Improving IoT Connection Resiliency in Wireless Networks
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Abstract
Internet of Things (IoT) wireless network is expected to connect billions of IoT devices in the next period of modern technologies. This is because IoT applications have more and more applicability in various fields, including services, health, agriculture, and so on. However, along with the significant benefits, IoT requires low-latency and high resilience of wireless communications in order to maintain a high quality of service. IoT networks should constantly maintain a high level of resilience in wireless communication in order to sustain the increasing number of new IoT devices connected to the networks. Since IoT networks consist of thousands of devices sharing the frequency spectrum in a given local area, IoT networks also address the problem of wireless interference that results in link degradation and low network connectivity. Therefore, the performance of In this thesis, we propose two technical solutions to improve the resilience of communications in IoT networks by suppressing wireless interference. We develop our system models that represent the interference with IoT network access and elements of graph theory for improving the resilience of connections. Our system models include node distribution following Point Poisson Process, wireless network as a graph, modeling interference in IoT network access, node criticality, and elasticity theory. Then, we utilize these models in our proposed solutions for improving the resilience of wireless communications. In order to avoid channel interference, we implement an algorithm based on the concept of graph theory to efficiently allocate channels used by IoT devices in the network. We observe that the number of colors labeled for each node can be minimized by eliminating several less important nodes, but it is a trade-off between color reduction and network connectivity. Also, we propose an additional solution using deep deterministic policy gradient (DDPG) based on graph coloring to determine the minimum number of colors used. Our simulation results indicate that the gain of eliminating the least important nodes is color reduction, but depending on each particular wireless network, the solution can achieve a high probability. Another proposed solution is to determine the chromatic number by using deep reinforcement learning-based channel allocation. Although several nodes in the network have the same colors, which leads to invalid/disconnected links, the number of colors using the DDPG algorithm is always smaller than the greedy coloring algorithm.