros modelos ya implementados en las herramientas, permite la realización de simulaciones precisas con datos ambientales del tiempo y de las condiciones marinas del lugar donde la aplicación objeto de estudio va a desplegarse. Seguidamente, este ecosistema se utiliza con éxito en el estudio y evaluación de diferentes protocolos de transmisión aplicados a una aplicación real de monitorización de una piscifactoría en la costa del mar Mediterráneo, que es parte de un proyecto de investigación español (CICYT CTM2011-2961-C02-01). Finalmente, utilizando el modelo de recolección de energía, esta plataforma de simulación se utiliza para medir los requisitos de energía de la aplicación y extraer las necesidades de hardware mínimas.Climent Bayarri, JS. (2014). Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3532

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3D Indoor Monitoring

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt and zoom (ptz), the sensing range of a video sensor can be further regarded as a fan-shape in 2d and pyramid-shape in 3d. Such uniqueness attributed to wireless video sensors and the challenges associated with deployment restrictions of indoor monitoring make the traditional sensor coverage, deployment and networked solutions in 2d sensing model environments for wsns ineffective and inapplicable in solving the wireless video sensor network (wvsn) issues for 3d indoor space, thus calling for novel solutions. In this dissertation, we propose optimization techniques and develop solutions that will address the coverage, deployment and network issues associated within wireless video sensor networks for a 3d indoor environment. We first model the general problem in a continuous 3d space to minimize the total number of required video sensors to monitor a given 3d indoor region. We then convert it into a discrete version problem by incorporating 3d grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. Due in part to the uniqueness of the visual sensor directional sensing range, we propose to exploit the directional feature to determine the optimal angular-coverage of each deployed visual sensor. Thus, we propose to deploy the visual sensors from divergent directional angles and further extend k-coverage to ``k-angular-coverage\u27\u27, while ensuring connectivity within the network. We then propose a series of mechanisms to handle obstacles in the 3d environment. We develop efficient greedy heuristic solutions that integrate all these aforementioned considerations one by one and can yield high quality results. Based on this, we also propose enhanced depth first search (dfs) algorithms that can not only further improve the solution quality, but also return optimal results if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced dfs solutions. Finally, this dissertation discusses some future research directions such as in-network traffic routing and scheduling issues

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Journal of Telecommunications and Information Technology, 2017, nr 1

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