Android Titles abstracts 2013-2014
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Android Titles abstracts 2013-2014

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Android Titles abstracts 2013-2014 Android Titles abstracts 2013-2014 Document Transcript

  • IEEE- Project Title 2013 1 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com 1. GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs ABSTRACT: This paper proposes a new location recognition algorithm for automatic check-in applications (LRACI), suited to be implemented within Smartphones, integrated in the Cloud platform and representing a service for Cloud end users. The algorithm, the performance of which is independent of the employed device, uses both global and hybrid positioning systems (GPS/HPS) and, in an opportunistic way, the presence of Wi-Fi access points (APs), through a new definition of Wi-Fi FingerPrint (FP), which is proposed in this paper. This FP definition considers the order relation among the received signal strength (RSS) rather than the absolute values. This is one of the main contributions of this paper. LRACI is designed to be employed where traditional approaches, usually based only on GPS/HPS, fail, and is aimed at finding user location, with a room-level resolution, in order to estimate the overall time spent in the location, called Permanence, instead of the simple presence. LRACI allows automatic check-in in a given location only if the users' Permanence is larger than a minimum amount of time, called Stay Length (SL), and may be exploited in the Cloud. For example, if many people check-in in a particular location (e.g., a supermarket or a post office), it means that the location is crowded. Using LRACI-based data, collected by smartphones in the Cloud and made available in the Cloud itself, end users can manage their daily activities (e.g., buying food or paying a bill) in a more efficient way. The proposal, practically implemented over Android operating system-based
  • IEEE- Project Title 2013 2 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com Smartphones, has been extensively tested. Experimental results have shown a location recognition accuracy of about 90%, opening the door to real LRACI employments. In this sense, a preliminary study of its application in the Cloud, obtained through simulation, has been provided to highlight the advantages of the LRACI features. 2. CloudMoV: Cloud-Based Mobile Social TV ABSTRACT: The rapidly increasing power of personal mobile devices (smartphones, tablets, etc.) is providing much richer contents and social interactions to users on the move. This trend however is throttled by the limited battery lifetime of mobile devices and unstable wireless connectivity, making the highest possible quality of service experienced by mobile users not feasible. The recent cloud computing technology, with its rich resources to compensate for the limitations of mobile devices and connections, can potentially provide an ideal platform to support the desired mobile services. Tough challenges arise on how to effectively exploit cloud resources to facilitate mobile services, especially those with stringent interaction delay requirements. In this paper, we propose the design of a Cloud-based, novel Mobile sOcial tV system (CloudMoV). The system effectively utilizes both PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service) cloud services to offer the living-room experience of video watching to a group of disparate mobile users who can interact socially while sharing the video. To guarantee good streaming quality as experienced by the mobile users with time-varying wireless connectivity, we employ a surrogate for each user in the IaaS cloud for video
  • IEEE- Project Title 2013 3 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com downloading and social exchanges on behalf of the user. The surrogate performs efficient stream transcoding that matches the current connectivity quality of the mobile user. Given the battery life as a key performance bottleneck, we advocate the use of burst transmission from the surrogates to the mobile users, and carefully decide the burst size which can lead to high energy efficiency and streaming quality. Social interactions among the users, in terms of spontaneous textual exchanges, are effectively achieved by efficient designs of data storage with BigTable and dynamic handling of large volumes of concurrent messages in a typical PaaS cloud. These various designs for flexible transcoding capabilities- battery efficiency of mobile devices and spontaneous social interactivity together provide an ideal platform for mobile social TV services. We have implemented CloudMoV on Amazon EC2 and Google App Engine and verified its superior performance based on real-world experiments. 3. Content Sharing over Smartphone-Based Delay-Tolerant Networks ABSTRACT: With the growing number of smartphone users, peer-to-peer ad hoc content sharing is expected to occur more often. Thus, new content sharing mechanisms should be developed as traditional data delivery schemes are not efficient for content sharing due to the sporadic connectivity between smartphones. To accomplish data delivery in such challenging environments, researchers have proposed the use of store-carry-forward protocols, in which a node stores a message and carries it until a forwarding opportunity arises through an encounter with other nodes. Most previous works in this field have focused on the prediction
