Selection Of Routing Protocol Research Articles

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

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.

Fuzzy-based optimization of AODV routing for efficient route in wireless mesh networks

The performance of any communication system heavily relies on the efficient routing of interventions. This article addresses the significant issue of routing protocol selection for optimal path determination in networks. Particularly, when wireless communication occurs among mobile nodes with limited resources, such as batteries, the routing problem becomes even more challenging. This article proposes the Fuzzy Control Energy Efficient (FCEE) routing protocol to overcome these challenges. The FCEE protocol combines the Ad-Hoc On-Demand Distance Vector (AODV) protocol with fuzzy logic techniques to enhance network lifetime and performance. The proposed approach introduces a memory-based channel integrated with fuzzy logic methodologies, which effectively restricts the forwarding of unnecessary broadcast packets based on the energy availability of the operating node. Through extensive simulations, demonstrate the promising capabilities of FCEE as a routing protocol for wireless mesh networks. To further assess the effectiveness of the FCEE protocol, the proposed article compares it with two existing routing protocols: AODV and Intelligent Routing AODV (IRAODV). The simulation results shows that the FCEE routing protocol significantly enhances the reliability of the conventional AODV, providing improved link connectivity and longer route lifetimes. Additionally, our proposed protocol enhances the quality of service (QoS) for mesh routing, with an average throughput of 351.374 (Kbps) compared to 90 (Kbps) for IRAODV and 39 (Kbps) for AODV. Moreover, FCEE exhibits superior energy efficiency with an average energy consumption of 14, while IRAODV and AODV consume 40 and 90 joules, respectively. In conclusion, the FCEE routing protocol demonstrates its potential to address the challenges of efficient routing in wireless mesh networks. By leveraging fuzzy logic and integrating it with AODV, FCEE enhances network lifetime, performance, and energy efficiency, making it a promising solution for future wireless communication systems.

Optimized scheme for cluster head selection and EAMPR routing protocol design in WSN

Wireless sensor networks (WSN) is a popularly emerging technology with several opportunities to sustain in various field that require multipurpose sensor nodes, less energy and non-expensive system. But in the WSN, the radio transmission needs high amount of energy and this creates the critical problem. Hence consumption of energy has to be decreased to extend the network durability. Even though there are so many techniques existing for clustering approach of WSN, they have limitations like increased energy consumption, less delivery rate of data, redundancy and unbalanced network load. Hence, these problems are solved by introducing the energy efficient deep learning techniques for clustering and finding the optimal route. Initially the initialization process of system model is performed with the implementation of energy model. In WSN, energy consumption should be reduced to enhance the QoS and balance the network traffic. Hence clustering method is used to group up the sensor nodes and the optimal cluster head is selected with the proposed technique of hybrid cuckoo search and particle swarm optimization (CSO-PSO). As the CH is chosen, the optimal path of routing data should be found in addition with the procedure of optimization and it is done through the proposed model of Optimization based routing protocol that incorporates the Energy Aware Multi Point Routing (EAMPR) protocol along with the Improved Tuna Search Optimization (ITSO) algorithm. Finally, by the use of ITSO-EAMPR technique the energy consumption will get reduced with the decrease in relative mobility and high stability of nodes would be achieved. The simulations are proceeded and the outcomes are validated. The result obtained is compared with the traditional methods to show the effectiveness of proposed technique. As per the results obtained the proposed ITSO-EAMPR attains maximized PDR and Throughput, higher energy efficiency with extension in lifetime of WSN along with decrease in BER, end-to-end latency as compared to the existing techniques.

A Deep Learning Approach for Wireless Network Performance Classification Based on UAV Mobility Features

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios.

Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols

The planet is the most water-rich place because the oceans cover more than 75% of its land area. Because of the unique activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while monitoring and recording their surroundings’ physical and environmental parameters. An Underwater Wireless Sensor Network (UWSN) is the name given to the network created by collecting these underwater wireless sensors. This particular technology has a random path loss model due to the time-varying nature of channel parameters. Data transmission between underwater wireless sensor nodes requires a careful selection of routing protocols. By changing the number of nodes in the model and the maximum speed of each node, performance parameters, such as average transmission delay, average jitter, percentage of utilization, and power used in transmit and receive modes, are explored. This paper focuses on UWSN performance analysis, comparing various routing protocols. A network path using the source-tree adaptive routing-least overhead routing approach (STAR-LORA) Protocol exhibits 85.3% lower jitter than conventional routing protocols. Interestingly, the fisheye routing protocol achieves a 91.4% higher utilization percentage than its counterparts. The results obtained using the QualNet 7.1 simulator suggest the suitability of routing protocols in UWSN.

