40120140503009

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 3, March (2014), pp. 58-69 © IAEME
58
EFFECTIVENESS OF ENERGY MANAGEMENT IN MOBILE DEVICES:
A STUDY
Shalini Prasad
Dept. of Electronics & Communication Engg.,
Research Scholar, Jain University,
Bangalore, India
S. Balaji
Centre for Emerging Technologies,
Jain University, Jakkasandra, Kanakapra Taluk
Ramanagara Dist-562112, India
ABSTRACT
With the increasing trends of accessibility of Internet and various advanced networking
system, the usage of mobile devices and applications running on them has exponentially increased
worldwide. It is known that smartphone consumes more energy as compared to legacy phones. The
prime reasons behind energy consumption are various applications running in the smartphone even if
the phone is in idle mode. There has been an extensive research contribution in the past decade to
mitigate this issue, but very few studies are found to have notable contribution. This paper attempts
to review the past research work and excavate the research gap.
Keywords: Energy Management, Mobile Device, Energy Consumption.
1. INTRODUCTION
The past decade has witnessed tremendous growth in the popularity of the Internet and
wireless handheld devices. For wireless Internet access, there is almost universal coverage with 2G,
3G, and Wi-Fi networks. The commonly used handheld devices are smartphones and PDAs
(Personal Digital Assistants), with Internet browsing capability. Some of the popular ones are iPAQ,
BlackBerry, iPhone, iPod, iPad, and Kindle. Application specific handheld devices, such as iPod for
music and Kindle for electronic book reading, are gaining much popularity. With advances in
microelectronics, there is every effort, subject to size constraint, to make a handheld device appear
INTERNATIONAL JOURNAL OF ELECTRONICS AND
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 5, Issue 3, March (2014), pp. 58-69
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2014): 7.2836 (Calculated by GISI)
www.jifactor.com
IJECET
© I A E M E

59
like a laptop computer in terms of application delivery. In addition to performance expectations, the
requirement of portability imposes severe constraints on the size and weight of a handheld system.
Portable devices commonly run on rechargeable batteries to support user mobility. The small size
and light weight requirements of a handheld device imply that its battery be proportionately small in
volume. Consequently, the system energy budget is severely limited. A battery must be charged
before its remaining energy falls below a threshold level to keep the device running. Battery charging
limits the mobility of users and the usability of the device. The full charge of a battery is one of the
key resources, and battery lifetime is an important characteristic of handheld devices. While
computing and communication capabilities of handheld devices have increased by orders-of-
magnitude in the past two decades, battery energy density has only tripled in the same period of time.
Therefore, hardware and software designers have adopted a variety of methodologies and techniques
to reduce the amount of energy drawn from the battery. However, the proposed study will basically
focus on implementing the computational model rather than working on hardware interface to
evaluate the efficiency of the algorithm implementation for determining and diminishing the extent
of energy consumption in mobile devices. Energy efficiency is a critical concern in mobile and
battery powered systems. Reducing energy consumption improves system lifetime. An OS can
improve energy efficiency by putting peripherals into low power modes and dropping the processor
to a sleep state when idle. The challenge lies is deciding when and how to do so: to manage energy
well, an OS must infer future application behavior. Despite all of these advances, most modern
operating systems still use very simplistic energy management policies. The problem is that, beneath
all of their advanced libraries, applications still use APIs which were designed before energy
constraints were a major concern. Energy consumption of network activity in mobile phones has
seen a large body of work in recent times. In modern smartphones, having the display on and
decoding the multimedia content can together consume a large portion of the energy. The energy
required to decode audio or video depends on the computational complexity of the CODEC and/or
compression algorithms used for encoding. Although display and decoding are often responsible for
a large portion of energy consumption, wireless interfaces can equally deplete the same amount of
energy while running audio or video streaming applications in mobile devices. This communication
energy spent by mobile devices while receiving multimedia content is the main focus. It has been
measured that Wi-Fi interface can use roughly three times of the energy required to decode audio or
video content whereas 3G interface requires around five times of the audio decoding energy. The
reason for such high energy consumption is the continuous flow of traffic which forces these
wireless radios to be powered up most of the time during streaming. Therefore, it can be seen that
with the rise of customers globally give rise to usage statistics of various applications that run on
mobile devices leading to faster rate of energy drainage. Referring to the research gap from the
current, it evidently proves that the domain requires to be further investigated as standard benchmark
is not yet reached in this field of study. Hence, this fact lays the basic foundation of motivation to
carry out the research work.
Many technical problems have to be fixed for application scenario becoming a reality.
Among them, one of the most critical is power management in mobile devices. To allow users’
mobility, devices must be battery-supplied. It is common experience that current mobile devices
(laptops, PDAs, etc.) can operate just for few hours before the battery gets drained. Even worse, the
difference between power requirements of electronic components and battery capacities is expected
to increase in the near future. In a nutshell, power management for mobile devices is mandatory for
the development of mobile and pervasive computing scenarios, and each (hardware or software)
component of a mobile device should be designed to be energy efficient. The networking subsystem
is one of the critical components from the power management standpoint, as it accounts for a
significant fraction of the total power consumption (around 10% for laptops, and up to 50% for small
hand-held devices, such as PDAs). Section 2 discusses about the significance of energy management
in mobile devices from the viewpoint of embedded computing system. Section 3 discusses few cases

