Robust and Energy-Efficient Data Gathering in Wireless Sensor Network
Author(s) -
Juan Feng,
Baowang Lian,
Hongwei Zhao
Publication year - 2014
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2014/960242
Subject(s) - computer science , wireless sensor network , energy consumption , computer network , robustness (evolution) , efficient energy use , sensor node , data transmission , grid , distributed computing , data aggregator , real time computing , sink (geography) , key distribution in wireless sensor networks , wireless , wireless network , telecommunications , ecology , biochemistry , chemistry , geometry , mathematics , cartography , gene , geography , electrical engineering , biology , engineering
Robustness and energy efficiency are critical for sensor information system, in which an abundance of wireless sensor nodes collects useful data from the deployed field. The chain-based protocols (like PEGASIS (Lindsey and Raghavendra, 2002)) are elegant solutions where sensor node has high energy efficiency. Unfortunately, if one node in the chain is failed due to some reasons such as energy exhaust, then the information cannot be forwarded to the sink. To improve system robustness and balance the energy consumption, this paper proposes a robust and energy-efficient data gathering (REEDG) approach, which is an improvement over the chain-based and grid-based network structures, in sensor information collecting system. In REEDG, data gathering is executed by a data transmitting chain which is composed by a series of virtual grids. Each grid communicates only with its neighbor grid and takes turns transmitting the information to the base station. Furthermore, an adaptive scheduling scheme is proposed to trade off energy consumption on each node and data forwarding delay. Experimental results show that, when compared with state-of-the-art approaches, REEDG achieves network lifetime extension of at least 13% as measured in terms of 20% dead nodes and improves the data transmission ratio at lowest 24% as 20% nodes fail.
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