Accepted 11 February 2012Available online 21 February 2012
Keywords:Power management and power controlalgorithmsMobile and wireless computing andcommunicationsMiddleware and operating system support
the amount of energy spent in sharing. In this paper, we present a framework for energy-
vide different subsets of these resources, and therefore,combining the resources of multiple devices can enablenew types of applications. For example, a camera mayadd a location tag to a picture by obtaining a GPS reading
for the Internet . Grid systems allow remote access todistributed resources and services in a standardized way.The potential of using Grid technology to implement mo-bile resource sharing systems has been widely discussedin recent years [3,4]. If Internet access is available, e.g.,by a cellular connection, mobile devices can be integratedinto existing infrastructure-based Grids, as proposed, e.g.,in . But even if no Internet access is available, devices
1389-1286/$ - see front matter 2012 Elsevier B.V. All rights reserved.
Corresponding author.E-mail addresses: firstname.lastname@example.org (J. Furthmller), waldhorst@
kit.edu (O.P. Waldhorst).
Computer Networks 56 (2012) 19201934
Contents lists available at SciVerse ScienceDirect
.e lsdoi:10.1016/j.comnet.2012.02.007The number, pervasiveness, and capabilities of mobiledevices like phones, PDAs, navigation devices, cameras,and mp3 players are growing fast and steadily. Nowadayswe live surrounded by a multitude of smart appliancesoffering plenty of resources, e.g., communication capabili-ties like 3G, WiFi, or Bluetooth interfaces, sensors likeGPS and acceleration, and data and information like digitalmaps for a navigation utility. Different devices may pro-
speed up downloads . Beyond offering remote accessto a single resource, a device can offer remote services thatcombine multiple resources in a non-trivial way. For exam-ple, by exploiting its GPS andWAN links a PDA can offer anintegrated service for location-tagging and gallery-uploadof a picture.
To enable exible resource sharing among mobiledevices, one can use an approach inspired by Grid systems1. Introductionaware resource sharing among mobile devices of various kinds that comprises (1) energy-aware strategies for selecting remote service providers and (2) a generic energy estimatorfor forecasting and accounting the energy consumption of a remote service call. To illustratethe benet of (1), we show by simulation that the battery lifetime of devices running theframework can be extended up to 40% by service selection strategies that take into accountthe energy cost of a requested service compared to energy-unaware (random) service selec-tion. For providing the energy-related input for service selection, we present (2) a genericestimator that can be customized easily for different hardware-platforms by solving a linearequation system with coefcients derived from benchmark measurements. We present aprototype-based case study for three different platforms, the Nokia N810, the HTC TouchCruise and the Samsung Galaxy S showing that for all of them the estimation error is below10% for 90% of the service calls. Furthermore, measurements conducted with a prototypeimplementation of the resource sharing framework show that battery lifetime can in factbe extended by energy-aware service selection strategies.
2012 Elsevier B.V. All rights reserved.
from a nearby navigation device, or PDAs may pool wire-less WAN links using local WiFi connectivity in order toReceived 1 February 2011Received in revised form 4 November 2011
ture enables new applications for mobile devices. However, the willingness of device own-ers to contribute resources to such applications remains low as long as they cannot controlEnergy-aware resource sharing with
Jochen Furthmller , Oliver P. WaldhorstInstitute of Telematics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Ger
a r t i c l e i n f o
a b s t r a c t
Ad hoc sharing of re
journal homepage: wwwobile devices
es by offering remote services through an appropriate infrastruc-
evier .com/locate /comnet
In a rst step, a benchmark program is executed on thespecic platform, which yields input values for deriving adevice-specic energy model by solving a linear equationsystem. The energy model describes the energy consump-tion of key resources such as CPU, WiFi, GPS, and display. Incombination with a resource demand vector that describesto what extent a service accesses each of the key resources,the energy consumption of a service call can be estimatedusing the energy model in a second step. The resource de-
Table 1Can you imagine sharing resources of your mobiledevice with other users?
Table 4Which resource are you willing to share?
CPU 42.1%GPS 42.1%WiFi 34.2%Storage 26.3%GSM/UMTS 21.1%Camera 18.4%Bluetooth 18.4%Display 13.2%Other 7.9%Microphone 5.3%
Table 5Which remote resources would you like to use?
