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This paper presents our work on the inception of RTDroid, a variant of Android that provides predictability to Android applications. Although there has been much interest in adopting Android in real-time contexts, surprisingly little work has been done to examine the suitability of the Android franework layer for real-time systems. Existing work only provides solutions to traditional problems, including adding support for real-time garbage collection at the virtual machine layer as well as kernel-level real-time scheduling and resource management. While it is critical to address these issues, it is by no means sufficient. After all, Android is a vast system that is more than a Java virtual machine and a kernel. Thus, this paper goes beyond existing work and examines the internals of Android, the Android programming model, libraries, and core systems services. We discuss the implications and challenges of adapting Android constructs and core system services for real-time and present a solution for each. Our system is unique in that it redesigns Androids internal components, replaces Androids Dalvik VM with a real-time VM, and leverages off-the-shelf real-time OSes. We demonstrate the feasibility and predictability of our solution on three different platforms. The evaluation results show that our design can successfully provide predictability to Android applications even under heavy loads. [ABSTRACT FROM PUBLISHER]

On current Android mobile devices, DVFS is a key power management technique. Besides CPU, memory and GPU support DVFS as well. OS modules known as governors implement algorithms that dynamically adjust operating frequency for their respective hardware components to balance performance and power. However, the stock governors do not consider application characteristics, manager power instead of energy, and operate independently of each other, thus leading to energy inefficiency. In this paper, we present an application-specific approach to energy optimization targeting Android mobile devices. In this two-step approach, we first perform offline profiling of the target app to obtain its performance and power measurements under different DVFS settings. The profiling data are then used by a feedback loop based online controller to dynamically select energy-optimal DVFS settings for the app while trying to meet a performance target. In contrast with the default governors, our approach is application-specific, manages energy, and controls both CPU and memory in unison. Tested on a few mainstream applications, our solution achieves considerable energy savings with minimal performance loss. This paper describes the design and evaluation of the system in detail with a particular focus on problems related to the robustness of the approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a driving assistant that makes recommendations in order to reduce the fuel consumption. The solution only requires a smartphone and an OBD/Bluetooth device. Eco-driving advices try to avoid situations that cause an increase in the fuel consumption such as inappropriate speed or slow reaction to the detection of traffic signs and traffic incidents. The main contribution of this paper is the use of artificial intelligence techniques in order to issue the eco-driving tips that are best adapted to the user profile, the characteristics of the vehicle, and the road state conditions. This is very important because the driver may lose the interest due to the high requirements that tend to be provided by general use eco-driving assistants. In order to properly assess and validate the proposed solution, it has been implemented on several Android mobile devices and has been validated using a dataset of 2,250 driving tests using three different models of vehicles with 25 different drivers on three distinct routes. The results show that the system reduces the fuel consumption by 11.04 percent on average and even, in certain cases, the fuel saving is greater than 15 percent. [ABSTRACT FROM PUBLISHER]

The pervasive penetration of mobile smart devices has significantly enriched Internet applications and undoubtedly reshaped the way that users access Internet services. Different from traditional desktop applications, mobile Internet applications require users to input via touch screens and view outputs on the displays with considerably limited size. The significant conflict between the limited-size of touch screens and the richness of online media contents widely exists in dynamic-viewport mobile applications, a class of mobile Internet applications that download contents beyond the user's viewing region (referred to as viewport). As dynamic-viewport mobile applications usually use HTTP for content downloading, to improve their quality of experience (QoE) and cost efficiency, in this paper, we present a Mobile-Friendly HTTP middleware (MF-HTTP), which can interpret user touch screen inputs and optimize the HTTP downloading of media objects for such applications. We first demystify screen scrolling in mobile operating systems and precisely break down the viewport moving process. We identify the key influential factors for media object downloading and develop an optimal download scheme. Towards building a practical middleware, we further discuss and address the implementation issues in detail. We implement a MF-HTTP prototype based on Android platforms and evaluate the performance of MF-HTTP by conducting concrete case studies on two representative dynamic-viewport mobile applications, namely, web browsing and 360-degree video streaming. [ABSTRACT FROM AUTHOR]

