The use of computational geometry techniques to resolve the issues of coverage and connectivity in wireless sensor networks

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.Web of Science2218art. no. 700

Optimal Deployment of Solar Insecticidal Lamps over Constrained Locations in Mixed-Crop Farmlands

Solar Insecticidal Lamps (SILs) play a vital role in green prevention and control of pests. By embedding SILs in Wireless Sensor Networks (WSNs), we establish a novel agricultural Internet of Things (IoT), referred to as the SILIoTs. In practice, the deployment of SIL nodes is determined by the geographical characteristics of an actual farmland, the constraints on the locations of SIL nodes, and the radio-wave propagation in complex agricultural environment. In this paper, we mainly focus on the constrained SIL Deployment Problem (cSILDP) in a mixed-crop farmland, where the locations used to deploy SIL nodes are a limited set of candidates located on the ridges. We formulate the cSILDP in this scenario as a Connected Set Cover (CSC) problem, and propose a Hole Aware Node Deployment Method (HANDM) based on the greedy algorithm to solve the constrained optimization problem. The HANDM is a two-phase method. In the first phase, a novel deployment strategy is utilised to guarantee only a single coverage hole in each iteration, based on which a set of suboptimal locations is found for the deployment of SIL nodes. In the second phase, according to the operations of deletion and fusion, the optimal locations are obtained to meet the requirements on complete coverage and connectivity. Experimental results show that our proposed method achieves better performance than the peer algorithms, specifically in terms of deployment cost

Full-View Coverage Problems in Camera Sensor Networks

Camera Sensor Networks (CSNs) have emerged as an information-rich sensing modality with many potential applications and have received much research attention over the past few years. One of the major challenges in research for CSNs is that camera sensors are different from traditional scalar sensors, as different cameras from different positions can form distinct views of the object in question. As a result, simply combining the sensing range of the cameras across the field does not necessarily form an effective camera coverage, since the face image (or the targeted aspect) of the object may be missed. The angle between the object\u27s facing direction and the camera\u27s viewing direction is used to measure the quality of sensing in CSNs instead. This distinction makes the coverage verification and deployment methodology dedicated to conventional sensor networks unsuitable. A new coverage model called full-view coverage can precisely characterize the features of coverage in CSNs. An object is full-view covered if there is always a camera to cover it no matter which direction it faces and the camera\u27s viewing direction is sufficiently close to the object\u27s facing direction. In this dissertation, we consider three areas of research for CSNS: 1. an analytical theory for full-view coverage; 2. energy efficiency issues in full-view coverage CSNs; 3. Multi-dimension full-view coverage theory. For the first topic, we propose a novel analytical full-view coverage theory, where the set of full-view covered points is produced by numerical methodology. Based on this theory, we solve the following problems. First, we address the full-view coverage holes detection problem and provide the healing solutions. Second, we propose $k$-Full-View-Coverage algorithms in camera sensor networks. Finally, we address the camera sensor density minimization problem for triangular lattice based deployment in full-view covered camera sensor networks, where we argue that there is a flaw in the previous literature, and present our corresponding solution. For the second topic, we discuss lifetime and full-view coverage guarantees through distributed algorithms in camera sensor networks. Another energy issue we discuss is about object tracking problems in full-view coverage camera sensor networks. Next, the third topic addresses multi-dimension full-view coverage problem where we propose a novel 3D full-view coverage model, and we tackle the full-view coverage optimization problem in order to minimize the number of camera sensors and demonstrate a valid solution. This research is important due to the numerous applications for CSNs. Especially some deployment can be in remote locations, it is critical to efficiently obtain accurate meaningful data

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Distributed navigation of multi-robot systems for sensing coverage

A team of coordinating mobile robots equipped with operation specific sensors can perform different coverage tasks. If the required number of robots in the team is very large then a centralized control system becomes a complex strategy. There are also some areas where centralized communication turns into an issue. So, a team of mobile robots for coverage tasks should have the ability of decentralized or distributed decision making. This thesis investigates decentralized control of mobile robots specifically for coverage problems. A decentralized control strategy is ideally based on local information and it can offer flexibility in case there is an increment or decrement in the number of mobile robots. We perform a broad survey of the existing literature for coverage control problems. There are different approaches associated with decentralized control strategy for coverage control problems. We perform a comparative review of these approaches and use the approach based on simple local coordination rules. These locally computed nearest neighbour rules are used to develop decentralized control algorithms for coverage control problems. We investigate this extensively used nearest neighbour rule-based approach for developing coverage control algorithms. In this approach, a mobile robot gives an equal importance to every neighbour robot coming under its communication range. We develop our control approach by making some of the mobile robots playing a more influential role than other members of the team. We develop the control algorithm based on nearest neighbour rules with weighted average functions. The approach based on this control strategy becomes efficient in terms of achieving a consensus on control inputs, say heading angle, velocity, etc. The decentralized control of mobile robots can also exhibit a cyclic behaviour under some physical constraints like a quantized orientation of the mobile robot. We further investigate the cyclic behaviour appearing due to the quantized control of mobile robots under some conditions. Our nearest neighbour rule-based approach offers a biased strategy in case of cyclic behaviour appearing in the team of mobile robots. We consider a clustering technique inside the team of mobile robots. Our decentralized control strategy calculates the similarity measure among the neighbours of a mobile robot. The team of mobile robots with the similarity measure based approach becomes efficient in achieving a fast consensus like on heading angle or velocity. We perform a rigorous mathematical analysis of our developed approach. We also develop a condition based on relaxed criteria for achieving consensus on velocity or heading angle of the mobile robots. Our validation approach is based on mathematical arguments and extensive computer simulations

Advance in optimal design and deployment of ambient intelligence systems

[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

Ant Colony Optimization for Jointly Solving Relay Node Placement and Trajectory Calculation in Hierarchical Wireless Sensor Networks

Given the locations of the Sensor Nodes in a Wireless Sensor Networks (WSN), finding the minimum number of Relays required and their locations such that each sensor is covered by at least one relay is called the Relay Node Placement (RNP) problem. Given the locations of the relays, finding an optimized trajectory for the Mobile Data Collector (MDC) is another important design problem of the WSN domain. Previous researchers have shown that jointly solving different design problems in the WSN domain often leads to better overall results. In recent years, Ant Colony Optimization (ACO) have emerged as an effective tool for solving complex optimization problems. An ACO based approach for solving the joint problem of Relay Node Placement & Trajectory calculation(RNPT) is presented in this thesis. We also present a deterministic, and a Continuous Ant Colony Optimization ([Special characters omitted.] ACOR ) approach for refining the trajectory produced by the ACO approach

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

Performance analysis of hybrid mobile sensor networks

Ph.DDOCTOR OF PHILOSOPH

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks