Managing motion triggered executables in distributed mobile databases

Mobile devices have brought new applications into our daily life. However, ecient man- agement of these objects to support new applications is challenging due to the distributed nature and mobility of mobile objects. This dissertation describes a new type of mobile peer- to-peer (M-P2P) computing, namely geotasking, and presents ecient management of mobile objects to support geotasking. Geotasking mimics human interaction with the physical world. Humans generate information using sensing ability and store information to geographical lo- cations. Humans also retrieve this information from the physical locations. For instance, an installation of a new stop sign at some intersection in town is analogous to an insertion of a new data item into the database. Instead of processing regular data as in traditional data management systems, geotasking manages a collection of geotasks, each dened as a computer program bound to a geographical region. The hardware platform for geotasking consists of popular networked position-aware mobile devices such as cell phones, personal digital assis- tants, and laptops. We design and implement novel system software to facilitate programming and ecient management of geotasks. Such management includes inserts, deletes, updates, retrieval and execution of a geotask triggered by mobile object correlations, geotask mobil- ity, and geotask dependency. Geotasking enables useful applications ranging from warning of dangerous areas for military and search-and-rescue missions to monitoring the population in a certain area for trac management to informing tourists of exciting events in an area and other such applications. Geotasking provides a distributed and unied solution for supporting various types of applications

Smart PIN: performance and cost-oriented context-aware personal information network

The next generation of networks will involve interconnection of heterogeneous individual networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore, there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches for data delivery across the heterogeneous network are also required. In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed: Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD). MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches

Managing continuous k-nearest neighbor queries in mobile peer-to-peer networks

A continuous k nearest neighbor (CKNN) query retrieves the set of k mobile nodes that are nearest to a query point, and provides real-time updates whenever this set of nodes changes. A CKNN query can be either stationary or mobile, depending on the mobility of its query point. Efficient processing of CKNN queries is essential to many applications, yet most existing techniques assume a centralized system, where one or more central servers are used for query management. In this thesis, we assume a fully distributed mobile peer-to-peer system, where mobile nodes are the only computing devices, and present a unified platform for efficient processing of both stationary and mobile CKNN queries. For each query, our technique computes a set of safe boundaries and lets mobile nodes monitor their movement with respect to these boundaries. We show that the result of a query does not change unless a node crosses over a safe boundary. As such, our technique requires a query to be re-evaluated only when there is a crossing event, thus minimizing the cost of query evaluation. For performance study, we model the communication cost incurred in query processing with a detailed mathematical analysis and verify its accuracy using simulation. Our extensive study shows that the proposed technique is able to provide real-time and accurate query results with a reasonable cost

THREE TEMPORAL PERSPECTIVES ON DECENTRALIZED LOCATION-AWARE COMPUTING: PAST, PRESENT, FUTURE

Durant les quatre dernières décennies, la miniaturisation a permis la diffusion à large échelle des ordinateurs, les rendant omniprésents. Aujourd’hui, le nombre d’objets connectés à Internet ne cesse de croitre et cette tendance n’a pas l’air de ralentir. Ces objets, qui peuvent être des téléphones mobiles, des véhicules ou des senseurs, génèrent de très grands volumes de données qui sont presque toujours associés à un contexte spatiotemporel. Le volume de ces données est souvent si grand que leur traitement requiert la création de système distribués qui impliquent la coopération de plusieurs ordinateurs. La capacité de traiter ces données revêt une importance sociétale. Par exemple: les données collectées lors de trajets en voiture permettent aujourd’hui d’éviter les em-bouteillages ou de partager son véhicule. Un autre exemple: dans un avenir proche, les données collectées à l’aide de gyroscopes capables de détecter les trous dans la chaussée permettront de mieux planifier les interventions de maintenance à effectuer sur le réseau routier. Les domaines d’applications sont par conséquent nombreux, de même que les problèmes qui y sont associés. Les articles qui composent cette thèse traitent de systèmes qui partagent deux caractéristiques clés: un contexte spatiotemporel et une architecture décentralisée. De plus, les systèmes décrits dans ces articles s’articulent autours de trois axes temporels: le présent, le passé, et le futur. Les systèmes axés sur le présent permettent à un très grand nombre d’objets connectés de communiquer en fonction d’un contexte spatial avec des temps de réponses proche du temps réel. Nos contributions dans ce domaine permettent à ce type de système décentralisé de s’adapter au volume de donnée à traiter en s’étendant sur du matériel bon marché. Les systèmes axés sur le passé ont pour but de faciliter l’accès a de très grands volumes données spatiotemporelles collectées par des objets connectés. En d’autres termes, il s’agit d’indexer des trajectoires et d’exploiter ces indexes. Nos contributions dans ce domaine permettent de traiter des jeux de trajectoires particulièrement denses, ce qui n’avait pas été fait auparavant. Enfin, les systèmes axés sur le futur utilisent les trajectoires passées pour prédire les trajectoires que des objets connectés suivront dans l’avenir. Nos contributions permettent de prédire les trajectoires suivies par des objets connectés avec une granularité jusque là inégalée. Bien qu’impliquant des domaines différents, ces contributions s’articulent autour de dénominateurs communs des systèmes sous-jacents, ouvrant la possibilité de pouvoir traiter ces problèmes avec plus de généricité dans un avenir proche. -- During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is in- creasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys already makes it possible to avoid traffic jams or to know about the need to organize a carpool. Another example: in the near future, the maintenance interventions to be carried out on the road network will be planned with data collected using gyroscopes that detect potholes. The application domains are therefore numerous, as are the prob- lems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architec- ture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future per- spective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these con- tributions are structured around the common denominators of the underlying systems, which opens the possibility of being able to deal with these problems more generically in the near future

