Capabilities Of Smartphones Research Articles

Implicit authentication method for smartphone users based on rank aggregation and random forest

Currently, the smartphone devices have become an essential part of our daily activities. Smartphone’ users run various essential applications (such as banking and e-health Apps), which contains very confidential information (e.g., credit card number and its PIN). Typically, the smartphone’s user authentication is achieved using mechanisms (password or security pattern) to verify the user identity. Although these mechanisms are cheap, simple, and quick enough for frequent logins, they are vulnerable to attacks such as shoulder surfing or smudge attack. This problem could be addressed by authenticating the users using their behaviour (i.e., touch behaviour) while using their smartphones. Such behaviours include finger’s pressure, size, and pressure time while tapping keys. Selecting features (from these behaviours) could play an important role in the authentication process’s performance. This paper aims to propose an efficient authentication method providing an implicit authentication for smartphone users while not imposing an additional cost of special hardware and addressing the limited smartphone capabilities. We first investigated feature selection techniques from the filter and wrapper approaches and then used the best one to propose our implicit authentication method. The random forest classifier is used to evaluate these techniques. It is also used to achieve the classification task in our authentication method. Using a public dataset, the experimental results showed that the filter-based technique (i.e., rank aggregation) is the best feature selection to build an implicit authentication method for the smartphone environment. It showed accuracy results around 97.80% using only 25 features out of 53 features (i.e., require less mobile resources (memory and processing power) to authenticate users. At the same time, the results showed that our method has less error rate: 2.03 FAR, 0.04 FRR, and 1.04 ERR, comparing to the related work. These promising results would be used to develop a mobile application that allows implicit authentication of legitimate owners while avoiding the traditional authentication problems and using fewer smartphone resources.

Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data

Exploiting the capabilities of smartphones for monitoring social anxiety shows promise for advancing our ability to both identify indicators of and treat social anxiety in natural settings. Smart devices allow researchers to collect passive data unobtrusively through built-in sensors and active data using subjective, self-report measures with Ecological Momentary Assessment (EMA) studies. Prior work has established the potential to predict subjective measures from passive data. However, the majority of the past work on social anxiety has focused on a limited subset of self-reported measures. Furthermore, the data collected in real-world studies often results in numerous missing values in one or more data streams, which ultimately reduces the usable data for analysis and limits the potential of machine learning algorithms. We explore several approaches for addressing these problems in a smartphone based monitoring and intervention study of eighty socially anxious participants over a five week period. Our work complements and extends prior work in two directions: (i) we show the predictability of seven different self-reported dimensions of social anxiety, and (ii) we explore four imputation methods to handle missing data and evaluate their effectiveness in the prediction of subjective measures from the passive data. Our evaluation shows imputation of missing data reduces prediction error by as much as 22%. We discuss the implications of these results for future research.

TraceTogether: Pandemic Response, Democracy, and Technology

On 20 March 2020, in the midst of the COVID-19 pandemic, the Singapore government released a new app called TraceTogether. Developed by the Ministry of Health, SG United, and GovTech Singapore, the app uses the Bluetooth capability of smartphones to store information about other smartphones that have come into close proximity with your own. These data facilitate the government’s process of “contact tracing” through which they track those who have potentially come into contact with the virus and place them in quarantine. This essay attempts to understand what kinds of citizens and civic behavior might be brought into being by this technology. By examining the workings and affordances of the TraceTogether app in detail, the authors argue that its peer-to-peer and open-source technology features mobilize the rhetorics and ideals of citizens science and democratic participation. However, by deploying these within a context that centralizes data, the app turns ideals born of dissent and protest on their head, using them to build trust not within a community but rather in government power and control. Rather than building social trust, TraceTogether becomes a technological substitute for it. The significant public support for TraceTogether shows both the possibilities and limitations of citizen science in less liberal political contexts and circumstances.

Enhancement of Neurocognitive Assessments Using Smartphone Capabilities: Systematic Review.