  • IEEE- Project Title 2013 4 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com of whether two nodes would encounter each other, without considering the place and time of the encounter. In this paper, we propose discover-predict-deliver as an efficient content sharing scheme for delay-tolerant smartphone networks. In our proposed scheme, contents are shared using the mobility information of individuals. Specifically, our approach employs a mobility learning algorithm to identify places indoors and outdoors. A hidden Markov model is used to predict an individual's future mobility information. Evaluation based on real traces indicates that with the proposed approach, 87 percent of contents can be correctly discovered and delivered within 2 hours when the content is available only in 30 percent of nodes in the network. We implement a sample application on commercial smartphones, and we validate its efficiency to analyze the practical feasibility of the content sharing application. Our system approximately results in a 2 percent CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at most. 4. Uncovering Measurements of Social and Demographic Behavior From Smartphone Location Data ABSTRACT: Human behavior, and in particular location behavior, is highly routine based. Modern mobile phones, through global position system (GPS) technology and cell tower and WiFi location identification, enable us to trace human location behavior at scales that were previously unattainable. The goal of this paper is to examine human location behavior, through mobile phone data, and investigate if links can be made between location behavior patterns and particular demographic and social
  • IEEE- Project Title 2013 5 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com characteristics about an individual. We hypothesize that an individual's daily predictability can be key to linking their behavior to certain characteristics, and we propose predictability and geographic areas of interest models to analyze this hypothesis. Experiments reveal that measurements, which are based on our proposed location predictability models, can correctly infer 17 different characteristics about an individual with an average accuracy of 85.5%. 5. On the Investigation of Cloud-Based Mobile Media Environments With Service-Populating and QoS-Aware Mechanisms ABTRACT: Recent advances in mobile devices and network technologies have set new trends in the way we use computers and access networks. Cloud Computing, where processing and storage resources are residing on the network is one of these trends. The other is Mobile Computing, where mobile devices such as smartphones and tablets are believed to replace personal computers by combining network connectivity, mobility, and software functionality. In the future, these devices are expected to seamlessly switch between different network providers using vertical handover mechanisms in order to maintain network connectivity at all times. This will enable mobile devices to access Cloud Services without interruption as users move around. Using current service delivery models, mobile devices moving from one geographical location to another will keep accessing those services from the local Cloud of their previous network, which might lead to moving a large volume of data over the Internet backbone over long distances. This scenario highlights the fact that user mobility will result in more congestion on the Internet. This will
  • IEEE- Project Title 2013 6 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com degrade the Quality of Service and by extension, the Quality of Experience offered by the services in the Cloud and especially multimedia services that have very tight temporal constraints in terms of bandwidth and jitter. We believe that a different approach is required to manage resources more efficiently, while improving the Quality of Service and Quality of Experience of mobile media services. This paper introduces a novel concept of Cloud-Based Mobile Media Service Delivery in which services run on localized public Clouds and are capable of populating other public Clouds in different geographical locations depending on service demands and network status. Using an analytical framework, this paper argues that as the demand for specific services increases in a location, it might be more efficient to move those services closer to that location. This will prevent th- Internet backbone from experiencing high traffic loads due to multimedia streams and will offer service providers an automated resource allocation and management mechanism for their services. 6. Distributed Cooperative Caching in Social Wireless Networks ABSTRACT: This paper introduces cooperative caching policies for minimizing electronic content provisioning cost in Social Wireless Networks (SWNET). SWNETs are formed by mobile devices, such as data enabled phones, electronic book readers etc., sharing common interests in electronic content, and physically gathering together in public places. Electronic object caching in such SWNETs are shown to be able to reduce the content provisioning cost which depends heavily on the service and pricing dependences among various stakeholders including content