Opportunistic communication, off-loading, and decentrlaized distribution have been proposed as a means of cost efficient disseminating content when users are geographically clustered into communities. Despite its promise, none of the proposed systems have not been widely adopted due to unbounded high content delivery latency, security, and privacy concerns. This paper, presents a novel hybrid content storage and distribution system addressing the trust and privacy concerns of users, lowering the cost of content distribution and storage, and shows how they can be combined uniquely to develop mobile social networking services. The system exploit the fact that users will trust their friends, and by replicating content on friends’ devices who are likely to consume that content it will be possible to disseminate it to other friends when connected to low cost networks. The paper provides a formal definition of this content replication problem, and show that it is NP hard. Then, it presents a community based greedy heuristic algorithm with novel dynamic centrality metrics that replicates the content on a minimum number of friends’ devices, to maximize availability. Then using both real world and synthetic datasets, the effectiveness of the proposed scheme is demonstrated. The practicality of the proposed system, is demonstrated through an implementation on Android smartphones. [ABSTRACT FROM AUTHOR]

Because of the smaller size of mobile devices, text entry with on-screen keyboards becomes inefficient. Therefore, we present CamK, a camera-based text-entry method, which can use a panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. With the built-in camera of the mobile device, CamK captures images during the typing process and utilizes image processing techniques to recognize the typing behavior, i.e., extract the keys, track the user's fingertips, detect, and locate keystrokes. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. To reduce the time latency, CamK optimizes computation-intensive modules by changing image sizes, focusing on target areas, introducing multiple threads, removing the operations of writing or reading images. Finally, we implement CamK on mobile devices running Android. Our experimental results show that CamK can achieve above 95 percent accuracy in keystroke localization, with only a 4.8 percent false positive rate. When compared with on-screen keyboards, CamK can achieve a 1.25X typing speedup for regular text input and 2.5X for random character input. In addition, we introduce word prediction to further improve the input speed for regular text by 13.4 percent. [ABSTRACT FROM AUTHOR]

The past decade has witnessed a tremendous growth in the variety and complexity of mobile applications (apps). Although considerable amount of efforts have been spent to improve app performance, smartphones nowadays still face many performance challenges. We discover that the resource contention of multiple running apps, caused by resource bottleneck(s), is a key factor that affects the smartphone performance. In this paper, we present APB, an A utomatic tool that detects Performance issues caused by resource B ottleneck(s) on commodity Android smartphones. APB employs an innovative bottleneck-hypersurface model to quantify performance issues given a specific system state. Then, based on the model, APB identifies a list of apps that contribute most to the resource contention, which can well inform the end user to take action such as killing background apps to resolve the performance issue. We implement APB on commodity Android platforms and widely evaluate its effectiveness with real user studies. Results show that APB outperforms three baseline approaches and helps users to improve smartphone performance by 10 to 67 percent, with less than 1 percent runtime overhead. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) era, smartphones are expected to frequently interact with IoT devices and even facilitate various IoT applications. Due to limited roles, energy constraints, etc., however, IoT devices may use mission-tailored or proprietary wireless protocols that smartphones do not speak natively. In this paper, we propose a novel approach to the wireless “language barrier” problem between the smartphones and IoT devices of the future. We first demonstrate that smartphones have become powerful enough to process software defined radio (SDR) for some known wireless protocols. Moreover, we show that the SDRs can be packaged as “apps” and be downloaded from app stores for OS-independent deployment. Second, we show different SDR protocols on the smartphone can concurrently run through a shared RF to serve multi-tasked applications on it as might happen in diversified IoT environments. For proof-of-concept, we implement a prototype architecture that has all the SDR logic and supporting middleware on an Android smartphone which uses a USRP as the simple RF-end. Finally, we demonstrate that IEEE 802.11p and IEEE 802.15.4 SDRs on a smartphone, respectively, communicate with a ZigBee sensor mote, a ZigBee smart lightbulb, and a commercial Wireless Access in Vehicular Environment (WAVE) device, concurrently. [ABSTRACT FROM AUTHOR]

The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants’ involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones. [ABSTRACT FROM AUTHOR]

Automatically triggering malicious behaviors is an essential step to understand malware for developing effective solutions. Existing automated dynamic analysis approaches usually try to trigger the malicious behaviors by relying on simple fuzzing or complex input generation techniques (e.g., concolic execution). However, advanced malware often adopt various evasion techniques to hide malicious behaviors, e.g., by introducing complex condition checks which are very hard to trigger. In this paper, we propose a new approach named DirectDroid, which bypasses related checks through on-demand forced execution while adopting fuzzing to feed the necessary program input. In this way, many hidden malicious behaviors can be successfully triggered. To ensure the normal execution towards the malicious behaviors, DirectDroid also largely handles potential program crashes caused by forced execution. Finally, we implement a prototype of DirectDroid and evaluate it against 951 recent malware samples. Our experiment results show that DirectDroid can trigger many more malicious behaviors than several previous works, even when crashes happened. Our further analysis shows that DirectDroid has a low false positive rate even though it adopts forced execution. [ABSTRACT FROM AUTHOR]

Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment. However, with the popularity of Android-based smartphones/tablets comes the rampancy of Android-based malware. In this paper, we propose a novel topological signature of Android apps based on the function call graphs (FCGs) extracted from their Android App PacKages (APKs). Specifically, by leveraging recent advances on graphlet mining, the proposed method fully captures the invocator-invocatee relationship at local neighborhoods in an FCG without exponentially inflating the state space. Using real benign app and malware samples, we demonstrate that our method, App topologiCal signature through graphleT Sampling (ACTS), can detect malware and identify malware families robustly and efficiently. More importantly, we demonstrate that, without augmenting the FCG with any semantic features such as bytecode-based vertex typing, local topological information captured by ACTS alone can achieve a high malware detection accuracy. Since ACTS only uses structural features, which are orthogonal to semantic features, it is expected that combining them would give a greater improvement in malware detection accuracy than combining non-orthogonal semantic features. [ABSTRACT FROM AUTHOR]

Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. This paper presents the design and implementation of SOAR—a vision-based surveillance robot system that integrates an off-the-shelf Android smartphone and a gliding robotic fish for debris monitoring in relatively calm waters. SOAR features real-time debris detection and coverage-based rotation scheduling algorithms. The image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. The rotation scheduling algorithm provides effective coverage for sporadic debris arrivals despite camera's limited angular view. Moreover, SOAR is able to dynamically offload compute-intensive processing tasks to the cloud for battery power conservation. We have implemented a SOAR prototype and conducted extensive experimental evaluation. The results show that SOAR can accurately detect debris in the presence of various environment and system dynamics, and the rotation scheduling algorithm enables SOAR to capture debris arrivals with reduced energy consumption. [ABSTRACT FROM AUTHOR]

Managing the power consumption of display-related components in mobile devices is difficult because of performance degradation. Therefore, eliminating hidden workloads, such as redundant frames, is preferable, as it directly reduces power without affecting the user experience. Our preliminary study shows that the default launcher of the Android Open Source Project (AOSP) and popular applications, such as Instagram and Pinterest, generate redundant frames. In this paper, we propose a scheme to optimize the power consumption of the smartphone's display-related components by preventing redundant frames generation. By analyzing the frame-generation process, we observe that redundant frame generation is possible in the current Android framework. We then propose a scheme that recognizes and prevents redundant frame generation before actual frame generation (i.e., frame rendering in the GPU). The proposed scheme utilizes a display list, which was introduced in recent Android smartphones for efficient frame generation. We implemented the proposed scheme on Nexus smartphones. On the Nexus 5, the proposed solution reduced the energy of the AOSP default launcher, Instagram, and Pinterest by 40, 35.4, and 39.6 percent, respectively. Furthermore, the experimental results with a general usage scenario showed that our scheme prevented about 35 percent of redundant frame generation with a false-positive rate of 1.8 percent. [ABSTRACT FROM AUTHOR]

Energy-efficient communications have become one fundamental aspect for today's cutting-edge wireless technologies due to its valuable impact on the environment. In this paper, we augment our earlier study for the user equipment's (UE) energy efficiency (EE) in the long-term evolution (LTE) downlink by looking at real-time heterogeneous traffic QoS requirements. In particular, we utilize the previously proposed cloud radio access network (C-RAN) and ray tracing (RT)-based scheduling model to optimize both of the EE and the packet delay jitter for real-time applications with fixed packet delay budget subject to other traffic types requirements. Using the utility-based scheduling approach, we formulate the resource allocation problem as a weighted sum binary integer programming (BIP) problem. Due to the inherent complexity of the problem formulation which hinders finding its solution directly, four heuristic algorithms are proposed to solve the optimization problem. Numerical simulations are conducted on three different traffic types each belonging to one of the popular QoS classes; best-effort class, rate, and delay-constrained classes. The obtained results demonstrate a substantial improvement in the system's performance achieved by our proposed schemes compared to other existing schemes. [ABSTRACT FROM AUTHOR]