BackgroundComprehensive exams such as the Dean-Woodcock Neuropsychological Assessment System, the Global Deterioration Scale, and the Boston Diagnostic Aphasia Examination are the gold standard for doctors and clinicians in the preliminary assessment and monitoring of neurocognitive function in conditions such as neurodegenerative diseases and acquired brain injuries (ABIs). In recent years, there has been an increased focus on implementing these exams on mobile devices to benefit from their configurable built-in sensors, in addition to scoring, interpretation, and storage capabilities. As smartphones become more accepted in health care among both users and clinicians, the ability to use device information (eg, device position, screen interactions, and app usage) for subject monitoring also increases. Sensor-based assessments (eg, functional gait using a mobile device’s accelerometer and/or gyroscope or collection of speech samples using recordings from the device’s microphone) include the potential for enhanced information for diagnoses of neurological conditions; mapping the development of these conditions over time; and monitoring efficient, evidence-based rehabilitation programs.ObjectiveThis paper provides an overview of neurocognitive conditions and relevant functions of interest, analysis of recent results using smartphone and/or tablet built-in sensor information for the assessment of these different neurocognitive conditions, and how human-device interactions and the assessment and monitoring of these neurocognitive functions can be enhanced for both the patient and health care provider.MethodsThis survey presents a review of current mobile technological capabilities to enhance the assessment of various neurocognitive conditions, including both neurodegenerative diseases and ABIs. It explores how device features can be configured for assessments as well as the enhanced capability and data monitoring that will arise due to the addition of these features. It also recognizes the challenges that will be apparent with the transfer of these current assessments to mobile devices.ResultsBuilt-in sensor information on mobile devices is found to provide information that can enhance neurocognitive assessment and monitoring across all functional categories. Configurations of positional sensors (eg, accelerometer, gyroscope, and GPS), media sensors (eg, microphone and camera), inherent sensors (eg, device timer), and participatory user-device interactions (eg, screen interactions, metadata input, app usage, and device lock and unlock) are all helpful for assessing these functions for the purposes of training, monitoring, diagnosis, or rehabilitation.ConclusionsThis survey discusses some of the many opportunities and challenges of implementing configured built-in sensors on mobile devices to enhance assessments and monitoring of neurocognitive functions as well as disease progression across neurodegenerative and acquired neurological conditions.

The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments

Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement units (IMUs), cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to the low-cost and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions. In this study, we proposed a scheme that integrated sensor data from smartphone IMUs, GNSS chipsets, and cameras using an extended Kalman filter (EKF) to enhance the navigation performance. The visual data from the camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration filter. In order to verify the performance of the integrated system, field test data was collected in a downtown area of Tainan City, Taiwan. Experimental results indicated that visual data contributed significantly to improving the accuracy of the navigation performance, demonstrating improvements of 43.0% and 51.3% in position and velocity, respectively. It was verified that the proposed integrated system, which used data from smartphone sensors, was efficient in terms of increasing navigation accuracy in GNSS-challenging environments.

A Portable Smartphone-linked Device for Direct, Rapid and Chemical-Free Hemoglobin Assay

We describe the development and clinical evaluation of an automated smartphone-linked sensor capable of chemical-free, quantitative measurement of hemoglobin concentration ([Hb]) in whole blood samples. We have demonstrated that our sensor could analyze an unprocessed blood specimen with a mean processing time of <8 s and provided the [Hb] results with ~99% accuracy against a reference hematology analyzer with coefficient of variation (CV) of 1.21% measured at [Hb] = 11.2 g/dL. Its diagnostic capability for anemia was evaluated by measuring [Hb] of 142 clinical blood specimens and comparing the results with those from an automated hematology analyzer (ADVIA 2120i, Siemens AG, Germany) and a portable hemoglobinomteter (Hb201+, Hemocue, Sweden). The sensor yielded comparable sensitivities and specificities of 87.50% and 100.00% for males, and 94.44% and 100.00% for females, respectively, for anemic detection. The results suggested that our optical sensor based on the intrinsic photothermal response of Hb molecules and advances in consumer electronics, particularly smartphone capabilities, enables a direct, chemical-free [Hb] assay accessible to people in both developed and developing countries.

“Thumb Exercise”: An Interpretative Phenomenological Analysis of Psychosocial Factors Encouraging Inactive Adults to Engage with Their Smartphones Rather than Physical Activity

Background: Physical inactivity accounts for up to 1.6 million deaths each year. With UK adults spending approximately eleven times longer using their smartphones than exercising, research suggests that frequent smartphone use is linked to poor physical fitness. Previous research on the psychosocial influences of both problem smartphone use, and physical activity barriers and facilitators exist, however insight into the psychosocial underpinnings of why inactive individuals choose to engage with their smartphones rather than physical activity is understudied. Aims: This study provides a qualitative exploration of the psychosocial factors that encourage inactive adults to engage with their smartphones rather than physical activity. Methods: Thirteen (female = 10) participants aged between 18 and 39 completed an online qualitative survey. The subjective experiences and perceptions from participants’ survey responses were analysed using interpretative phenomenological analysis, with a phenomenological epistemological approach. Results: Three themes were identified: the first theme identified that the psychosocial influence of smartphone engagement was to escape unpleasant realities; the second theme depicts that the psychosocial barrier of physical activity engagement was perceptions in relation to the financial and task-oriented costs that physical activity incurs; the third theme captured that social support necessities are being fulfilled through smartphone communication, therefore as a counterpart, physical activity is deemed to be a desolate operation. Conclusions: The findings from this study provide recommendations that harness social support and smartphone capabilities for motivating inactive adults to maintain physically active lifestyles.

