CAFOR: congestion avoidance using fuzzy logic to find an optimal routing path in 6LoWPAN networks

Shreyas, J.; Singh, Hemant Kumar; Tiwari, Soumya; Srinidhi, N. N.; Kumar, Sharad

doi:10.1007/s40860-021-00134-5

Cited by 21 publications

(9 citation statements)

References 19 publications

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“…Whereas, in order to optimize the dangerous goods transportation management (DGTM), it is important to take into account other factors than can have an effect on DGTM as trip duration and related cost. Therefore, the problem becomes a multi-criteria decision making (MCDM) problem [57], [58], [59], [60], and [61]. And an appropriate method is needed for risk analysis of dangerous goods transportation to solve the interdependent issues in an effective manner.…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Kanj,

Kotb,

Alakkoumi

et al. 2024

IEEE Access

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Dangerous Goods Transportation in smart cities is one of the most intriguing topics that researchers are interested in to reduce the risks caused by hazardous events. This article proposes a novel technique to avoid hazardous events during the transportation of dangerous goods using realtime information provided by the cloud. This article applies Fuzzy AHP-TOPSIS hybrid method for risk analysis applied to Dangerous goods transportation in a smart city. The article assumes that we have three criteria that dictate the choice of routs the vehicle follows: cost, duration, and risk. The main objective is to choose the best route through calculated weights assigned to the three criteria based on predefined pairwise comparisons. The study is done on both static and dynamic environments. In static environments, the decision is made and is followed prior to actions and no change of actions is made even if criteria values change. In dynamic environments on the other hand, the decision could change during the transportation process due to the change of the valuation the criteria contribute to. The study shows that risk is reduced and therefore safety is increased for the transportation of dangerous goods.

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Section: Literature Surveymentioning

confidence: 99%

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Kanj,

Kotb,

Alakkoumi

et al. 2024

IEEE Access

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“…The congestion is detected by the loss of packets at the sink node. In [83], Kang et al proposed a Topology-Aware Resource Adaptation (TARA) solution to minimize congestion in IoT networks. The focus of this scheme is an additional resource adaptation for congestion alleviation in WSNs.…”

Section: Ai-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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“…Authors in [19] suggested a method for determining congestion problems in IoT networks by offering the use of fuzzy logic. The difficulty of parent choice is formed and then solved applying the fuzzy weighted sum procedure.…”

Section: Related Workmentioning

confidence: 99%

Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

Lu¹,

Li²,

Wang³

et al. 2022

Wireless Communications and Mobile Computing

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Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

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CAFOR: congestion avoidance using fuzzy logic to find an optimal routing path in 6LoWPAN networks

Cited by 21 publications

References 19 publications

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

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

Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

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