Designing a context-aware model for RPL load balancing of low power and lossy networks in the internet of things

Royaee, Zohreh; Mirvaziri, Hamid; Bardsiri, Amid Khatibi

doi:10.1007/s12652-020-02382-4

Cited by 18 publications

(10 citation statements)

References 38 publications

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“…This limitation can be removed via using pre-trained CNN models that are optimized for energy efficient load balancing. Furthermore, existing models like SLA based model [15], AMRRPL (Royaee et al, 2021)16], EDF (Ben Alla et al, 2019) [22], MU (Mandal et al, 2020) [15], MMT [15], RALBHC (Ahmed et al, 2021) [27], etc. can be further improved via addition of soft computing for optimization https://doi.org/10.17993/3cemp.2022.110250.232-248 of energy consumption.…”

Section: Quantitative Analysismentioning

confidence: 99%

“…The quantitative results are tabulated in table 1, wherein the aforementioned parameters are compared across different algorithms.Based on this analysis it can be observed that the FLPSO (Ben Alla et al, 2019)[22], AMRRPL(Royaee et al, 2021)[16], MU(Mandal et al, 2020)[15], SLA based model(Mandal et al, 2020)[15], EDF(?, ? )[22], and GSO SCA(Dong et al, 2019)[7] model outperform other models in terms of relative energy efficiency.This performance evaluation can also be observed from the visualization in figure8, wherein different algorithms and their accuracies are compared.…”

mentioning

confidence: 94%

“…Each of these models utilize soft computing techniques like ACO, PSO, GA, and GSO in order to achieve high energy efficiency. A resource aware load balancing model can be observed in (Ahmed, Aleem, Noman Khalid, Arshad Islam, and Azhar Iqbal, 2021)[27], wherein heterogenous clustering is used in order to perform resource-based task mapping. The model performs job to resource mapping depending upon resource availability, and resource aware load balancing for obtaining higher utilization ratio.…”

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confidence: 99%

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Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Singh¹,

Gaikwad²,

Kamble³

2022

3C Empresa

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Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

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Section: Quantitative Analysismentioning

confidence: 99%

mentioning

confidence: 94%

mentioning

confidence: 99%

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Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Singh¹,

Gaikwad²,

Kamble³

2022

3C Empresa

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“…6LoWPAN networks do not support traditional IoT routing protocols due to the loss and minimal power nature of 6LoWPAN networks. In order to enable effective routing in 6LoWPAN networks, the RPL network was introduced [8]. The main advantages of the control messages are following a specific pattern during the repair and building an IoT network.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

Alkanhel¹,

Khafaga²,

El-kenawy³

et al. 2023

Computers, Materials &Amp; Continua

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The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That's why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique (LSH-SMOTE). The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms. To prove the effectiveness of the proposed approach, a set of experiments were conducted to evaluate the performance of NIDS for three cases, namely, detection without dataset balancing, detection with SMOTE balancing, and detection with the proposed optimized LSH-SOMTE balancing. Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy. In addition, a statistical analysis is performed to study the significance and stability of the proposed approach. The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset. Based on the 2696 CMC, 2023, vol.74, no.2 proposed approach, the achieved accuracy is (98.1%), sensitivity is (97.8%), and specificity is (98.8%).

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“…As a result, researchers can simulate realistic scenarios using standard application-layer protocols like message queue telemetry transport (MQTT) and constrained application protocol (CoAP) over 802.15.4 and lowpower wireless personal area networks using IPv6 (6LoWPAN). With modest modifications, the simulation firmware running on virtual nodes can be deployed to real physical hardware, bridging the gap between the proof-of-concept and prototype phases [16]. For the above reasons, the current work uses the Cooja simulator.…”

Section: Introductionmentioning

confidence: 99%

Efficient data transmission using trusted third party in smart home environments

Panda

Supriya

2022

J Wireless Com Network

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Currently, deployed Internet of Things (IoT) technology acts as a passive observer of the environment that sends data to a remote location. Developing and deploying future IoT applications will need re-tasking this one-way behaviour in a reliable manner. A novel computationally tractable optimization technique that can accept cross-layer resource configurations and focus on network enhancement with longevity should be created for the smart home, one of the heterogeneous IoT applications. This study shows different smart home architectures in static and mobile environments, taking into account some of the challenges like orchestration, mobility, and range in IoT. For network communication, routing protocol over 6LoWPAN (RPL) is used. The goal of the work is to optimize the communication network in both static and mobile environment. To attain the goal, this paper proposes an algorithm that improves the path selection by modifying the existing objective functions of RPL. The proposed smart home architectures are analysed and compared based on different parameters such as packet reception ratio, network overhead, throughput, average latency, and total energy consumption. Even when some of the devices in the smart home are mobile, the modified smart home-optimized path (MSHOP) is found to achieve a packet reception ratio of 99.93%, minimum latency of 0.9 s, and the highest total energy usage in the network of 3373 millijoules. In conclusion, proposed MSHOP outperforms all existing smart home architectures when considering network efficiency, time, and usability.

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Designing a context-aware model for RPL load balancing of low power and lossy networks in the internet of things

Cited by 18 publications

References 38 publications

Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

Efficient data transmission using trusted third party in smart home environments

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