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Improving Energy Efficiency in QoS-Constrained Wireless Sensor Networks
Author(s) -
Mohamed Abdelaal,
Oliver Theel,
Christian Kuka,
Peilin Zhang,
Yang Gao,
Vasilisa Bashlovkina,
Daniela Nicklas,
Martin Fränzle
Publication year - 2016
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 - 10.1155/2016/1576038
Subject(s) - computer science , quality of service , energy consumption , unavailability , throughput , wireless sensor network , energy (signal processing) , correctness , exploit , efficient energy use , computer network , distributed computing , mobile qos , real time computing , wireless , service (business) , service provider , reliability engineering , telecommunications , algorithm , economy , economics , engineering , ecology , statistics , mathematics , computer security , electrical engineering , biology
Energy saving is often achieved via “squeezing” other application-sensitive Quality of Service (QoS) parameters such as delay and throughput. Accordingly, energy-saving methods must consider those QoS parameters. In this paper, we survey the most recent work on energy efficiency in WSNs and we discuss the impacts of these methods on the QoS provided. Moreover, we propose a novel divide-and-conquer procedure to deal with the trade-off between energy consumption and other QoS parameters. The idea is to tackle a certain source of energy consumption to minimize the drawn energy. Subsequently, this energy-saving method is refined to consider other service qualities. To support the correctness of our claim, three energy-saving methods, taking the QoS issues into consideration, are given as examples. The first method exploits a so-called Fuzzy transform for shrinking the wireless traffic with highly precise lossy data compression. In the second method, the sensing module is targeted by employing reliable virtual sensors. Such sensors compensate the unavailability of main energy-hungry sensors during sleep periods. The third method exploits a self-adaptive mechanism to improve the QoS parameters via deliberately reducing the lifetime below the maximum time and exploiting design-time knowledge.

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