60
for energy management in mobile devices followed by discussion of various models of energy
management in Section 4. Section 5 discusses about existing energy saving architectures while
Section 6 discusses about past research work. Finally, Section 7 gives concluding remarks stating
research gap found from the study.
2. SIGNIFICANCE OF ENERGY MANAGEMENT
Energy management in mobile device is important for the following reasons.
• Limited Size and Battery: For battery-operated mobile device, energy supply is a crucial
limitation. Energy consumption leads to heating, which is unacceptable in several new
applications and accessories (wearable accessories of mobile devices). Further, the small size of
these systems also limits the amount of heat-dissipation that can be managed. Lower power
consumption enables use of smaller power supplies and reduced heat-dissipation overhead, which
also reduces the cost, weight and area of embedded systems. Thus energy management can lead
to easier system design.
• Ensuring Longevity: A 15-degree Celsius rise in temperature increases the device failure rates
by up to a factor of two. Thus, energy dissipation has deleterious effect on reliability of mobile
devices and this phenomenon may be crucial in mission-critical systems.
• Addressing Inefficiency Arising due to Over-provisioning of Resources: In embedded
systems, idle intervals arise for several reasons, such as pessimistic estimate of worst-case
execution time and inherent slack due to relaxed deadline etc. Despite this, the designers need to
provision resources to meet the worst-case performance requirement which leads to energy
wastage. Thus, dynamic energy saving techniques can use runtime adaption to trade performance
for saving energy. Also, since the embedded systems are typically used for well-defined
applications, static techniques can be easily used for per-application tuning of resources.
• Meeting Performance Requirements: In recent years, embedded processors are used to execute
resource-intensive applications that were originally designed for general-purpose processors. To
meet these performance demands, modern embedded processors use many complex features such
as multi-cores, multi-level caches etc. These trends have influenced the design of embedded
systems to be optimized for higher performance, instead of lower power consumption.
• Energy Challenges Posed by CMOS Scaling: The advancements in CMOS technology have
greatly increased the on-chip transistor densities and speeds. These trends have led to a
technology-imposed utilization wall which limits the fraction of the chip that can be
simultaneously used at full speed within the power budget. Thus, the processor performance is
primarily constrained by energy efficiency and it has been estimated that, if left unaddressed,
power challenges may end future performance scaling. Conversely, techniques for improving
energy efficiency can enable the designers to scale performance by executing parallel
computations without violating the power budget.
• Trends in Usage Pattern: In recent years, mobile computing devices have become the key
platform for the mobile convergence applications, e.g. web browsing, imaging, and video
streaming. Due to these features, embedded systems have become ubiquitous. Thus, while an
individual portable system consumes much less power than a server in the data center, the large
user-base of embedded systems makes their total power consumption very high.

