The Influence of k-Constant to Delay Performance of RI-MAC Protocol for Wireless Sensor Networks

In this paper, we introduce a new method to improve the delay performance of RI-MAC protocol, that was originated from the basic concept of IEEE 802.11e EDCA. The delay issue is critical in wireless sensor network (WSN) because it brings up other issues such as energy wastage and limiting its applications. Therefore, the parameter that determine the delay performance, the BeaconTimeout of the RI-MAC protocol will need to be further optimized in order to achieve better delay performance particularly in applications that involved long transmission range. Hence, we introduce the k constant to go with the conventional RI-MAC protocol in order to achieve that. We first evaluate the delay performance of the conventional RI-MAC in long transmission range application focuses on range from 250 to 850 m. The performance evaluation has been carried out through computer simulation of WSN where the relationship and the influence of the proposed k constant in relation to different transmission ranges are also determined. The reason to focus on extended transmission range is to enable wider physical coverage of WSN to use even lesser sensor nodes for better implementation cost effectiveness. The significance of this paper is to highlight that by applying an optimized k constant, the delay performance of the overall network can be maintained at its optimum level. © 2017 Springer Science+Business Media New Yor

Deep reinforcement learning-based resource allocation strategy for energy harvesting-powered cognitive machine-to-machine networks

Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the escalating spectrum deficient to guarantee the Quality of Service (QoS) meanwhile decreasing the energy consumption to achieve Green Communication (GC) became an important research topic. In this paper, we investigate the resource allocation problem for EH-CMNs underlaying cellular uplinks. We aim to maximize the energy efficiency of EH-CMNs with consideration of the QoS of Human-to-Human (H2H) networks and the available energy in EH-devices. In view of the characteristic of EH-CMNs, we formulate the problem to be a decentralized Discrete-time and Finite-state Markov Decision Process (DFMDP), in which each device acts as agent and effectively learns from the environment to make allocation decision without the complete and global network information. Owing to the complexity of the problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm to solve the problem. Numerical results validate that the proposed scheme outperforms other schemes in terms of average energy efficiency with an acceptable convergence speed