Squirrel Search Algorithm Based Support Vector Machine for Congestion Control in WSN-IoT

Sankari, B. Siva; Nemani, Ramya

doi:10.1007/s11277-021-09437-5

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…In [13], a strategy based on ML was introduced to detect congestion in IoT-based WSNs. The learning strategy used in this method is a Support Vector Machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

“…Based on the anticipated congestion situation, a decision is made in the context of offloading the node to the server. The methods in [13,14] suffer the same problem as the method presented in [12], and it is difficult to collect the training data to construct the SVM models for different network configurations. On the other hand, due to the time-consuming process of SVM optimization, this operation should be executed offline; however, this process requires online network data.…”

Section: Related Workmentioning

confidence: 99%

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Congestion Avoidance in Intelligent Transport Networks Based on WSN-IoT through Controlling Data Rate of Zigbee Protocol by Learning Automata

Zhang

Chen

et al. 2023

Electronics

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Congestion control is one of the primary challenges in improving the performance of wireless sensor networks (WSNs). With the development of this network based on the Internet of Things (IoT), the importance of congestion control increases, and the need to provide more efficient strategies to deal with this problem is strongly felt. This problem is even more important in applications such as Intelligent Transport Systems (ITSs). This article introduces a new method for congestion control in ITSs based on WSN-IoT infrastructure, namely, the Congestion Avoidance by Reinforcement Learning algorithm (CARLA). The purpose of the research was to improve the performance of the Zigbee protocol in congestion control through more efficient routing and also the intelligent adjustment of the data rate of the nodes. For this purpose, a topology control and routing strategy based on the multiple Bloom filter (MBF) is proposed in this research. Further, learning automata (LA) was used as a reinforcement learning model to adjust the data rate of network nodes in a distributed manner. These strategies distinguish the current research from previous efforts and can be effective in reducing the probability of congestion in the network. The performance evaluation results of the proposed algorithm in a simulated ITS environment were compared with conventional Zigbee and state of the art methods. According to the results, CARLA can improve PDR by 4.64%, and at the same time, reduce energy consumption and end-to-end delay by 11.44% and 25.26%, respectively. The results confirm that by using CARLA, in addition to congestion control in the ITS, energy consumption and the end-to-end delay can also be reduced.

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“…In [13], a strategy based on ML was introduced to detect congestion in IoT-based WSNs. The learning strategy used in this method is a Support Vector Machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Congestion Avoidance in Intelligent Transport Networks Based on WSN-IoT through Controlling Data Rate of Zigbee Protocol by Learning Automata

Zhang

Chen

et al. 2023

Electronics

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“…Many IoT applications require that the "things" know their environmental conditions, and adjust their behaviour to them and to the other objects that are nearby. In this way, Wireless Sensor Networks (WSNs) can be considered the extension of the Internet towards the physical environment in the IoT, and thus they are one of the most valuable parts of any IoT system [2][3][4]. The IoT accommodates extensive applications that include industry 4.0, advanced parking, healthcare, animal, and security monitoring applications.…”

Section: Introductionmentioning

confidence: 99%

Multi‐hop similarity‐based‐clustering framework for IoT‐Oriented Software‐Defined wireless sensor networks

Shafique

Asad

Aslam

et al. 2022

IET Wireless Sensor Systems

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The performance of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) depends on the routing protocol and the deployment technique in modern applications. In a plethora of IoT-WSNs applications, the IoT nodes are essential equipment to prolong the network lifetime with limited resources. Data similarity-based clustering protocols exploit the temporal correlation among the neighbouring sensor nodes through the subset of data. In bendy supervision, IoT-based Software Defined WSNs provide an optimistic resolution by allowing the control logic to be separated from the sensor nodes. The benefit of this SDN-based IoT architecture, allows the unified control of the entire IoT network, making it easier to implement on-demand network management protocols and applications. To this end, in this paper, we design a Multi-hop Similarity-based Clustering framework for IoT-oriented Software-Defined wireless sensor Networks (MSCSDNs). In particular, we construct data-similar application-aware clusters in order to minimise the communication overhead. Also, we adapt inter-cluster and intra-cluster multi-hop communication using adaptive normalised least mean square and merged them with the proposed MSCSDN framework that helps prolong the network lifespan. The proposed framework is compared with the state-of-the-art approaches in terms of network lifespan, stability period, instability period, report delay, report delivery, and cluster leader nodes generations. The MSCSDN achieves optimal data accuracy concerning the collected data. K E Y W O R D S internet of things, multi-hoping, similarity-based clustering, software defined networkingThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Applying an Improved Squirrel Search Algorithm (ISSA) for Clustering and Low-Energy Routing in Wireless Sensor Networks (WSNs)

Qaffas

2023

Mobile Netw Appl

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Squirrel Search Algorithm Based Support Vector Machine for Congestion Control in WSN-IoT

Cited by 5 publications

References 23 publications

Congestion Avoidance in Intelligent Transport Networks Based on WSN-IoT through Controlling Data Rate of Zigbee Protocol by Learning Automata

Congestion Avoidance in Intelligent Transport Networks Based on WSN-IoT through Controlling Data Rate of Zigbee Protocol by Learning Automata

Multi‐hop similarity‐based‐clustering framework for IoT‐Oriented Software‐Defined wireless sensor networks

Applying an Improved Squirrel Search Algorithm (ISSA) for Clustering and Low-Energy Routing in Wireless Sensor Networks (WSNs)

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