Traffic Management in Ad-Hoc Network by Selection of Suitable Routing Protocol

The need for traffic management has risen at the same pace as the number of cars has changed dramatically. Even though VANET and MANET have seen tremendous growth, traffic management is still essential. There is a demand for effective traffic management among the vehicles, which necessitates their communication ability. Routing protocols facilitate the transmission of data between vehicles. There are several routing protocols in a MANET, and choosing the optimal one is challenging. The IEEE802.11p standard is used for data transfer via routing protocols in the current operational environment. C-V2X is a developing technology superior to IEEE802.11p in terms of efficiency. Qualcomm is the creator of C-V2X. AODV, AMODV, and MDART are some appropriate routing protocols for evaluation. The Average End-to-End Delay, Average Throughput, and Packet Delivery Ratio of each protocol are analyzed. The Network Simulator 2.35 (NS2) simulates these routing protocols. This article concludes that the performance of C-V2X using AOMDV protocols is superior to that of IEEE802.11p. This research claims that AOMDV’s architecture might be improved to provide more accurate figures for the average end-to-end latency.

Dynamic Routing Protocol Selection in Multi-Hop Device-to-Device Wireless Networks

Until now, multi-hop device-to-device (MD2D) routing in mobile wireless networks has been designed to operate over a single-protocol routing framework. Consequently, to the best of our knowledge, no framework has been developed to enable the dynamic deployment and switching of multiple MD2D routing protocols under one framework. This paper proposes a multi-protocol framework to introduce the idea of clustering a cell and deploying different MD2D routing protocols for each cluster based on the cluster requirements. To this end, four clusters are developed with varying network conditions (i.e., node density, mobility rate, and the number of flows). Then, the performance of our two previously designed multi-hop routing protocols, namely hybrid SDN architecture for wireless distributed networks (HSAW) and source-based virtual ad hoc routing protocol (VARP-S), are investigated in each cluster in terms of energy consumption, end-to-end (E2E) delay, packet loss, and cellular-band overhead. Based on the achieved simulation results, a multiple-criteria decision-making (MCDM) approach based on the analytic hierarchy process (AHP) is developed to choose the most suitable protocol for each cluster to provide the best performance. The simulation results indicate that our proposed multi-protocol framework provides better performance compared to traditional single-protocol architectures.

Investigating Energy Efficiency of Mobile Ad-hoc Network Routing Protocols

Mobile Ad-hoc Network (MANET) is a wireless network that comes with a few routing protocols which have varied mechanisms. Studies show that routing operations consume energy while current research focuses more on MANET routing protocol operation and its performance evaluation, the required energy for successful routing operations equally demands quality attention of researchers. Hence, the need to expand the scope of study on MANET routing protocols to the neglected area of studies. To bridge the research gap between MANET routing protocols and energy consumption, this paper used residual energy to analyze routing protocols’ energy efficiency as a metric to analyze selected routing protocols; Destination Sequence Distance Vector (DSDV) and Ad-hoc On-demand Distance Vector (AODV) via simulation. It also compared the amount of energy required to transmit the data packets to their destinations in DSDV and AODV. Results, in terms of energy efficiency, indicated that AODV was better than DSDV because it consumed less energy for its successful routing operations

Performance and statistical analysis of ant colony route in mobile ad-hoc networks

<div class="WordSection1"><p>Research on mobile ad-hoc networks (MANETs) is increasing in popularity due to its rapid, budget-friendly, and easily altered implementation, and relevance to emergencies such as forest firefighting and health care provisioning. The main concerns that ad-hoc networks face is dynamic topology, energy usage, packet drop rate, and throughput. Routing protocol selection is a critical point to surmount alterations in topology and maintain quality in MANET networks. The effectiveness of any network can be vastly enhanced with a well-designed routing protocol. In recent decades, standard MANET protocols have not been able to keep pace with growing demands for MANET applications. The current study investigates and contrasts ant colony optimization (ACO) with various routing protocols. This paper compares ad-hoc on-demand multi-path distance vector (AOMDV), dynamic source routing protocol (DSR), ad-hoc on-demand distance vector routing (AODV), and AntHocNet protocols regarding the quality of service (QoS) and statistical analysis. The current research aims to study the behavior of the state-of-the-art MANET protocols with the ACO technique. The ACO technique is a hybrid technique, integrating a reactive route maintaining technique with a proactive method. The reason and motivation for including the ACO algorithm in the current study is to improve by using optimization algorithms proved in other domains. The ACO algorithm appears to have substantial use in large-scale MANET simulation.</p></div>

