Utilizing Dual Information for Moving Target Search Trajectory Optimization

Various recent events have shown the enormous importance of maritime search-and-rescue missions. By reducing the time to find floating victims at sea, the number of casualties can be reduced. A major improvement can be achieved by employing autonomous aerial systems for autonomous search missions, allowed by the recent rise in technological development. In this context, the need for efficient search trajectory planning methods arises. The objective is to maximize the probability of detecting the target at a certain time k, which depends on the estimation of the position of the target. For stationary target search, this is a function of the observation at time k. When considering the target movement, this is a function of all previous observations up until time k. This is the main difficulty arising in solving moving target search problems when the duration of the search mission increases. We present an intermediate result for the single searcher single target case towards an efficient algorithm for longer missions with multiple aerial vehicles. Our primary aim in the development of this algorithm is to disconnect the networks of the target and platform, which we have achieved by applying Benders decomposition. Consequently, we solve two much smaller problems sequentially in iterations. Between the problems, primal and dual information is exchanged. To the best of our knowledge, this is the first approach utilizing dual information within the category of moving target search problems. We show the applicability in computational experiments and provide an analysis of the results. Furthermore, we propose well-founded improvements for further research towards solving real-life instances with multiple searchers

Yellow Tree: A Distributed Main-memory Spatial Index Structure for Moving Objects

Mobile devices equipped with wireless technologies to communicate and positioning systems to locate objects of interest are common place today, providing the impetus to develop location-aware applications. At the heart of location-aware applications are moving objects or objects that continuously change location over time, such as cars in transportation networks or pedestrians or postal packages. Location-aware applications tend to support the tracking of very large numbers of such moving objects as well as many users that are interested in finding out about the locations of other moving objects. Such location-aware applications rely on support from database management systems to model, store, and query moving object data. The management of moving object data exposes the limitations of traditional (spatial) database management systems as well as their index structures designed to keep track of objects\u27 locations. Spatial index structures that have been designed for geographic objects in the past primarily assume data are foremost of static nature (e.g., land parcels, road networks, or airport locations), thus requiring a limited amount of index structure updates and reorganization over a period of time. While handling moving objects however, there is an incumbent need for continuous reorganization of spatial index structures to remain up to date with constantly and rapidly changing object locations. This research addresses some of the key issues surrounding the efficient database management of moving objects whose location update rate to the database system varies from 1 to 30 minutes. Furthermore, we address the design of a highly scaleable and efficient spatial index structure to support location tracking and querying of large amounts of moving objects. We explore the possible architectural and the data structure level changes that are required to handle large numbers of moving objects. We focus specifically on the index structures that are needed to process spatial range queries and object-based queries on constantly changing moving object data. We argue for the case of main memory spatial index structures that dynamically adapt to continuously changing moving object data and concurrently answer spatial range queries efficiently. A proof-of concept implementation called the yellow tree, which is a distributed main-memory index structure, and a simulated environment to generate moving objects is demonstrated. Using experiments conducted on simulated moving object data, we conclude that a distributed main-memory based spatial index structure is required to handle dynamic location updates and efficiently answer spatial range queries on moving objects. Future work on enhancing the query processing performance of yellow tree is also discussed

Efficient MaxCount and threshold operators of moving objects

Calculating operators of continuously moving objects presents some unique challenges, especially when the operators involve aggregation or the concept of congestion, which happens when the number of moving objects in a changing or dynamic query space exceeds some threshold value. This paper presents the following six d-dimensional moving object operators: (1) MaxCount (or MinCount), which finds the Maximum (or Minimum) number of moving objects simultaneously present in the dynamic query space at any time during the query time interval. (2) CountRange, which finds a count of point objects whose trajectories intersect the dynamic query space during the query time interval. (3) ThresholdRange, which finds the set of time intervals during which the dynamic query space is congested. (4) ThresholdSum, which finds the total length of all the time intervals during which the dynamic query space is congested. (5) ThresholdCount, which finds the number of disjoint time intervals during which the dynamic query space is congested. And (6) ThresholdAverage, which finds the average length of time of all the time intervals when the dynamic query space is congested. For these operators separate algorithms are given to find only estimate or only precise values. Experimental results from more than 7,500 queries indicate that the estimation algorithms produce fast, efficient results with error under 5%

Efficient Indexing Structure for Trajectories in Geographical Information Systems

Technologies dealing with location such as GPS are producing more and more data of moving objects. Spatio-temporal databases store information about the positions of individual objects over time. Real-world applications of spatio-temporal data include vehicle navigation, migration of people, tracking and monitoring air-based, sea or land-based vehicles. Also the location technologies, such as GPS and telegraphy, are producing more and more data of moving objects. Spatio-temporal database is needed to manage these data, so as to solve the problems in spatio-temporal applications. A spatio-temporal database adopts an exhaustive search strategy for querying the trajectories. This is very time-consuming when processing large datasets for the given spatio-temporal query conditions. As a result, efficient Spatio-Temporal indexing methods are highly demanded to improve the performance of the system in searching such large datasets.Computer Science Departmen

GET_MOVE: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects

International audienceRecent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, they are called spatio-temporal pat- terns. Due to the emergence of many different kinds of spatio-temporal patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of pattern. In addition to the fact that it is a painstaking task due to the large number of algorithms used to mine and manage patterns, it is also time consuming. Additionally, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine spatio-temporal patterns in the itemset context. Secondly, we propose a unifying approach, named GeT Move, using a frequent closed itemset-based spatio-temporal pattern-mining algorithm to mine and manage different spatio-temporal patterns. GeT Move is implemented in two versions which are GeT Move and Incremental GeT Move. Experiments are per- formed on real and synthetic datasets and the experimental results show that our approaches are very effective and outperform existing algorithms in terms of efficiency

Collaborative Concealment of Spatio-Temporal Mobile Sequential Patterns

Recent advances in communication and information technology such as the increasing accuracy of GPS technology and the portability of wireless communication devices coat the way for Location Based Services LBS Based on the data collected from the location aware mobile devices data mining techniques are used to meet the quality requirements of expected services The efficient management of moving object databases has gained much interest in recent years due to the development of mobile communication and positioning technologies A typical way of representing moving objects is to use the trajectories Much work has focused on the topics of indexing query processing and data mining of moving object trajectories but little attention has been paid to the preservation of privacy in this setting The major contribution of this paper is to provide privacy to the users of Location Based Services along with capturing interesting user s behavior pattern by broaden the ideas presented in the datamining-literatur