Smartphone Apps for the Treatment of Mental Disorders: Systematic Review.

BackgroundSmartphone apps are an increasingly popular means for delivering psychological interventions to patients suffering from a mental disorder. In line with this popularity, there is a need to analyze and summarize the state of the art, both from a psychological and technical perspective.ObjectiveThis study aimed to systematically review the literature on the use of smartphones for psychological interventions. Our systematic review has the following objectives: (1) analyze the coverage of mental disorders in research articles per year; (2) study the types of assessment in research articles per mental disorder per year; (3) map the use of advanced technical features, such as sensors, and novel software features, such as personalization and social media, per mental disorder; (4) provide an overview of smartphone apps per mental disorder; and (5) provide an overview of the key characteristics of empirical assessments with rigorous designs (ie, randomized controlled trials [RCTs]).MethodsThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews were followed. We performed searches in Scopus, Web of Science, American Psychological Association PsycNET, and Medical Literature Analysis and Retrieval System Online, covering a period of 6 years (2013-2018). We included papers that described the use of smartphone apps to deliver psychological interventions for known mental disorders. We formed multidisciplinary teams, comprising experts in psychology and computer science, to select and classify articles based on psychological and technical features.ResultsWe found 158 articles that met the inclusion criteria. We observed an increasing interest in smartphone-based interventions over time. Most research targeted disorders with high prevalence, that is, depressive (31/158,19.6%) and anxiety disorders (18/158, 11.4%). Of the total, 72.7% (115/158) of the papers focused on six mental disorders: depression, anxiety, trauma and stressor-related, substance-related and addiction, schizophrenia spectrum, and other psychotic disorders, or a combination of disorders. More than half of known mental disorders were not or very scarcely (<3%) represented. An increasing number of studies were dedicated to assessing clinical effects, but RCTs were still a minority (25/158, 15.8%). From a technical viewpoint, interventions were leveraging the improved modalities (screen and sound) and interactivity of smartphones but only sparingly leveraged their truly novel capabilities, such as sensors, alternative delivery paradigms, and analytical methods.ConclusionsThere is a need for designing interventions for the full breadth of mental disorders, rather than primarily focusing on most prevalent disorders. We further contend that an increasingly systematic focus, that is, involving RCTs, is needed to improve the robustness and trustworthiness of assessments. Regarding technical aspects, we argue that further exploration and innovative use of the novel capabilities of smartphones are needed to fully realize their potential for the treatment of mental health disorders.

Edge-Based Collision Avoidance for Vehicles and Vulnerable Users: An Architecture Based on MEC

Collision avoidance, one of the most promising applications for vehicular networks, dramatically improves the safety of vehicles that support it. In this article, we investigate how it can be extended to benefit vulnerable users, such as pedestrians and bicyclists, equipped with a smartphone. Owing to the reduced capabilities of smartphones compared to vehicular onboard units (OBUs), traditional distributed approaches are not viable, and multi-access edge computing (MEC) support is needed. Thus, we propose an MECbased collision-avoidance system, discuss its architecture, and evaluate its performance. We find that, thanks to MEC, we are able to extend to vulnerable users the collision avoidance protection traditionally applied to vehicles, without impacting its effectiveness or latency.

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors.

Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

Feature extraction and feature selection in smartphone-based activity recognition

Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance.

A Context-Aware and Self-Adaptation Strategy for Cloud Service Selection and Configuration in Run-Time

Day after day, the number of mobile applications deployed on cloud computing continues in increasing because of smartphone capabilities improvement. Cloud computing has already succeeded in the web-based application, for that reason, the demand for context-aware services provided by cloud computing increases. To customize a cloud service that takes into account the consumer requirements, which depend on information change, it brings to light many recent challenges to cloud computing about environment-aware, location-aware, time-aware. The cloud provider, moreover, has to manage personalized applications and the constraints of mobile devices in matters of interaction abilities and communication restrictions. This paper proposes a strategy for selecting automatically an appropriate cloud environment that runs out whole requirements, defines a configuration for the associated cloud environment and able to easily adapt to the change of the environment on either the user or the cloud side or both. This process builds on the principles of dynamic software product lines, Agent-oriented software engineering, and the MAPE-k model to select and configure cloud environments according to the consumer needs and the context change.

