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| Author | : Vadiraj K. Hombal |
| Publisher | : |
| Total Pages | : 322 |
| Release | : 2009 |
| Genre | : |
| ISBN | : 9781109683943 |
| Author | : Koushil Sreenath |
| Publisher | : |
| Total Pages | : |
| Release | : 2005 |
| Genre | : Electrical engineering and electronics |
| ISBN | : 9780542466519 |
The spatiotemporally varying network topology of mobile sensor networks makes it very suitable for applications such as reconstruction of environmental fields through sampling at locations that maximally reduce the largest uncertainty in the field estimate. Mobile sensor networks comprise of multiple heterogeneous resources and a deadlock-free resource scheduling in the presence of shared and routing resources becomes necessary to schedule the most efficient (cost/energy/time) resource for a task. Location information is imperative in sensor networks for most applications for localized sensing where localizing the network adaptively with no additional hardware is important. Adaptive sampling approaches for spatially distributed static linear and Gaussian fields with mobile robotic sensors are formulated and experimentally validated. Resource scheduling algorithms for dispatching resources in a deadlock-free manner in systems with shared and routing resources are mathematically formulated and experimentally validated. Simultaneous and Adaptive localization algorithms for sensor network localization through simple geometric constraints are validated through simulations. (Abstract shortened by UMI.).
| Author | : Yunfei Xu |
| Publisher | : Springer |
| Total Pages | : 124 |
| Release | : 2015-10-27 |
| Genre | : Technology & Engineering |
| ISBN | : 3319219219 |
This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.
| Author | : Yunfei Xu |
| Publisher | : |
| Total Pages | : 166 |
| Release | : 2011 |
| Genre | : Adaptive sampling (Statistics) |
| ISBN | : 9781267073488 |
| Author | : Koushil Sreenath |
| Publisher | : Institution of Engineering and Technology |
| Total Pages | : 0 |
| Release | : 2011-02-11 |
| Genre | : Technology & Engineering |
| ISBN | : 9781849192576 |
Adaptive Sampling with Mobile WSN develops algorithms for optimal estimation of environmental parametric fields. With a single mobile sensor, several approaches are presented to solve the problem of where to sample next to maximally and simultaneously reduce uncertainty in the field estimate and uncertainty in the localisation of the mobile sensor while respecting the dynamics of the time-varying field and the mobile sensor. A case study of mapping a forest fire is presented. Multiple static and mobile sensors are considered next, and distributed algorithms for adaptive sampling are developed resulting in the Distributed Federated Kalman Filter. However, with multiple resources a possibility of deadlock arises and a matrix-based discrete-event controller is used to implement a deadlock avoidance policy. Deadlock prevention in the presence of shared and routing resources is also considered. Finally, a simultaneous and adaptive localisation strategy is developed to simultaneously localise static and mobile sensors in the WSN in an adaptive manner. Experimental validation of several of these algorithms is discussed throughout the book.
| Author | : Yingshu Li |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 444 |
| Release | : 2008-02-10 |
| Genre | : Computers |
| ISBN | : 0387495924 |
A crucial reference tool for the increasing number of scientists who depend upon sensor networks in a widening variety of ways. Coverage includes network design and modeling, network management, data management, security and applications. The topic covered in each chapter receives expository as well as scholarly treatment, covering its history, reviewing state-of-the-art thinking relative to the topic, and discussing currently unsolved problems of special interest.
| Author | : Shuo Huang |
| Publisher | : |
| Total Pages | : 220 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
| Author | : G.A. Kaminka |
| Publisher | : IOS Press |
| Total Pages | : 1860 |
| Release | : 2016-08-24 |
| Genre | : Computers |
| ISBN | : 1614996725 |
Artificial Intelligence continues to be one of the most exciting and fast-developing fields of computer science. This book presents the 177 long papers and 123 short papers accepted for ECAI 2016, the latest edition of the biennial European Conference on Artificial Intelligence, Europe’s premier venue for presenting scientific results in AI. The conference was held in The Hague, the Netherlands, from August 29 to September 2, 2016. ECAI 2016 also incorporated the conference on Prestigious Applications of Intelligent Systems (PAIS) 2016, and the Starting AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume; the papers from STAIRS are published in a separate volume in the Frontiers in Artificial Intelligence and Applications (FAIA) series. Organized by the European Association for Artificial Intelligence (EurAI) and the Benelux Association for Artificial Intelligence (BNVKI), the ECAI conference provides an opportunity for researchers to present and hear about the very best research in contemporary AI. This proceedings will be of interest to all those seeking an overview of the very latest innovations and developments in this field.
| Author | : Sreeja Banerjee |
| Publisher | : |
| Total Pages | : 179 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex large-scale ecosystems. We introduce a method of underwater monitoring using semi-mobile underwater sensor networks and mobile underwater robots in this thesis. The underwater robots can move freely in all dimension while the sensor nodes are anchored to the bottom of the water column and can move only up and down along the depth of the water column. We develop three different algorithms to optimize the path of the underwater robot and the positions of the sensors to improve the overall quality of sensing of an area of water. The algorithms fall into three categories based on knowledge of the environment: global knowledge, local knowledge, and a decentralized approach. The first algorithm,VoronoiPath, is a global path planning algorithm that uses the concept of Voronoi Tessellation. The second algorithm, TanBugPath, is a local path planning algorithm, inspired from the Tangent Bug method for obstacle avoidance. Finally, the third path planning algorithm, AdaptivePath, optimizes the path by balancing the distance covered by the underwater robot and maximizing the sensing efficiency of both the sensor and the robot. It is based on an adaptive decentralized algorithm and plans the path of the underwater robot by assigning robot waypoints along the depth of the water column, and then adapting them alongside the sensor nodes to obtain the path of the robot. It uses a stable gradient-descent based controller which, we show, converges to a local minimum. We verify the algorithms through simulations and experiments. The VoronoiPath algorithm, generally, results in more efficient sensing paths. However, it is difficult to implement in real world as it needs global information and results in longer robot paths. The TanBugPath algorithm, on the other hand, has good sensing and it plans paths which are a usually shorter under varying conditions. However, all the processing takes place on-board the mobile robot, hence, this approach needs a more advanced robot than other algorithms. Finally, in case of the AdaptivePath algorithm, the in-network sensors calculate the path of the mobile robot in a decentralized manner. A major advantage of this approach is that the the positions of the sensors in the water column also get optimized depending on the path of the mobile robot. However, this algorithm can get stuck in a local minima, and is also dependent on the starting positions of the robot waypoints. For each of the algorithms we perform a detailed analysis and comparison. We identify limitations of each, and provide framework for future improvements.
