Energy-driven distribution of signal processing applications across wireless sensor networks
ACM transactions on sensor networks
Wireless sensor network (WSN) applications have been studied extensively in recent years. Such applications involve resource-limited embedded sensor nodes that have small size and low power requirements. Based on the need for extended network lifetimes in WSNs in terms of energy use, the energy efficiency of computation and communication operations in the sensor nodes becomes critical. Digital signal processing (DSP) applications typically require intensive data processing operations and as a
... sult are difficult to implement directly in resource-limited WSNs. In this paper, we present a novel design methodology for modeling and implementing computationallyintensive DSP applications applied to wireless sensor networks. This methodology explores efficient modeling techniques for DSP applications, including data sensing and processing; derives formulations of energy-driven partitioning (EDP) for distributing such applications across wireless sensor networks; and develops efficient heuristic algorithms for finding partitioning results that maximize the network lifetime. To address such an energy-driven partitioning problem, this paper provides a new way of aggregating data and reducing communication traffic among nodes based on application analysis. By considering low data token delivery points and the distribution of computation in the application, our approach finds energy-efficient trade-offs between data communication and computation. Data collection and management play an important role in WSNs, especially for the applications, such as digital signal processing (DSP) applications, that need to sense and process large amounts of data. For example, a distributed automatic speech recognition (DASR) system [Shen et al. 2008 ] which is a DSP-based WSN application senses and processes large amounts of data, and communicates required information across WSN. The DASR system can be applied to application scenarios. The first scenario involves using a DASR system as a speech-based speakerdependent command and control system in a battlefield environment. When the system is applied in a battlefield for recognizing command words, the speakerdependent property provides a benefit by rejecting command words that are spoken by unauthorized people. The other application scenario is to use a DASR system as a surveillance system for collecting large amounts of speech data with similar patterns from arbitrary speakers. Since sensor nodes are usually designed to be small, they can be hidden in a battlefield environment that is being monitoring. Therefore, acoustic signals from the enemy can be secretly sensed, collected, and translated into useful data on the sensor nodes. Through a well-designed communication protocol, this data can then be transmitted to a central node for further processing and backend analysis. The recognized words, for example, can be used to distinguish among a diversity of languages or to survey specific words in a crowd for special monitoring and detection purposes.