61
• Enabling Green Computing: It has been estimated that the ICT (Information and
Communications Technology) contributes nearly 3% in the overall carbon footprint. Thus,
energy management in mobile devices is also important for achieving the goals of green
computing.
3. A CASE FOR ENERGY MANAGEMENT
This section motivates the need for low-level, fine-grained energy control in a mobile device
operating system. It starts by reviewing some of the prior work on energy visibility and the few
examples of coarse energy control. Using several application examples as motivation, it describes
three mechanisms an OS needs to provide for energy: isolation, delegation, and subdivision.
• Visibility and Control: Managing energy requires accurately measuring its consumption. A
great deal of prior work has examined this problem for mobile systems, including ECOSystem
[1], Currentcy [2], PowerScope [3], and PowerBooter [4]. These systems use a model of the
power consumption of hardware components based on hardware states. Early systems like
ECOSystem [1] proposed mechanisms by which a user could control per-application energy
expenditure. ECOSystem, in particular, introduced an abstraction called Currentcy [2], which
gives an application the ability to spend a certain amount of energy, up to a fixed cap. This flat
hierarchy of energy principals – applications – is reasonable for simple large applications. Mobile
applications and systems today, however, are far more complex and involve multiple principals.
• Isolation, Delegation, and Subdivision: It is believed that for applications to effectively control
energy, an operating system must provide three energy management mechanisms: i) isolation, ii)
delegation, and iii) subdivision. Isolation is a fundamental part of an operating system. Memory
and Inter-Process Communication (IPC) isolation provide security, while CPU and disk space
isolation ensure that processes cannot starve others. Isolating energy consumption is similarly
important. An application should not be permitted to consume inordinate amounts of energy, nor
should it be able to deprive other applications. Consider two processes in a system, each with
some share of system energy. To improve system reliability and simplify system design, the
operating system should isolate each process’ share from the others. If one process forks
additional processes, the children must not be able to consume the energy of the other. The
second mechanism is delegation that allows a principal to loan any of its available energy and
power to another principal. After delegation, either the resource donor or the recipient can freely
consume the delegated resources. Furthermore, if there are multiple donors delegating to this
recipient, the resources are pooled for use by the recipient. Resource delegation is an important
enabler of inter-application cooperation. For example, the Cinder nets networking stack transfers
energy into common radio activation pool when an application cannot afford the high initial
expense of powering up the radio. By delegating their energy to the radio, multiple processes can
contribute to expensive operations; this may not only improve quality of service, but even reduce
energy consumption. The third mechanism is subdivision that allows applications to partition
their available energy. Combined with isolation, subdivision allows an application to give
another principal a partial share of its energy, while being assured that the rest will remain for its
own use. For example, modern web browsers commonly run plug-ins, some of which may even
be untrusted. If a browser is granted a finite amount of power, it might want to protect itself from
buggy or poorly written plug-ins that could waste CPU energy. Subdivision lets the browser give
full control over a fraction of its energy allotment to plug-ins. Isolation further ensures that each
plug-in component does not consume more than its share.

62
Issues: Prior systems like ECOSystem [1-2] only partially support isolation and subdivision:
child processes share the resources of their parent. This is sufficient when applications are static
entities, but not when they spawn new processes and invoke complex services. The web browser
demonstrates the problem: it has no way to prevent its plugins from consuming its own resources
once they are spawned. Cinder’s subdivision lends naturally to familiar and standard abstractions
such as process trees, resource containers, and quotas. Furthermore, prior systems do not permit
delegation, which is akin to priority inheritance. For always-on systems which have small
variations in power consumption, such as the laptops for which they were designed, this is not a
serious limitation. On mobile phones, however, which have almost two orders of magnitude
difference in active and sleep power, the cost of powering up peripherals, such as the wireless
data interface, can be significant. Delegation provides a means to facilitate application
cooperation.
4. MODELS FOR ENERGY MANAGEMENT
Designers of modern Smartphone hardware and vendors have incorporated power-saving
features to allow hardware components to dynamically adjust their power consumption based on
required functionality and performance. Many of these features are available for software
developers; however, making an efficient use of them requires software developers to have a good
understanding of the implications of their design decisions in terms of energy. In fact, mobile phones
present significant differences in terms of power consumption signatures depending on the
manufacturer, operating system and other contextual factors such as network coverage. This section
introduces several analyses of the power consumption in modern smart phones. The work by
Balasubramanian et al. in [5] goes a bit deeper in the analysis of IEEE 802.11 standards and cellular
networks (using exclusively Nokia Energy Profiler as measurement tool). They found that cellular
networks present high tail energy overhead by staying in high energy-states after completing a
transfer. This effect is much lower in GSM than in 3G networks. On the other hand, IEEE 802.11
networks do not present any tail energy and they are more efficient than cellular networks. However,
they have an energy overhead caused by associating to the access point procedures. The authors
modelled the energy consumption required by the wireless interfaces in the devices they studied.
Those findings were used to implement a protocol called TailEnder. Table 2 shows the comparison
of the different energy measurements and power models. The table highlights the mobile platform,
whether or not the power measurements were done with an external multimeter, and the resources
under study. Resources such as camera, audio support, SD card and accelerometer are not considered
in this table. Another interesting power model for wireless interfaces in Symbian devices has been
done by Xiao et al. in [6]. In this case, the authors aim to model the energy impact of data
transmission over IEEE 802.11g as a function of the traffic burstiness and an off-line measurement
of the power consumed by the devices at a specific power state. Their model, validated using both an
external multimeter and Nokia Energy Profiler, can be used to estimate the energy consumption of
IEEE 802.11g interfaces in runtime but it is not clear about the power overhead that this technique
will have in the system due to the computation requirements. The work by Rice and Hay [7] is
probably the more accurate energy measurement of Wi-Fi interfaces in smartphones. In this paper,
the authors present a platform to run automatic measurements in mobile phones using high-
resolution power meters. Their platform synchronizes the device and the measurement tool which is
sampling at 250KHz with minimal error; using short screen pulses for synchronization. The paper
also incorporates a detailed analysis of the cost of sending messages over a IEEE 802.11 links. Their
results reveal that the energy cost per KB transmitted varies with the buffer size and interesting
effects during transmissions and idle power states.

