info@itechprosolutions.in | +91 9790176891

Android 2017 Projects

Category Archives

Buy Your Coffee with Bitcoin: Real-World Deployment of a Bitcoin Point of Sale Terminal

Abstract:
In this paper we discuss existing approaches for Bitcoin payments, as suitable for a small business for small-value transactions. We develop an evaluation framework utilizing security, usability, deployability criteria,, examine several existing systems, tools. Following a requirements engineering approach, we designed, implemented a new Point of Sale (PoS) system that satisfies an optimal set of criteria within our evaluation framework. Our open source system, Aunja PoS, has been deployed in a real world café since October 2014.


A distributed open-close access for Small-Cell networks: A random matrix game analysis

Abstract:
Nowadays, Small-Cells are widely being deployed to assist and improve performance of mobile networks. Indeed, they are a promising solution to improve coverage and to offload data traffic in mobile networks. In this paper, we propose a signaling-less architecture of the heterogeneous network composed of one single Macro Base Station and a Single Small-Cell. First, we construct a game theoretic framework for channel-state independent interaction. We present many conditions for the existence of Pure Nash equilibrium. Next, and in order to capture the continuous change of the channel state, we build a random matrix game where the channel state is considered to be random (potentially ruled by some given distribution). A characterization of Nash equilibrium is provided in terms of pure strategies and mixed strategies. Convergence to Nash equilibrium is furthermore guaranteed using a variant of the well-known Combined fully distributed payoff and strategy learning. Our algorithm converges faster (only 10-20 iterations are required to converge to Nash equilibrium) and only need a limited amount of local information. This is quite promising since it says that our scheme is almost applicable for all environments (fast fading included).


ShakeIn: Secure User Authentication of Smartphones with Habitual Single-handed Shakes

Abstract:
Smartphones have been widely used with a vast array of sensitive and private information stored on these devices. To secure such information from being leaked, user authentication schemes are necessary. Current password/pattern-based user authentication schemes are vulnerable to shoulder surfing attacks and smudge attacks. In contrast, stroke/gait-based schemes are secure but inconvenient for users to input. In this paper, we propose ShakeIn, a handy user authentication scheme for secure unlocking of a smartphone by simply shaking the phone. With embedded motion sensors, ShakeIn can effectively capture the unique and reliable biometrical features of users about how they shake. In this way, even if an attacker sees a user shaking his/her phone, the attacker can hardly reproduce the same behaviour. Furthermore, by allowing users to customise the way how they shake the phone, ShakeIn endows users with the maximum operation flexibility. We implement ShakeIn and conduct both intensive trace-driven simulations and real experiments on 20 volunteers with about 530; 555 shaking samples collected over multiple months. The results show that ShakeIn achieves an average equal error rate of 1:2% with a small number of shakes using only 35 training samples even in the presence of shoulder-surfing attacks.


Efficient and Privacy-Preserving Min and k th Min Computations in Mobile Sensing Systems

Abstract:
Protecting the privacy of mobile phone user participants is extremely important for mobile phone sensing applications. In this paper, we study how an aggregator can expeditiously compute the minimum value or the kth minimum value of all users’ data without knowing them. We construct two secure protocols using probabilistic coding schemes and a cipher system that allows homomorphic bitwise XOR computations for our problems. Following the standard cryptographic security definition in the semi-honest model, we formally prove our protocols’ security. The protocols proposed by us can support time-series data and need not to assume the aggregator is trusted. Moreover, different from existing protocols that are based on secure arithmetic sum computations, our protocols are based on secure bitwise XOR computations, thus are more efficient.


A Classroom Scheduling Service for Smart Classes

Abstract:
During past decades, the classroom scheduling problem has posed significant challenges to educational programmers and teaching secretaries. In order to alleviate the burden of the programmers, this paper presents SmartClass, which allows the programmers to solve this problem using web services. By introducing service-oriented architecture (SOA), SmartClass is able to provide classroom scheduling services with back-stage design space exploration and greedy algorithms. Furthermore, the SmartClass architecture can be dynamically coupled to different scheduling algorithms (e.g. Greedy, DSE, etc.) to fit in specific demands. A typical case study demonstrates that SmartClass provides a new efficient paradigm to the traditional classroom scheduling problem, which could achieve high flexibility by software services reuse and ease the burden of educational programmers. Evaluation results on efficiency, overheads and scheduling performance demonstrate the SmartClass has lower scheduling overheads with higher efficiency.