  • IEEE- Project Title 2013 7 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com providers (CP), network service providers, and End Consumers (EC). Drawing motivation from Amazon's Kindle electronic book delivery business, this paper develops practical network, service, and pricing models which are then used for creating two object caching strategies for minimizing content provisioning costs in networks with homogenous and heterogeneous object demands. The paper constructs analytical and simulation models for analyzing the proposed caching strategies in the presence of selfish users that deviate from network-wide cost- optimal policies. It also reports results from an Android phone-based prototype SWNET, validating the presented analytical and simulation results. 7. Validating the Impact on Reducing Fuel Consumption by Using an EcoDriving Assistant Based on Traffic Sign Detection and Optimal Deceleration Patterns ABSTRACT: This paper implements and validates an expert system that, based on the detection or previous knowledge of certain types of traffic signals, proposes a method to reduce fuel consumption by calculating optimal deceleration patterns, minimizing the use of braking. The expert system uses a mobile device's embedded camera to monitor the environment and to recognize certain types of static traffic signals that force or can force a vehicle to stop. The system uses an adaptation of the algorithm proposed by Viola and Jones for the recognition of faces in real time, adapted to the detection of traffic signals. Detected signals are also incorporated into a central database for future use. When the vehicle approaches an upcoming traffic signal, the algorithm estimates the distance required to stop the vehicle
  • IEEE- Project Title 2013 8 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com without using the brakes, taking into account the rolling resistance coefficient and the road slope angle. Appropriate advice and feedback are provided to the driver to release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a data set of 180 tests with five different models of vehicles and nine different drivers. The main contribution of this paper is the proposal of an assistant that uses information from the environment and from the vehicle to calculate optimal deceleration patterns when approaching traffic signals that force or may force the vehicle to stop. In addition, the proposed solution does not require the installation of infrastructure on the road, and it can be installed into any vehicle. 8. Adaptive Mobile Cloud Computing to Enable Rich Mobile Multimedia Applications ABSTRACT: With worldwide shipments of smartphones (487.7 million) exceeding PCs (414.6 million including tablets) in 2011, and in the US alone, more users predicted to access the Internet from mobile devices than from PCs by 2015, clearly there is a desire to be able to use mobile devices and networks like we use PCs and wireline networks today. However, in spite of advances in the capabilities of mobile devices, a gap will continue to exist, and may even widen, with the requirements of rich multimedia applications. Mobile cloud computing can help bridge this gap, providing mobile applications the capabilities of cloud servers and storage together with the benefits of mobile devices and mobile connectivity,
  • IEEE- Project Title 2013 9 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com possibly enabling a new generation of truly ubiquitous multimedia applications on mobile devices: Cloud Mobile Media (CMM) applications. 9. Predicting Human Movement Based on Telecom's Handoff in Mobile Networks ABSTRACT: Investigating human movement behavior is important for studying issues such as prediction of vehicle traffic and spread of contagious diseases. Since mobile telecom network can efficiently monitor the movement of mobile users, the telecom's mobility management is an ideal mechanism for studying human movement issues. The problem can be abstracted as follows: What is the probability that a person at location A will move to location B after T hours. The answer cannot be directly obtained because commercial telecom networks do not exactly trace the movement history of every mobile user. In this paper, we show how to use the standard outputs (handover rates, call arrival rates, call holding time, and call traffic) measured in a mobile telecom network to derive the answer for this problem. 10.A Robust Indoor Pedestrian Tracking System with Sparse Infrastructure Support ABSTRACT: Existing approaches to indoor tracking have various limitations. Location- fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for
