A Survey on Congestion Control for RPL-Based Wireless Sensor Networks

Lim, Chansook

doi:10.3390/s19112567

Cited by 44 publications

(22 citation statements)

References 57 publications

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“…Data aggregation (DA) is counterproductive to this distributed raw data from various sources within the network. The accumulation in the network has a significant impact on the number of packets transmitted or their length, thereby reducing energy consumption and prolonging network life [15]. The three main scheduling policies for DA are summarized in [16]: (i) Quadruple simple aggregation requires each router to collect all data items periodically and collect them after a period of time Preset.…”

Section: System Model and Related Workmentioning

confidence: 99%

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Homaei¹,

Salwana

Shamshirband

2019

Sensors

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“Internet of Things (IoT)” has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g. human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several 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 increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.

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Section: System Model and Related Workmentioning

confidence: 99%

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Homaei¹,

Salwana

Shamshirband

2019

Sensors

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“…The classical internet of things network and low power and lossy networks (LLNs) are useful domains of wireless sensor networks, which can provide area monitoring and control operations with high potential in unpredictable and dynamic environments [1]. However, these networks face challenges in transferring information to the base station in the network The associate editor coordinating the review of this manuscript and approving it for publication was Wei Wang .…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

et al. 2020

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The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms. INDEX TERMS Internet of Things, wireless sensor network, congestion control, fuzzy decision making, and back-pressure.

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“…One of the most common currently among WSNs is the RPL where it is possible to configure OF (Objective Functions) with multiple parameters. However, multiples OF may obtain routes that converge on the same nodes, and this is reflected in congestion at the central nodes [ 38 ].…”

Section: Sdn Over Tschmentioning

confidence: 99%

Enhancing SDN WISE with Slicing Over TSCH

Orozco-Santos

Sempere-Payá

Albero-Albero

et al. 2021

Sensors

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IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic sources with different characteristics. Due to the great diversity of application scenarios, there is a need to implement additional capabilities that can guarantee an adequate level of reliability and that can adapt to the dynamic behavior of the applications in use. The use of SDNs (Software Defined Networks) extends the possibilities of control over the network and enables its deployment at an industrial level. The signaling traffic exchanged between nodes and controller is heavy and must occupy the same channel as the data traffic. This difficulty can be overcome with the segmentation of the traffic into flows, and correct scheduling at the MAC (Medium Access Control) level, known as slices. This article proposes the integration in the SDN controller of a traffic manager, a routing process in charge of assigning different routes according to the different flows, as well as the introduction of the Time Slotted Channel Hopping (TSCH) Scheduler. In addition, the TSCH (Time Slotted Channel Hopping) is incorporated in the SDN-WISE framework (Software Defined Networking solution for Wireless Sensor Networks), and this protocol has been modified to send the TSCH schedule. These elements are jointly responsible for scheduling and segmenting the traffic that will be sent to the nodes through a single packet from the controller and its performance has been evaluated through simulation and a testbed. The results obtained show how flexibility, adaptability, and determinism increase thanks to the joint use of the routing process and the TSCH Scheduler, which makes it possible to create a slicing by flows, which have different quality of service requirements. This in turn helps guarantee their QoS characteristics, increase the PDR (Packet Delivery Ratio) for the flow with the highest priority, maintain the DMR (Deadline Miss Ratio), and increase the network lifetime.

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A Survey on Congestion Control for RPL-Based Wireless Sensor Networks

Cited by 44 publications

References 57 publications

An Enhanced Distributed Data Aggregation Method in the Internet of Things

An Enhanced Distributed Data Aggregation Method in the Internet of Things

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

Enhancing SDN WISE with Slicing Over TSCH

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