Modeling and Analysis of Data Aggregation From Convergecast in Mobile Sensor Networks for Industrial IoT

Qin, Zhijing; Wu, Di; Xiao, Zhu; Fu, Bin; Qin, Zhijin

doi:10.1109/tii.2018.2846687

Cited by 44 publications

(14 citation statements)

References 24 publications

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“…Smart Phone, Smart Watch, Smart Mote, Tablet, Laptop, Drone, AR/VR Glass, Activity Tracker, Versatile Camera, Robot, Vehicle) that are configured with multiple radio access capabilities (e.g. WiFi, 3G, 4G, Bluetooth, Zigbee, WiMax, DSRC, TV White Space, mmWave) [16], [3], [17].…”

Section: Motivationsmentioning

confidence: 99%

“…R ECENT developments in wireless communications and embedded systems have resulted in consumer devices becoming highly ubiquitous creating a strong interest in the Internet of Things (IoT) [1], [2] as part of smart city solutions. Real world urban IoT applications are expected to be heterogeneous, due to various access networks and connectivity capabilities [3], [4], resulting in geographically wide-scale multinetworks [5] where there is a coexistence of multiple wireless communication solutions (e.g. WiFi, Bluetooth, Cellular).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Distributed SDN: Mobility Management and Flow Scheduling in Software Defined Urban IoT

Nie

Asmare

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g. WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g. 67.21% throughput improvement, 72.99% reduced delay, and 69.59% jitter improvements, compared with alternative SDN systems.

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Section: Motivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Distributed SDN: Mobility Management and Flow Scheduling in Software Defined Urban IoT

Nie

Asmare

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

Self Cite

View full text Add to dashboard Cite

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Section: Data Aggregation Strategies In Iotmentioning

confidence: 99%

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Homaei¹,

Salwana²,

Shamshirband³

2019

Preprint

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As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested based approaches in terms of energy consumption, network control overhead, and packet loss rate.

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“…Since each network node not only has limited capacity to generate, process, and save data; but it is also responsible for data exchange of its neighbor nodes; the necessity of efficient utilization of network resources is axiomatic. Toward this goal, a number of data aggregation methods are proposed, in order to remove the redundancy of unnecessary and digressive data, and reduce communication costs as well [18,19]. The process and architecture of data aggregation in LLN networks and IoT is illustrated in Figure 2 Base Station / Sink Authors in [20] proposed a three-layer data aggregation infrastructure, called dynamic data aggregation scheme (PDDA) based on priorities for sensor networks because the sensors collect a large amount of redundant data.…”

Section: Data Aggregation Strategies In Iotmentioning

confidence: 99%

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Homaei¹,

Rafiee²

2019

Preprint

View full text Add to dashboard Cite

As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced avail- ability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested base approaches in terms of energy consumption, network control overhead, and packet loss rate.

show abstract

Modeling and Analysis of Data Aggregation From Convergecast in Mobile Sensor Networks for Industrial IoT

Cited by 44 publications

References 24 publications

Towards Distributed SDN: Mobility Management and Flow Scheduling in Software Defined Urban IoT

Towards Distributed SDN: Mobility Management and Flow Scheduling in Software Defined Urban IoT

An Enhanced Distributed Data Aggregation Method in the Internet of Things

An Enhanced Distributed Data Aggregation Method in the Internet of Things

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