Abstract
The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.
Highlights
The Internet of Things (IoT) is a promising communication technology that can provide connectivity to physical objects anywhere and anytime
The information of the neighboring node is obtained from the ith sensor node, and Pcoll (Ni ) is the probability of collision of Ni node to be used during the construction network layer
The computational complexity of the proposed intelligent collision probability learning algorithm (iCPLA) mechanism is O(n)(a), where n is the number of neighbors that are candidates for being selected as parents, and a is the number of actions available at every state
Summary
The Internet of Things (IoT) is a promising communication technology that can provide connectivity to physical objects anywhere and anytime. Instead of utilizing the ETX-based mechanism, it is more efficient to exploit the probability of collision information at the MAC layer to learn the network dynamics and improve the overall network performance. The self-sustainability of low-power and lossy IoT nodes, according to network condition, is one of the open issues of the IoT network To solve this issue, we present a Q-learning (QL)-based intelligent collision probability learning algorithm (iCPLA). In iCPLA, each node learns the channel collision probability and uses this information at the network layer. In this way, it tunes the RPL-based network layer using MAC layer collision information through interacting with the environment.
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