Knowledge-Enhanced Mobile Video Broadcasting Framework With Cloud Support

Abstract:
The convergence of mobile communications and cloud computing facilitates the cross-layer network design and content-assisted communication. Mobile video broadcasting can benefit from this trend by utilizing joint source-channel coding and strong information correlation in clouds. In this paper, a knowledge-enhanced mobile video broadcasting (KMV-Cast) is proposed. The KMV-Cast is built on a linear video transmission instead of a traditional digital video system, and exploits the hierarchical Bayesian model to integrate the correlated information into the video reconstruction at the receiver. The correlated information is distilled to obtain its intrinsic features, and the Bayesian estimation algorithm is used to maximize the video quality. The KMV-Cast system consists of both likelihood broadcasting and prior knowledge broadcasting. The simulation results show that the proposed KMV-Cast scheme outperforms the typical linear video transmission scheme called Softcast, and achieves 8 dB more of the peak signal-to-noise ratio (PSNR) gain at low-SNR channels (i.e., -10 dB), and 5 dB more of PSNR gain at high-SNR channels (i.e., 25 dB). Compared with the traditional digital video system, the proposed scheme has 7 dB more of PSNR gain than the JPEG2000 + 802.11a scheme at a 10-dB channel SNR.


Searching Trajectories by Regions of Interest

Abstract:
With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.


ShakeIn: Secure User Authentication of Smartphones with Habitual Single-handed Shakes

Abstract:
Smartphones have been widely used with a vast array of sensitive and private information stored on these devices. To secure such information from being leaked, user authentication schemes are necessary. Current password/pattern-based user authentication schemes are vulnerable to shoulder surfing attacks and smudge attacks. In contrast, stroke/gait-based schemes are secure but inconvenient for users to input. In this paper, we propose ShakeIn, a handy user authentication scheme for secure unlocking of a smartphone by simply shaking the phone. With embedded motion sensors, ShakeIn can effectively capture the unique and reliable biometrical features of users about how they shake. In this way, even if an attacker sees a user shaking his/her phone, the attacker can hardly reproduce the same behaviour. Furthermore, by allowing users to customise the way how they shake the phone, ShakeIn endows users with the maximum operation flexibility. We implement ShakeIn and conduct both intensive trace-driven simulations and real experiments on 20 volunteers with about 530; 555 shaking samples collected over multiple months. The results show that ShakeIn achieves an average equal error rate of 1:2% with a small number of shakes using only 35 training samples even in the presence of shoulder-surfing attacks.


Efficient and Privacy-Preserving Min and k th Min Computations in Mobile Sensing Systems

Abstract:
Protecting the privacy of mobile phone user participants is extremely important for mobile phone sensing applications. In this paper, we study how an aggregator can expeditiously compute the minimum value or the kth minimum value of all users’ data without knowing them. We construct two secure protocols using probabilistic coding schemes and a cipher system that allows homomorphic bitwise XOR computations for our problems. Following the standard cryptographic security definition in the semi-honest model, we formally prove our protocols’ security. The protocols proposed by us can support time-series data and need not to assume the aggregator is trusted. Moreover, different from existing protocols that are based on secure arithmetic sum computations, our protocols are based on secure bitwise XOR computations, thus are more efficient.


A Classroom Scheduling Service for Smart Classes

Abstract:
During past decades, the classroom scheduling problem has posed significant challenges to educational programmers and teaching secretaries. In order to alleviate the burden of the programmers, this paper presents SmartClass, which allows the programmers to solve this problem using web services. By introducing service-oriented architecture (SOA), SmartClass is able to provide classroom scheduling services with back-stage design space exploration and greedy algorithms. Furthermore, the SmartClass architecture can be dynamically coupled to different scheduling algorithms (e.g. Greedy, DSE, etc.) to fit in specific demands. A typical case study demonstrates that SmartClass provides a new efficient paradigm to the traditional classroom scheduling problem, which could achieve high flexibility by software services reuse and ease the burden of educational programmers. Evaluation results on efficiency, overheads and scheduling performance demonstrate the SmartClass has lower scheduling overheads with higher efficiency.


