Improve Routing Performance Research Articles

Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

In this study, a research was done to examine the effectiveness of deep learning models, such as the Artificial Neural Networks, Convolutional Neural Networks, Gated Recurrent Units and Generative Adversarial Networks, to boost routing’s efficiency and performance in the Mobile Ad Hoc Networks through mobility prediction. In total, 10 experiments were conducted, and ANNs demonstrated an average prediction accuracy of 0.86, MAE of 0.10, RMSE of 0.16, and correlation coefficient of 0.93. CNNs showed the most impressive performance, featuring the following indicators: the average prediction accuracy of 0.87, MAE of 0.09, RMSE of 0.15, and the correlation coefficient of 0.94. GRUs, in their turn, displayed quite decent performance, with an average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94; in the meanwhile, GANs can be also seen as offering a decent performance, with the average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94. In the end, the identified findings imply that deep learning models can be used to enhance the routing efficiency in MANETs, since they can predict the node movement with the high level of accuracy. The identified changes are possible to lead to the creation of effective wireless communication networks.

Improving routing performance in social internet of things with FNRA: The free node-based approach

In the distributed environment of the Social Internet of Things (SIoT), where nodes rely primarily on the “store-carry-forward” mechanism for message delivery, it has been determined that forming community structures is advantageous for message propagation. Nevertheless, due to the dynamic nature of nodal social relationships and inaccurate community division, routing algorithm performance is inferior. This paper proposes a novel SIoT routing algorithm, the Free Node-based Routing Algorithm (FNRA) to resolve these issues. The FNRA algorithm presents a comprehensive framework for message forwarding, differentiating between in-community and out-of-community scenarios. The algorithm compares the forwarding capabilities of relay and source nodes within the community. A graph-theoretic approach is used to classify source, relay, and destination nodes into four distinct structures outside the community. Considering node-node, node-community, community-node, and community-community interactions, the algorithm addresses situations where the source, destination, and relay nodes do not belong to the same community. FNRA dynamically selects appropriate forwarding strategies to expedite message delivery to the destination node by leveraging the community structure and the presence of free nodes. The FNRA algorithm exhibits superior performance through detailed experimental simulations by enhancing message delivery rate, decreasing network overhead, and reducing average latency compared to existing routing algorithms. This demonstrates the algorithm's effectiveness in optimizing overall network performance.

Policy Gradient-Based Core Placement Optimization for Multichip Many-Core Systems.

As many deep neural network models become deeper and more complex, processing devices with stronger computing performance and communication capability are required. Following this trend, the dependence on multichip many-core systems that have high parallelism and reasonable transmission costs is on the rise. In this work, in order to improve routing performance of the system, such as routing runtime and power consumption, we propose a reinforcement learning (RL)- based core placement optimization approach, considering application constraints, such as deadlock caused by multicast paths. We leverage the capability of deep RL from indirect supervision as a direct nonlinear optimizer, and the parameters of the policy network are updated by proximal policy optimization. We treat the routing topology as a network graph, so we utilize a graph convolutional network to embed the features into the policy network. One step size environment is designed, so all cores are placed simultaneously. To handle large dimensional action space, we use continuous values matching with the number of cores as the output of the policy network and discretize them again for obtaining the new placement. For multichip system mapping, we developed a community detection algorithm. We use several datasets of multilayer perceptron and convolutional neural networks to evaluate our agent. We compare the optimal results obtained by our agent with other baselines under different multicast conditions. Our approach achieves a significant reduction of routing runtime, communication cost, and average traffic load, along with deadlock-free performance for inner chip data transmission. The traffic of interchip routing is also significantly reduced after integrating the community detection algorithm to our agent.

Architecture and performance evaluation of fiber-granularity routing networks with supplemental grooming by wavelength conversion

Fiber-granularity routing networks that adopt wavelength-granularity add/drop and a limited number of wavelength converters are proposed. The express parts of nodes can be optical switches, used as fiber cross-connects (FXCs), as they offer low-cost scaling. However, FXCs cannot route optical signals on a wavelength basis, unlike present wavelength cross-connects (WXCs). This routing restriction degrades the statistical multiplexing effect and decreases fiber utilization efficiency. The restriction is slightly relaxed by the use of wavelength-granularity add/drop operations; however, our numerical simulations show that the degradation can reach around 50% when the number of optical paths in a fiber is 25. This substantial degradation is cost-effectively alleviated by introducing wavelength/optical path grooming with a limited number of wavelength converters, which enhances routing flexibility. Numerical simulations show that our network architecture improves routing performance by 86% compared to fiber-granularity routing networks without wavelength converters, and the performance deterioration relative to present WXC-based networks is suppressed to ∼14%.

