Development of Efficient Role-Based Sensor Network Applications with Excel Spreadsheets

Abstract

Natural scientists use large scale sensor networks for gathering and analyzing environmental data. However, the implementation work requires expert programmers. The problem is complicated by limited battery lifetime, processing power and memory capacity of the nodes, because this requires a low-level programming language. Since scientists are used to analyzing data with spreadsheets, researchers have studied the possibility of applying spreadsheet-based programming to sensor networks. The approaches so far either require a central server to execute the spreadsheet, or they execute a spreadsheet run-time on each node. The first approach causes higher communication cost since all data has to be routed to the central server and the second one causes computational overhead, because evaluating a spreadsheet is slower than executing handcrafted NesC-code. Hence, we present a spreadsheet driven tool-chain that can create efficient NesC-code and allows for simulation in the spreadsheet itself. The nodes have to recompute the spreadsheet formulas upon new data. However, we can avoid a large fraction of this recomputation by applying several optimization strategies during code generation. In our example scenario, sensor nodes compute the variance across a series of sensor readings. We can show that the optimizations save 65% CPU cycles and the code size decreases by 12% when compared to non-optimized execution of the spreadsheet. Thus, our approach can deliver an easy way of developing sensor network programs while yielding very efficient code.