In recent years, the use of smartphones has increased exponentially, and so have their capabilities. Together with an increase in processing power, smartphones are now equipped with a variety of sensors and provide an extensive set of API. These capabilities allow us to extract data related to environment, user habits, and operating system itself. This data is extremely valuable in many research fields such as user authentication, intrusion, and information leaks detection. For these reasons, researchers need a solid and reliable logging tool to collect data from mobile devices. In this paper, we first survey the existing logging tools available on the Android platform, comparing their features and their impact on the system. Then, we present DELTA - Data Extraction and Logging Tool for Android, which improves the existing Android logging solutions in terms of flexibility, fine-grained tuning capabilities, extensibility, and available set of logging features. We fully implement DELTA and we run a thorough performance evaluation. The results show that our tool has a low impact on the performance of the system, on battery consumption, and on user experience. Finally, we make the DELTA source code available to the research community. [ABSTRACT FROM AUTHOR]

Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps—Ookla Speedtest and MobiPerf—have their RTT measurements inflated. We build three test apps for three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete trace of packets and their timestamps using multiple sniffers and frame-based synchronization. Our multi-layer analysis reveals that the delay inflation can be introduced both in the user space and kernel space. The long path of subfunction invocations accounts for the majority of the delay overhead in the Android runtime (both Dalvik VM and ART), and the sleeping functions in the drivers are the major source of the delay overhead between the kernel and physical layer. We propose and implement a native measurement app to mitigate the delay overhead in the Android runtime, and the resulted delay inflation in the user space can be kept under 1.5 ms for almost all cases. [ABSTRACT FROM AUTHOR]

Monitoring context depends on continuous collection of raw data from sensors which are either embedded in smart mobile devices or worn by the user. However, continuous sensing constitutes a major source of energy consumption; on the other hand, lowering the sensing rate may lead to missing the detection of critical contextual events. In this paper, we propose VCAMS: a Viterbi-based Context Aware Mobile Sensing mechanism that adaptively finds an optimized sensing schedule to decide when to trigger the sensors for data collection while trading off the sensing energy and the delay to detect a state change. The sensing schedule is adaptive from two aspects: 1) the decision rules are learned from the user’s past behavior, and 2) these rules are updated over real time whenever there is a significant change in the user’s behavior. VCAMS is validated using multiple experiments, which include evaluation of model success when considering binary and multi-user states. We also implemented VCAMS on an Android-based device to estimate its computational costs under realistic operational conditions. Test results show that our proposed strategy provides better trade-off than previous state-of-the-art methods under comparable conditions. Furthermore, the method provides 78 percent energy saving when compared to continuous sensing. [ABSTRACT FROM PUBLISHER]

The prevalence of Android platform has attracted adversaries to craft malicious payloads for illegal profit. Such malicious artifacts are frequently reused and embedded in benign, paid apps to lure victims that the apps have been cracked for free. To discover these fraudulent apps, administrators of app markets desire an automated scanning process to maintain the health of app ecosystem. However, conventional approaches cannot be efficiently applied due to the lack of a scalable, effective approach to malware characteristics aggregation. On the other hand, the vast number of apps significantly increases the analysis complexity. In this paper, we propose Petridish which generates discriminative models against the repacked malicious apps. These representative models of malicious semantics can be progressively distilled with malign and benign samples. These models can further detect repacked malicious apps. Our experiment shows that, after two retraining rounds, Petridish achieved an average of 28 percent progressive detection improvement from 63 to 91.2 percent for the large families, exceeding 38 test samples in size. With noise reduction, it accomplished 88 percent detection rate and 1.7 percent false alarm rate. The characteristics aggregation approach will become critical in the age of app explosion. [ABSTRACT FROM AUTHOR]

Numerous complains have been made by Android users who severely suffer from the sluggish response when interacting with their devices. However, very few studies have been conducted to understand the user-perceived latency or mitigate the UI-lagging problem. In this paper, we conduct the first systematic measurement study to quantify the user-perceived latency using typical interaction-intensive Android apps in running with and without background workloads. We reveal the insufficiency of Android system in ensuring the performance of foreground apps and therefore design a new system to address the insufficiency accordingly. We develop a lightweight tracker to accurately identify all delay-critical threads that contribute to the slow response of user interactions. We then build a resource manager that can efficiently schedule various system resources including CPU, I/O, and GPU, for optimizing the performance of these threads. We implement the proposed system on commercial smartphones and conduct comprehensive experiments to evaluate our implementation. Evaluation results show that our system is able to significantly reduce the user-perceived latency of foreground apps in running with aggressive background workloads, up to 10x, while incurring negligible system overhead of less than 3.1 percent CPU and 7 MB memory. [ABSTRACT FROM AUTHOR]

