2022, Vol.8, No.4

Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89

pISSN : 2466-0078
https://journal.kci.go.kr/kiots

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.

NAND flash memory is used as a medium for various storage devices due to its high data processing speed with low power consumption. However, since the read processing speed of data is about 10 times faster than the write processing speed, various studies are being conducted to improve the speed difference. In particular, flash dedicated buffer management policies have been studied to improve write speed. However, SSD(solid state disks), which has recently been used for various purposes, is more vulnerable to read performance than write performance. In this paper, we find out why read performance is slower than write performance in SSD composed of NAND flash memory and study buffer management policies to improve it. The buffer management policy proposed in this paper proposes a method of improving the speed of a flash-based storage device by analyzing the pattern of read data and applying a policy of pre-reading data to be requested in the future from NAND flash memory. It also proves the effectiveness of the read-ahead policy through simulation.

NB-IoT and LTE Cat.M1 based on LPWA(Low Power Wide Area) are commercialized and serviced by mobile carriers. As the demand for IoT devices is increased, the number of subscribers to these services is also increasing. In the beginning of service, there was no issue that eNB capacity for NB-IoT and LTE Cat.M1. However, as the number of subscribers increases, there is an issue that the eNB capacity for these service is insufficient. Active UE capacity issue may cause overload by continuous increase and temporary increase. In this paper, we propose a solution to solve the problem of LTE RRC(Radio Resource Control) Active UE capacity shortage and base station overload caused by the increase of NB-IoT and LTE Cat.M1 UE in same eNB. The proposed solution can increase a cell capacity without cell division and additional eNB, and can also improve the service quality of these UEs.