
Priority‐aware hybrid scheduling for fast, energy‐efficient max function computation in single‐hop networks
Author(s) -
Huang Jiajia,
Soong BoonHee
Publication year - 2016
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2016.0311
Subject(s) - time division multiple access , computer science , scheduling (production processes) , computation , energy consumption , computer network , wireless sensor network , scalability , real time computing , algorithm , distributed computing , mathematical optimization , mathematics , ecology , database , biology
In recent years, to efficiently deliver a specific function value is of great importance in sensing and monitoring wireless sensor networks. In this study, a priority‐aware hybrid scheduling scheme is proposed to achieve fast decentralised energy‐efficient max function computation in a single‐hop network. In the authors’ proposed scheme, priority‐aware time division multiple access (TDMA) scheme is used to first schedule the transmission. In contrast to traditional TDMA, where all the nodes in a network take turns to transmit, in the authors’ proposed scheme, the node with the maximum value is given the priority to transmit. However, the problem of collision exists and this still limits the performance of TDMA‐based scheme. Therefore, in order to improve the performance with the influence of collision, code division multiple access (CDMA) is introduced to recover information whenever collision occurs. To solve this, an optimisation problem is formulated to minimise average computation time subjected to the constraints on failure probability. The synergy of CDMA and TDMA contributes to reduction of computation failure probability as well as average computation delay. Moreover, when compared with the traditional round‐robin TDMA transmission, the scheduling rule in the proposed scheme helps to reduce number of transmissions for a single computation. Therefore, a reduction in energy consumption occurs as well. Numerical results show that our algorithm reduces average computation time and energy consumption for one‐shot max function computation, and with the advantage of better scalability.