Abstract
Body Sensor Networks (BSNs) have become prominent in research and industry alike as a powerful enabler of novel applications in human-centered domains. However, developing applications on such systems is still a cumbersome process, due to the lack of suitable software abstractions and the difficulties in managing wearable computing application within the stringent constraints of embedded systems. In this paper, we introduce a novel framework, SPINE2 (Signal Processing In Node Environment), which allows task-oriented programming on a platform-independent architecture. We demonstrate how fairly sophisticated signal-processing applications can be realized in the form of easy-to-implement embedded processes. The proposed architecture is tested experimentally and its features are illustrated through a nontrivial case study. In the last years, several frameworks and middlewares have been conceived and made available to support high-level programming in WSNs. These provide a generic set of features that can only be used for the most common application domains. However, it is hard to efficiently support the more specific domain of BSNs, which requires specific capabilities. In order to fully satisfy the BSN-based requirements, SPINE2 has been conceived as an effective and efficient tool for developing distributed signal-processing applications. Its task-oriented paradigm allows developers to specify the applications' behavior by abstracting away any low-level details concerning the platform hardware and the communication protocol. Moreover, its platform-independent architecture enables code reusability and portability, as well as application interoperability and platform heterogeneity. To demonstrate the effectiveness of the proposed framework and the efficiency of the runtime environment, a BSN-based activity recognition system has been developed through SPINE2. The easiness in implementing such a complex system thanks to both the provided programming abstractions and the framework components reusability is shown, as well as the efficiency of the whole system whose performance has been evaluated under a range of metrics.
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