A growing number of mobile computing applications are centered around the user’s location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone’s camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87 % when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.

Abstract. Indoor localization is an open problem. In this paper, we explore the possibility of fingerprinting rooms based on the intensity of light incident on a light sensor worn by the user, under static lighting conditions. We present three algorithms for fingerprinting, namely Bayesian, Range-max and Spatial and present experimental results for the first two. Bayesian performs better, achieving an accuracy (i.e success probability) of more than 90 % when the light sensor is worn on top of a hat, and more than 80 % when the light sensor is worn as a pendant. This approach does not require any infrastructure; existing light sources in rooms are used for localization. 1

Finding a person in a public place, such as in a library, conference hotel, or shopping mall, can be difficult. The difficulty arises from not knowing where the person may be at that time; even if known, navigating through an unfamiliar place may be frustrating. Maps and floor plans help in some occasions, but such maps may not be always handy. In a small scale poll, 80 % of users responded that the ideal solution would be “to have an escort walk me to the desired person”. This paper identifies the possibility of using mobile phone sensors and opportunistic user-intersections to develop an electronic escort service. By periodically learning the walking trails of different individuals, as well as how they encounter each other in space-time, a route can be computed between any pair of persons. The problem bears resemblance to routing packets in delay tolerant networks, however, its application in the context of human localization raises distinct research challenges. We design and implement Escort, a system that guides a user to the vicinity of a desired person in a public place. We only use an audio beacon, randomly placed in the building, to enable a reference frame. We do not rely on GPS, WiFi, or war-driving to locate a person – the Escort user only needs to follow an arrow displayed on the phone. Evaluation results from experiments in parking lots and university buildings show that, on average, the user is brought to within 8m of the destination. We believe this is an encouraging result, opening new possibilities in mobile, social localization.

In this paper, we exemplify outdoor distributed computing and point out the key challenges. We present Split Smart Messages, a lightweight, portable, network failure resilient and relatively secure middleware that enables a large subset of outdoor distributed computing applications. We also present a Service Discovery, Interaction and Payment Protocol (SDIPP) tailored for mobile phones. We evaluate our middleware and protocol on Sony Ericsson P900 phones and present experimental results.

Pervasive computing is an application-driven field and as such is ill-defined and unstructured for the lack of definitions, algorithms and architectures. Research is primarily guided by visions and beliefs because it is hard to tell, in a scientific way, which systems/applications will penetrate the market. To realize the diverse set of applications, research has to be multidisciplinary, which adds to the complexity. The exploratory and unstructured nature of pervasive computing research is, therefore, justified to some extent. Besides, this field is still in a nascent stage. Unlike networks/compilers/operating systems, which exist and are widely used and deployed, pervasive computing exists mostly in labs or in specialized environments or in the form of single isolated applications. As a result, progress is hard to measure and prove. In this position paper, we express our view on what should be the sub-areas of

Pervasive computing is centered around the idea of provisioning computing services to the user anywhere anytime. If realized, pervasive computing can have a significant impact on our daily lives, ranging from the the way we dress to the way we work and travel. Two key hurdles prohibit the widescale adoption of pervasive computing. First, human cognitive bandwidth is a limited resource; the burden of interacting with pervasive computing applications may outweigh the functionality obtained. Second, pervasive computing applications often assume a smart environment which does not exist today; dependency on a ubiquitous computing infrastructure hampers deployment. In this dissertation, we propose location-aware personal computing as a way of getting close to the pervasive computing vision with minimal overhead. Central to locationaware personal computing is the use of smart phones and location information. Smart phones personify a ubiquitous personal device that can execute client frontends, and connect wirelessly to backend services. Location information serves as a proxy for the user. Smart phones and location information can minimize both user involvement and