ENERGY SAVING SYSTEM FOR ANDROID SMARTPHONE

International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
ENERGY SAVING SYSTEM FOR ANDROID SMARTPHONE
APPLICATION DEVELOPMENT
Dipika K. Nimbokar1 , Ranjit M. Shende 2
1
2
B.E.,IT,J.D.I.E.T.,Yavatmal,Maharashtra,India,[email protected]
Assistant Prof, IT,J.D.I.E.T.,Yavatmal,Maharashtra,India, [email protected]
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
IJRISE| www.ijrise.org|[email protected] [396-400]
International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
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
IJRISE| www.ijrise.org|[email protected] [396-400]
International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
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.
IJRISE| www.ijrise.org|[email protected] [396-400]
International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
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 WiFi network AP by using user’s location. Secondly, it calculates the time needed for user to arrive at the nearest WiFi network AP. Finally, after that time, it scans and connects to the Wi-Fi network.
IJRISE| www.ijrise.org|[email protected] [396-400]
International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
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 loc alization 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
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[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 36th International Conference on Software Engineering (ICSE), 2014.
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