An Energy-Efficient Protocol based on Recursive Geographic Forwarding Mechanisms for Improving Routing Performance in WSN

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols.

Q-learning-based routing inspired by adaptive flocking control for collaborative unmanned aerial vehicle swarms

A collaborative unmanned aerial vehicle (UAV) swarm can support various applications, including aerial surveillance and emergency communication. Owing to the high mobility, limited energy of UAVs, and frequent link breakages, data routing from remote UAVs to a base station may produce retransmissions, loops, energy holes, and long delay. In a UAV swarm network, therefore, relative mobility, path stability, and delay should be jointly taken into consideration to improve routing performance because they are highly coupled with each other. However, they are not properly exploited in the existing literature. To address these issues, in this paper, a Q-learning (QL)-based routing protocol inspired by adaptive flocking control (QRIFC) is proposed. The proposed adaptive flocking control algorithm generates optimal mobility with fairness in travel distance for each UAV to control the optimal node density. It also addresses the trade-off between aerial coverage and quality of service in connectivity by imposing constraints on the minimum separation distance and maximum allowable inter-UAV spacing using two-hop neighbor information. Additionally, it provides a stable link duration (LD) between neighboring UAVs and optimizes the control overhead. Furthermore, QL performs multi-objective optimization by utilizing a new state exploration and exploitation strategy to select an optimal routing path in terms of delay, stable path selection defined by predictive LD, and energy consumption. According to an extensive performance study, the proposed QRIFC outperforms existing routing protocols by 21-40 percent of average end-to-end delay and 9-23 percent of average packet delivery ratio, with less retransmissions.

Energy Based Random Repeat Trust Computation in Delay Tolerant Network

As the use of mobile devices continues to rise, trust administration will significantly improve security in routing the guaranteed quality of service (QoS) supply in Mobile Ad Hoc Networks (MANET) due to the mobility of the nodes. There is no continuance of network communication between nodes in a delay-tolerant network (DTN). DTN is designed to complete recurring connections between nodes. This approach proposes a dynamic source routing protocol (DSR) based on a feed-forward neural network (FFNN) and energy-based random repetition trust calculation in DTN. If another node is looking for a node that swerved off of its path in this situation, routing will fail since it won’t recognize it. However, in the suggested strategy, nodes do not stray from their pathways for routing. It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route, based on their pattern of mobility. The EBRRTC-DTN algorithm (Energy based random repeat trust computation) is based on the time that has passed since nodes last encountered the destination node. Compared to other existing techniques, simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node, which improves routing performance, increases the number of delivered messages, and decreases delivery delay. Therefore, the suggested method is better at providing better QoS (Quality of Service) and increasing network lifetime, tolerating network system latency.

Improving the geo-drone-based route for effective communication and connection stability improvement in the emergency area ad-hoc network

Current work focuses on establishing communication connectivity that is specially designed for wireless mobile sensor networks, especially the high connectivity stability of the disaster area network. Existing system focuses on location initial communications and utilizes networks based on most Internet Protocol (IP) frameworks. To solve these problems, reduce the number of neighboring countries (node density) and avoid high mobility notes (node speed). This proposed Geographic Node Speed Drone Based Routing Path Optimization (GNSDRPO) system to significantly reduce overall overhead and node speed improves overall performance by detecting the area of disaster. To keep the drone-based disaster network in the area, this drone communicates from time to time and shares information with the anchor tip. Geo-tracking appears to be a promising approach to improving routing performance on the MANET. Geographic navigation appears to be a promising approach to improving routing performance on MANET. This method is to reach the anchor end node using a geographical map-based mapping (GGMBM). The mobile anchor enables GPS on the mobile network and constantly broadcasts its location to assist locations. In the first step, the node looks for the expected ETX (Expected number of Transfers) locally and remotely and calculates the distance estimated by it. RSSI results are stored in the node's local memory. The node calculates the expected ETX (Expected number of Transfers), connection loss rate and new location information over short distances. Freeway heuristic algorithm improves data speed and performance and determines path, optimization problem. This proposed method improves efficient communication, increases efficiency and reduces latency in the disaster area network compared to other methods.

