دکتر محمدرضا خیام باشی

With the growing demand for Internet of Things (IoT) applications, supporting massive access to the media is a necessary requirement in 5G cellular networks. Accommodating the stringent requirements of Ultra-Reliable Low Latency Communications (URLLC) is a challenge in massive access to the medium. The random-access procedure is of the most challenging issues in massive IoT (mIoT) networks with URLL requirements as a high number of channel access requests result in high channel access latency or low reliability. In previous works, some solutions have been proposed to solve this challenge including grant-free access, priority-based access, and grouping nodes to restrict random access requests to groups’ leaders. Particularly, previous idea that is based on grouping, clusters the devices with similar reaction against an event to a group, which is not always applicable for various IoT applications. This research proposes a novel device grouping to improve the random-access procedure of mIoT devices with URLLC requirements. In the proposed method, device grouping is accomplished based on the analysis of devices’ traffic. A similarity index is used to obtain the similarity of time series made from historical traffic patterns of devices and then, an innovative algorithm is proposed to group the devices based on this index. Grouping devices based on similar traffic patterns, provides access to the media with less complexity and more efficiency for a large number of devices. Performance of the proposed approach is evaluated using simulations and real traffic dataset. The evaluation results show higher suitability of proposed method compared to the baseline mechanism of LTE and the previous method in terms of access failures (which affects delay and reliability) and energy consumption. For a usual setting, the channel access failure decreases by about 94 % compared to the previous method and by 0.88 % compared to LTE. The energy consumption also improves by about 1.8 % compared to LTE and by 1.2 % compared to previous method. Moreover, the results show that the proposed method is appropriate for IoT applications with regular traffic patterns. © 2024 Elsevier B.V.

IoT data management for caching performance improvement in NDN

( Article )

Khayyambashi, M.R., Bohlooli, A.

Publication Date: 2024

Cluster Computing (13867857)27(4)pp. 4537-4550

Connected devices in IoT continuously generate monitoring and measurement data to be delivered to application servers or end-users. Transmitting IoT data through networks would lead to congestion and long delays. NDN is an emerging network paradigm based on name-identified data known to be an appropriate architecture for supporting IoT networks. In-network caching is one of the main advantages of NDN, a major issue discussed in many studies. One of the significant challenges for some IoT data is the transiency nature, making the data caching mechanism different. IoT data such as ambient monitoring in urban areas and tracking current traffic conditions are often transient, which means these data have a limited lifetime and then expire. In the proposed approach, data placement is decided upon based on the data lifetime and node position. Data lifetime is an essential property that must be involved in caching methods; consequently, the data are classified based on the data lifetime, and specific nodes are selected for caching according to defined classes and nodes’ positions in topology. Based on the proposed scheme, the nodes with the highest outgoing interface count or the edge nodes are selected for data caching. By considering both data lifetime and node location, we determine the suitable caching location for each data class separately. In addition, we remove data that has a short lifetime and is not suitable for caching from the caching mechanism of NDN nodes. By considering both the cache and data placements for transient data, a more comprehensive view is grasped in improving the caching performance. This issue, which has not been addressed in the available studies run on IoT data caching, can lead to the appropriate use of available storage and reduce redundancy. Eventually, the simulation results performed by the ndnSIM simulator show the proposed method could improve the cache mechanism efficiency in terms of both delay and hit ratio. Comparison results of the proposed method with CE2 and Btw indicate that this method can provide a reduction in average delay and an increase in cache hit ratio. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

Multiobjective Placement of Edge Servers in MEC Environment Using a Hybrid Algorithm Based on NSGA-II and MOPSO

( Article )

Khayyambashi, M.R.

Publication Date: 2024

IEEE Internet of Things Journal (23274662)11(18)pp. 29819-29837

نمایش همه (86)