Energy Balanced Zone Based Routing Protocol to Mitigate Congestion in Wireless Sensor Networks

Sunitha, G.P.; Kumar, Sharad; Kumar, Bhavnesh

doi:10.1007/s11277-017-4630-4

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…RPL is proposed for limited IoT devices and WSNs [63]. Numerous studies have been conducted to apply RPL in different contexts, including node mobility [38,64], different traffic patterns [65], forwarding, and multi-casting.…”

Section: Rpl-based Algorithms For Congestion Mitigation In Wsnmentioning

confidence: 99%

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

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The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.

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Section: Rpl-based Algorithms For Congestion Mitigation In Wsnmentioning

confidence: 99%

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

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“…Cloud model is the concrete implementation method of cloud and also the basis of cloud-based operational reasoning and control. The process from qualitative concept to quantitative representation, namely, the concrete realization of cloud droplets generated by the digital features of cloud, is called forward cloud generator [5,6].…”

Section: Cloud Model Theory and Congestionmentioning

confidence: 99%

Congestion and Computer Program Control Algorithm Strategy for Wireless Sensor Networks Based on Cloud Model

Fei

2022

Wireless Communications and Mobile Computing

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Cloud model and sensor network are the research hotspots in recent years. This paper proposes a congestion and rate control strategy for wireless sensor networks based on cloud model. It adjusts the input rate of nodes based on cloud model through node congestion detection. Aiming at the problem of network congestion control, two congestion adjustment algorithms based on red are improved. Congestion threshold and congestion degree are used as the basis of packet transmission rate adjustment to realize network support plug control. In this paper, the congestion control strategy NP starts to alleviate congestion at about 40 s and controls the packet loss rate at about 116 packets/s, which is 45.3% lower than the multipath congestion control strategy and is more stable. However, when β = 0 , the packet loss rate of the double congestion threshold algorithm is 20% lower than that of the single congestion threshold algorithm. The reason is that the double congestion threshold algorithm allows a stronger source rate control mechanism earlier than the single congestion threshold algorithm a large number of packets are lost in a point, but this also makes the network performance relatively low.

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“…Using the z id all the sensor nodes are mapped to unique zones. The reader is referred to Sunitha G P et al, [12] for assigning z id and node mapping algorithms. Example of assigning z id is shown in Fig The reason for excluding the merge of diagonally adjacent zones is illustrated with the Fig.1.…”

Section: A Cluster Formationmentioning

confidence: 99%

“…Most fireflies produces short and rhythmic flashes and uses this flashes to attract other fireflies. All the FAs uses the following three idealized C Yogarajan et al, [12] rules:  Regardless of the gender fireflies attracts other fireflies.…”

Section: A General Description Of Firefly Behaviormentioning

confidence: 99%

A Nature Inspired Optimal Path Finding Algorithm to Mitigate Congestion in WSNs

Sunitha¹,

Brajesh²,

Kumar³

2018

IJSRNSC

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In resource constrained wireless sensor networks, congestion control is an extremely important issue that need to be addressed. The individual capacities of the channels are exceeded by the bulk traffic and creates adverse effects on the performance of the network. Therefore, to resolve the congestion problems in wireless sensor network the challenge lies in developing more sophisticated routing techniques which are able to fairly deliver the data between source and destination with minimum consumption of energy and reduced congestion. In the recent times, various swarm intelligence based routing approaches are proposed that aided in congestion detection and control mechanisms. Most of them are found to be with lower convergence rate. Therefore, a nature inspired hierarchical routing technique which aims to reduce congestion and energy consumption with network longevity and faster convergence rate is proposed. In this technique, a static partition of the target area based on node density is done to optimize energy efficiency. Firefly behavior based routing is modeled to select the optimal path for data transmission. This approach is concerned with exploiting global behavioral patterns emerging from local interactions. The proposed technique aims to minimize congestion by applying network load balance. network lifetime, energy consumption and pathlength. This ideally makes the proposed algorithm suitable for diverse WSN applications.

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Energy Balanced Zone Based Routing Protocol to Mitigate Congestion in Wireless Sensor Networks

Cited by 13 publications

References 27 publications

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Congestion and Computer Program Control Algorithm Strategy for Wireless Sensor Networks Based on Cloud Model

A Nature Inspired Optimal Path Finding Algorithm to Mitigate Congestion in WSNs

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