63
Table-2: Existing Energy Measurement techniques and Power Models
Platform Power
Meter
CPU Display GPS Bluetooth WiFi GSM 3G Description
Symbian
[5]
Energy costs of wireless
interfaces. Impact of tail
energy.
Symbian
[6]
Energy model for data
transmissions on WiFi as a
function of the traffic
burstiness.
Android
[7]
High resolution analysis of
802.11 interfaces.
Android
[8]
Power Model for Android
using application
benchmarks.
Android
[9]
PowerTutor: online Power
Model based on the
voltage curve and linear
regression techniques to
infer power consumption
at each different power
state.
Symbian
[10]
Power Model using linear
regression.
Xiao et al. [8] consider the processors, wireless LAN interface and display in Symbian
devices. Their model uses linear regression with non-negative coefficients and the Nokia Energy
Profiler to know the total energy consumption in the handset. In the case of Android devices,
PowerTutor4 uses information about the discharging rate of the voltage curve to estimate the power
consumption [9]. Despite that it is probably the most complete model, it does not consider resources
like accelerometer and camera, and it does not take into account the impact of signal strength and
burstiness on wireless interfaces. In order to obtain the power model, PowerTutor uses linear
regression to compute the coefficients about the energy consumption of each individual resource by
combining all the hardware power modes. In theory, this model will not require using an external
multimeter to measure the power consumption and it enables online estimation of the power
consumption looking at the power state and the resources usage in the handset. However, one of its
limitations is that it requires a quite expensive computational training to obtain the model and it does
not present an evaluation of the overhead caused by estimating the power consumption in runtime
and how frequently this action is done. A different approach compared to PowerTutor is the one
suggested by Shye et al. [10]. This solution uses a background logger that samples resources
utilization at 1Hz to estimate the power consumption of mobile devices during normal users activity.
As in the previous models, this model has been derived by linear regression techniques using a
power meter. However, they used application benchmarks rather than power states to derive the
model. As a result, its measurements can be inaccurate because of relying exclusively on
applications. It has been validated for HTC G1 devices and it only considers EDGE as possible
cellular interfaces.
5. EXISTING ENERGY SAVING ARCHITECTURES
Various approaches save energy during Wi-Fi communication. Some data transfer scheduling
saves the energy or delaying communication until low energy network is available. There are some
general approaches and some are application specific approaches. This section describes all such
approaches and at the end of the section, it compares and contrasts the approaches.

64
A) Catnap: Catnap is an architecture, which exploits high bandwidth on Wireless link and allows
mobile device to sleep to save energy. This architecture buffers the data at middle-box and sends it in
a burst on high bandwidth wireless link so that device can sleep for rest of the time. Catnap
decouples wired and wireless segments. It uses Application Data Unit as a unit of transfer. It uses
middle box for bandwidth estimation on wired and wireless links. When the client sends the request
to Catnap proxy, it forwards it to the server. The server response also has hint about data. The
scheduler on the proxy combines the packet and schedules them together. Catnap decouples wired
and wireless segment with middle box proxy, which has some storage. To provide workload hint to
proxy, ADU uses header (ID, Length, and Mode). Mode provides information whether to use batch
mode or not. Scheduler precisely schedules ADU transfer. It also dynamically reschedules when
conditions change [11]. Catnap provides 2 modes. In normal mode it allows maximum sleep time
without increase in transfer time. In batch mode, it provides additional savings by batching and
delaying data. In normal mode, scheduler estimates capacity of wired link and available bandwidth
and wireless capacity. It also calculates Finish Time (FT) and Virtual Slot Time (VST). The transfer
scheduled at (FT {V, ST}). It periodically checks for rescheduling. In batch mode, it finds batch size
and threshold for which it can wait at most. It batches data up to the point and then bursts it. The
reschedule allows more time to sleep and energy is saved for the data transfer. More energy is saved
in case of longer transfer.
Advantages:
o Evaluation shows that Catnap allows long sleep time for mobile devices especially for larger data
transfers.
o Implementation does not require any changes on client side. Just middle box implementation is
needed.
o Scheduling is done dynamically and it is rescheduled to save maximum possible energy.
Disadvantages:
o Due to congestion on wireless side, the transfer time may increase causing delay. So there is no
guarantee that transfer time will not increase.
o In the larger data transfer, more energy is saved using S3 mode only if client in idle for that
period.
o It saves energy only for non-interactive data transfers.
B) NAPman: NAPman (Network-Assisted Power Management for Wi-Fi Devices) is a system that
provides energy savings and overcomes the negatives of Wi-Fi PSM. NAPman provides solution to
overcome all negatives. Implementation NAPman requires changes only at AP, so it is easy to
implement. 802.11 Wi-Fi provides two modes of scheduling, normal and high priority. In normal
scheduling, it enquires all buffered packets of a PSM client to tail. It increases time for which PSM
client stays in CAM (Continuous Awake Mode). This incurs more energy consumption. High
priority solution for PSM using priority queue, unfair to CAM clients [12]. AP checks if it is fair to
transmit one or more PSM packets for a given client at the next available opportunity. If the check
passes, AP notifies presence of PSM packets for that client. Client informs AP that it is ready to
receive its packets through a PS-POLL or NULL frame. AP prepares to transmit PSM packets using
high priority queue. Before sending the packets, AP must ensure that it would not result in
unfairness. NAPman as in the following scenarios:
• Static PSM: Attach a time-stamp with each packet on arrival. If the time-stamp of the packet at
the head of PSM queue for client is less than the time-stamp of packet at the head of main FIFO
queue, transmit PAM packet at next opportunity.