GPS 52.6%WiFi 52.6%CPU 50%GSM/UMTS 42.1%Storage 36.8%Camera 26.3%Bluetooth 15.8%Microphone 13.2%Display 10.5%Other 2.6%
J. Furthmller, O.P. Waldhorst / Computer Networks 56 (2012) 19201934 1921can share resources with other nearby devices in local mo-bile ad hoc Grids, as discussed, e.g., in . Furthmller andWaldhorst  gives an elaborate survey of the variousapproaches to establishing a Grid-like infrastructureamong mobile devices.
Although creating an infrastructure for resource sharingamong mobile devices is technically feasible, the questionremains of whether device owners are willing to share atall. To answer this question, we conducted a user poll inseveral Internet forums1. We received 38 responses fromrandom forum visitors as well as from students and col-leagues we encouraged to participate. The exact results ofthe poll are shown in Tables 15. We found that almost 23of the participants were willing to share resources. Conrm-ing the claim in , 53% of the participants stated that theirbiggest concern besides security was the limitation of theavailable energy budget. Thus, energy-awareness is a keydriver for user acceptance of mobile resource sharing.
Motivated by the results of the poll, this paper presentsan OSGi-based  framework for energy-aware resourcesharing among mobile devices that comprises two importantparts. On the one hand, to chose an appropriate serviceprovider in the case that a remote service is offered bymultiple devices, the framework provides multiple en-ergy-aware service selection strategies (1). On the otherhand, to gather energy-related information for serviceselection independently of the actual device-hardware,an energy estimator (2) for forecasting and accounting theenergy required by a particular service call is incorporated.
Using simulation, we shed light on the impact of en-ergy-aware service selection strategies (1) on the batterylifetime of the participating mobile devices and serviceavailability in the system. We nd that in the scenariosconsidered, the baseline energy consumption, i. e., the en-ergy consumed for listening for remote service calls has ahuge impact on the battery lifetime. This signicantlyreduces the gain in battery lifetime due to the serviceselection strategy. Nevertheless, using an appropriatestrategy can extend the time until the rst device failureby up to 40%, as compared to choosing the service providerat random, given a reasonable baseline energy consump-tion. Such strategy requires knowledge of both the energyavailable on a device and the energy required for a partic-ular service call.
Since the energy consumption of a service call is on the
Yes 65.8%No 31.6%No answer 2.6%one hand highly platform-specic, and on the other handdepends on the characteristics of the particular service,we present an approach for an energy estimator (2) thatcan be exibly customized to different hardware platformsand services. For this purpose, we use a two step-approach.
1 http://www.fragebogen-tool.de/f.php?i=12586&c=mhdcd, http://talk.maemo.org, http://www.handy-faq.de/.Table 2Can you imagine using the resources of other usersdevices?
Yes 81.6%No 15.8%No answer 2.6%
Table 3If you do not want to share, whynot?
Privacy concerns 53%Security concerns 27%Uncomfortable to
Monetary concerns 2%mand vector is iteratively rened by tracking the history ofprevious service calls at run-time. Note that the energymodel must be derived only once per device and can be eas-ily generated. Thus the burden of doing energy measure-ments for each service is eliminated.
For validation, we customize the energy model for threedifferent platforms Nokia N810 (Maemo Linux), HTCTouch Cruise (Windows Mobile) and Samsung Galaxy S
functionality for advertisement, discovery, selection, and
energy consumption of a service call may consume as littleenergy as a fraction of a milliwatt, and, thus cannot bemeasured using methods provided by the platform .Thus, the framework includes a generic energy estimatordescribed in Section 4.
Our framework is implemented as extension of an exist-ing framework provided by the ETH Zrich. The basicframework consists of Concierge , a lightweight OSGiimplementation, R-OSGi  and jSLP . R-OSGi pro-vides means for remote access on OSGi bundles executedon remote devices, and jSLP is a Java implementation ofthe Service Location Protocol for Concierge.
We add three more components to the existing frame-work, as depicted in Fig. 1: The rst is the local energy man-
1922 J. Furthmller, O.P. Waldhorst / Computer Networks 56 (2012) 19201934remote access to shared services. As we show in Section 3,among all thi