Chronus: A Spatiotemporal Macroprogramming Language for Autonomic Wireless Sensor Networks

• Main Authors: Wada,H., Boonma,P., Suzuki,J.
• Format: Chapter
• Published: 2015
• Online Access: http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84882786930&origin=inward; http://cmuir.cmu.ac.th/handle/6653943832/39023
• Institution: Chiang Mai University

This chapter considers autonomic wireless sensor networks (WSNs) to detect and monitor spatiotemporally dynamic events, which dynamically scatter along spatiotemporal dimensions, such as oil spills, chemical/gas dispersions, and toxic contaminant spreads. Each WSN application is expected to autonomously detect these events and collect sensor data from individual sensor nodes according to a given spatiotemporal resolution. For this type of autonomic WSN, this chapter proposes a new programming paradigm, spatiotemporal macroprogramming. This paradigm is designed to reduce the complexity of programming event detection and data collection in autonomic WSNs by (1) specifying them from a global network viewpoint as a whole rather than a viewpoint of sensor nodes as individuals and (2) making applications behave autonomously to satisfy the spatiotemporal resolutions for event detection and data collection. The proposed programming language, Chronus, treats space and time as first-class programming primitives and combines them as a spacetime continuum. A spacetime is a three-dimensional object that consists of two spatial dimensions and a time playing the role of the third dimension. Chronus allows application developers to program event detection and data collection to spacetime, and abstracts away low-level details in WSNs. The notion of spacetime provides an integrated abstraction for seamlessly expressing event detection and data collection as well as consistently specifying data collection for both the past and future in arbitrary spatiotemporal resolutions. This chapter describes Chronus' design, implementation, runtime environment, and performance implications. © 2011 Elsevier Inc. All rights reserved.