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Multiobjective ant lion optimizer based network planning for Internet of Things on TV white space
Author(s) -
Ding Lianghui,
Jin Yiqing,
Tian Feng,
Yang Feng,
Qian Liang,
Zhi Cheng
Publication year - 2021
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.4195
Subject(s) - white spaces , computer science , mathematical optimization , particle swarm optimization , nondeterministic algorithm , the internet , ant colony optimization algorithms , algorithm , mathematics , wireless , telecommunications , cognitive radio , world wide web
Summary In this article, we consider the planning problem of Internet of Things networks working on TV White Space (TVWS), that is, optimizing the number of gateways and their locations to cover the M2M devices using the available TVWS spectrum. We first formulate it into a nondeterministic polynomial optimization problem, and then present the multiobjective ant lion optimizer (MOALO) algorithm with low complexity to solve it. Simulation results show that the proposed MOALO solution can achieve better performance than the classical multiobjective particle swarm optimization (MOPSO) algorithm. Considering the typical case with M2M devices distributed Gaussianly with the variance 80, to achieve 99% coverage, the number of gateways selected by the proposed MOALO algorithm can be 14% less than that obtained from the MOPSO algorithm.