Multi-Armed-Bandit Based Channel Selection Algorithm for Massive Heterogeneous Internet of Things Networks
Multi-Armed-Bandit Based Channel Selection Algorithm for Massive Heterogeneous Internet of Things Networks
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DOI：
10.3390/app12157424
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发表时间：
2022-08-01
影响因子：
2.7
通讯作者：
Hasegawa, Mikio
中科院分区：
文献类型：
--
作者：
Hasegawa, So;Kitagawa, Ryoma;Hasegawa, Mikio
近年来，物联网设备的数量大幅增加。大量的物联网设备产生巨大的流量，导致网络拥塞和丢包。为了解决海量物联网系统中的网络拥塞问题，需要一种高效的信道分配方法。虽然已经研究了一些通道分配方法，但据我们所知，考虑到同一物联网系统中同时存在不同种类的物联网设备的海量异构物联网系统，还没有针对物联网设备实施阶段的研究。本文研究了一种基于多臂强盗的信道分配方法，该方法可以在资源受限、计算处理能力较低的物联网设备上实现，同时避免大规模物联网系统中的拥塞。本文首先对大型物联网系统中一些知名的基于多臂强盗的信道分配方法进行了评价。仿真结果表明，一种改进的基于多臂强盗的信道选择方法——修正拔河算法在大多数情况下都能获得最高的帧成功率。具体来说，当通道数和物联网设备数分别为60和10000时，帧成功率可达95%，而12%的通道受到其他类型物联网设备的流量负载。此外，在帧成功率方面的性能比平等信道分配可提高20%。在50台支持IEEE 802.15.4g/4e通信的Wi-SUN物联网设备上实现了基于多臂强盗的信道分配方法，并在与LoRa设备共存的实际木屋中以帧成功率进行了性能评估。实验结果表明，与其他已知的基于帧成功率的信道选择方法相比，改进的多臂强盗方法可以获得最高的帧成功率。
In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.