ENERGY SAVING SYSTEM FOR ANDROID SMARTPHONE APPLICATION DEVELOPMENT Dipika K. Nimbokar 1, Ranjit M. Shende 2 1 B.E.,IT,J.D.I.E.T.,Yavatmal,Maharashtra,India,dipika23nimbokar@gmail.com 2 Assistant Prof, IT,J.D.I.E.T.,Yavatmal,Maharashtra,India, ranjeetmshende@rediffmail.com ABSTRACT Mobile devices such as smartphones and tablets have become almost ubiquitous in our daily lives. Smartphone applications energy efficiency is vital, but many Android applications suffer from serious energy inefficiency problems. The smartphone application market is growing rapidly. The one million Android applications on Google Play store had received more than 50 billion downloads. Many of these applications leverage Smartphone s rich features to provide desirable user experiences. Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developer slack viable techniques for estimating the energy consumption of their applications. Developer needs special tools to check whether there apps are running smoothly, checking apps efficiency, most app testing is done on computer using emulator. But this is not same as your phone. When you download any app on phone, battery life, and bad usage are the important parameter. To develop energy efficient application is the important goal of developer. Developing an app so that it is energy efficient is challenging and implementations can vary widely in terms of their energy consumption. As a result, battery usage has become an important, albeit informal, quality metric for marketplace apps. A cursory examination of marketplace reviews shows that many users complain about battery usage and this can inform their decision to give positive or negative ratings to an app. Our paper focuses on this useful parameter that can in turn effect the usability of mobile device. This paper focuses on the analysis of energy saving and the result can give us the technique for performance enhancing coding practice Keywords: mobile monitor; device; smartphone; energy consumption; energy android ----------------------------------------------------------------------------------------------------------------------------- INTRODUCTION With so many people using mobile app today, there is huge demand of exiting feature and technology. These devices accompany us constantly and the apps they run provide helpful information and services by combining cloud data and sensor measurements in new and innovative ways. Unfortunately, these devices are limited in terms of their battery power and the extensive usage of sensors and network data can rapidly drain the devices' batteries and limit the usefulness of the device and its apps. Although advances in hardware and battery technology have helped decrease a device's energy consumption, these improvements cannot prevent an inefficient or poorly designed app from needlessly draining the device's battery.. This makes improving energy consumption an important goal for mobile app developers. There are existing tools that can help developers to gain insight into the energy usage patterns of their applications. Examples of such techniques are cycle-accurate simulators power monitors program analyses and statistical based measurement techniques. Although these techniques allow developers to understand where energy is consumed within their application (e.g., by which source lines), they do not provide direct guidance as to how to improve the app's energy consumption[4]. That is, they do not address the gap between understanding where energy is consumed and understanding how the code can be changed to reduce the energy consumed. The connection between observed energy consumptio n and opportunities for energy optimization is not always straightforward. For example, although a particular method may consume a lot of energy, there may not be any alternative implementation mechanisms for that functionality that consume less energy. At the same time, there may be another location that consumes less energy, but has alternative implementation mechanisms that consume even less energy. This situation can make it difficult for developers to readily identify areas for code
improvement. To end energy saving best practices, developers can make use of conventional wisdom consult development blogs written by fellow software engineers or simply search online for tips. In our own online searches, we found many such sites offering development advice. Unfortunately, many of these suggestions are not supported by empirical evidence and it is not clear how effective they will be in practice. 1.1 Review Techniques: 1.1.1 Monitoring Energy Consumption of Smartphones SEMO SYSTEM DESIGN To analyze the energy consumption of the applications on mobile devices, we designed SEMO system. First, it is used to check the battery s status, such as its power remaining and the temperature of its battery. Second, it collects the energy consumption data of the mobile devices, and then it analyzes the energy consumption of the applications on mobile devices according to the data it collects. The collected data include the time, the battery s power remaining at the time and the names of the applications which are running at the time. Third, its data analysis and corresponding algorithms can find the rate of the energy consumption of the applications. It s very useful to the developers and the users of the mobile devices. As shown in Fig. 1[1], SEMO consists of the following three main parts: an inspector, a recorder and an analyzer. The inspector is designed to check the information of the battery. The recorder is used to record the information of battery and applications, especially the energy consumption information. Then, the analyzer analyses the data that recorder records to get the rate of the energy consumption of the applications and ranks the applications by these energy consumption rates. In the following sections, we will introduce each part of the SEMO system and explain their functions in detail[ 1]. Fig -1: SEMO system structure[1] 1.2.2. Estimating Mobile Application Energy Consumption using Program Analysis
elens Technique: elens is the combines two ideas that have not previously been explored together.program analysis to determine paths traversed and track energy-related information during execution, and per-instruction energy modelling that enables elens to obtain fine-grained estimates of application energy.[3] elens, there are three components: the Workload Generator translates the workload into sets of paths through the software artefact; the Analyzer uses the paths and system profiles to compute an energy estimate; and, the Source Code Annotator combines the paths and energy estimate to create an annotated version of the source code that is provided to the developer. The output of elens is a visualization that shows the estimated energy consumption of the software at the path, method, source line, and whole program granularity[3]. Fig-2: Overview of elens[3] 2. PROPOSED WORK In this paper, we consider the energy savings that can be achieved by using different coding practices that are commonly suggested or proposed in the official Android developers web site.
