A hybrid objective function with empirical stability aware to improve RPL for IoT applications

Hassani, Abdelhadi Eloudrhiri; Sahel, Aïcha; Badri, Abdelmajid; Mourabit, Ilham El

doi:10.11591/ijece.v11i3.pp2350-2359

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…Initially, RPL protocol relied on directed acyclic graph (DAG) that introduced the problem of routing loop (algorithm not converging to outgoing links) [15]. Therefore, destination-oriented DAG (DODAG) was introduced to help achieve a loop-free network, converging to single destination [27]. Point-to-multipoint (P2MP), Multipoint to point (MP2P), and Point to point (P2P) are three types of traffic that RPL supports [28], [29].…”

Section: Rpl Protocolmentioning

confidence: 99%

A Review on the Security of IoT Networks: From Network Layer’s Perspective

et al. 2023

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Internet of Things (IoT) has revolutionized the world in the last decade. Today millions of devices are connected to each other utilizing IoT technology in one way or the other. With the significant growth in IoT devices, the provision of IoT security is imperative. Routing protocol for low power and lossy networks (RPL) is a network layer protocol, specially designed for routing in IoT devices. RPL protocol faces many attacks such as selective forwarding attacks, blackhole attacks, sybil attacks, wormhole attacks, and sinkhole attacks. All these attacks pose great threats to IoT networks and can substantially affect the performance of the network. In this work, a comprehensive review of internal attacks on the network layer is presented. Specifically, we focus on the literature that considers presenting solutions for the detection and prevention of sinkhole attacks. We reviewed the state-of-the-art works and different performance parameters like energy consumption, scalability, threshold value, packet delivery ratio, and throughput. Moreover, we also present a detailed analysis of machine learning-based algorithms and techniques proposed for the security of RPL protocol against internal attacks.

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Section: Rpl Protocolmentioning

confidence: 99%

A Review on the Security of IoT Networks: From Network Layer’s Perspective

et al. 2023

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“…Hassani et al [14] designed a hybrid objective function for RPL, integrating metrics such as RSSI, hop count, and energy consumption. Their approach employed static and empirical thresholds to address frequent changes in preferred parent nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Effect of Different ObjectiveFunctions on Energy Efficiency for RPL-basedRouting in Low Power Lossy Networks for IoTapplication

Telgote

Mande²

2023

Preprint

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Low power lossy networks (LLNs) are designed to enhance network lifetime byminimizing energy consumption. In this research, we focus on the routing pro-tocol for LLNs known as RPL and investigate the impact of different objectivefunctions on energy consumption. Previous studies have highlighted the impor-tance of objective function selection in optimizing energy efficiency in LLNs.However, there is still a need to thoroughly understand the effects of objectivefunctions on energy consumption in RPL. The primary objective of this researchis to compare the energy consumption of different objective functions in RPLfor various node configurations. We specifically evaluate Objective function 0, Minimum Rank Hysteresis Objective Function (MRHOF), MRHOF with energy(MRHOF Energy), and MRHOF with expected transmission (MRHOF ETX).Our research methodology involves simulating LLNs with varying numbers ofnodes and analyzing the energy consumption patterns of the different objectivefunctions. We also investigate the impact of alpha weighting as well as tricklealgorithm on the MRHOF algorithms. The results show that as the number ofnodes increases, the average power consumed gradually rises. Among the objec-tive functions under consideration, MRHOF Energy exhibits the highest powerconsumption, while MRHOF demonstrates the most efficient performance withsignificantly lower power consumption. Furthermore, our findings reveal thatMRHOF offers better energy efficiency compared to other objective functions,including MRHOF Energy and MRHOF ETX. The superiority of MRHOF isconsistent across different node configurations. However, after experimenting on performance parameter like delay and through-put it is observed that the choice of objective function depends on the specificrequirements and trade-offs of the IoT application, with MRHOF offeringimproved network lifespan, OF0 prioritizing low delay and high throughputin smaller networks, and MRHOF ETX providing a balanced approach forscalability and trade-offs. The significance of this research lies in its ability to provide valuable insightsinto the influence of objective function selection on energy consumption andperformance in low-power lossy networks (LLN). These findings serve as a guid-ing resource for the development and implementation of energy-efficient routingprotocols. By incorporating the recommendations from this research, LLN per-formance can be enhanced, leading to improved network longevity and overallefficiency. The research outcomes contribute to the advancement of LLN tech-nologies and pave the way for more sustainable and effective network solutionsin various IoT applications.

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“…In [36] the authors proposed a hybrid OF which has empirical stability awareness (HOFESA). It is a RPL implementation which combines linearly three weighty metrics namely hop count, RSSI and node energy consumption.…”

Section: Calculus Based Workmentioning

confidence: 99%

RPL protocol enhancement using K-Nearest Neighbor machine learning approach for IoT application

Kuwelkar¹,

Virani²

2023

Preprint

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It is predicted that the coming decade will witness an upsurge of devices, mobile phones, sensors and actuators communicating over the Internet to enrich human lifestyle. The network design will have to ensure energy efficiency, speed and reliability for Internet of Things to be sustainable in long run. Routing of data, is one aspect which largely governs the quality of service in wireless networks. The IETF ROLL working group proposed the IPv6 Routing protocol for Low power Lossy Networks (RPL) as de-facto routing standard keeping in mind the sustainability of constrained IoT networks. The standard RPL is designed to select best route based on single parameter like hop count. The standard was kept open for researchers to experiment and augment it further. The main aim of this work is to improve the performance of RPL protocol by considering multiple metrics during selection of best route. Four routing parameters namely, hop count, delay, residual energy and link quality are chosen as criteria for deciding the best route. The K-Nearest Neighbor machine learning technique is proposed for multi criteria decision making. The K-NN computes the quality score for each candidate neighbor node and the one with highest score is selected as the most suitable parent in route towards the sink node This novel version, RPL-ML is tested in the COOJA simulator of Contiki OS. Deep dive analysis of RPL-ML by varying data rates and network density reveals that it lowers energy consumption by 18%, improves PDR by 3%, lowers delay by 20% and reduces the control overhead by 48% as compared to standard RPL implementations.

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A hybrid objective function with empirical stability aware to improve RPL for IoT applications

Cited by 6 publications

References 21 publications

A Review on the Security of IoT Networks: From Network Layer’s Perspective

A Review on the Security of IoT Networks: From Network Layer’s Perspective

Analyzing the Effect of Different ObjectiveFunctions on Energy Efficiency for RPL-basedRouting in Low Power Lossy Networks for IoTapplication

RPL protocol enhancement using K-Nearest Neighbor machine learning approach for IoT application

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