A Versatile MANET Experimentation Platform and Its Evaluation through Experiments on the Performance of Routing Protocols under Diverse Conditions

Mobile Ad hoc Networks (MANETs) are characterized by highly dynamic phenomena and volatility. These features have a significant impact on network performance and should be present in the scenarios of experiments for the assessment of MANET-related technologies. However, the currently available experimentation approaches suffer from limitations, either employing overly abstract simulation-based models that cannot capture real-world imperfections or drawing upon “monolithic” testbeds suited only to a narrow set of predetermined technologies, operational scenarios, or environmental conditions. Toward addressing these limitations, this work proposes a versatile platform that can accommodate many of the complexities present in real-world scenarios while still remaining highly flexible and customizable to enable a wide variety of MANET-related experiments. The platform is characterized by a modular architecture with clearly defined modules for the signaling between peer mobile nodes, the tracking of each node’s location and motion, the routing protocol functionality, and the management of communication messages at each node. The relevant software runs on inexpensive Raspberry Pi-based commodity hardware, which can be readily attached to robotic devices for moving the network nodes in accordance with controlled mobility patterns. Moreover, through an appropriate tuning of certain modules, a number of important operational conditions can be precisely controlled through software, e.g., restricting the communications range (thus reducing the network density) or for emulating the mobility patterns of nodes. The effectiveness and versatility of the proposed platform are demonstrated through the realization of a series of experiments on the performance comparison of selected routing protocols under diverse network density conditions.

A Directional Selective Power Routing Protocol for the Internet of Underwater Things

The Internet of Underwater Things (IoUT) has lately gained popularity as a means of facilitating a wide range of underwater applications. In the IoUT, underwater communication is best accomplished by the usage of acoustic waves, whereas the terrestrial communication between the surface sinks and the on-shore control stations is typically achieved using radio waves. As a result, the greatest portion of an IoUT is enabled by the underwater acoustic sensor network (UASN), where the specific issues provided by the use of acoustic waves, the underwater node mobility, and the localization difficulties have yet to be addressed. In this paper, we discuss the challenges faced by the IoUT in terms of the currently proposed routing protocols and propose a Directional Selective Power Routing Protocol (DSPR) to cope with most of these challenges. The proposed protocol (i.e., DSPR) uses the angle of arrival and the sender depth information to find the best direction to the surface sink. In addition, the DSPR uses selective power control to enhance the delivery ratio and ensure connectivity while reducing energy consumption. To testify the performance of the proposed protocol, intensive simulation experiments have been conducted. The simulation results show that the proposed DSPR protocol outperforms two variations of the fixed directional routing (DR) protocol and the variable power depth-based routing (VDBR) protocol in terms of energy consumption and delivery ratio. For instance, the proposed DSPR protocol achieves at least 8 times enhancement in energy consumption compared with VDBR. In addition, DSPR saves around 30% of energy consumption over the DR protocols when the network is mobile. Moreover, the DSPR protocol acquires a delivery ratio above 90% for static/dynamic scenarios in both sparse and dense networks.

Improved spider monkey optimization‐based multi‐objective software‐defined networking routing with block chain technology for Internet of Things security

AbstractInternet of Things (IoT) can be connected with remote smart devices to offer global connectivity, which consists of handling, sensing, and transmission of real‐time data gathered from different devices with human intervention. The conventional security schemes are also faced with limitations like collecting and monitoring data through the IoT network to provide defense against cyber‐attacks for delivering the optimal security. To address the current challenges, this proposed model plans for integrating the energy‐efficient software‐defined networking (SDN) and block chain‐based IoT networks. The proposed model adopts a new meta‐heuristic‐based clustering protocol for optimal communication. The architecture uses the private and public block chains for offering the peer to peer communication to eliminate the proof‐of‐work (POW) issue. The contribution of a deep learning algorithm called optimized deep neural network (ODNN) is employed for detecting the malicious IoT device based on the energy characteristics of each node. Once the malicious nodes are detected, the optimal cluster head selection and optimal routing is accomplished by the adoption of neighborhood‐based spider monkey optimization (N‐SMO). The experimentation results show that the proposed model ensures the efficient cluster head selection and optimal routing protocol on the cluster structure with the traditional meta‐heuristic‐based algorithms.

Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedingly flexible manner during the second stage. Extensive simulation has been done and results are evaluated to point out the strength of the proposed work, while dynamically varying the specific requirements of IoT applications. We observe that the work proposed is path-breaking the prevailing methods, where a specific routing protocol is employed throughout the period of time. It’s clearly shown that the proposed, Low-cost Context-Aware Protocol Switching (LCAPS) scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.

A QoS Aware Cluster Head Selection and Hybrid Cryptography Routing Protocol for Enhancing Efficiency and Security of VANETs

Nowadays, VANET (vehicular ad hoc network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. Additionally, the dynamic change in the network routing affects the network’s security, makes it vulnerable to various attacks, and causes data loss. In order to overcome these drawbacks an efficient and highly secured routing protocol is needed. Subsequently, in this research, a new routing protocol is proposed that has the combination of quality of service (QoS)-aware cluster head (CH) selection and hybrid cryptography, which is named QoS+. The QoS+ protocol is mainly divided into QoS-based CH selection and hybrid cryptography modules. The CH selection module performs based on QoS parameters attempt to provide reliable and stable clusters and improve the firmness and connectivity during the communication process of the network. The hybrid cryptography module contains advanced encryption standard (AES) and elliptic curve cryptosystems (ECC) algorithms. It attempts to improve the security and privacy of the network. The QoS+ protocol is evaluated by a developed VANET simulator using NS2 software. The simulator consists of a network model, a load model, and an attack model. Various speed and transmission ranges and gray hole and wormhole attacks are used in the simulator. The outcome calculated from the performance analysis shows that the proposed QoS+ protocol has 7% to 24% higher message success rate, 500 to 800 higher packets normalized routing load, 350 to 550 Kbps higher throughput, 5% to 17% higher efficiency and 50ms to 12ms lower end to end delay when compared with the earlier works of ECHS and KMSUNET. The proposed QoS+ protocol also achieves superior performance in terms of CH efficiency, cluster member efficiency, and average cluster number with various speeds and transmission ranges.

Multiple intersection selection routing protocol based on road section connectivity probability for urban VANETs

An efficient routing protocol is of great significance to improve vehicular ad hoc networks (VANETs) performance. However, due to complex road conditions, intermittent connectivity among vehicles and rapid changes of network topology, how to design an efficient routing protocol in an urban environment to transmit the data packet to the destination remains a challenging problem. In this paper, we propose a multiple intersection selection based on road section connectivity probability routing protocol for vehicle–vehicle (V2V) communication in urban VANETs, which considers the vehicle distribution influenced by traffic lights. First, we present a method to calculate the road section connectivity probability of a two-way lane with traffic lights consideration. Then, we establish an optimization model to select the optimal road path from the source node to the destination node based on the road section connectivity probability. Furthermore, we analyze the relay node selection on the road section after the optimal road path is determined, which discusses the number of neighbor vehicles in the communication range of the vehicle carrying packets and jointly considers the position, speed and direction of vehicles. Simulation results demonstrate that our proposed routing protocol increases the packet delivery rate and reduces the end-to-end delay.

Performance Comparison of Fresh and Spray & Wait Protocol through ONE Simulator

Opportunistic Networks (OppNets) are becoming the prime interest for researchers day-by-day due to the large scope of further research into it. An opportunistic network is used to transmit data in an environment of intermittent connectivity. OppNet offers a variety of routing protocols based on different strategies. Each protocol has some pros and cons. Among the available ones, Fresh Routing Protocol and Spray-and-wait Routing Protocol are the most efficient routing protocols in terms of performance during data transmission. This paper aims to compare these two different routing protocols through simulation on the ground of standard performance metrics. It is believed that this simulation comparison will help upcoming researchers in the selection of appropriate routing protocol as per their requirement

Wireless Sensor Networks: A Review on Sensor Deployment and Routing Protocols for Different Application

In the past years, many research works has been performed on the basics of the wireless sensor networks for different application namely precision agriculture, environment, medical care, security and surveillance. Whereas, WSN faces the different kinds of challenges that includes energy consumption, routing protocol algorithm and selection of the sensor node from the particular application environment. Therefore, the implementation of the wireless sensor networks has overcome a numerous amount of challenges in the practical life. In this paper, we mainly focused on the two issues which mainly affect the transmission of the data to the base station they are sensor node deployment which is used to improve the coverage area of the environment and the routing protocol which is used to select the node to maintain a particular route for a network. Atlast, we have discussed about design challenges that are faced during the designation of the wireless sensor networks.

