Performance Analysis of Unicasting Routing Protocols for Mobile Ad-Hoc Network

Sahu, Prabhat Kumar; Acharya, Biswa Mohan

doi:10.1109/icaml48257.2019.00060

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…This approach is derived from the differential evolution technique. The node mobility creates network congestion since it can't improve the QoS [14]. This approach uses a hybrid routing protocol like RED and Droptail queue management method to minimize network congestion.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence Machine Learning in Healthcare System for improving Quality of Service

Reddy¹,

Mungara²

2023

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A vital element of widespread patient monitoring is consistency in transmission between the patients and the healthcare professionals not including time and position dependencies. Artificial intelligence (AI) and machine learning (ML) techniques have a vast possibility to proficiently handle the automated function of the mobile nodes distributed in the Mobile ad-hoc network (MANET). ML is a part of AI in that the computer algorithms learn themselves by improving from historical experiences. The main issues in MANET are autonomous operation, maximization of a lifetime, coverage of the network, energy utilization, connectivity issues, quality of service, high bandwidth necessity, communication protocol design, etc. ML is valuable for data aggregation, and it saves the energy of mobile nodes and enhances the network lifetime. In this paper, we propose an Artificial Intelligence Machine Learning Algorithm for improving the Quality of Service in MANET (AIMQ). ML techniques based on artificial neural networks (ANN) algorithm is helpful for data aggregation tasks. This approach formed the clusters by node mobility and connectivity. Glowworm Swarm Optimization (GSO) algorithm is applied in every cluster to choose a proficient Cluster Head (CH). Here, we choose the CH by GSO fitness function based on mobile node degree, node distance, node reliability, and energy. ANN algorithm recognizes and chooses the data aggregator with great energy and more extended stability. It updates the weight of input parameters such as node energy, node degree, packet loss ratio, and node delay to reduce the errors. It minimizes the repetitive CHs selection and member nodes’ re-affiliation in a cluster.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence Machine Learning in Healthcare System for improving Quality of Service

Reddy¹,

Mungara²

2023

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“…Regardless of the length of its own queue, it will put the data message in the queue and wait to be sent. However, when the data flow is large, the queue length has exceeded the set buffer capacity value, and the network node has no space to temporarily store these new data messages [30].…”

Section: Droptail Algorithmmentioning

confidence: 99%

Can Multipath TCP Be Robust to Cyber Attacks? A Measuring Study of MPTCP with Active Queue Management Algorithms

Cao

et al. 2021

Security and Communication Networks

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With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

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