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

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

Energy Efficient Rectangular Indexing for Mobile Peer-to-Peer Environment

Now a days in wireless environment there are many challenges. One of them which is need to be addressed in mobile Peer-to-Peer environment is getting the information of interest quickly and efficiently. Wherein whenever the node tries to get the desired data it has to wait too long or have to contact to unnecessary nodes which are not having their data of interest. This causes the node to waste the limited power resources and incurs more cost in terms of energy wastage. Here we proposed an energy efficient rectangular indexing called PMBR (Peer-to-Peer Minimum Bounding Rectangle) which allows the user to get the information of interest in energy efficient manner. We proposed algorithms namely PMBR_DSS, PMBR_HB and PMBR_CP and processed Nearest Neighbor & Range type queries. The experimental results carried out shows that the proposed algorithm PMBR_CP provides the efficient, quick and assured access to information of interest by saving the scarce power resources

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

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Shortest Path Computation with No Information Leakage

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client's position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client's location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as black-box building blocks. Experiments on real, large-scale road networks assess the practicality of our schemes.Comment: VLDB201

ILARS: An Improved Empirical Analysis for Lars* Using Partitioning and Travel Penalty

In this paper we develop an improved web based location-aware recommender software system, ILARS, that uses location-based ratings to provide proper advice and counseling. Present recommender systems don’t consider about spatial attributes of users and also of items; But, ILARS*considers major classes regarding location such as spatial scores rate for the non-spatial things, non-spatial score rate for the spatial things, and spatial score rate for the spatial things. ILARS* deals with recommendation points for accomplishing user ranking locations with help of user partitioning methods, which that are spatially near querying users in an effective way that maximizes system computability by not reducing the systems quality. A style that supports recommendation successors nearer in travel distance to querying users is used by ILARS* to exploits item locations using travel penalty. For avoiding thorough access to any or all spatial things. ILARS* will apply these art singly, or based on the rating that is obtained. The experimental results show information from various location based social networks. Various social network tells that LARS* is magnified , most expanded ,inexpensive ,reasonable ,capable of showing recommendations which are accurate as compared to existing recommendation software systems. DOI: 10.17762/ijritcc2321-8169.15073