Home > Publications
Home University of Twente
Prospective Students
Intranet (internal)

EEMCS EPrints Service

25676 OPS: Opportunistic pipeline scheduling in long-strip wireless sensor networks with unreliable links
Home Policy Brochure Browse Search User Area Contact Help

Guo, Peng and Meratnia, N. and Havinga, P.J.M. and Jiang, Hongbo and Zhang, Kui (2015) OPS: Opportunistic pipeline scheduling in long-strip wireless sensor networks with unreliable links. Wireless Networks, 21 (5). pp. 1669-1682. ISSN 1022-0038 *** ISI Impact 1,006 ***

Full text available as:

- Univ. of Twente only
1198 Kb

Official URL:

Exported to Metis


Being deployed in narrow but long area, strip wireless sensor networks (SWSNs) have drawn much attention in applications such as coal mines, pipeline and structure monitoring. One of typical characteristics of SWSNs is the large hop counts, which leads to long end-to-end delivery delay in low-duty-cycle SWSNs. To reduce the delay, pipeline scheduling is a promising technique, which assigns sensor nodes sequential active time slots along the data forwarding path. However, pipeline scheduling is prone to failure when communication links are unreliable. In this paper, we propose an opportunistic pipeline scheduling algorithm (OPS) for SWSNs, based on the observation that sensor nodes in SWSNs can overhear data transmissions passing by them. OPS exploits nodes outside the data forwarding path to opportunistically provide links when transmission failure happens, and hence maintains the pipeline forwarding instead of retransmission in the next duty cycle. Theoretical calculation shows that the expectation delay of OPS is always smaller than that of existing methods when the link quality is <100 %. Both extensive simulations and experiments are conducted. The results verify that the average end-to-end delivery delay of OPS is usually <60 % of that of existing methods, while the energy cost is almost the same.

Item Type:Article
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-General
Research Project:GENESIS: Green sEnsor NEtworks for Structural monItoring
ID Code:25676
Deposited On:10 February 2015
ISI Impact Factor:1,006
More Information:statisticsmetis

Export this item as:

To request a copy of the PDF please email us request copy

To correct this item please ask your editor

Repository Staff Only: edit this item