Android Application Framework is an integral and foundational part of the Android system. Each of the two billion (as of 2017) Android devices relies on the system services of Android Framework to manage applications and system resources. Given its critical role, a vulnerability in the framework can be exploited to launch large-scale cyber attacks and cause severe harms to user security and privacy. Recently, many vulnerabilities in Android Framework were exposed, showing that it is indeed vulnerable and exploitable. While there is a large body of studies on Android application analysis, research on Android Framework analysis is very limited. In particular, to our knowledge, there is no prior work that investigates how to enable symbolic execution of the framework, an approach that has proven to be very powerful for vulnerability discovery and exploit generation. We design and build the first system, Centaur, that enables symbolic execution of Android Framework. Due to the middleware nature and technical peculiarities of the framework that impinge on the analysis, many unique challenges arise and are addressed in Centaur. The system has been applied to discovering new vulnerability instances, which can be exploited by recently uncovered attacks against the framework, and to generating PoC exploits. [ABSTRACT FROM AUTHOR]

While much effort has been made to detect and measure the privacy leakage caused by the advertising (ad) libraries integrated in mobile applications, analytics libraries, which are also widely used in mobile apps have not been systematically studied for their privacy risks. Different from ad libraries, the main function of analytics libraries is to collect users’ in-app actions. Hence, by design analytics libraries are more likely to leak users’ private information. In this work, we study what information is collected by the analytics libraries integrated in popular Android apps. We design and implement a framework called “Alde”. Given an app, Alde employs both static analysis and dynamic analysis to detect the users’ in-app actions collected by analytics libraries. We also study what private information can be leaked by the apps that use the same analytics library. Moreover, we analyze apps’ privacy policies to see whether app developers have notified the users that their in-app action data is collected by analytics libraries. Finally, we select eight widely used analytics libraries to study and apply our method to 300 popular apps downloaded from both Chinese app markets and Google play. Our experimental results show that some apps indeed leak users’ personal information through analytics libraries even though their genuine purposes of using analytics services are legal. To mitigate such threats, we have developed an app named “ALManager” that leverages the Xposed framework to manage analytics libraries in other apps. [ABSTRACT FROM AUTHOR]

Malware collusion is a technique utilized by attackers to evade standard detection. It is a new threat where two or more applications, appearing benign, communicate to perform a malicious task. Most proposed approaches aim at detecting stand-alone malicious applications. We point out the need for analyzing data flows across multiple Android apps, a problem referred to as end-to-end flow analysis. In this work, we present a flow analysis for app pairs that computes the risk level associated with their potential communications. Our approach statically analyzes the sensitivity and context of each inter-app flow based on inter-component communication (ICC) between communicating apps, and defines fine-grained security policies for inter-app ICC risk classification. We perform an empirical study on 7,251 apps from the Google Play store to identify the apps that communicate with each other via ICC channels. Our results report four times fewer warnings on our dataset of 197 real app pairs communicating via explicit external ICCs than the state-of-the-art permission-based collusion detection. [ABSTRACT FROM AUTHOR]

WiFi continues to be a prime source of energy consumption in mobile devices. This paper observes that, despite a rich body of research in WiFi energy management, there is room for improvement. Our key finding is that WiFi energy optimizations have conventionally been designed with a single AP in mind. However, network contention among different APs can dramatically increase a client's energy consumption. Each client may have to keep awake for long durations before its own AP gets a chance to send it packets to it. As AP density increases, the waiting time inflates, resulting in a proportional decrease in battery life. We design SleepWell, a system that achieves energy efficiency by evading network contention. The APs regulate the sleeping window of their clients in a way that different APs are active/inactive during nonoverlapping time windows. The solution is analogous to the common wisdom of going late to office and coming back late, thereby avoiding the rush hours. We implement SleepWell on a testbed of eight Laptops and nine Android phones, and evaluate it over a wide variety of scenarios and traffic patterns. Results show a median gain of up to 2x when WiFi links are strong; when links are weak and the network density is high, the gains can be even more. [ABSTRACT FROM PUBLISHER]

