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Book Chapter: Distributed Storage and Parallel Processing in Large-Scale Wireless Sensor Networks

TitleDistributed Storage and Parallel Processing in Large-Scale Wireless Sensor Networks
Authors
Issue Date2011
PublisherIOS Press
Citation
Distributed Storage and Parallel Processing in Large-Scale Wireless Sensor Networks. In Foster, I ... [et al] (Eds.), High Performance Computing: From Grids and Clouds to Exascale, p. 288-305. Amsterdam: IOS Press, 2011 How to Cite?
AbstractA Large-scale Wireless Sensor Network (LWSN). such as an environment monitoring system deployed in a city, could yield data on the order of petabytes each year. Storage and computation of such vast quantities of data pose difficult challenges to the LWSN, particularly because sensors are highly constrained by their scarce resources. Distributed storage and parallel processing are solutions that deal with the massive amount of data by utilizing the collective computational power of the large number of sensors, all while keeping inter-node communication minimized to save energy. In this chapter, we conduct a survey on the state-of-the-art of distributed storage and parallel processing in LWSNs. We will focus on the LWSN scenario in which vast amounts of data are collected prior to intensive computation on that data. We argue that current research results in this direction fall into three categories: 1) hierarchical system architecture to support parallel and distributed computation; 2) distributed data aggregation and storage strategies; 3) parallel processing, scheduling and programming methods. We highlight some important results for each of these categories and discuss existing problems and future directions.
Persistent Identifierhttp://hdl.handle.net/10722/230363
ISBN
Series/Report no.Advances in Parallel Computing; v. 20

 

DC FieldValueLanguage
dc.contributor.authorWang, AYX-
dc.contributor.authorWang, YC-
dc.date.accessioned2016-08-23T14:16:37Z-
dc.date.available2016-08-23T14:16:37Z-
dc.date.issued2011-
dc.identifier.citationDistributed Storage and Parallel Processing in Large-Scale Wireless Sensor Networks. In Foster, I ... [et al] (Eds.), High Performance Computing: From Grids and Clouds to Exascale, p. 288-305. Amsterdam: IOS Press, 2011-
dc.identifier.isbn978-1-60750-802-1-
dc.identifier.urihttp://hdl.handle.net/10722/230363-
dc.description.abstractA Large-scale Wireless Sensor Network (LWSN). such as an environment monitoring system deployed in a city, could yield data on the order of petabytes each year. Storage and computation of such vast quantities of data pose difficult challenges to the LWSN, particularly because sensors are highly constrained by their scarce resources. Distributed storage and parallel processing are solutions that deal with the massive amount of data by utilizing the collective computational power of the large number of sensors, all while keeping inter-node communication minimized to save energy. In this chapter, we conduct a survey on the state-of-the-art of distributed storage and parallel processing in LWSNs. We will focus on the LWSN scenario in which vast amounts of data are collected prior to intensive computation on that data. We argue that current research results in this direction fall into three categories: 1) hierarchical system architecture to support parallel and distributed computation; 2) distributed data aggregation and storage strategies; 3) parallel processing, scheduling and programming methods. We highlight some important results for each of these categories and discuss existing problems and future directions.-
dc.languageeng-
dc.publisherIOS Press-
dc.relation.ispartofHigh Performance Computing: From Grids and Clouds to Exascale-
dc.relation.ispartofseriesAdvances in Parallel Computing; v. 20-
dc.titleDistributed Storage and Parallel Processing in Large-Scale Wireless Sensor Networks-
dc.typeBook_Chapter-
dc.identifier.emailWang, AYX: amywang at hku.hk-
dc.identifier.doi10.3233/978-1-60750-803-8-288-
dc.identifier.hkuros260184-
dc.identifier.spage288-
dc.identifier.epage305-
dc.publisher.placeAmsterdam-

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