A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET

The vehicular ad hoc network (VANET) has traditional routing protocols that evolved from mobile ad hoc networks (MANET). The standard routing protocols of VANET are geocast, topology, broadcast, geographic, and cluster-based routing protocols. They have their limitations and are not suitable for all types of VANET traffic scenarios. Hence, metaheuristics algorithms like evolutionary, trajectory, nature-inspired, and ancient-inspired algorithms can be integrated with standard routing algorithms of VANET to achieve optimized routing performance results in desired VANET traffic scenarios. This paper proposes integrating genetic algorithm (GA) in ant colony optimization (ACO) technique (GAACO) for an optimized routing algorithm in three different realistic VANET network traffic scenarios. The paper compares the traditional VANET routing algorithm along with the metaheuristics approaches and also discusses the VANET simulation scenario for experimental purposes. The implementation of the proposed approach is tested on the open-source network and traffic simulation tools to verify the results. The three different traffic scenarios were deployed on Simulation of Urban Mobility (SUMO) and tested using NS3.2. After comparing them, the results were satisfactory and it is found that the GAACO algorithm has performed better in all three different traffic scenarios. The realistic traffic network scenarios are taken from Dehradun City with four performance metric parameters including the average throughput, packet delivery ratio, end-to-end delay, and packet loss in a network. The experimental results conclude that the proposed GAACO algorithm outperforms particle swarm intelligence (PSO), ACO, and Ad-hoc on Demand Distance Vector Routing (AODV) routing protocols with an average significant value of 1.55%, 1.45%, and 1.23% in three different VANET network scenarios.

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

In the case of new technology application, the cognitive radio network (CRN) addresses the bandwidth shortfall and the fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity of resources and mobility. In this paper, we present a reconfigurable CRN-based cross-layer routing protocol with the purpose of increasing routing performance and optimizing data transfer in reconfigurable networks. Recently developed spotted hyena optimizer (SHO) is used for tuning the hyperparameters of machine-learning models. The system produces a distributor built with a number of tasks, such as load balance, quarter sensing and the development path of machine learning. The proposed technique is sensitive to traffic and charges, as well as a series of other network metrics and interference (2bps/Hz/W average). The tests are performed with classic models that demonstrate the residual energy and strength of the resistant scalability and resource.

Comparative analysis of routing techniques in chord overlay network

Overlay networks are not a new field or area of study. This domain of computing will someday drive P2P systems in various application areas such as block-chain, energy trading, video multicasting, and distributed file storage. This study highlights the two widely known methods of routing information employed in one of such overlay networks called chord. In this study, simulations of both routing modes (iterative and recursive) and their variations under no-churn (leaving and joining of nodes) and churn conditions was carried out. The routing parameter (successor list size) was varied for each of the routing techniques in a simulation study. The results obtained show that semi recursive routing gives a better routing performance under churn scenarios.

Cooperative Channel Selection With Q-Reinforcement Learning and Power Distribution in Cognitive Radio Networks

With the increasing number of wireless communication devices, there may be a shortage of non-licensed spectrum, and at the same time, licensed spectrum may be underutilized by the primary users. The utilization of licensed spectrum can be improved using cognitive radio techniques. The proposed work allows secondary users to use the correct slot period of the channel as per their need. Particle swarm optimization technique is used to optimize the resource allocation. The aim of the proposed work is to determine the optimal throughput and power of available channels between the communicating nodes and improve the routing performance by selecting the best channel. Mathematical equation is derived that represents the channel selection relationship from the Q-value, congestion throughput, and benefit value. Network simulator-2 is used to simulate the proposed work and compared with the existing work. From the simulation results, it is observed that routing performance is improved in terms of throughput, packet delivery ratio, delay, packet dropped, and normalized routing overhead.

Routing Performance Evaluation of a Multi-Domain Hybrid SDN for Its Implementation in Carrier Grade ISP Networks

Legacy IPv4 networks are strenuous to manage and operate. Network operators are in need of minimizing the capital and operational expenditure of running network infrastructure. The implementation of software-defined networking (SDN) addresses those issues by minimizing the expenditures in the long run. Legacy networks need to integrate with the SDN networks for smooth migration towards the fully functional SDN environment. In this paper, we compare the network performance of the legacy network with the SDN network for IP routing in order to determine the feasibility of the SDN deployment in the Internet Service provider (ISP) network. The simulation of the network is performed in the Mininet test-bed and the network traffic is generated using a distributed Internet traffic generator. An open network operating system is used as a controller for the SDN network, in which the SDN-IP application is used for IP routing. Round trip time, bandwidth, and packet transmission rate from both SDN and legacy networks are first collected and then the comparison is made. We found that SDN-IP performs better in terms of bandwidth and latency as compared to legacy routing. The experimental analysis of interoperability between SDN and legacy networks shows that SDN implementation in a production level carrier-grade ISP network is viable and progressive.

