GetMobile is the new incarnation of the quarterly ACM SIGMOBILE Mobile Computing and Communications Review, which was formerly known as MC2R. GetMobile is the premier forum for addressing networks, systems, algorithms, and applications that support the symbiosis of portable computers and wireless networks.
MOBILE SENSING: Retrospectives and Trends
It is difficult to think back to a time before smartphones existed, with their ubiquitous computing and communication capabilities, and with detailed location sensing easily available from Global Positioning Systems (GPS). In the late 1990s, when my research group began work on mobile sensing, smartphones had not yet been invented. While GPS did exist, GPS receivers were expensive, power-hungry and not widely available. Our first mobile computing project started as a powerefficiency study for a GPS-based interactive campus tour. GPS-based tour applications are familiar now, but were unheard of then, and the physical implementation was a challenge. We used a Palm Pilot PDA (personal digital assistant) connected to an external GPS receiver and an external Wi-Fi card. In those days, PDAs had neither GPS nor any wireless communication capability! Given the bulkiness of the various pieces of our "app," we carried them and their batteries around in a shoebox. Since both the GPS and the radio were quite high power (over 1W), they greatly impacted the system's battery life. Our power-efficiency work explored methods to locally cache maps on the PDA, and to power down modules when not in use.
Battery-Free Connected Machine Vision with WISPCam
Sustained exponential improvements in the energy efficiency of microelectronics has recently enabled us to build battery-free camera systems that are powered entirely by propagating radio waves. This paper describes primitive machine vision applications built using this highly constrained, battery-free camera system. After describing the WISPCam system and its constraints, we show how to use it to capture (relatively) high-resolution images of faces, without ever capturing a full frame at high resolution. This example application illustrates the issues that arise in partitioning a demanding vision application across mobile hardware that is highly constrained in power, storage, computation and communication.