Location cloaking for location privacy protection and location safety protection

Many applications today rely on location information, yet disclosing such information can present heightened privacy and safety risks. A person\u27s whereabouts, for example, may reveal sensitive private information such as health condition and lifestyle. Location information also has the potential to allow an adversary to physically locate and destroy a subject, which is particularly concerned in digital battlefields. This research investigates two problems. The first one is location privacy protection in location-based services. Our goal is to provide a desired level of guarantee that the location data collected by the service providers cannot be correlated with restricted spaces such as home and office to derive who\u27s where at what time. We propose 1) leveraging historical location samples for location depersonalization and 2) allowing a user to express her location privacy requirement by identifying a spatial region. With these two ideas in place, we develop a suite of techniques for location-privacy aware uses of location-based services, which can be either sporadic or continuous. An experimental system has been implemented with these techniques. The second problem investigated in this research is location safety protection in ad hoc networks. Unlike location privacy intrusion, the adversary here is not interested in finding the individual identities of the nodes in a spatial region, but simply wants to locate and destroy them. We define the safety level of a spatial region as the inverse of its node density and develop a suite of techniques for location safety-aware cloaking and routing. These schemes allow nodes to disclose their location as accurately as possible, while preventing such information from being used to identify any region with a safety level lower than a required threshold. The performance of the proposed techniques is evaluated through analysis and simulation

Novel techniques for location-cloaked applications

Location cloaking has been shown to be cost-effective in mitigating location privacy and safety risks. This strategy, however, has significant impact on the applications that rely on location information. They may suffer efficiency loss; some may not even work with reduced location resolution. This research investigates two problems. 1) How to process location-cloaked queries. Processing such queries incurs significant more workload for both server and client. While the server needs to retrieve more query results and transmit them to the client, the client downloading these results wastes its battery power because most of them are useless. To address these problems, we propose a suite of novel techniques including query decomposition, scheduling, and personalized air indexing. These techniques are integrated into a single unified platform that is capable of handling various types of queries. 2) How a node V can verify whether or not another node P indeed locates in a cloaking region it claims. This problem is challenging due to the fact that the process of location verification may allow V to refine P\u27s location within the region. We identify two types of attacks, transmission coverage attack and distance bounding attack. In the former, V refines a cloaking region by adjusting its transmission range to partially overlap with the region, whereas in the latter, by measuring the round trip time of its communication with P. We present two corresponding counter strategies, and built on top of them, propose a novel technique that allows P to participate in location verification while providing a certain level of guarantee that its cloaking region will not be refined during the process

Query Processing In Location-based Services

With the advances in wireless communication technology and advanced positioning systems, a variety of Location-Based Services (LBS) become available to the public. Mobile users can issue location-based queries to probe their surrounding environments. One important type of query in LBS is moving monitoring queries over mobile objects. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. To address both of the scalability factors, distributed computing has been considered. These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. In the first part of this dissertation, we propose a distributed framework to process moving monitoring queries over moving objects in a spatial network environment. In the second part of this dissertation, in order to reduce the communication cost, we leverage both on-demand data access and periodic broadcast to design a new hybrid distributed solution for moving monitoring queries in an open space environment. Location-based services make our daily life more convenient. However, to receive the services, one has to reveal his/her location and query information when issuing locationbased queries. This could lead to privacy breach if these personal information are possessed by some untrusted parties. In the third part of this dissertation, we introduce a new privacy protection measure called query l-diversity, and provide two cloaking algorithms to achieve both location kanonymity and query l-diversity to better protect user privacy. In the fourth part of this dissertation, we design a hybrid three-tier architecture to help reduce privacy exposure. In the fifth part of this dissertation, we propose to use Road Network Embedding technique to process privacy protected queries