Refining Location-Aided Routing (LAR) through Proactive Algorithm

One of the weaknesses in Location-Aided Routing (LAR) is the delay due to partial flooding of data packets throughout the ad hoc network during route discovery. Systematic literature review indicates that very little or no studies conducted to seek a solution to this routing weakness in LAR. This study proposes introduction of periodic updates of location information among the nodes as a solution to minimizing latency. Proactive-LAR (P-LAR) eliminates partial flooding, thus reducing latency while advancing routing performance of traditional LAR. As a research scope, this study uses Angle of Arrival (AoA), Time of Arrival (ToA), Time Difference of Arrival (TDoA) and the expected distance of nodes and the direction of movement as the only location information details. Moreover, the simulation on OMNET ++ is limited to the initial expected zone of LAR Scheme 1. Simulation of the modified LAR Scheme 1 algorithm indicates that inclusion of proactivity as an algorithmic aspect of LAR augments general data packets throughput, latency, packets delivery ratio while minimizing the number of packets dropped. Nodes mobility in simulation was considered stationary. The simulation results—as analyzed through RapidMiner, suggest that proactive algorithmic element in LAR routing algorithm can potentially minimize partial flooding thus improving routing performance while minimizing routing overheads such as jitter and delay.

Exploiting trust, influence, and physical proximity for improving routing performance in vehicular ad hoc networks: The SCALE protocol

SummaryVehicular ad hoc networks (VANETs) are receiving increased attention, and several routing protocols have been proposed already by the research community. In such a framework, social aspects and human mobility are important performance enablers, which however are mostly overlooked. In this work, we propose the socially aware CLWPR (SCALE) protocol, significantly enhancing cross‐layer weighted position‐based routing (CLWPR), a routing protocol for urban VANET environments, combining social properties such as trust, influence, and users' individual mobility patterns to its core design in order to support an efficient content dissemination scheme. The design principles of SCALE that rely on both online and offline social metrics quantifying relationships on social networking platforms and opportunistic contacts of nodes due to physical proximity, respectively, are presented, while nodes with close online and offline social relationship are favored as next forwarder nodes. Subsequently we provide the performance evaluation of SCALE against other routing protocols in distributed vehicular networks, employing representative urban scenarios with synthetic and real traffic. It is shown that the proposed approach presents improved performance in terms of Packet Delivery Ratio (PDR) and throughput when compared with other similar protocols by an average of 37% in scenarios with synthetic dataset and 58% with real dataset for PDR and by an average of 45% in scenarios with synthetic dataset and 61% with real dataset for throughput.

R-SOR: Ranked Social-based Routing Protocol in Opportunistic Mobile Social Networks

Exploiting social information to improve routing performance is an increasing trend in Opportunistic Mobile Social Networks (OMSNs). Selecting the next message’s relay node based on the user’s social behavior is a critical factor in attaining a high delivery rate. So, to ascertain the most efficient selection of the next relay, the correlation between daily social activities and the social characteristics in the user profiles can be exploited. In this paper, we consider the impact of the social characteristics on mobile user activities during certain periods of the day and then rank these characteristics based on their relative importance in order to be included in the routing protocol. These processes consolidate the proposed Ranked Social-based Routing (R-SOR) protocol to provide an effective way for data dissemination in OMSN. We use the real data set INFOCOM06 to evaluate the proposed protocol. The experimental results show that the proposed protocol has higher routing efficiency than flooding-based protocols such as Epsoc and Epidemic, prediction-based protocols such as PRoPHET, and social-based protocols such as MSM and Bubble Rap.

An Efficient and Stable Routing Algorithm in Mobile Ad Hoc Network

Mobile Ad hoc Network (MANET) is mainly designed to set up communication among devices in infrastructure-less wireless communication network. Routing in this kind of communication network is highly affected by its restricted characteristics such as frequent topological changes and limited battery power. Several research works have been carried out to improve routing performance in MANET. However, the overall performance enhancement in terms of packet delivery, delay and control message overhead is still not come into the wrapping up. In order to overcome the addressed issues, an Efficient and Stable-AODV (EFST-AODV) routing scheme has been proposed which is an improvement over AODV to establish a better quality route between source and destination. In this method, we have modified the route request and route reply phase. During the route request phase, cost metric of a route is calculated on the basis of parameters such as residual energy, delay and distance. In a route reply phase, average residual energy and average delay of overall path is calculated and the data forwarding decision is taken at the source node accordingly. Simulation outcomes reveal that the proposed approach gives better results in terms of packet delivery ratio, delay, throughput, normalized routing load and control message overhead as compared to AODV.

Topology Independent Multipath Routing for Data Center Networks

Data center communication networks are characterized by fast and intensive traffic changes, to the extent that a statistical approach to routing has significant drawbacks. This paper proposes the first routing solution that takes into account every data center flow. In order to achieve the required performance, the proposed algorithm operates in the data plane, i.e. in the packet processing pipeline of the programmable packet forwarding chips. Route updates are triggered by the arrival of new flows, and are immediately transferred to the upstream switches in order for them to direct newly arriving flows along the best path. This enables the proposed algorithm to handle fast and intensive traffic changes, such as large-scale flow incast communication patterns which historically presented challenges to the data center routing algorithms. With the explicit knowledge of the current path state, the proposed algorithm can improve routing performance in the data center topologies used today. Additionally, being able to operate with high performance in arbitrary topology, the proposed solution can enable the introduction of new topologies into the data center networks, instead of topologies designed with the goal of supporting equal-cost multipath routing which are dominant in modern data centers.