65
• Adaptive PSM: Consider adaptive PSM clients as normal CAM clients. Maintain a state for
adaptive PSM clients even after they transition to CAM. On receiving a NULL frame, only those
packets that are buffered in the queue and are fair to transmit immediately get enquired in high
priority queue for transmission. Client receives all those packets and enters in idle timeout phase.
• Multiple PSM clients: NAPman use virtualization support to address this issue. It assigns
dedicated virtual AP for each PSM client. Limited numbers of virtual APs are possible. When
maximum number of APs has been reached, then NAPman can assign multiple PSM clients to
the same virtual AP using heuristics.
• Enhancements with client support: AP will inform the client to wake up at arbitrary times
instead of fixed interval. AP can inform client whether there any more packets available for
immediate transmission using more bits. If more bits are not set, the client can immediately go to
sleep.
• Advantages
o It is simple to implement, as it requires changes only at AP.
o It suggests further enhancements with client support to save more energy. The evaluations of this
enhancements shows that significant savings in energy consumption.
o It is not evaluated for applications that require QoS.
o It does not put additional possible delay to give more time to sleep to client device.
o With higher background traffic, latency increases in NAPman in comparison with High Priority
802.11 PSM.
C) Energy-Delay Tradeoffs in Smartphone Applications: Delay-tolerant applications can delay
the upload until a low-energy Wi-Fi connection becomes available. SALSA (Stable and Adaptive
Link Selection Algorithm) presents an online algorithm for this energy-delay trade-off using
Lyapunov optimization framework, which minimizes the total energy consumption subject to
keeping the average queue length finite. The problem can be formulated as link selection problem:
“given a set of links, determine whether to use any of the available links to transfer data or to defer a
transmission in anticipation of a lower energy link becoming available in the future, without
increasing delay indefinitely”. [13] Consider, E is estimate function. Control decision chooses link l
only when either a queue backlog U[t] is high or the available rate on link l is high. Performance
depends on the value of V. Here L[t] is available links [t] Tells whether to transfer the data or not. Pl
provides power consumption by link l. Framework includes energy expenditure, fairness, and
throughput maximization. Salsa provides near to optimal power consumption. V controls Energy-
delay tradeoff. It is threshold on the queue backlog beyond which the control algorithm decides to
transmit. It can be selected online: Adapt to V using binary search. It has long convergence time.
There two goals to choose V. Good power consumption vs. Delay tradeoff and Degree of explicit
control over the energy delay tradeoff. Power consumption is proportional to 1/V. Online rate
estimation average rate achieved over last few time slots. Offline estimation is used when history is
not available. Estimate rate by sampling several access points and obtain a distribution of achievable
rates as a function of Received Signal Strength Indicator [14].
Advantages:
o SALSA is evaluated using trace-driven evaluation on simulator with large number of traces on
different locations. It is also implemented and evaluated.
o SALSA clearly explains how to select single parameter V of the algorithm.
o SALSA suggests extension for energy efficient download and peer-assisted uploads.