Analyzing The Performance Of On-Demand Routing Protocols Based On Rainfall Intensity In Wi-Fi Networks

During rainfall the information signal while transmission gets attenuated and the network performance is affected greatly in the regions where frequent rainfall occurs as they are prone to varying climatic conditions. Therefore in order to maintain a reliable communication the implementation of a MANET protocols during rainstorm or similar unfavorable environmental conditions should be paid great attention for the selection of routing protocols. The routing protocol must provide efficient and effective improvement in performance aspects such as total number of packets received, throughput, mean jitter and an end to end delay. This paper undergoes evaluation based study of MANET reactive/on-demand routing protocols namely LANMAR, OSPFV2, ANODR and DYMO in Wi-Fi networks (IEEE802.11n). The behavior of routing protocols is simulated and evaluated using Exata 5.1 network simulator by varying the intensity level of rainfall from light rain (2mm/hr) to cloud burst (100 mm/hr) to predict rainfall efficient routing protocol which can render maximum throughput and minimum delay.

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

Routing protocols in Mobile Ad Hoc Networks (MANETs) play a pivotal role in ensuring quality of service (QoS) and improving network performance. Selection of optimal routing protocol and suitable parameters for a given network scenario is a major task that ultimately affects the behavior of network. This work exploits machine learning (ML) techniques for the selection of adequate routing parameters and protocol by regression of parameters in given network scenario to ensure optimal performance. The network is trained based on parametric setup of expanding ring search mechanism (ERS) and random early detection (RED) technique to estimate network throughput, end to end (E2E) delay and packets delivery ratio (PDR) and is tested via wide-ranging simulations in varying network topologies. Both RED and ERS mechanisms are aimed to control link and node level congestion in the reactive routing protocols and our aim is to select the best suited parameters for given network topologies based on ERS and RED parametric setups and improve performance for ensuring QoS. ML algorithms are trained and tested for their performance in varying network topologies. We have exploited these models with best performance for ERS and RED based routing in given topological arrangements. The performance of the ML algorithms is evaluated on the basis of root mean squared error (RMSE) and mean absolute error (MAE) for regression settings. Prediction models with up to par RMSE and MAE out-turns are attained and exploited for selection of suitable ERS and RED parameters and routing protocols in order to ensure the QoS for given network scenario. Variants of standard routing protocols are devised based on their performance and the ML techniques are exploited for prediction of QoS parameters to decide on the optimal variant that attains significant improvement in performance. Results are shown to confirm that considerable improvement in QoS is attained.

New Development of Physarum Routing Algorithm With Adaptive Power Control

Internet of Things (IoT) network infrastructure now days growing very significantly, along with the number of smart devices being used in everyday life. The existence of obstacles in the Internet of Things (IoT) network area is the biggest challenge in the IoT routing network. This obstacle in the IoT network can be a wall or the absence of a router in a specific area. The routing development to facing obstacles problems has been done a lot, however, there no routing algorithm that can overcome that obstacle. This research proposed a Physarum Routing Algorithm with Adaptive Power Control (PRA-APC) to solve the problem of IoT obstacles in the network. PRA-APC method provides sufficient power to the last node before the obstacle so it can be able to pass through the obstacle to the final destination node. PRA-APC routing method will compare with other routing algorithms such as Physarum inspired Routing Protocol (P-iRP) and Robust Selection Physarum-inspired Routing Protocol (RS-PRP). The PRA-APC protocol can develop the shortest routing path, 70% better than P-iRP and RS-PRP. The result of the delay more efficient, network lifetime is longer, throughput is much better than other routing algorithm protocols. The PRA-APC routing performance improvement results in efficiency and balance of resources in the network so that it can extend the life span of IoT networks that have limited resources in the local IoT network infrastructure. The new development of the PRA-APC routing protocol can provide solutions to overcome the problem of obstacles in the IoT network.