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Evolutionary multi-objective set cover problem for task allocation in the Internet of Things
Applied Soft Computing ( IF 7.2 ) Pub Date : 2021-01-12 , DOI: 10.1016/j.asoc.2021.107097
Hussein M. Burhan , Bara’a A. Attea , Amenah D. Abbood , Mustafa N. Abbas , Mayyadah Al-Ani

Efficient distribution of tasks in an Internet of Things (IoT) network ensures the fulfillment for all objects to dynamically cooperate with their limited energy, processing and memory capabilities. The main contribution of this paper is threefold. Firstly, we address the task allocation in the IoT as an optimization problem with a new formulation derived from the context of set cover problem. To the best of our knowledge, no such study has been considered in the literature. Secondly, we extend the set cover problem to further express the conflict that meets with both operational period and stability. Thirdly, an evolutionary single objective and multi-objective algorithms are developed to tackle the formulated problem. Two heuristic operators are also introduced and injected within the framework of the evolutionary algorithms where the need arises to harness their strength in terms of both operational period and network stability. Performance evaluation is reported while different problem dimensions are experimented with in the simulations. The results show that the proposed multi-objective evolutionary algorithm is quite appropriate to converge to more accurate solutions than the counterpart single objective evolutionary algorithm. Further, the results give plausible evidence supporting the importance of the proposed heuristic operators to mitigate against the contradictory nature of the network lifetime in terms of operational period and stability.



中文翻译:

物联网中任务分配的进化多目标集覆盖问题

物联网(IoT)网络中任务的高效分配可确保满足所有对象的动态需求,以与其有限的能量,处理和存储功能进行动态协作。本文的主要贡献是三方面的。首先,我们通过从集合覆盖问题的上下文中得出的新公式,将物联网中的任务分配作为一个优化问题来解决。据我们所知,文献中没有考虑过此类研究。其次,我们扩展了集合覆盖问题,以进一步表达既满足运营期又满足稳定性的冲突。第三,发展了进化的单目标和多目标算法来解决所提出的问题。还引入了两个启发式运算符,并将其注入到进化算法的框架中,在这种情况下,有必要在运营周期和网络稳定性方面利用其优势。报告了性能评估,同时在模拟中尝试了不同的问题维度。结果表明,与对应的单目标进化算法相比,所提出的多目标进化算法非常适合收敛到更精确的解。此外,结果提供了合理的证据,证明了拟议的启发式运营商在运营周期和稳定性方面减轻网络寿命的矛盾性质的重要性。报告了性能评估,同时在模拟中尝试了不同的问题维度。结果表明,与对应的单目标进化算法相比,所提出的多目标进化算法非常适合收敛到更精确的解。此外,结果提供了合理的证据,证明了拟议的启发式运营商在运营周期和稳定性方面减轻网络寿命的矛盾性质的重要性。报告了性能评估,同时在模拟中尝试了不同的问题维度。结果表明,与对应的单目标进化算法相比,所提出的多目标进化算法非常适合收敛到更精确的解。此外,结果提供了合理的证据,证明了拟议的启发式运营商在运营周期和稳定性方面减轻网络寿命的矛盾性质的重要性。

更新日期:2021-01-22
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