Greedy path planning for maximizing value of information in underwater sensor networks

Underwater sensor networks (UWSNs) face specific challenges due to the transmission properties in the underwater environment. Radio waves propagate only for short distances under water, and acoustic transmissions have limited data rate and relatively high latency. One of the possible solutions to these challenges involves the use of autonomous underwater vehicles (AUVs) to visit and offload data from the individual sensor nodes. We consider an underwater sensor network visually monitoring an offshore oil platform for hazards such as oil spills from pipes and blowups. To each observation chunk (image or video) we attach a numerical value of information (VoI). This value monotonically decreases in time with a speeed which depends on the urgency of the captured data. An AUV visits different nodes along a specific path and collects data to be transmitted to the customer. Our objective is to develop path planners for the movement of the AUV which maximizes the total VoI collected. We consider three different path planners: the lawn mower path planner (LPP), the greedy planner (GPP) and the random planner (RPP). In a simulation study we compare the total VoI collected by these algorithms and show that the GPP outperforms the other two proposed algorithms on the studied scenarios

Decision-making for Vehicle Path Planning

This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors. There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle. The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem. The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected

A pragmatic value-of-information approach for intruder tracking sensor networks

Sensor networks are distributed systems where nodes embedded in the environment collect readings through their sensors and transmit data to customers. The overall goal of these systems can be stated as maximizing a metric of the sensing quality while limiting the consumption of a set of scarce resources. In this paper we consider an intruder detection and tracking system where the sensing quality is a metric of the pragmatic value of the information provided by the network. This metric depends not only on the quantity and accuracy of information, but also on when and how the customers will use this information. We design a system which adapts its information transmission to the disruptive decisions made by the user, including a consideration for the cost of incorrect decisions. © 2012 IEEE

A Pragmatic Value-Of-Information Approach For Intruder Tracking Sensor Networks

Sensor networks are distributed systems where nodes embedded in the environment collect readings through their sensors and transmit data to customers. The overall goal of these systems can be stated as maximizing a metric of the sensing quality while limiting the consumption of a set of scarce resources. In this paper we consider an intruder detection and tracking system where the sensing quality is a metric of the pragmatic value of the information provided by the network. This metric depends not only on the quantity and accuracy of information, but also on when and how the customers will use this information. We design a system which adapts its information transmission to the disruptive decisions made by the user, including a consideration for the cost of incorrect decisions. © 2012 IEEE

Modeling Crowd Mobility and Communication in Wireless Networks

This dissertation presents contributions to the fields of mobility modeling, wireless sensor networks (WSNs) with mobile sinks, and opportunistic communication in theme parks. The two main directions of our contributions are human mobility models and strategies for the mobile sink positioning and communication in wireless networks. The first direction of the dissertation is related to human mobility modeling. Modeling the movement of human subjects is important to improve the performance of wireless networks with human participants and the validation of such networks through simulations. The movements in areas such as theme parks follow specific patterns that are not taken into consideration by the general purpose mobility models. We develop two types of mobility models of theme park visitors. The first model represents the typical movement of visitors as they are visiting various attractions and landmarks of the park. The second model represents the movement of the visitors as they aim to evacuate the park after a natural or man-made disaster. The second direction focuses on the movement patterns of mobile sinks and their communication in responding to various events and incidents within the theme park. When an event occurs, the system needs to determine which mobile sink will respond to the event and its trajectory. The overall objective is to optimize the event coverage by minimizing the time needed for the chosen mobile sink to reach the incident area. We extend this work by considering the positioning problem of mobile sinks and preservation of the connected topology. We propose a new variant of p-center problem for optimal placement and communication of the mobile sinks. We provide a solution to this problem through collaborative event coverage of the WSNs with mobile sinks. Finally, we develop a network model with opportunistic communication for tracking the evacuation of theme park visitors during disasters. This model involves people with smartphones that store and carry messages. The mobile sinks are responsible for communicating with the smartphones and reaching out to the regions of the emergent events