Increasing and Evolving Role of Smart Devices in Modern Medicine.

Today is an age of rapid digital integration, yet the capabilities of modern-day smartphones and smartwatches are underappreciated in daily clinical practice. Smartphones are ubiquitous, and smartwatches are very common and on the rise. This creates a wealth of information simply waiting to be accessed, studied and applied clinically, ranging from activity level to various heart rate metrics. This review considers commonly used devices, the validity and accuracy of the data they obtain and potential clinical application of the data.

A Deep Learning Model for Transportation Mode Detection Based on Smartphone Sensing Data

Understanding people’s transportation modes is beneficial for empowering many intelligent transportation systems, such as supporting urban transportation planning. Yet, current methodologies in collecting travelers’ transportation modes are costly and inaccurate. Fortunately, the increasing sensing and computing capabilities of smartphones and their high penetration rate offer a promising approach to automatic transportation mode detection via mobile computation. This paper introduces a light-weighted and energy-efficient transportation mode detection system using only accelerometer sensors in smartphones. The system collects accelerometer data in an efficient way and leverages a deep learning model to determine transportation modes. Different architectures and classification methods are tested with the proposed deep learning model to optimize the system design. Performance evaluation shows that the proposed new approach achieves a better accuracy than existing work in detecting people’s transportation modes.

An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients

Abstract Despite the proliferation of mobile interventions that are delivered through smartphones, and their potential impact on promoting mental health in chronic health populations, little research has examined how to leverage the advanced technological capabilities of smartphones to also monitor response to app-based interventions. Current methods used to evaluate response to digital interventions rely on static and retrospective self-report measures to infer crucial behavioral and affective patterns, which provide little contextual information and limits generalizability. Importantly, this approach can also increase burden among chronic health populations that are already experiencing a heavy treatment load. Using data collected from 7 recently diagnosed breast cancer patients undergoing active cancer treatment (average time from initial diagnosis to study enrollment is 13 days), we propose a framework for integrating self-report surveys and fine-grained mobile sensing data. Preliminary results demonstrate the feasibility and utility of this framework to reduce burden and improve detection of mood in response to an app-based intervention among chronic health populations.

A multidisciplinary approach for improving the outcome of out-of-hospital cardiac arrest in South Korea.

Aim:Direct medical control using video conferencing capabilities of smartphones has never been conducted in out-of-hospital cardiac arrest patients. This study was conducted to investigate the feasibility and treatment effectiveness of real-time smartphone video conferencing calls for the management of out-of-hospital cardiac arrest.Methods:This study was a pre–post-intervention prospective cohort study conducted from January 2013 to July 2015. The intervention was pre-hospital advanced life support under a physician’s direction using a smartphone video call.Results:In total, 942 cardiac arrests occurred over the 2-year period; 308 patients were excluded, and 314 (49.5%) and 320 (50.5%) cardiac arrest patients were enrolled during the pre- and post-intervention study periods, respectively. There were 248/320 (77.5%) cases of smartphone video-assisted advanced life support during the post-intervention period. For patients in the pre- and post-intervention groups, the pre-hospital return of spontaneous circulation was 6.7 and 20%, respectively (adjusted odds ratio 3.3, 95% confidence interval 1.6–6.8, P < 0.01), and favourable neurological outcomes were ascertained in 1.9 and 6.9%, respectively (adjusted odds ratio 23.6, 95% confidence interval 3.4–164.0, P < 0.01). The smartphone voice and video quality were rated 8.5 and 8.2 out of 10, respectively, in physician evaluation, while the overall utility was rated 9.1.Conclusion:We concluded that a multidisciplinary approach including the re-education of basic life support, simulation training for advanced life support, real-time medical direction via video call, and dispatching two teams rather than one team improved the outcome of out-of-hospital cardiac arrest.

