Latency Minimization in Wireless IoT Using Prioritized Channel Access and Data Aggregation

Bhandari, Sabin; Sharma, Shree Krishna; Wang, Xianbin

doi:10.1109/glocom.2017.8255038

Cited by 33 publications

(11 citation statements)

References 19 publications

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“…IoT networks generate a huge amount of data [ 113 ]. Critical IoT applications, such as e-health applications [ 114 ] require retrieving data with low latency [ 115 , 116 ]. ECAs-IoT face issues in placing IoT data in the appropriate edge nodes.…”

Section: Classifications Of Ecas-iotmentioning

confidence: 99%

Edge-Computing Architectures for Internet of Things Applications: A Survey

Hamdan

Ayyash

Almajali

2020

Sensors

139

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The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

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Section: Classifications Of Ecas-iotmentioning

confidence: 99%

Edge-Computing Architectures for Internet of Things Applications: A Survey

Hamdan

Ayyash

Almajali

2020

Sensors

139

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“…If they share the same buffer, then the QoS experienced by packets that belong to real-time applications may be degraded as the packet aggregation mechanism may introduce additional delays. The authors in [ 40 ] proposed priority-based channel access and data aggregation scheme to reduce packet delays in clustered Industrial IoT networks. When the IoT packets arrive at the access point or fog node, they are classified into high priority (that is, packets that belong to real-time applications) and low priority packets (those that belong to non-real-time applications).…”

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

confidence: 99%

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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“…The priority-based channel access and aggregation of the data scheme in [Bhandari, et al( 2017)] facilitate the cluster head to reduce the channel access latency. The preemptive queuing model in [Bhandari, et al( 2017)] supports an efficient data aggregation by separating the high and low priority queue before forwarding the aggregated data packets to the gateway. The priority-based two aggregation schemes are suggested in [Ghate, and Vijayakumar, ( 2018)].…”

Section: Cluster Head-based Data Aggregation Schemesmentioning

confidence: 99%

Untitled

2021

INDJCSE

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Recently, tremendous growth and interest in the deployment of tiny sensors in the Internet of Things (IoT) for smart applications improves human lives. With the increasing need for energy-efficient mechanisms in IoT communication, the data aggregation technique for reducing data transmissions is considered a significant research problem. The basic idea in most of the aggregation mechanisms is to build the clustering or aggregation tree in an application layer over IoT, resulting in high complexity. To solve such a problem, the proposed MLDA designs an energy-aware aggregation layer that focuses on utilizing the network layer factors in data aggregation by providing transparency of accessing the topology structure from the network layer. Moreover, the proposed work also focuses on the design of the load-balanced topology structure in the network layer for efficient routing and also takes support from such network structure for energy-efficient data aggregation. The proposed Multi-Layer based Data Aggregation approach (MLDA) avoids the hotspot problem and inefficient data aggregation. The MLDA achieves such goals by improving the network layer protocol, RPL activities and designing the aggregation layer to eliminate redundant transmissions. By using an energy-efficient network structure, the impact of redundant data transmissions on network resources and data aggregation efficiency are eliminated. To support SUM, AVG, MAX, and MIN aggregation functions without redundant data transmissions, the Double Hash Bloom Filter (DHBF), observation scheme, and merge sort are used in the developed aggregation layer. Thus, the proposed MLDA improves the data aggregation efficiency in terms of both energy and accuracy.

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Latency Minimization in Wireless IoT Using Prioritized Channel Access and Data Aggregation

Cited by 33 publications

References 19 publications

Edge-Computing Architectures for Internet of Things Applications: A Survey

Edge-Computing Architectures for Internet of Things Applications: A Survey

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

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