Routing Performance Evaluation of a Multi-Domain Hybrid SDN for its Implementation in Carrier Grade ISP Networks

Legacy IPv4 networks are strenuous to manage and operate. Network operators are in need to minimize the capital and operational expenditure of running network infrastructure. The implementation of Software-defined networking (SDN) addresses those issues by minimizing the expenditures in the long run. Legacy networks need to integrate with the SDN networks for the smooth migration towards the fully functional SDN environment. In this paper, we compare the network performance of the legacy network with the SDN network for IP routing in order to determine the feasibility of the SDN deployment in the Internet Service provider (ISP) network. The simulation of the network is performed in the Mininet test-bed and the network traffic is generated using distributed Internet traffic generator. Open network operating system is used as a controller for the SDN network in which SDN-IP application is used for IP routing. Round trip time, bandwidth, and packet transmission rate from both SDN and legacy networks are first collected and then the comparison is done. We found that SDN-IP performs better in terms of bandwidth and latency as compared to legacy routing. The experimental analysis of interoperability between SDN and legacy networks shows that SDN implementation in production level carrier-grade ISP network is viable and progressive.

PERFORMANCE EVALUATION OF ENHANCED MANHATTAN MOBILITY MODEL OVER GM, RWP, MANHATTAN GRID, SLAW, AND TLW MOBILITY MODELS IN MANETS

: The mobility model is the base of simulation experiments in the Mobile Ad-hoc Network. A composite model for mobility for city scenarios which includes a realistic model of obstacle avoidance and movement in the vertical direction, is proposed. The comparison of its performance with those of other available mobility models is encouraging. We believe that it can upgrade the routing performance. Here, we discuss the synthetic mobility models (Gauss-Markov, Random Waypoint, Manhattan Grid), and trace-based mobility models (Truncated Levy Walk, Self-Similar Least Action Walk). Then, we propose a new mobility model by replacing a speed calculating formula using Bonnmotion-3.0.1 on simulator NS2. The proposed mobility model, named Enhanced Manhattan Mobility Model, is compared with the existing Manhattan Grid mobility model in a tabulated form. AODV, DSR, and DSDV are analysed for above-mentioned mobility models against the proposed one. Furthermore, the accuracy of the best protocol over the best mobility model is investigated through Packet Delivery Ratio (PDR), throughput, average end-to-end delay, packet overhead, and packet drop rate performance metrics. Due to the smooth movements created by the proposed model, it shows an improvement of 1 percent to 7 percent in throughput, 0.8 percent to 1.7 percent in packet overhead, 1 percent to 7 percent in PDR, and 1 percent in dropped packets.

Delta Ruled First Order Iterative Deep Neural Learning for Sybil and Wormhole Attacks Detection in Healthcare Wireless Sensor Network

Detection and isolation of Sybil and wormhole attack nodes in healthcare WSN is a significant problem to be resolved. Few research works have been designed to identify Sybil and wormhole attack nodes in the network. However, the detection performance of Sybil and wormhole attack nodes was not effectual as the false alarm rate was higher. In order to overcome such limitations, Delta Ruled First Order Iterative Deep Learning based Intrusion Detection (DRFOIDL-ID) Technique is proposed. The DRFOIDL-ID Technique includes two main phase namely attack detection and isolation. The DRFOIDL-ID Technique constructs Delta Ruled First Order Iterative Deep Learning in attack detection phase with aim of detecting the occurrence of Sybil and wormhole attacks in healthcare WSN. After detecting the attack nodes, DRFOIDL-ID Technique carried outs isolation process with the objective of increasing the routing performance. During the isolation phase, DRFOIDL-ID Technique keep always the identified Sybil and wormhole attack nodes through transmitting the isolation messages to all sensor nodes in healthcare WSN. Hence, DRFOIDL-ID Technique improves the routing performance with lower packet loss rate. The DRFOIDL-ID Technique conducts the simulation process using factors such as attack detection rate, attack detection time, false alarm rate and packet loss rate with respect to a diverse number of sensor nodes and data packets. The simulation result proves that the DRFOIDL-ID Technique is able to improve the attack detection rate and also reduces the attack detection time as compared to state-of-the-art works.

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

The diverse applications of the internet of things (IoT) require adaptable routing protocol able to cope with several constraints. Thus, RPL protocol was designed to meet the needs for IoT networks categorized as low power and lossy networks (LLN). RPL uses an objective function based on specific metrics for preferred parents selection through these packets are sent to root. The single routing metric issue generally doesn’t satisfy all routing performance requirements, whereas some are improved others are degraded. In that purpose, we propose a hybrid objective function with empirical stability aware (HOFESA), implemented in the network layer of the embedded operating system CONTIKI, which combines linearly three weighty metrics namely hop count, RSSI and node energy consumption. Also, To remedy to frequent preferred parents changes problems caused by taking into account more than one metric, our proposal relies on static and empirical thresholds. The designed HOFESA, evaluated under COOJA emulator against Standard-RPL and EC-OF, showed a packet delivery ratio improvement, a decrease in the power consumption, the convergence time and DIO control messages as well as it gives network stability through an adequate churn.