Energy consumption patterns of mobile applications in android platform: a systematic literature review

Studies related to resource consumption of mobile devices and mobile applications have been brought to the fore lately as mobile applications depend largely on their resource consumption. The study aims to identify the key factors and holistic understanding of how a factor influences Consumption Pattern (CP) effectiveness for an android platform mobile application. The study presents a Systematic Literature Review (SLR) on existing studies that examined factors influencing the effectiveness of CP for android mobile application and measured the effectiveness of CP. Therefore, the current SLR is conducted to answer the following questions: (1) What is the evidence of CP factors that drain the battery of a mobile device? (2) What are the energy conservation techniques to overcome all the factors that drain battery life? and (3) How can developers measure the effectiveness of an energy conservation technique?. The SLR investigated factors affecting the effectiveness of CP for android platform mobile application. The analyses of forty papers were used in our synthesis of the evidence related to the research questions above. Therefore, the analyses showed 22 studies that investigated how to measure the energy conservation technique effectiveness while 18 studies focused on better understanding of how the resources of mobile devices are actually spent. In this sense, 2 studies show the effectiveness of early analysis of software application design. Additionally, five factors i.e., architecture, interface, behavior of the application, resources, and network technologies that affect CP effectiveness were identified. This study investigated a SLR targeting at studies of CP effectiveness in android platform. The total of 40 studies were identified and selected for result synthesis purpose in this work (SLR). The evidences show there are five factors affecting the CP’s effectiveness. Three of them have received a little attention among developers regarding choosing the most suitable: software architecture, application interface and behavior of the application in terms of resource consumption

Android Malware Family Classification Based on Resource Consumption over Time

The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years. An important task of malware analysis is the classification of malware samples into known families. Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques. To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScribe. With respect to DroidScribe, our approach is easier to reproduce. Our methodology only employs publicly available tools, does not require any modification to the emulated environment or Android OS, and can collect data from physical devices. The latter is a key factor, since modern mobile malware can detect the emulated environment and hide their malicious behavior. Our approach relies on resource consumption metrics available from the proc file system. Features are extracted through detrended fluctuation analysis and correlation. Finally, a SVM is employed to classify malware into families. We provide an experimental evaluation on malware samples from the Drebin dataset, where we obtain a classification accuracy of 82%, proving that our methodology achieves an accuracy comparable to that of DroidScribe. Furthermore, we make the software we developed publicly available, to ease the reproducibility of our results.Comment: Extended Versio

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

COMPSs-Mobile: parallel programming for mobile-cloud computing

The advent of Cloud and the popularization of mobile devices have led us to a shift in computing access. Computing users will have an interaction display while the real computation will be performed remotely, in the Cloud. COMPSs-Mobile is a framework that aims to ease the development of energy-efficient and high-performing applications for this environment. The framework provides an infrastructure-unaware programming model that allows developers to code regular Android applications that, transparently, are parallelized, and partially offloaded to remote resources. This paper gives an overview of the programming model and describes the internal components of the toolkit which supports it focusing on the offloading and checkpointing mechanisms. It also presents the results of some tests conducted to evaluate the behavior of the solution and to measure the potential benefits in Android applications.Peer ReviewedPostprint (published version