Method for Handling Massive IoT Traffic in 5G Networks

Marwat, Safdar Nawaz Khan; Mehmood, Yasir; Khan, Ahmad; Ahmed, Salman; Hafeez, Abdul; Kamal, Tariq; Khan, Aftab

doi:10.3390/s18113966

Cited by 15 publications

(12 citation statements)

References 28 publications

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“…With the widespread adoption of Low Power Wide Area (LPWA) technologies to enable long-range communication for IoT devices, NB-IoT (NarrowBand-IoT) [ 41 ] has been introduced and can coexist with existing mobile networks (e.g., 2G/3G/4G/5G). Existing mobile networks may be overwhelmed in the future by the massive growth in IoT traffic [ 10 ] when hundreds of billions of IoT devices are connected to the Internet via mobile networks. Allocating radio resources for each IoT packet will result in spectral inefficiency and inefficient radio resources utilisation.…”

Section: A Review Of Recent Applications Of Packet Aggregation In Computer and Telecommunication Networkmentioning

confidence: 99%

“…The authors in [ 10 ] proposed introducing a Relay Node (RN) between the IoT devices and the 5G radio access network. It receives small IoT packets, stores them in a buffer and then aggregates them into larger packets that are transmitted to the cellular radio access network through a wireless connection.…”

Section: A Review Of Recent Applications Of Packet Aggregation In Computer and Telecommunication Networkmentioning

confidence: 99%

“…The amount of IoT data carried over mobile networks is expected to increase significantly and poses challenges for service providers [ 9 ]. To ensure efficient utilisation of radio resource for IoT over mobile network deployment, a packet aggregation scheme can be implemented at an intermediate node between the IoT devices and the radio access network [ 10 ]. In this case, multiple IoT packets are aggregated into a larger packet whose size is almost equivalent to the maximum packet size that can be transmitted over the mobile network to efficiently utilise the radio resources.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Kuaban

Atmaca

Kamli

et al. 2021

Sensors

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The transmission of massive amounts of small packets generated by access networks through high-speed Internet core networks to other access networks or cloud computing data centres has introduced several challenges such as poor throughput, underutilisation of network resources, and higher energy consumption. Therefore, it is essential to develop strategies to deal with these challenges. One of them is to aggregate smaller packets into a larger payload packet, and these groups of aggregated packets will share the same header, hence increasing throughput, improved resource utilisation, and reduction in energy consumption. This paper presents a review of packet aggregation applications in access networks (e.g., IoT and 4G/5G mobile networks), optical core networks, and cloud computing data centre networks. Then we propose new analytical models based on diffusion approximation for the evaluation of the performance of packet aggregation mechanisms. We demonstrate the use of measured traffic from real networks to evaluate the performance of packet aggregation mechanisms analytically. The use of diffusion approximation allows us to consider time-dependent queueing models with general interarrival and service time distributions. Therefore these models are more general than others presented till now.

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Section: A Review Of Recent Applications Of Packet Aggregation In Computer and Telecommunication Networkmentioning

confidence: 99%

Section: A Review Of Recent Applications Of Packet Aggregation In Computer and Telecommunication Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Kuaban

Atmaca

Kamli

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…whereΨ i (t) is the data rate in bits that user i can achieve in the current TTI if the whole bandwidth is allocated for scheduling to user i. The determination ofΨ i (t) is realized by finding the system level SINR of user i and using the Transport Block Size (TBS) tables of 3GPP to get the total number of bits that the whole system bandwidth can accomplish [28].…”

Section: Time Domain Maximum Throughputmentioning

confidence: 99%

Medium Access-Based Scheduling Scheme for Cyber Physical Systems in 5G Networks

et al. 2020

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The development of the 5G mobile communication standard attempts to meet the future needs of data users. The impact of Cyber Physical Systems (CPS) is crucial in Internet of Things (IoT) and other emerging technologies. The design of medium access mechanisms for CPS such as radio resource scheduling schemes has a significant effect on network performance. Recent literature shows that limited work is available on uplink scheduling schemes, particularly in the 5G domain. Planning a network that can address the modern needs of users entails efficient CPS scheduling mechanisms such that resources are amicably distributed between users of contrasting priorities. The prime focus of this work is to design and develop an uplink radio resource scheduling framework for CPS-based future networks such as 5G. In the designed framework, scarce radio resources are sought to be distributed efficiently according to the service-based needs of users. The proposed scheduling scheme is a service aware (SA) scheduler designed for CPS in accordance with the 5G network peculiarities, intended to achieve higher throughput and reduced latency. The proposed SA scheduler supports multi-bearer traffic and is capable of providing resources in adverse channel conditions in an efficient manner. The SA scheduling mechanism’s performance is evaluated and compared with renowned scheduling algorithms such as blind equal throughput (BET), maximum throughput (MT), and proportional fair (PF) scheduling schemes. The simulation results obtained in a cellular environment demonstrate that the SA scheduler achieves acceptable cell throughput and end-to-end delay results in all scenarios and out-performs other contemporary scheduling schemes.

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“…For cellular operators, traffic time series are fundamental for evaluating traffic patterns from different types of IoT devices. It is noteworthy that these devices typically employ a cellular network [4], competing for resources with smartphone users [5]; therefore, network dimensioning requires traffic profiles for different cellular user types.…”

Section: Introductionmentioning

confidence: 99%

Computation of Traffic Time Series for Large Populations of IoT Devices

Izal

Morató

Magaña

et al. 2018

Sensors

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The Internet of Things (IoT) contains sets of hundreds of thousands of network-enabled devices communicating with central controlling nodes or information collectors. The correct behaviour of these devices can be monitored by inspecting the traffic that they create. This passive monitoring methodology allows the detection of device failures or security breaches. However, the creation of hundreds of thousands of traffic time series in real time is not achievable without highly optimised algorithms. We herein compare three algorithms for time-series extraction from traffic captured in real time. We demonstrate how a single-core central processing unit (CPU) can extract more than three bidirectional traffic time series for each one of more than 20,000 IoT devices in real time using the algorithm DStries with recursive search. This proposal also enables the fast reconfiguration of the analysis computer when new IoT devices are added to the network.

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Method for Handling Massive IoT Traffic in 5G Networks

Cited by 15 publications

References 28 publications

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Medium Access-Based Scheduling Scheme for Cyber Physical Systems in 5G Networks

Computation of Traffic Time Series for Large Populations of IoT Devices

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