66
Disadvantages:
o Use of complicated Lypunov Framework, which is not explained properly.
o Approach is application specific. Used only for one application to upload the videos.
6. RELATED WORK
This section briefly discusses about the most recent and significant contribution of the past
researchers in mitigating the energy issues over mobile devices.
Chen et al. [15] performed a study to extend client-server collaboration by offloading some of
the computations (i.e., method execution and dynamic compilation) normally performed by the
mobile client to the resource rich server in order to conserve energy consumed by the client in a
wireless Java environment.
Weissel et al. [16] discussed that there is no general-purpose policy that maximizes energy
savings for every workload and present system services that dynamically switch between different,
specialized power management algorithms. The operating system automatically learns which policy
performs best for a specific workload.
Klues et al. [17] presented an Integrated Concurrency and Energy Management (ICEM), a
device driver architecture that enables simple, energy efficient wireless sensor net applications. The
key insight behind ICEM is that the most valuable information an application can give the OS for
energy management is its concurrency.
Balasubramanian et al. [18] present a measurement study of the energy consumption
characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find
that 3G and GSM incur high tail energy overhead because of lingering in high power states after
completing a transfer. Based on these measurements, we develop a model for the energy consumed
by network activity for each technology.
Roy et al. [19] demonstrated how Cinder maintains system lifetime in the presence of
malicious applications, reserves energy for critical functions such as 911, supports energy-aware
applications, easily augments existing Unix applications with energy polices, properly amortizes
costs across multiple principals, and allows applications to sandbox untrusted subcomponents (such
as browser plug-in).
Harvey et al. [20] proposed an algorithm that employs the dead reckoning error rate to
dynamically control the state of the wireless interface. An algorithm is designed into a dead
reckoning simulator that is based on a real open-source game. The experimental result shows that the
proposed algorithm can achieve up to 36% energy savings for mobile devices.
Lin et al. [21] designed and prototyped an adaptive location service for mobile devices, a-
Loc, that helps in reducing battery drainage. The design is based on the observation that the required
location accuracy varies with location, and hence lower energy and lower accuracy localization
methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used.
Xiao et al. [22] proposed CasCap, a novel cloud-assisted context-aware power management
framework, which takes advantage of the processing, storage and networking resources in the cloud
to provide secure, low-cost and efficient power management for mobile devices. CasCap is featured
by crowd-sourced context monitoring, function offloading to the cloud, and providing adaptations as
services.
Hoque et al. [23] discuss, propose and apply energy efficient streaming techniques for
constant bit rate streaming. The author studied the energy savings at the streaming mobile devices
with different cellular network configuration.
Johnson and Hawick [24] discussed some power management strategies and present results
showing how some quite dramatic energy savings are possible on a typical modern mobile device
running Android and Linux. The implications for future mobile computing device architectures are
also discussed.

67
Rodriguez and Crowcroft [25] classified their study in six categories based on the type of
optimization: operating system and efficient resource management, energy measurements and power
models, users’ interaction with mobile resources, wireless interfaces and sensors management, and
finally, the new opportunities that process and system migration to the cloud can offer is also
discussed.
Sankaran et al. [26] developed a statistical user model to predict available energy at a given
time using historical user data and further describe a generic multi-level security model for mobile
devices. The available energy from the user model in conjunction with the energy estimates from the
security model can be used for energy-aware security adaptation in mobile devices.
Chandra et al. [27] described an alternative that controls the behavior of an energy hungry
application rather than kill it. The system offers a finer-grained approach to energy drain and is
cognizant of specific application energy characteristics as well as interactions amongst multiple
applications that can affect energy drain in unexpected ways.
Qin and Zhang [28] proposed a ZigBee-assisted PSM system to improve energy efficiency in
WiFi communication. Simulation results have shown significant improvement on energy efficiency,
compared to the standard PSM system.
7. CONCLUSIONS
After studying the literature in-depth from the previous section, following research gap has
been surfaced: i) Majority of the prior studies has considered enhancing the conventional energy
consumption frameworks which is based on only amount of moved data; such approaches are
becoming outdated with the evolution of the current standards in mobile communication system, ii)
Although there are massive research work conducted towards evaluating energy consumption in
mobile devices, very few studies have considered empirical model with various parameters of energy
depletion both with respect to routing and data transfer, iii) Some of the significant studies (e.g.
Balasubramanian et al. [18]) stated that energy depletion is highly significant in the peer mode when
compared to client mode. The prime reason behind it is recurrent maintenance signaling. However,
very few research works were witnessed to address this issue while formulating the design of energy
consumption model. Therefore, it can be seen that mobile handsets are still power-hungry devices
despite the tremendous efforts done by hardware manufacturers and operating system vendors in the
last years. Modern mobile platforms such as Android and iPhone are built as modifications of
general-purpose operating systems which do not consider energy-efficiency as a key performance
goal. In fact, modern handsets incorporate power-hungry hardware resources such as touchscreen
displays and location sensors, and they support Internet data services so they are always connected to
the network. All these resources bootstrapped a rich ecosystem of mobile applications but their
design is clearly driven by usability factors rather than energy efficiency. Since the mid-90s,
researchers have been emphasizing the need for considering energy as a fundamental system
resource in mobile devices. In this survey, we covered the most relevant articles about energy-
efficient resource management in mobile systems that can be implemented in current mobile
handsets. As far as we know, this is the first survey about mobile green computing in the last decade
and we strongly believe that some of the improvements highlighted in this survey will be part of
future mobile OS designs. Managing mobile resources from an energy-efficiency perspective without
diminishing the user experience is clearly one of the most challenging problems in mobile computing
nowadays. Power management considerations often require certain actions to be deferred, avoided or
slowed down to prolong battery life. It can even require changing dynamically the power states of
the hardware components and applications behavior depending on the available resources. However,
these techniques can impact the user experience with the handsets. Moreover, limitations such as the
lack of energy-aware support from hardware components make this problem even harder to solve.

