A Green Hybrid Congestion Management Scheme for IoT-Enabled WSNs

Kaur, Gagandeep; Chanak, Prasenjit; Bhattacharya, Mahua

doi:10.1109/tgcn.2022.3179388

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…Secondly, quantum computing-based solutions were formulated for optimized issues concentrating on energy-centric solution representation, rotation angle, and measurement. Kaur et al [17] presented a green hybrid congestion control system for IoT-assisted WSNs. It employs an unequal clustering system which would save power of battery-limited SNs and solve energy hole problems.…”

Section: Related Workmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network's efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

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Section: Related Workmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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“…In [17], a green hybrid congestion control mechanism for IoT-based WSNs is presented. This method uses an unequal clustering mechanism that saves the energy of sensor nodes.…”

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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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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“…Tao et al [14] and Kaur et al [34] showed benefit of using unequal clustering in addressing hot-spot problem; however, the unequal clustering model (the clusters size varies at different level 𝑗 i.e., the node closer to base station has lower cluster size in comparison with cluster far away from bas station) presented in [35] addressed both hotspot and coverage problem. Let the senor deployed in random manner with density 𝛿 and same communication range 𝑆.…”

Section: Cluster Head Selection Modelmentioning

confidence: 99%

“…where 𝑆 ̅ defines member node which has not been selected as CH yet for corresponding gathering period, 𝑑 symbolizes the CH for round 1 𝑟(𝑑) ⁄ ; thus, different devices will have a dissimilar probability of being CH [20], [34]. the existing model [14], [35] failed to address the load optimization in inter-cluster routing which is addressed below in more adaptive manner with respect to varying traffic with minimal energy consumption.…”

Section: Cluster Head Selection Modelmentioning

confidence: 99%

“…The purpose of CRPSH is to lengthen the life of the network by decreasing the amount of time spent on control packets. Kaur et al [34], authors detail a hybrid congestion control technique for IoT-enabled WSNs that is energy efficient. Using an unequal clustering method helps conserve energy for sensor nodes with short battery life and addresses issues with energy holes.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive traffic aware clustering and routing model for wireless sensor networks

Veeregowdanadoddi Chandraiah,

Ramalingappa

2023

IJEECS

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<span>Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.</span>

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A Green Hybrid Congestion Management Scheme for IoT-Enabled WSNs

Cited by 15 publications

References 33 publications

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

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

Adaptive traffic aware clustering and routing model for wireless sensor networks

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