Towards an Audio-Locative Mobile Application for Immersive Storytelling

We live in an age in which digital media is omnipresent and augmented reality is beginning to find its way into our everyday lives, GPS allows us to determine our position with meter precision and the sensor capabilities of smartphones are increasing. All these technologies in combination enable us to explore one of the oldest human art forms in a new way: storytelling. The presented work aims at developing approaches that combine different elements of audio media playback, geolocation and other sensor capabilities of smartphones to allow the creation of immersive geolocated narratives within a mobile application. Thus, the narrative can be precisely adjusted to the specific spatial and temporal context of the user. One of the main goals of such a geolocated narrative would be to influence the cognitive processing of the users. As the visual perception of the location stays the same while using the app, it is only the auditory cognition that causes a subtle enhancement and thus alteration of the reality which could result in a very strong sense of immersion. There are many location-based audio applications on the market. Most of them are intended for tourism in the form of an audio tour guide. Some of those applications have been analyzed before developing our own ideas on how to intertwine sound, space and time into an immersive mobile application for storytelling with semi-linear narrative structures including a map. Possible features could be influenceability of the narrative by, e.g., speed or approach direction of the user, sound movement over time on predetermined routes, etc. The first prototype implemented some audiovisual presentations among others.

A Mobile Solution for Augmenting a Manufacturing Environment with User-Generated Annotations

The ever-increasing complexity in manufacturing environments has caused delay and uncertainty for on-site personnel when retrieving critical information. Currently, information associated with manufacturing environments is created and stored in centralized terminals and even paper-based logbooks. Any delays in accessing this information can hinder critical decision-making processes that are essential to ensure maximum efficiency and productivity. To increase the competitiveness of manufacturing companies, the authors present a user-oriented markerless augmented reality authoring solution for the manufacturing environment utilizing the capability of a modern smartphone or head-mounted display. With a one-time setup and calibration, the end user is able to augment a physical workspace with a digital layer and annotate any stationary object with application data including maintenance records, service tickets, and usage logs. By directly mapping the data onto a relevant physical object, it allows more efficient reuse and better contextualization of manufacturing information. The backend expandable data structure also enhances extensibility so that the users can modify the digital layer with ease when the physical environment changes. This annotation tool defines a novel way of managing manufacturing information to help companies work more efficiently and effectively.

The Impact of Using the Iranian Red Crescent Society Educational Mobile App on Improving the Students’ Awareness of First Aids

Background: The first aid educational mobile app (Khadem) developed by the Iranian Red Crescent Society provides the most important and simplest training of the first aids in crises for people. Objectives: The present study aimed to investigate the impact of this mobile application on improving high school students’ awareness of first aids. Methods: In this quasi-experimental study (before and after using the mobile application), 51 students studying at high schools of Ahvaz city in the southwest of Iran were selected as the sample using simple random sampling method. Before the intervention, their awareness and mastery of the first aids were measured by a pre-test. Then, the mobile software was offered to them and one month later, they were asked to give another test. The scores obtained before and after the intervention were evaluated. Descriptive and inferential statistics were used for data analysis using SPSS V.22. Results: Overall, the students' awareness of first aids was very low. In the intervention group, the mean score of the students’ awareness significantly increased after the intervention (offering mobile app) (P < 0.01). According to Spearman, Kruskal-Wallis, and Mann-Whitney tests, age, gender, and school grade had no impact on the mean scores obtained before and after the intervention (P < 0.05). Conclusions: Due to the various advantages and capabilities of smartphones and their popularity among students, they can provide a good opportunity to teach the students vital subjects such as the first aids, although this technology is yet to be officially included in the curriculum.

Comparative Analysis of Network Libraries for Offloading Efficiency in Mobile Cloud Environment

In the modern era, smartphones are increasingly becoming an integral and essential part of our daily life. Although the hardware capabilities of the smartphones (i.e., processing, memory, battery, and communication) are improving every day, however, it is not enough to handle computation-intensive applications, such as image processing, data analytics, and encryption. To overcome these limitations, mobile cloud computing (MCC) is introduced, which augments the capabilities of smartphones and resources of the cloud to provide better QoS performance to the user. The idea is to save resources in the smartphones by offloading the computationally intensive tasks to the cloud. In this context, researchers have proposed several offloading frameworks, mainly addressing challenges of why-what-when and where to offload. In this paper, however, we explore another challenging issue of offloading, i.e., how-to-offload. More specifically, we analyze different networking libraries (HttpURLConnection, OkHttp, Volley, Retrofit) and study their performance on various dynamic factors such as data size, communication media, hardware and software of the smartphone. Our objective is to explore if an application can use the same networking library for all the smartphones and all purposes or there is a need to make an adaptive decision based on the local constraints. To understand this, we perform a comprehensive analysis of the networking libraries on different Andriod smart-phones in the real environment and found that there is a need of adaptive network library selection because libraries perform changes in different scenarios.