68
Hardware manufacturers do not offer enough information about the energy consumption in runtime
to the operating system and applications.
8. REFERENCES
1. H.Zeng, S.C.Carla, A.R. Lebeck, A. Vahdat, “ECOSystem: managing energy as a first class
operating system resource”, In Proceedings of the 10th International Conference on
Architectural Support for Programming Languages and Operating Systems, pp. 123–132, San
Jose, CA, 2002.
2. H.Zeng, S.C. Ellis, A. R. Lebeck, A. Vahdat, “Currentcy: A unifying abstraction for
expressing energy management policies”, In Proceedings of the 2003 USENIX Annual
Technical Conference, pages 43–56, San Antonio, TX, 2003.
3. J.Flinn and M. Satyanarayanan, “PowerScope: A Tool for Profiling the Energy Usage of
Mobile Applications”, In Proceedings of the 2nd IEEE Workshop on Mobile Computer
Systems and Applications, New Orleans, LA, 1999.
4. L. Zhang, B.Tiwana, Z.Qian, Z.Wang, R.P. Dick, “Zhuoqing Morley Mao, and Lei Yang.
Accurate online power estimation and automatic battery behavior based power model
generation for smartphones”, In Proceedings of the eighth IEEE/ACM/IFIP international
conference on Hardware/software codesign and system synthesis, CODES/ISSS ’10, pages
105–114, New York, NY, USA, ACM. ISBN 978-1-60558-905-3, 2010.
5. N. Balasubramanian, A. Balasubramanian, and A. Venkataramani, “Energy consumption in
mobile phones: a measurement study and implications for network applications,” in
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, ser.
IMC ’09. New York, NY, USA: ACM, pp. 280–293, 2009.
6. Y. Xiao, P. Savolainen, A. Karppanen, M. Siekkinen, and A. Ylä- Jääski, “Practical power
modeling of data transmission over 802.11g for wireless applications,” in Proceedings of the
1st International Conference on Energy-Efficient Computing and Networking, ser. e- Energy
’10. New York, NY, USA: ACM, pp. 75–84, 2010.
7. A. Rice and S. Hay, “Decomposing power measurements for mobile devices,” in Pervasive
Computing and Communications (PerCom), IEEE, 2010.
8. Y. Xiao, R. Bhaumik, Z. Yang, M. Siekkinen, P. Savolainen, and A. Yla-Jaaski, “A System-
Level Model for Runtime Power Estimation on Mobile Devices,” in Proceedings of the 2010
IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference
on Cyber, Physical and Social Computing, ser. GREENCOMCPSCOM ’10. Washington, DC,
USA: IEEE Computer Society, pp. 27–34, 2010.
9. L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang, “Accurate
online power estimation and automatic battery behavior based power model generation for
smartphones,” in Proceedings of the eighth IEEE/ACM/IFIP international conference on
Hardware/software codesign and system synthesis, ser. CODES/ISSS ’10. New York, NY,
USA: ACM, 2010, pp. 105–114.
10. A. Shye, B. Scholbrock, and G. Memik, “Into the wild: studying real user activity patterns to
guide power optimizations for mobile architectures,” in Proceedings of the 42nd Annual
IEEE/ACM International Symposium on Microarchitecture, ser. MICRO 42. New York, NY,
USA: ACM, 2009, pp. 168–178.
11. F. Ben Abdesslem, A. Phillips, and T. Henderson. Less is more: energy-efficient mobile
sensing with senseless. ACM MobiHeld, August 2009.
12. E.Cuervo, A.Balasubramanian, D.k.Cho, A.Wolman, StefanSaroiu, Ranveer Chandra, and
Paramvir Bahl, “Maui: Making smartphones lastlonger with code offroad”, ACM MobiSys,
2010.

