Energy Efficient Data Management in Smartphone Networks

Smartphone devices have emerged into powerful computational platforms equipped with multitude of sensors and capable of generating vast amounts of data (geo-location, a udio, video, etc.) On the other hand, these devices operate on a str ic energy budget, thus have a limited lifetime on a single charg e. Consequently, we need to identify new energy-aware algorit hms and techniques to provide innovative, feature-rich applications and services. In this white paper, we start out by providing recent trends in Smartphone technology and Smartphone networks. Our description is succeeded by an anatomy of the energy cost s associated with data processing in a Smartphone Network. We conclude with prominent research directions in energy-awa re data management for Smartphone networks. I. I NTRODUCTION The widespread deployment of Smartphone devices featuring geo-location (e.g., AGPS, Cell tower and WLAN positioning) and other sensing capabilities (e.g., proxim ity, ambient light, accelerometer, camera, microphone, etc.) a long with Internet connectivity through WLAN, WCDMA/UMTS (3G), HSPA (3.5G) and LTE/WiMAX (4G) networks, have brought a revolution in location-oriented mobile applicat ons and services. IMS Research and Comscore reported over 225M Smartphone sales in February 2010 (i.e., RIM, Apple, Microsoft, Google and Palm) and according to the Focal Point Group, handheld smart devices (including mobile phones and PDAs) could number 1 billion in 2010. We define a Smartphone Network as “ a set of Smartphone devices that communicate in an unobtrusive manner, without explicit user interactions, in order to realize a collaborative or social task. ” There is already a proliferation of innovative applications founded on Smartphone networks. One example is opportunistic and participatory sensing [6], [2], [3], w here applications can task mobile nodes in a given region to provi de information about their vicinity using their sensing capab ilities. Another example is road traffic delay estimation [9] using WiFi beams collected by Smartphone devices rather tha n invoking energy-demanding GPS acquisition. On the social site, Google Latitude enables users to track the places they and their social network have visited. The given service alread y reports millions of users, despite the controversial priva cy concerns. Similarly, mobile social networking applicatio ns like Foursquare, Gowalla and Loopt enjoy enormous success in the Smartphone community. A Smartphone is a battery-operated device, thus has a limited lifetime on a single charge. Consider a recent 4.3inch Smartphone device, which features a rechargeable 3.7V lithium-ion battery at a capacity of 1730 mAh (i.e., 1.73 * 3600 seconds * 3.7V = 23,044 Joules). Such a device is advertised to offer 450 minutes (i.e., almost 8 hours) of tal k time using WCDMA and up to 355 hours (i.e., almost 15 days! ) in stand-by mode. These number assume that the users are not running any of their favorite applications nor use po wer hungry features such as a bright LCD, GPS, WiFi, 3G/4G and others. But even in the absence of all aforementioned featur s and applications, the lifetime of a Smartphone is considera bly lower due to location, movement, signal strength, cell traf fic and battery age. Consequently, this brings the energy lifet ime of a Smartphone on a single charge, down to a day or so, depending on the Smartphone vendor and model. Finally, external battery chargers are not very practical for mobile users and fuel cells for smartphones have not reached the wid e masses either (e.g., Aquafairy’s AF-M3000 model requires only water to generate electricity that can charge an i-Phon e in 90 minutes! ) Consequently, we need to identify new energy-aware algorithms and techniques to provide innovative, feature-ri ch applications and services. We start our description out wit h some measurements we obtained using a real Smartphone. These measurements provide an anatomy of energy costs associated with data processing on a Smartphone, enabling the following two observations: i) Local processing is an expensive operation with respect to energy consumption and must be avoided whenever possible; and ii) Data transfer ove r a wireless link (3G or WiFi) is again an expensive operation and must be avoided whenever possible. We conclude with prominent research directions in energy-aware data manage ment for Smartphone networks. II. ENERGY ANATOMY OF A SMARTPHONE In this subsection we present a set of real measurements we obtained from a prototype trajectory similarity search sys tem implemented for Android smartphones [4]. Our experimental platform is an Android-based HTC Hero 2.1 Smartphone equipped with 802.11b/g and a Qualcomm MSM 7200A 528 MHz processor. We use benchmarking tools like 3gtest [1] and PowerTutor [8] to quantify the energy drain of a Smartphone device.