Knowledge-Enhanced Mobile Video Broadcasting Framework With Cloud Support

Abstract:
The convergence of mobile communications and cloud computing facilitates the cross-layer network design and content-assisted communication. Mobile video broadcasting can benefit from this trend by utilizing joint source-channel coding and strong information correlation in clouds. In this paper, a knowledge-enhanced mobile video broadcasting (KMV-Cast) is proposed. The KMV-Cast is built on a linear video transmission instead of a traditional digital video system, and exploits the hierarchical Bayesian model to integrate the correlated information into the video reconstruction at the receiver. The correlated information is distilled to obtain its intrinsic features, and the Bayesian estimation algorithm is used to maximize the video quality. The KMV-Cast system consists of both likelihood broadcasting and prior knowledge broadcasting. The simulation results show that the proposed KMV-Cast scheme outperforms the typical linear video transmission scheme called Softcast, and achieves 8 dB more of the peak signal-to-noise ratio (PSNR) gain at low-SNR channels (i.e., -10 dB), and 5 dB more of PSNR gain at high-SNR channels (i.e., 25 dB). Compared with the traditional digital video system, the proposed scheme has 7 dB more of PSNR gain than the JPEG2000 + 802.11a scheme at a 10-dB channel SNR.


Searching Trajectories by Regions of Interest

Abstract:
With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.


A Lightweight Secure Data Sharing Scheme for Mobile Cloud Computing

Abstract:
With the popularity of cloud computing, mobile devices can store/retrieve personal data from anywhere at any time. Consequently, the data security problem in mobile cloud becomes more and more severe and prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However, most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. Solutions with low computational overhead are in great need for mobile cloud applications. In this paper, we propose a lightweight data sharing scheme (LDSS) for mobile cloud computing. It adopts CP-ABE, an access control technology used in normal cloud environment, but changes the structure of access control tree to make it suitable for mobile cloud environments. LDSS moves a large portion of the computational intensive access control tree transformation in CP-ABE from mobile devices to external proxy servers. Furthermore, to reduce the user revocation cost, it introduces attribute description fields to implement lazy-revocation, which is a thorny issue in program based CP-ABE systems. The experimental results show that LDSS can effectively reduce the overhead on the mobile device side when users are sharing data in mobile cloud environments.


Cooperative Query Answer Authentication Scheme Over Anonymous Sensing Data

Abstract:
In cloud service over crowd-sensing data, the data owner (DO) publishes the sensing data through the cloud server, so that the user can obtain the information of interest on demand. But the cloud service providers (CSP) are often untrustworthy. The privacy and security concerns emerge over the authenticity of the query answer and the leakage of the DO identity. To solve these issues, many researchers study the query answer authentication scheme for cloud service system. The traditional technique is providing DO’s signature for the published data. But the signature would always reveal DO’s identity. To deal with this disadvantage, this paper proposes a cooperative query answer authentication scheme, based on the ring signature, the Merkle hash tree (MHT) and the non-repudiable service protocol. Through the cooperation among the entities in cloud service system, the proposed scheme could not only verify the query answer, but also protect the DO’s identity. First, it picks up the internal nodes of MHT to sign, as well as the root node. Thus, the verification computation complexity could be significantly reduced from O(log2N) to O(log2N0.5) in the best case. Then, it improves an existing ring signature to sign the selected nodes. Furthermore, the proposed scheme employs the non-repudiation protocol during the transmission of query answer and verification object to protect trading behavior between the CSP and users. The security and performance analysis prove the security and feasibility of the proposed scheme. Extensive experimental results demonstrate its superiority of verification efficiency and communication overhead


Privacy-Preserving Location-Proximity for Mobile Apps

Abstract:
Location Based Services (LBS) have seen alarming privacy breaches in recent years. While there has been much recent progress by the research community on developing privacy-enhancing mechanisms for LBS, their evaluation has been often focused on the privacy guarantees, while the question of whether these mechanisms can be adopted by practical LBS applications has received limited attention. This paper studies the applicability of Privacy-Preserving Location Proximity (PPLP) protocols in the setting of mobile apps. We categorize popular location social apps and analyze the trade-offs of privacy and functionality with respect to PPLP enhancements. To investigate the practical performance trade-offs, we present an in-depth case study of an Android application that implements InnerCircle, a state-of-the-art protocol for privacy-preserving location proximity. This study indicates that the performance of the privacy-preserving application for coarse-grained precision is comparable to real applications with the same feature set.


