Energy Efficient Scheduling of Fine-granularity Tasks in a Sensor Cloud
Abstract
Wireless Sensor Networks (WSNs) are frequently used in number of applications like unattended environmental monitoring. WSNs have low battery power hence schemes have been proposed to reduce the energy consumption during sensor task processing. Consider a Sensor Cloud where owners of heterogeneous WSNs come together to offer sensing as a service to the users of multiple applications. In a Sensor Cloud environment, it is important to manage different types of tasks requests from multiple applications efficiently. In our work, we have proposed a scheduling scheme suitable for the multiple applications in a Sensor Cloud. The scheduling scheme proposed is based on TDMA which considers the fine granularity of tasks. In our performance evaluation, we show that the proposed scheme saves energy of sensors and provides better throughput and response time in comparison to a most recent work.
Recommended Citation
R. Dalvi and S. K. Madria, "Energy Efficient Scheduling of Fine-granularity Tasks in a Sensor Cloud," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9050, pp. 498 - 513, Springer, Jan 2015.
The definitive version is available at https://doi.org/10.1007/978-3-319-18123-3_30
Department(s)
Computer Science
International Standard Book Number (ISBN)
978-331918122-6
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2015
