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Mission-Oriented Sensor Networks and Systems: Art and Science pp 193–226Cite as

Energy-Aware Task Allocation in WSNs

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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 163))

Abstract

Complex wireless sensor network applications as those in Internet of Things or in-network processing are pushing the requirements for energy efficiency and data processing drastically. Executing the tasks of such complex applications in a single node may lead it to die soon, since the nodes in WSNs are usually with limited and generally irreplaceable power sources. How to distribute the tasks across the network and simultaneously balance the energy consumption of each node to achieve energy efficiency and to extend the network lifetime are crucial and urgent requirements in WSNs. Energy-aware task allocation (sometimes also called workload distribution) technologies, which have been deeply studied in multiprocessor systems, grid computing, and system on chip (SoC), are attracting the attention of the research community in WSNs. Due to the limited energy source and computing capability as well as the wireless communication, the task allocation problem in WSNs is different from traditional wired systems. This chapter provides an application-level taxonomy and an in-depth review of task allocation approaches in WSNs. It enables the readers to gain a clear view of current task allocation approaches, by taking the evaluation metrics and the modeling methods of the problem into account.

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Notes

  1. 1.

    The number of the source tasks are not limited to one, it is determined by specific applications.

  2. 2.

    Note that, the network structures of WSNs are not limited to multi-hop mesh and hierarchical cluster, there are also other types, such as location-based network structures.

  3. 3.

    MEPS is the maximum entropy power spectrum (MEPS) computation which is adapted from Ptolemy II design environment and the spectrum computation refers to convert signals from time domain to frequency domain.

  4. 4.

    The important partition cuts are represented by the binary vectors as the same as the partition cut \(\mathbb {X}\) in Sect. 5.2.

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Authors and Affiliations

  1. University of Bremen, Bremen, Germany

    Wanli Yu, Yanqiu Huang & Alberto Garcia-Ortiz

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  1. Wanli Yu
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  2. Yanqiu Huang
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  3. Alberto Garcia-Ortiz
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Correspondence to Alberto Garcia-Ortiz .

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Editors and Affiliations

  1. Wireless Sensor and Mobile Ad-hoc Network Applied Cryptography Engineering (WiSeMAN-ACE) Research Lab, Department of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University-Kingsville, Kingsville, TX, USA

    Habib M. Ammari

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Yu, W., Huang, Y., Garcia-Ortiz, A. (2019). Energy-Aware Task Allocation in WSNs. In: Ammari, H. (eds) Mission-Oriented Sensor Networks and Systems: Art and Science. Studies in Systems, Decision and Control, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-91146-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-91146-5_6

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  • Online ISBN: 978-3-319-91146-5

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