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ANDROID 2013 Projects

Category Archives

T-Drive: Enhancing Driving Directions with Taxi Drivers’ Intelligence

ABSTRACT

This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers’ intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.

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Collaborative Learning Assistant for Android

ABSTRACT:

The quantitative and qualitative increase in mobile devices that reach the average user opens more and more topics for research. In education, m-Learning has been an interesting topic for several years. However, the smartphones, that today display an unprecedented mix of computing capability, connectivity and interactivity, leverage new possibilities for m-Learning applications. Such applications can seamlessly connect remote individuals, but can also provide access to various resources, such as media, or interactive quizzes. We focus on collaborative learning and peer-review learning, two closely related concepts, promoting methods such as sharing educational resources, organizing study sessions and giving feedback to fellow students. We propose a client-server system to provide all these features and we study it under performance considerations such as scalability and mobility.

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Collaborative Policy Administration

ABSTRACT:

Policy based management is a very effective method to protect sensitive information. However, the over claim of privileges is widespread in emerging applications, including mobile applications and social network services, because the applications’ users involved in policy administration have little knowledge of policy based management. The over claim can be leveraged by malicious applications, then lead to serious privacy leakages and financial loss. To resolve this issue, this paper proposes a novel policy administration mechanism, referred to as Collaborative Policy Administration (CPA for short), to simplify the policy administration. In CPA, a policy administrator can refer to other similar policies to set up their own policies to protect privacy and other sensitive information. This paper formally defines CPA, and proposes its enforcement framework. Furthermore, in order to obtain similar policies more effectively, which is the key step of CPA, a text mining based similarity measure method is presented. We evaluate CPA with the data of Android applications, and demonstrate that the text mining based similarity measure method is more effective in obtaining similar policies than the previous category based method.

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Cloud FTP: A Case Study of Migrating Traditional Applications to the Cloud

ABSTRACT:

The cloud computing is growing rapidly for it offers on-demand computing power and capacity. The power of cloud enables dynamic scalability of applications facing various business requirements. However, challenges arise when considering the large amount of existing applications. In this work we propose to move the traditional FTP service to the cloud. We implement FTP service on Windows Azure Platform along with the auto-scaling cloud feature. Based on this, we implement a benchmark to measure the performance of our Cloud FTP. This case study illustrates the potential benefits and technical issues associated with the migration of the traditional applications to the clouds.

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Secure Encounter-based Mobile Social Networks: Requirements, Designs, and Tradeoffs

ABSTRACT:

Encounter-based social networks and encounter-based systems link users who share a location at the same time, as opposed to the traditional social network paradigm of linking users who have an offline friendship. This new approach presents challenges that are fundamentally different from those tackled by previous social network designs. In this paper, we explore the functional and security requirements for these new systems, such as availability, security, and privacy, and present several design options for building secure encounter-based social networks. To highlight these challenges we examine one recently proposed encounter-based social network design and compare it to a set of idealized security and functionality requirements. We show that it is vulnerable to several attacks, including impersonation, collusion, and privacy breaching, even though it was designed specifically for security. Mindful of the possible pitfalls, we construct a flexible framework for secure encounter-based social networks, which can be used to construct networks that offer different security, privacy, and availability guarantees. We describe two example constructions derived from this framework, and consider each in terms of the ideal requirements. Some of our new designs fulfill more requirements in terms of system security, reliability, and privacy than previous work. We also evaluate real-world performance of one of our designs by implementing a proof-of-concept iPhone application called Meet Up. Experiments highlight the potential of our system and hint at the deployability of our designs on a large scale.

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Research in Progress- Defending Android Smartphones from Malware Attacks

ABSTRACT:

Smartphones are becoming enriched with confidential information due to their powerful computational capabilities and attractive communications features. The Android smartphone is one of the most widely used platforms by businesses and users alike. This is partially because Android smartphones use the free, open-source Linux as the underlying operating system, which allows development of applications by any software developer. This research study aims to explore security risks associated with the use of Android smartphones and the sensitive information they contain; the researcher devised a survey questionnaire to investigate and further understand security threats targeting Android smartphones. The survey also intended to study the scope of malware attacks targeting Android phones and the effectiveness of existing defense measures. The study surveyed the average Android users as the target population to understand how they perceive security and what security controls they use to protect their smartphones.

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Review of Behavior Malware Analysis for Android

ABSTRACT:

Android based Smartphone are now a day’s getting more popularity. With the use of Smartphone user must always concern about the security breaching and malicious attacks. Here we introduce an approach for proactive malware detection working by abstraction of program behaviors.  Suspicious behaviors are detected by comparing trace abstractions to reference malicious behaviors.  The  sensitive  power  of  concept allows  us  to  grip  common  mistrustful  behaviors  rather  than specific  malware  code  and  then,  to  distinguish  malware transformation. We present and discuss an implementation validating our approach. First have to analyze the programs or apps,  then  represented  them  as  trace  languages,  which  are abstracted  by  altering  with  respect  to  elementary  behavior patterns, defined as regular string rephrasing systems. This paper review the state of the art on threats, vulnerabilities , We aimed at existing  approaches  to  protecting  mobile  devices  against  these classes  of  attacks  into  different  categories,  based  upon  the detection  principles,  architectures,  collected  data  and  operating systems, especially focusing on IDS-based models and tools.