69
13. I. Constandache, S. Gaonkar, M. Sayler, R. R. Choudhury, and L. Cox, “Enloc: Energy-
efficient localization for mobile phones”, IEEE Infocom Mini Conference, April 2009.
14. R.Fahad. Dogar, P.Steenkiste, K. Papagiannaki, “Catnap: Exploiting high bandwidth wireless
interfaces to save energy for mobile devices”, ACMMobiSys, 2010.
15. G.Chen., B.Kang, M.Kandemir, N.Vijaykrishnan, M.J.Irwin, R.Chandramouli, "Energy-aware
compilation and execution in Java-enabled mobile devices", Parallel and Distributed
Processing Symposium, Proceedings. International, pp.8 pp. 22-26, 2003.
16. A.Weissel and F. Bellosa. "Self-learning hard disk power management for mobile devices."
Proceedings of the Second International Workshop on Software Support for Portable Storage
(IWSSPS), 2006.
17. K.Klues, V.Handziski, C.Lu, A.Wolisz, D.Culler, D.Gay, P.Levis, “Integrating concurrency
control and energy management in device drivers”, In ACM SIGOPS Operating Systems
Review,Vol. 41, No. 6, pp. 251-264, 2007.
18. N.Balasubramanian, A.Balasubramanian, A. Venkataramani. "Energy consumption in mobile
phones: a measurement study and implications for network applications." Proceedings of the
9th ACM SIGCOMM conference on Internet measurement conference, 2009.
19. A.Roy, S.M.Rumble, R.Stutsman, P. Levis, D.Mazières, N.Zeldovich, “Energy management
in mobile devices with the Cinder operating system”, In Proceedings of the sixth conference
on Computer systems, pp. 139-152, 2011.
20. R.C.Harvey, A.Hamza, C.Ly, M.Hefeeda, “Energy-efficient gaming on mobile devices using
dead reckoning-based power management. In Network and Systems Support for Games
(NetGames), 2010 9th Annual Workshop, pp. 1-6, 2010.
21. K.Lin, A. Kansal, D. Lymberopoulos, F.Zhao. "Energy-accuracy aware localization for mobile
devices." In Proceedings of 8th International Conference on Mobile Systems, Applications,
and Services 2010.
22. Y.Xiao, P.Hui, P. Savolainen, A. Ylä-Jääski, "CasCap: cloud-assisted context-aware power
management for mobile devices." In Proceedings of the second international workshop on
Mobile cloud computing and services, pp. 13-18, 2011.
23. M.Hoque, S.Matti, J. Nurminen. "Energy efficient multimedia streaming to mobile devices—a
survey." Pp. 1-19, 2012.
24. M.J. Johnson and K.A. Hawick, “Optimising Energy Management of Mobile Computing
Devices”.
25. V.Rodriguez, Narseo, J.Crowcroft. "Energy management techniques in modern mobile
handsets." Communications Surveys & Tutorials, IEEE, Vol. 15, No. 1, pp. 179-198, 2013.
26. S.Sankaran, R.Sridhar, "User-adaptive energy-aware security for mobile devices,"
Communications and Network Security (CNS), 2013 IEEE Conference, pp.391- 392, 2013
27. R.Chandray, O. Fatemieh, P. Moinzadehz, “End-to-End Energy Management of Mobile
Devices”, 2013.
28. Q.Hua, W. Zhang. "ZigBee-assisted Power Saving Management for mobile devices." In
Mobile Adhoc and Sensor Systems (MASS), IEEE 9th International Conference, pp. 93-101,
2012.
29. Khaja Mizbahuddin Quadry, Dr. Mohammed Misbahuddin and Dr. A.Govardhan, “Security
Issues Vs User Awareness in Mobile Devices: A Survey”, International Journal of Advanced
Research in Engineering & Technology (IJARET), Volume 4, Issue 3, 2013, pp. 217 - 225,
ISSN Print: 0976-6480, ISSN Online: 0976-6499.
30. S.Mohan Raj and Dr.G.Kalivarathan, “Feasibility Study of Pervasive Computing Approach
for Energy Management in Mobiles”, International Journal of Computer Engineering &
Technology (IJCET), Volume 3, Issue 3, 2012, pp. 312 - 319, ISSN Print: 0976 – 6367,
ISSN Online: 0976 – 6375.

40120140503009

Recommended

Recommended

More Related Content

What's hot

What's hot (18)

Viewers also liked

Viewers also liked (20)

Similar to 40120140503009

Similar to 40120140503009 (20)

More from IAEME Publication

More from IAEME Publication (20)

40120140503009