Node failures are known to be among the generic problems in Internet of Things (IoT) networks. These failures can be caused by communication disturbances, battery depletion, or even hardware faults. The larger the IoT network and the larger the task to be executed in the network, the higher is the probability of a node failure in the relevant part of the network. This article studies the node failures and proposes a new task allocation algorithm based on multiobjective optimization to address this issue. This article proposes a specialized archive-selection mechanism to enhance diversity in the search space of the so-called multiobjective task allocation algorithm (MOTA). High diversity in the archive allows a reliable selection of alternative task assignments in the case of node failures in the IoT network. We evaluate the performance of the proposed approach regarding the network lifetime, its latency, and its availability, using a network simulation model and compare the results with the baseline MOTA and the dynamic task allocation scheduler (DTAS). The results show that the proposed approach provides significant performance improvements over the existing algorithms, especially in scenarios with high task-to-node ratios.

中文翻译：

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物联网网络的可用性感知多目标任务分配算法

众所周知，节点故障是物联网 (IoT) 网络中的常见问题之一。这些故障可能是由通信干扰、电池耗尽甚至硬件故障引起的。物联网网络越大，网络中要执行的任务越大，网络相关部分出现节点故障的概率就越高。本文研究了节点故障，提出了一种基于多目标优化的新任务分配算法来解决这个问题。本文提出了一种专门的档案选择机制，以增强所谓的多目标任务分配算法（MOTA）的搜索空间的多样性。档案的高度多样性允许在物联网网络中节点故障的情况下可靠地选择替代任务分配。我们使用网络模拟模型评估所提出的方法在网络生命周期、延迟和可用性方面的性能，并将结果与​​基线 MOTA 和动态任务分配调度程序 (DTAS) 进行比较。结果表明，所提出的方法比现有算法提供了显着的性能改进，特别是在任务与节点比率高的场景中。