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Summary. Accident detection systems help reduce fatalities stemming from car accidents by decreasing the response time of emergency responders. Smartphones and their onboard sensors (such as GPS receivers and accelerometers) are promising platforms for constructing such systems. This paper provides three contributions to the study of using smartphone-based accident detection systems. First, we describe solutions to key issues associated with detecting traffic accidents, such as preventing false positives by utilizing mobile context information and polling onboard sensors to detect large accelerations. Second, we present the architecture of our prototype smartphone-based accident detection system and empirically analyze its ability to resist false positives as well as its capabilities for accident reconstruction. Third, we discuss how smartphone-based accident detection can reduce overall traffic congestion and increase the preparedness of emergency responders.

The powerful processors and variety of sensors in new and planned mobile Internet devices, such as Apple's iPhone and Android-based smartphones, can be leveraged to build cyber-physical applications that collect sensor data from the real world and communicate it back to Internet services for processing and aggregation. This article presents key R&D challenges facing developers of mobile cyber-physical applications that integrate with Internet services and summarizes emerging solutions to address these challenges. For example, application software should be architected to conserve power, which motivates R&D on tools that can predict the power consumption characteristics of mobile software architectures. Other R&D challenges involve the relative paucity of work on software and sensor data collection architectures that cater to the powerful capabilities and cyber-physical aspects of mobile Internet de-J. White ( ) ·

Abstract:Smartphones are mobile devices that travel with their owners and provide increasingly powerful services. The software implementing these services must conserve battery power since smartphones may operate for days without being recharged. It is hard, however, to design smartphone software that minimizes power consumption. For example, multiple layers of abstractions and middleware sit between an application and the hardware, which make it hard to predict the power consumption of a potential application design accurately. Application developers must therefore wait until after implementation (when changes are more expensive) to determine the power consumption characteristics of a design. This paper provides three contributions to the study of applying model-driven engineering to analyze power consumption early in the lifecycle of smartphone applications. First, it presents a model-driven methodology for accurately emulating the power consumption of smartphone application architectures. Second, it describes the System Power Optimization Tool (SPOT), which is a model-driven tool that automates power consumption emulation code generation and simplifies analysis. Third, it empirically demonstrates how SPOT can estimate power consumption to within ∼3-4% of actual power consumption for representative smartphone applications.