FogRoute: DTN-Based Data Dissemination Model in Fog Computing

Abstract:
Fog computing, known as “cloud closed to ground,” deploys light-weight compute facility, called Fog servers, at the proximity of mobile users. By precatching contents in the Fog servers, an important application of Fog computing is to provide high-quality low-cost data distributions to proximity mobile users, e.g., video/live streaming and ads dissemination, using the single-hop low-latency wireless links. A Fog computing system is of a three tier Mobile-Fog-Cloud structure; mobile user gets service from Fog servers using local wireless connections, and Fog servers update their contents from Cloud using the cellular or wired networks. This, however, may incur high content update cost when the bandwidth between the Fog and Cloud servers is expensive, e.g., using the cellular network, and is therefore inefficient for nonurgent, high volume contents. How to economically utilize the Fog-Cloud bandwidth with guaranteed download performance of users thus represents a fundamental issue in Fog computing. In this paper, we address the issue by proposing a hybrid data dissemination framework which applies software-defined network and delay-tolerable network (DTN) approaches in Fog computing. Specifically, we decompose the Fog computing network with two planes, where the cloud is a control plane to process content update queries and organize data flows, and the geometrically distributed Fog servers form a data plane to disseminate data among Fog servers with a DTN technique. Using extensive simulations, we show that the proposed framework is efficient in terms of data-dissemination success ratio and content convergence time among Fog servers.


Drive Now, Text Later: Nonintrusive Texting-While-Driving Detection Using Smartphones

Abstract:
Texting-while-driving (T&D) is one of the top dangerous behaviors for drivers. Many interesting systems and mobile phone applications have been designed to help to detect or combat T&D. However, for a T&D detection system to be practical, a key property is its capability to distinguish driver’s mobile phone from passengers’. Existing solutions to this problem generally rely on the user’s manual input, or utilize specific localization devices to determine whether a mobile phone is at the driver’s location. In this paper, we propose a method which is able to detect T&D automatically without using any extra devices. The idea is very simple: when a user is composing messages, the smartphone embedded sensors (i.e., gyroscopes, accelerometers, and GPS) collect the associated information such as touchstrokes, holding orientation and vehicle speed. This information will then be analyzed to see whether there exists some specific T&D patterns. Extensive experiments have been conducted by different persons and in different driving scenarios. The results show that our approach can achieve a good detection accuracy with low false positive rate. Besides being infrastructurefree and with high accuracy, the method does not access the content of messages and therefore is privacy-preserving.


Detecting Driver’s Smartphone Usage via Nonintrusively Sensing Driving Dynamics

Abstract:
In this paper, we address a critical task of dynamically detecting the simultaneous behavior of driving and texting using smartphone as the sensor. We propose, design, and implement TEXIVE which achieves the goal of detecting texting operations during driving utilizing irregularities and rich micro-movements of users. Without relying on any external infrastructures and additional devices, and no need to bring any modification to vehicles, TEXIVE is able to successfully detect dangerous operations with good sensitivity, specificity, and accuracy by leveraging the inertial sensors integrated in regular smartphones. To validate our approach, we conduct extensive experiments involving in a number of volunteers on various of vehicles and smartphones. Our evaluation results show that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.


DELTA++: Reducing the Size of Android Application Updates

Abstract:
Compression can be a useful tool to reduce network bandwidth usage. We have developed an improved compression method for Android application updates called DELTA++ that achieves an additional 50% traffic reduction when compared to Google Smart Application Update. We estimate that the extra reduction in network bandwidth use from using DELTA++ would be about 1.8% of all annual cellular traffic in the US. Increased battery discharge from DELTA++ was found to be negligible. If similar methods were to be used for iPhone application updates even larger savings could be achieved.


An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context

Abstract:
Today, there is widespread use of mobile applications that take advantage of a user’s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user’s behalf can significantly improve a smartphone’s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy.


Active Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location

Abstract:
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.


RECENT PAPERS