Abstract

Abstract. Macroprogramming-the technique of specifying the behavior of the system, as opposed to the constituent nodes-provides application developers with high level abstractions that alleviate the programming burden in developing wireless sensor network (WSN) applications. However, as the semantic gap between macroprogramming abstractions and node-level code is considerably wider than in traditional programming, converting the high level specification to running code is a daunting process, and a major hurdle to the acceptance of macroprogramming. In this paper, we propose a general compilation framework for a data-driven macroprogramming language that allows for plugging in different modules implementing various stages of compilation. We also demonstrate an actual instantiation of our framework by showing an end-to-end solution for compiling macroprograms. Our compiler provides the final code to be deployed on real nodes as well as an estimate of the costs the running system will incur, e.g., in terms of messages exchanged. We compared the auto-generated code against a handcoded version for the same application behavior to verify the outcome of our compiler.

Wireless Sensor Networks (WSNs)

In model based oil field operations, engineers rely on simulations (and hence simulation models) to make important operational decisions on a daily basis. Three problems that are commonly encountered in such operations are: on-demand access to information, integrated view of information, and knowledge management. The first two problems of on-demand access and information integration arise because a number of different kinds of simulation models are created and used. Since these models are created by different processes and people, the same information could be represented differently across models. A unified view of the models and their simulations is desirable for decision making, and thus the necessity for information integration. Knowledge management refers to a systematic way to capture the rationale (knowledge) behind the various analyses performed by an engineer and decisions taken based on the analyses. It is critical to capture this knowledge for auditing, archiving, and training purposes. In this paper, we propose the application of semantic web technologies to address these problems. The key elements of the semantic web approach are the ontologies or the information schemas that model various elements from the domain, and a knowledge base (KB) which is a central repository of the instance information in the system. We present a modular approach for organizing the ontologies and outline the process that was followed to define the ontologies. We also describe the workflow that was used to populate the KB and briefly discuss some of our prototype applications that address the problems mentioned above. Based on our experience, semantic web technologies appear to be a highly promising approach to deal with these information management issues in the oilfield domain, although performance and tool support remain the key areas of concern at this stage.

Abstract