Fuzzy-Based Objective Function for Routing Protocol in the Internet of Things

Fabian, Philippe; Rachedi, Abderrezak; Gueguen, Cédric; Lohier, Stéphane

doi:10.1109/glocom.2018.8647969

Cited by 19 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bahramlou and Javidan [167] use the aggregation method to make the most scarce capital. Researchers estimate the number of children to calculate each parent's rank, a technique that, in heavy traffic, A new objective function based on fuzzy routines by Fabian et al [169] provided for the environment dynamic adjustment using EC and ETX metrics. When the node battery level exceeds the defined threshold, ETX is used to calculate the node rank.…”

Section: Available Improvements For Rpl Using Intelligencementioning

confidence: 99%

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Seyfollahi

Ghaffari

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

IPv6 routing protocol for low-power and lossy networks (RPL) has been developed as a routing agent in low-power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low-power, high-throughput IPv6 routing protocol that uses distance vectors. Each sensor-to-wire network router has a collection of fixed parents and a preferred parent on the path to the Destination-oriented directed acyclic graph (DODAG) graph’s root in steady-state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.

show abstract

Section: Available Improvements For Rpl Using Intelligencementioning

confidence: 99%

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Seyfollahi

Ghaffari

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Limiting control packets improves energy consumption; however, it increases the network convergence time. The fuzzy logic-based mechanism is based on ETX and energy consumption for rank calculation [36]. It shows improvement in the network throughput but increases overall energy consumption.…”

Section: Of 26mentioning

confidence: 99%

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

Musaddiq

Nain

Qadri

et al. 2020

Sensors

View full text Add to dashboard Cite

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.

show abstract

“…17 In Reference 12, author proposed a novel improved information conveyance structure, fuzzy system-based delivery framework, that uses fuzzy logic for successful message conveyance in improved constraint-based artificial neural network. 18 In the proposed component, at whatever point any two channels meet each other in the system, every channel utilizes the fuzzy network to assess the conveyance inclinations of every conceivable message dependent on different fuzzy sets. Following such an assessment, the channels select and store messages that have higher conveyance inclination later on.…”

Section: Related Workmentioning

confidence: 99%

Advanced event‐base exhibition management mobile applications social networks using fuzzy reasoning‐based routing cum forwarding algorithm

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

5G is the fifth-generation mobile innovation that not only just expands the downloading and transferring speeds over the mobile networks, however, it additionally decreases the latency, which is the time taken by a system to respond, builds energy proficiency, and offers increasingly stable system associations. It has been seen that the present Internet structure is not appropriate for current and developing traffic requests. Many content distributions advances such as content Delivery Networks and shared frameworks have developed to allow content access by name as instead of server area or address. In addition, to respond to the expanding volumes of traffic for such applications as video on demand and distributed cloud computing, numerous works have been attempted to empower reserving, content replication, and preparing inside the system. Such advancements are exceptionally dependent on the distribution channel and execute as an overlay on the current Internet structure, which results in various inefficient result aspects. In recent years, it is highly difficult to develop a routing cum forwarding algorithm in order to overcome the existing problems, the introduced fuzzy reasoning-based routing cum forwarding algorithm. In this fuzzy method, the mobile devices evaluate the transmission priority and decide the transmission decision too. On comparing to the existing routing algorithms, the fuzzy concept should have reduced energy consumption, increased time delay, and high transmission efficiency and the above parametric results are verified by the fact of the fuzzy algorithm.

show abstract

Fuzzy-Based Objective Function for Routing Protocol in the Internet of Things

Cited by 19 publications

References 6 publications

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

Advanced event‐base exhibition management mobile applications social networks using fuzzy reasoning‐based routing cum forwarding algorithm

Contact Info

Product

Resources

About