Moppet: A Model-Driven Performance Engineering Framework for Wireless Sensor Networks

This paper describes a model-driven performance engineering framework for applications embedded in individual nodes of wireless sensor networks (WSNs). The framework, called Moppet, is designed for developers, even non-programmers, to rapidly implement WSN applications, estimate their performance and feedback the estimated performance results for customizing their design/implementation. By leveraging the notion of feature modeling, Moppet allows developers to graphically and intuitively specify features (e.g. functionalities and configuration policies) required in their applications. It also validates a set of constraints among features and generates a lightweight application code. Moreover, with event calculus and network calculus, Moppet estimates a WSN application's performance without generating its code nor running it on simulators and real networks. It can approximate data yield, data transmission latency and network lifetime as well as each node's power and bandwidth consumption. Evaluation results show that, in a large-scale WSN of 400 nodes, Moppet's performance estimation is 46% more efficient than empirical performance measurement and its estimation error is lower than 10%. Moppet scales well to network size with respect to estimation efficiency and accuracy.