As mobile customers gradually occupying the largest share of cloud service users, the effective and cost-sensitive provisioning of mobile cloud services quickly becomes a main theme in cloud computing. The key issues involved are much more than just enabling mobile users to access remote cloud resources through wireless networks. The resource limited and intermittent disconnection problems of mobile environments have intrinsic conflict with the continuous connection assumption of the cloud service usage patterns. We advocate that seamless service provisioning in mobile cloud can only be achieved with full exploitation of all available resources around mobile users. An elastic framework is proposed to automatically and dynamically deploy cloud services on data center, base stations, client units, even peer devices. The best deployment location is dynamically determined based on a context-aware and cost-sensitive evaluation model. To facilitate easy adoption of the proposed framework, a service development model and associated semi-automatic tools are provided such that cloud service developers can easily convert a service for execution on different platforms without porting. Prototype implementation and evaluation on the Google Cloud and Android platforms demonstrate that our mechanism can successfully maintain seamless services with very low overhead. [ABSTRACT FROM AUTHOR]

In cellular networks, due to many practical deployment issues, some areas have good wireless coverage while other areas may not. This results in significant throughput (service quality) difference between wireless carriers at some locations. We first analyze the factors that affect the service quality and then validate the existence of service quality difference between different carriers via extensive measurements. To deal with this problem, a mobile device (node) with low service quality can offload its data traffic to nearby nodes with better service quality through Device-to-Device interfaces, such as WiFi direct, to save energy and reduce delay. To achieve this goal, we propose a Quality-Aware Traffic Offloading (QATO) framework to offload network tasks to neighboring nodes with better service quality. QATO can identify neighbors with better service quality and motivate nodes to help each other using incentive schemes. To validate our design, we have implemented QATO on Android platform and have developed a web browser and a photo uploader on top of it. Experimental results show that QATO can significantly reduce energy and delay for both data downloading and uploading. Through trace-driven simulations, we also show that all users can benefit from data offloading in the long run. [ABSTRACT FROM AUTHOR]

User surveys have shown that a typical user has over a 100 apps on his/her smartphone  [1] , but stops using many of them. We conduct a user study to identify such unused apps, which we call zombies, and show via experiments that zombie apps consume significant resources on a user’s smartphone and access his/her private information. We then design and build ZapDroid, which enables users to detect and silo zombie apps in an effective way to prevent their undesired activities. If and when the user wishes to resume using such an app, ZapDroid restores the app quickly and effectively. Our evaluations show that: (i) ZapDroid saves twice the energy from unwanted zombie app behaviors as compared to apps from the Play Store that kill background unwanted processes, and (ii) it effectively prevents zombie apps from using undesired permissions. In addition, ZapDroid is energy-efficient, consuming $<4$ percent of the battery per day. [ABSTRACT FROM AUTHOR]

A lot of personal daily contexts and activities may be inferred by analyzing acoustic signals in vicinity. Conversations play an important role in one's social communications. In this work, we consider the inference of conversation partners via acoustic sensing conducted by a group of smartphones in vicinity. By considering the continuity and overlap of speeches, we propose novel inference methods to identify conversational relationships among co-located users. In our system, each smartphone individually processes the acoustic data to understand its owner's talking turns and emotions. Via direct wireless communications, smartphones then cooperatively conduct the inference to retrieve conversational groups. Compared to existing work, which only exploits peer-to-peer conversational relationships, our approach is able to capture group conversational relationships in a more real-time manner. A prototype on Android smartphones is demonstrated to verify the feasibility of our approach. We also collect conversation data from movie clips and real life with $2$ to $14$ speakers to validate our result, which shows promising performance. [ABSTRACT FROM AUTHOR]

We present Mantis, a framework for predicting the computational resource consumption (CRC) of Android applications on given inputs accurately, and efficiently. A key insight underlying Mantis is that program codes often contain features that correlate with performance and these features can be automatically computed efficiently. Mantis synergistically combines techniques from program analysis and machine learning. It constructs concise CRC models by choosing from many program execution features only a handful that are most correlated with the program’s CRC metric yet can be evaluated efficiently from the program’s input. We apply program slicing to reduce evaluation time of a feature and automatically generate executable code snippets for efficiently evaluating features. Our evaluation shows that Mantis predicts four CRC metrics of seven Android apps with estimation error in the range of 0-11.1 percent by executing predictor code spending at most 1.3 percent of their execution time on Galaxy Nexus. [ABSTRACT FROM AUTHOR]