  • IEEE- Project Title 2013 10 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially. 11.Leveraging smartphone cameras for collaborative road advisories ABSTRACT: Ubiquitous smartphones are increasingly becoming the dominant platform for collaborative sensing. Smartphones, with their ever richer set of sensors, are being used to enable collaborative driver-assistance services like traffic advisory and road condition monitoring. To enable such services, the smartphones' GPS, accelerometer, and gyro sensors have been widely used. On the contrary, smartphone cameras, despite being very powerful sensors, have largely been neglected. In this paper, we introduce a collaborative sensing platform that exploits the cameras of windshield-mounted smartphones. To demonstrate the potential of this platform, we propose several services that it can support, and prototype
  • IEEE- Project Title 2013 11 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com SignalGuru, a novel service that leverages windshield-mounted smartphones and their cameras to collaboratively detect and predict the schedule of traffic signals, enabling Green Light Optimal Speed Advisory (GLOSA) and other novel applications. Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66 s, for pretimed traffic signals and within 2.45 s, for traffic-adaptive traffic signals. Feeding SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3 percent, on average. 12.An efficient approach for mobile asset tracking using contexts ABSTRACT: Due to the heterogeneity involved in smart interconnected devices, cellular applications, and surrounding (GPS-aware) environments there is a need to develop a realistic approach to track mobile assets. Current tracking systems are costly and inefficient over wireless data transmission systems where cost is based on the rate of data being sent. Our aim is to develop an efficient and improved geographical asset tracking solution and conserve valuable mobile resources by dynamically adapting the tracking scheme by means of context-aware personalized route learning techniques. We intend to perform this tracking by proactively monitoring the context information in a distributed, efficient, and scalable fashion. Context profiles, which indicate the characteristics of a route based on environmental conditions, are utilized to dynamically represent the values of the asset's properties. We designed and implemented an adaptive learning based
  • IEEE- Project Title 2013 12 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com scheme that makes an optimized judgment of data transmission. This manuscript is complemented with theoretical and practical evaluations that prove that significant costs can be saved and operational efficiency can be achieved. 13.Enhancing privacy and accuracy in probe vehicle-based traffic Monitoring via virtual trip lines ABSTRACT: Traffic monitoring using probe vehicles with GPS receivers promises significant improvements in cost, coverage, and accuracy over dedicated infrastructure systems. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we describe a system based on virtual trip lines and an associated cloaking technique, followed by another system design in which we relax the privacy requirements to maximize the accuracy of real-time traffic estimation. We introduce virtual trip lines which are geographic markers that indicate where vehicles should provide speed updates. These markers are placed to avoid specific privacy sensitive locations. They also allow aggregating and cloaking several location updates based on trip line identifiers, without knowing the actual geographic locations of these trip lines. Thus, they facilitate the design of a distributed architecture, in which no single entity has a complete knowledge of probe identities and fine-grained location information. We have implemented the system with GPS smartphone clients and conducted a controlled
  • IEEE- Project Title 2013 13 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com experiment with 100 phone-equipped drivers circling a highway segment, which was later extended into a year-long public deployment. 14.Mobile data offloading through opportunistic communications and Social participation ABSTRACT: 3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device/service discovery and content transfer.
  • IEEE- Project Title 2013 14 33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551 Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com 15.Smartphone-based collaborative and autonomous radio Fingerprinting ABSTRACT: Although active research has recently been conducted on received signal strength (RSS) fingerprint-based indoor localization, most of the current systems hardly overcome the costly and time-consuming offline training phase. In this paper, we propose an autonomous and collaborative RSS fingerprint collection and localization system. Mobile users track their position with inertial sensors and measure RSS from the surrounding access points. In this scenario, anonymous mobile users automatically collect data in daily life without purposefully surveying an entire building. The server progressively builds up a precise radio map as more users interact with their fingerprint data. The time drift error of inertial sensors is also compromised at run-time with the fingerprint-based localization, which runs with the collective fingerprints being currently built by the server. The proposed system has been implemented on a recent Android smartphone. The experiment results show that reasonable location accuracy is obtained with automatic fingerprinting in indoor environments.