Ant Colony Inspired Energy Efficient OLSR (AC-OLSR) Routing Protocol in MANETS

Jain, Rachna

doi:10.1007/s11277-022-09514-3

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…Consequently, every search agent is a possible fix for the problem, represented statistically by a vector. All search agents comprise an OOA populace, which is given in Equation (17). When the OOA is initially applied, Equation ( 18) is used to initialize at random search agents' locations in the search area.…”

Section: Initializationmentioning

confidence: 99%

“…These two procedures take a long time, which prolongs the end-to-end delay. Proactive routing protocols cover many protocols such as the destination sequenced distance vector routing protocol (DSDV), wireless routing protocol (WRP), cluster head gateway switch routing protocol (CGSR), and optimized linked state routing protocol (OLSR) [17,18]. The OLSR generates a maximum throughput that is scalable and manageable.…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Link State Routing Protocol with a Blockchain Framework for Efficient Video-Packet Transmission and Security over Mobile Ad-Hoc Networks

Ahmed,

AL-Asadi

2024

JSAN

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A mobile ad-hoc network (MANET) necessitates appropriate routing techniques to enable optimal data transfer. The selection of appropriate routing protocols while utilizing the default settings is required to solve the existing problems. To enable effective video streaming in MANETs, this study proposes a novel optimized link state routing (OLSR) protocol that incorporates a deep-learning model. Initially, the input videos are collected from the Kaggle dataset. Then, the black-hole node is detected using a novel twin-attention-based dense convolutional bidirectional gated network (SA_ DCBiGNet) model. Next, the neighboring nodes are analyzed using trust values, and routing is performed using the extended osprey-aided optimized link state routing protocol (EO_OLSRP) technique. Similarly, the extended osprey optimization algorithm (EOOA) selects the optimal feature based on parameters such as node stability and link stability. Finally, blockchain storage is included to improve the security of MANET data using interplanetary file system (IPFS) technology. Additionally, the proposed blockchain system is validated utilizing a consensus technique based on delegated proof-of-stake (DPoS). The proposed method utilizes Python and it is evaluated using data acquired from various mobile simulator models accompanied by the NS3 simulator. The proposed model performs better with a packet-delivery ratio (PDR) of 91.6%, average end delay (AED) of 23.6 s, and throughput of 2110 bytes when compared with the existing methods which have a PDR of 89.1%, AED of 22 s, and throughput of 1780 bytes, respectively.

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Section: Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimized Link State Routing Protocol with a Blockchain Framework for Efficient Video-Packet Transmission and Security over Mobile Ad-Hoc Networks

Ahmed,

AL-Asadi

2024

JSAN

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“…In [44], the routing protocol Ant Colony Inspired Energy efficient OLSR (AC-OLSR) was presented. Pheromone concentration was computed using a combined link quality technique that could enhance wireless network performance.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization ACO Based Autonomous Secure Routing Protocol for Mobile Surveillance Systems

Saleem

Ahmad

2022

Drones

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Sensing plays a vital role in enabling smart cities. The mobile surveillance of different sectors, the retransmission of radio signals, and package delivery are the main applications conducted by unmanned vehicles in smart cities. Multiple unmanned vehicles or miniaturized real-time flying machines with onboard sensors, whether land- or air-based, communicate with each other to form a flying sensor network. Almost all of these machines are battery-operated. Therefore, power preservation is an extremely important factor to be taken into consideration. This paper proposes a power-aware biologically inspired secure autonomous routing protocol (P-BIOSARP) that depends on enhanced ant colony optimization (eACO). eACO autonomously and securely routes the data packet, and the power awareness maintains the power consumption of the flying sensor network. The novel intelligent power-aware routing protocol was implemented in network simulator 2 to perform a number of experiments with different scenarios. The scenarios included varying numbers of total nodes and mobile nodes, different packet rates, mobile source nodes, multiple mobile routing nodes, and, on the side of security, the injection of malicious nodes. The proposed protocol is compared with BIOSARP, E-BIOSARP, and SRTLD in terms of energy consumption, the delivery ratio, and traffic overhead. The analysis shows that the P-BIOSARP remarkably reduces energy consumption compared to other well-known protocols implemented on real testbeds.

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“…IoT objects deployed with dynamic topology results in network vulnerabilities. Battery life of devices is an important criterion to prolong the life of sensor networks [22][23][24][25]. Critical factors determining the life of the battery are the transmission range of the sensor nodes and sensor reading frequency [1].…”

Section: Introductionmentioning

confidence: 99%

Detection Mechanism in IoT framework using Artificial Neural Networks

Jain¹,

Dhand

Sheoran

et al. 2023

Preprint

Self Cite

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Internet of Things (IoT) applications are now used more frequently due to the rapid expansion of wireless networking and the digital revolution. IoT helps in user-to-machine and machine-to-machine interaction. IoT objects have gained popularity because they can be accessed from anywhere. Healthcare, agriculture, smart cities, and the military are different domains where IoT objects are communicating with each other. The goal of anomaly-based techniques is to figure out which patterns are normal and which are aberrant. This approach of intrusion detection has the benefit of detecting original works of authorship intrusions. However, this technique has the drawback of frequently producing false positive results. To increase the effectiveness of anomaly-based intrusion detection methods, machine learning techniques are being evaluated. Anomaly-based intrusion detection techniques can be used by machine learning algorithms to watch active behavior and compare it to known intrusion footprints in order to stay aware of potential future attacks. In a hybrid approach, different identifying methods are combined in the same scheme. This technique will eliminate the weaknesses of a particular operation while improving the overall IoT system's reliability. In this research, we study intrusion-based systems using comparative analysis of several machine learning and deep learning algorithms. In the proposed work one hot encoding technique is used to deal with the categorical data. Different parameters like accuracy, F-1 score, precision, and recall value have been calculated. Experimental results prove that ANN yields 99.61% accuracy over other hybrid models. However, in Machine Learning, RandomForestClassifier yields the best results.

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Ant Colony Inspired Energy Efficient OLSR (AC-OLSR) Routing Protocol in MANETS

Cited by 8 publications

References 28 publications

An Optimized Link State Routing Protocol with a Blockchain Framework for Efficient Video-Packet Transmission and Security over Mobile Ad-Hoc Networks

An Optimized Link State Routing Protocol with a Blockchain Framework for Efficient Video-Packet Transmission and Security over Mobile Ad-Hoc Networks

Ant Colony Optimization ACO Based Autonomous Secure Routing Protocol for Mobile Surveillance Systems

Detection Mechanism in IoT framework using Artificial Neural Networks

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