An Enhanced Random Access Algorithm Based on the Clustering-Reuse Preamble Allocation in NB-IoT System

Wu, Fan; Zhang, Baohou; Fan, Wenhao; Tian, Xingkang; Huang, Sijia; Yu, Cuiping; Liu, Yuanan

doi:10.1109/access.2019.2960436

Cited by 14 publications

(10 citation statements)

References 42 publications

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“…We simulate the proposed DCMI algorithm in MATLAB 2018a high-end simulation tool. After simulation, we compare the performance of the DCMI algorithm with the existing algorithms DWSC 28 , R-LEACH 30 , ERA-CRPA 29 , and DOUCA 27 . In order to analyze and compare the performance of the algorithms, we incorporate the parameters such as Energy Consumption, End-to-End Latency, Packet Delivery Ratio, Network Lifetime, Throughput, and Packet Loss Ratio.…”

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

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DCMI: Dynamic Clustering Approach using Multi-Verse Optimizer for Fog-assisted IoT Devices

Arikumar

Natarajan

Satapathy

et al. 2022

Preprint

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The massive growth of sensor devices has assisted the Internet of Things (IoT) technology to reach a greater extent. However, the IoT devices are associated with various limitations, as the attached sensor devices are prone to limited battery lifetime. Though clustering seems to be the outstanding solution, it suffers from various disadvantages including unbalanced energy consumption and high packet loss ratio. In order to overcome these issues, we propose a fog-assisted Multi-Verse Optimization algorithm (MVO) for dynamic clustering of IoT devices (DCMI). The MVO algorithm is a meta-heuristic nature-inspired optimization algorithm possessing powerful operators, which makes the dynamic partitional clustering strategy more efficient. The MVO algorithm in the proposed DCMI approach employs inner and outer fitness function to produce more stable and optimal number of clusters. Moreover, the fog nodes helps the IoT network in transmitting the data with minimal latency. Thus, DCMI approach enhances the network lifetime and minimizes the packet loss ratio. The proposed DCMI approach is simulated and the results are compared with the various existing optimization based clustering approaches. The outcome analysis prove that the DCMI approach outperforms the existing approaches in terms of network lifetime, latency, packet loss ratio, throughput, packet delivery ratio, and energy consumption.

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Section: Experimental Results and Performance Analysismentioning

confidence: 99%

“…A clustering Reuse Preample Allocation (ERA-CRPA) technique proposed in 29 aims at reducing the probability of random access preampling. Clustering the massive IoT devices through preampling technique reduces energy consumption in parallel.…”

Section: Clustering In Fog-assisted Iot Devicesmentioning

confidence: 99%

DCMI: Dynamic Clustering Approach using Multi-Verse Optimizer for Fog-assisted IoT Devices

Arikumar

Natarajan

Satapathy

et al. 2022

Preprint

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“…Wu et al focused on the IoT's massive access and proposed a cluster-based reusable preamble allocation to improve random access algorithms for the NB-IoT environment. The simulation results show that the algorithm performs well and has a low probability of preamble collision by distributing the stations into clusters and allotting appropriate preamble sets [55]. In classical clustering LEACH algorithm, cluster heads are selected randomly to balance the energy usage of wireless sensor network nodes.…”

Section: The Average Number Of Remaining Brokers In Different Roundmentioning

confidence: 99%

An Energy-Aware, Highly Available, and Fault-Tolerant Method for Reliable IoT Systems

Bukhsh

Abdullah²,

Rahman

et al. 2021

IEEE Access

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The Internet of Things (IoT) is one of the highly influencing and promising technologies of today's world, consisting of sensor devices. The internet smoothly changes from an internet of people towards an Internet of Things (IoT), which allows different things and objects to connect wirelessly. Things and objects are grouped into IoT subgroups in the IoT system, which are called clusters, and each cluster is controlled by a central authority and checked by the broker's help. A concept of keeping backup data is used to increase the lifespan of IoT subgroups by avoiding re-clustering overhead for smooth transmission of packets and increasing availability concerns. A novel approach is used for the selection of cluster head/broker and backup nodes simultaneously. Cluster head and Backup Storage Point node (BSP) remain the same unless and until the residual power of the broker/cluster head is greater than the threshold energy. A novel Energy Efficient Message scheduling algorithm EAAFTMS (An Energy-Aware Available and Fault-Tolerant System with Message Scheduling in IoT) is incorporated at broker node for smooth transmission of messages. This proposed approach is not only solving availability issues over the wireless network but also proved to be energy efficient by prolonging the battery-powered network lifetime. Simulation results prove EAAFTMS, many folds better than benchmark protocols. This system ensures fault-tolerant and available schemes for IoT systems while stabilizing the energy of the overall system. The results shown prove the effectiveness and efficiency of the proposed system.

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“…The high delay because most NB-IoT frameworks do not incorporate delay-tolerant methodologies [19][20][21][22]. Additionally, the transmission time is affected by the large data size generated by the large number of terminals or by the healthcare applications, e.g., high-definition images [9,20], particularly because the NB-IoT depends on the UDP protocol for sending small-sized data in real time [20].…”

Section: Introductionmentioning

confidence: 99%

Edge–Fog–Cloud Computing Hierarchy for Improving Performance and Security of NB-IoT-Based Health Monitoring Systems

Daraghmi

Daraghma

et al. 2022

Sensors

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This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.

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An Enhanced Random Access Algorithm Based on the Clustering-Reuse Preamble Allocation in NB-IoT System

Cited by 14 publications

References 42 publications

DCMI: Dynamic Clustering Approach using Multi-Verse Optimizer for Fog-assisted IoT Devices

DCMI: Dynamic Clustering Approach using Multi-Verse Optimizer for Fog-assisted IoT Devices

An Energy-Aware, Highly Available, and Fault-Tolerant Method for Reliable IoT Systems

Edge–Fog–Cloud Computing Hierarchy for Improving Performance and Security of NB-IoT-Based Health Monitoring Systems

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