Abstract

The Internet of Things (IoT) comprises smart objects capable of sensing, processing, and transmitting application-specific data. These objects collect and transmit a huge amount of correlated and redundant data due to overlapped sensing regions, causing unnecessary exploitation of spectral bands and load balancing issues in the network. As a result, time-critical and delay-sensitive data experience a higher delay, lower throughput, and quality of service degradation. To circumvent these issues, in this paper, we propose a model that is energy efficient and is capable of maximizing the spectrum utilization with optimal Device-to-Gateway configuration. Initially, the network gateways perform spectrum sensing for available channels using an energy detection technique and forward them to a cognitive engine (CE). The CE assigns the best available channels in the licensed band to the network devices for communication. Each channel is divided into equal-length time slots for the timely delivery of critical data. In addition, the CE calculates the load on each gateway and uses particle swarm optimization algorithm for optimal load distribution among the network gateways. Our experimental results show that the proposed model is efficient for the resource-constrained IoT devices in terms of packet drop ratio, delay, and throughput of the network. Moreover, the proposed scheme also achieves optimal Device-to-Gateway configuration with efficient spectrum utilization in the licensed band.

中文翻译：

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认知物联网中基于人工智能的负载优化

摘要

物联网（IoT）包括能够感应，处理和传输特定于应用程序的数据的智能对象。由于重叠的传感区域，这些对象收集并传输了大量相关和冗余数据，从而导致频谱带的不必要利用和网络中的负载平衡问题。结果，对时间要求严格且对延迟敏感的数据会遇到较高的延迟，较低的吞吐量和服务质量下降。为了避免这些问题，在本文中，我们提出了一个能源效率高的模型，该模型能够通过最佳的设备到网关配置来最大化频谱利用率。最初，网络网关使用能量检测技术对可用信道执行频谱感测，并将其转发给认知引擎（CE）。CE将许可频段中的最佳可用信道分配给网络设备进行通信。每个通道被划分为等长的时隙，以便及时传送关键数据。另外，CE计算每个网关上的负载，并使用粒子群优化算法在网络网关之间实现最佳负载分配。我们的实验结果表明，从丢包率，延迟和网络吞吐量方面来看，该模型对于资源受限的IoT设备是有效的。此外，提出的方案还通过在许可频段内有效地利用频谱来实现最佳的设备到网关配置。