Fig-3:Flow of Energy saving system In order to reduce energy consumption of smartphones and extend the lifetime of batteries, it is essential to manage energy consumption of networks and sensors. 2.1 Network Switching Network switching enables smartphone to intelligently switch between GPRS and Wi-Fi wireless networks. This scheme is designed for Wi-Fi discovery with low energy demand, which is also of high efficiency. Based on the information collected from users, this scheme checks whether they need to switch to Wi-Fi network. If they do, it initiates the switching program to find the nearest Wi-Fi network AP (access point) and switch to it. There are three major modules involved in this scheme, as detailed below. 2.1.1 Information Collecting Module: On account of the frequent changes in network conditions, information of both mobile device s and wireless networks conditions needs special attention. In that paper, for sake of simplicity, the data rate of user s smartphone is used to guide switching decision making. When a significant change of data rate occurs, it is the responsibility of the switching decision making module to decide whether to switch to Wi-Fi network or not. 2.2.2 Switching Decision Making Module: With the help of information collecting module, it is possible to examine changes of user s data rate. Then, the switching decision making module judges whether the change is significant enough to trigger switching. In addition, it gets the threshold of bandwidth before switching. If the bandwidth that user needs is more than or equal to the threshold value, switching module will get a signal to switch to a wide bandwidth Wi-Fi network. Considering individual s preference, users are allowed to set their own bandwidth thresholds. 2.2.3 Switching Module: This module decides when and how to switch to Wi-Fi networks, and it takes the following three steps to complete the switching program. Firstly, it gets user s location and discovers the nearest Wi- Fi network AP by using user s location. Secondly, it calculates the time needed for user to arrive at the nearest Wi- Fi network AP. Finally, after that time, it scans and connects to the Wi-Fi network.
2.2 GPS Usage Management GPS is a very popular localization technique because of its high accuracy. However, GPS should not be used frequently because it is energy-hungry. To address this issue, an efficient method which reduces energy consumption of smartphones from two aspects. On the one hand, it chooses optional localization technique when accuracy requirement is not very high. On the other hand, it dynamically estimates the next localization time point to avoid unnecessary localization operations when the application s location accuracy requirement is satisfied. The basic idea of this method is to reduce the energy consumption of localization by avoiding the use of GPS sensor whenever possible. It first determines the localization accuracy requirements of running applications, and then selects proper localization method. Here only smartphones equipped with GPS, Wi-Fi, and GSM positioning interfaces will be considered. Unnecessary localization operations will be avoided by dynamically estimating the next localization time point and sampling the movement velocity of the user. If the user has been in movement for some time within the range of application accuracy limit, for instance, it is not necessary to locate the user. When it is time to locate the user again, an energy-optimal method will be used to calculate the average energy consumption of each localization strategy and select the least energy-consuming one. 3.CONCLUSION Developing energy efficient mobile applications is an important goal for software developers as energy usage can directly affect the usability of a mobile device. Unfortunately, existing energy-oriented techniques tend to focus on understanding where energy is consumed within an application and how much is consumed. The resulting situation is that developers lack guidance as to how to improve the energy efficiency of their implementation and which practices are most useful. REFERENCES [ 1] Fangwei Ding, Feng Xia, Wei Zhang, Xuhai Zhao, Monitoring Energy Consumption of Smartphone s Chengchuan Ma School of Software, Dalian University of Technology, Dalian 116620, China. [2] M. Chaudron, C. Szyperski, and R. Reussner, Eds. Springer Berlin Heidelberg, Component-Level Energy Consumption Estimation for Distributed Java-Based Software Systems," in Component-Based Software Engineering, ser. Lecture Notes in Computer Science 2008. [3] Shuai Hao Ding Li,Williams G.,J.Halfond,Ramesh Govindan Estimating Mobile Application Energy comsumption using program analysis University of Southern California, USA,2011. [4]L. Ding, T. Angelica, Huyen, and H. William, G.J., Making Web Applications More Energy Efficient for OLED Smartphones," in Proceedings of the 36 th International Conference on Software Engineering (ICSE), 2014. [5]A. Pathak, Y. C. Hu, M. Zhang, P. Bahl, and Y.-M. Wang, Fine-grained Power Modelling for Smartphones Using System Call Tracing," in Computer Systems, 2011. Proceedings., Sixth Conference on, ser. EuroSys '11. New York, NY, USA: ACM, 2011.