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Crowd sourced Trace Similarity with Smartphones

ABSTRACT:

Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS, accelerometers, etc. This capability allows smartphone users to easily engage in crowdsourced computing services, which contribute to the solution of complex problems in a distributed manner. In this work, we leverage such a computing paradigm to solve efficiently the following problem: comparing a query trace Q against a crowd of traces generated and stored on distributed smartphones. Our proposed framework, coined SmartTraceþ, provides an effective solution without disclosing any part of the crowd traces to the query processor. SmartTraceþ, relies on an in-situ data storage model and intelligent top-K query processing algorithms that exploit distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q. We evaluate our algorithms on both synthetic and real workloads. We describe our prototype system developed on the Android OS. The solution is deployed over our own SmartLab testbed of 25 smartphones. Our study reveals that computations over SmartTraceþ result in substantial energy conservation; in addition, results can be computed faster than competitive approaches.

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PMSE: A Personalized Mobile Search Engine

ABSTRACT:

We propose a personalized mobile search engine (PMSE) that captures the users’ preferences in the form of concepts by mining their click through data. Due to the importance of location information in mobile search, PMSE classifies these concepts into content concepts and location concepts. In addition, users’ locations (positioned by GPS) are used to supplement the location concepts in PMSE. The user preferences are organized in an ontology-based, multifacet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevances to the user’s need, four entropies are introduced to balance the weights between the content and location facets. Based on the client-server model, we also present a detailed architecture and design for implementation of PMSE. In our design, the client collects and stores locally the click through data to protect privacy, whereas heavy tasks such as concept extraction, training, and re-ranking are performed at the PMSE server. Moreover, we address the privacy issue by restricting the information in the user profile exposed to the PMSE server with two privacy parameters. We prototype PMSE on the Google Android platform. Experimental results show that PMSE significantly improves the precision comparing to the baseline.

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A Proxy-Based Approach to Continuous Location-Based Spatial Queries in Mobile Environments

ABSTRACT:

Caching valid regions of spatial queries at mobile clients is effective in reducing the number of queries submitted by mobile clients and query load on the server. However, mobile clients suffer from longer waiting time for the server to compute valid regions. We propose in this paper a proxy-based approach to continuous nearest-neighbor (NN) and window queries. The proxy creates estimated valid regions (EVRs) for mobile clients by exploiting spatial and temporal locality of spatial queries. For NN queries, we devise two new algorithms to accelerate EVR growth, leading the proxy to build effective EVRs even when the cache size is small. On the other hand, we propose to represent the EVRs of window queries in the form of vectors, called estimated window vectors (EWVs), to achieve larger estimated valid regions. This novel representation and the associated creation algorithm result in more effective EVRs of window queries. In addition, due to the distinct characteristics, we use separate index structures, namely EVR-tree and grid index, for NN queries and window queries, respectively. To further increase efficiency, we develop algorithms to exploit the results of NN queries to aid grid index growth, benefiting EWV creation of window queries. Similarly, the grid index is utilized to support NN query answering and EVR updating. We conduct several experiments for performance evaluation. The experimental results show that the proposed approach significantly outperforms the existing proxy-based approaches.

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Meet You – Social Networking on Android

ABSTRACT:

This paper aims to present a system that illustrates the social nature of a human being – the need to be always in touch with family and friends – taking into account facilities available on Android platform. The role of this application is to create a social network in which the users are being alerted when their friends are around. This gives them the possibility to set up a meeting or to avoid one. The users have the possibility to check in some locations and allow their friends to follow their activity. Taking into account the security of the users, we included in the facilities of the application an option which allows close friends or family to check the user’s location based on a keyword text message. For this purpose, available Android location and messages services are used for finding an approximate location of a mobile phone running this program and then sharing it through MeetYou or via SMS. Information is being displayed using default components provided by Android platform and also more complex elements including heterogeneous lists CWAC, Google Maps and augmented reality using Mixare Library.

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Spatial Query Integrity with Voronoi Neighbors

ABSTRACT:

With the popularity of location-based services and the abundant usage of smart phones and GPS-enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Meanwhile, the fast a rising trend of cloud storage and cloud computing services has provided a flexible and cost-effective platform for hosting data from businesses and individuals, further enabling many location-based applications. Nevertheless, in this database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB) model and propose an efficient scheme, called VN-Auth, which allows a client to verify the correctness and completeness of the result set. Our approach is based on neighborhood information derived from the Voronoi diagram of the underlying spatial data set and can handle fundamental spatial query types, such as k nearest neighbor and range queries, as well as more advanced query types like reverse k nearest neighbor, aggregate nearest neighbor, and spatial skyline. We evaluated VN-Auth based on real-world data sets using mobile devices (Google Droid smart phones with Android OS) as query clients. Compared to the current state-of-the-art approaches (i.e., methods based on Merkle Hash Trees), our experiments show that VN-Auth produces significantly smaller verification objects and is more computationally efficient, especially for queries with low selectivity.

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Dynamic Personalized Recommendation on Sparse Data

ABSTRACT:

Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm has satisfying performance.

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T-Drive: Enhancing Driving Directions with Taxi Drivers’ Intelligence

ABSTRACT

This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers’ intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.

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