Data Collection Mechanism Based on Wavelet Multi-Resolution for Opportunistic Social Networks

With the shaping of universal computing concept and the development of microelectronics technology, the mobile terminal devices have the strong functions of computing, storage and communication. The opportunistic social networks composed of a large number of terminal devices can be widely used in various scenarios by deploying them anytime and anywhere. One of its most important tasks is to collect data in order to communicate between people and things. The existing researches mainly focus on routing strategies that aim to improve the performance of data collection by optimizing the routing algorithm. However, the inherent characteristics of the opportunistic social networks such as the intermittent communication opportunities make it difficult to improve routing performance because nodes can not get global network topology information. In order to solve this problem, we should establish an efficient data collection mechanism based on wavelet multi-resolution to improve the efficiency of data collection from the source, which mainly studies the multi-resolution compression storage method of node data, the spatial multi-resolution data hierarchical storage framework, and the multi-resolution data management mechanism of mobile node. The experimental results show that the multi-resolution communication mode based on integer wavelet transform can greatly reduce the amount of data in the network and the energy consumption of nodes, and it is beneficial to the data collection of opportunistic social networks.

A Vehicular Service-Oriented Dynamical Routing Searching Algorithm in Software-Defined BusNet

With the development of the vehicular network, BusNet-based service is of great interest to both bus companies and public users. How to provide service and ensure service quality is considered as key points for the promotion of such kinds of applications. In this paper, a system architecture, which is designed based on multiaccess edge computing (MEC) architecture and software-defined network (SDN), is proposed. Moreover, considering the problem of the dynamicity of the network, a new routing path optimization algorithm, SPSO (serial particle swarm optimization), is proposed, while a dynamic weight matrix is used to improve routing performance. The comprehensive simulation results demonstrate that the proposed method can well adapt to the dynamic change of network topology and improve communication performance significantly.

Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks.

The swarm intelligence (SI)-based bio-inspired algorithm demonstrates features of heterogeneous individual agents, such as stability, scalability, and adaptability, in distributed and autonomous environments. The said algorithm will be applied to the communication network environment to overcome the limitations of wireless sensor networks (WSNs). Herein, the swarm-intelligence-centric routing algorithm (SICROA) is presented for use in WSNs that aim to leverage the advantages of the ant colony optimization (ACO) algorithm. The proposed routing protocol addresses the problems of the ad hoc on-demand distance vector (AODV) and improves routing performance via collision avoidance, link-quality prediction, and maintenance methods. The proposed method was found to improve network performance by replacing the periodic “Hello” message with an interrupt that facilitates the prediction and detection of link disconnections. Consequently, the overall network performance can be further improved by prescribing appropriate procedures for processing each control message. Therefore, it is inferred that the proposed SI-based approach provides an optimal solution to problems encountered in a complex environment, while operating in a distributed manner and adhering to simple rules of behavior.

Toward Swarm Coordination: Topology-Aware Inter-UAV Routing Optimization

Swarm unmanned aerial vehicles (UAVs) is an approach to the coordination of multiple UAVs as a system, which has huge advantages on mission capabilities, such as cooperative search, border surveillance, and situation awareness. For better swarm coordination, a robust inter-UAV network, especially in ad hoc mode, is needed. Due to the dynamic nature of UAV networks owing to mobility and topology change, adapting routing quickly to topology changes is one of the key components of UAV networks. Based on investigating the relationship between the swarm formation control and the network topology, we propose a proactive topology-aware scheme to track the network topology change. Through simulations on Qualnet and real-world experiments with five quadrotors, the results confirmed that the proposed scheme reduces the average delay and improves routing performance significantly.

Evaluating TCP performance of routing protocols for traffic exchange in street-parked vehicles based fog computing infrastructure

As most vehicles remain parked 95% of its time, this suggests that leveraging the use of On-board Units (OBUs) in parked vehicles would provide communication and computation services to other mobile and fixed nodes for delivery of services such as multimedia streaming, data storage and data processing. The nearby vehicles can form an infrastructure using IEEE 802.11p communication interface, facilitating communication, computation and storage services to the end users. We refer to this as a Vehicular Fog Computing (VFC) infrastructure. In this study, using NS-2 simulator, we investigate how six routing protocols consisting of two proactive routing protocols, Destination Sequence Destination Vector (DSDV) and Fisheye State Routing (FSR); two reactive routing protocols, Ad Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR); and two geographic routing protocols, Distance Routing Effect Algorithm for Mobility (DREAM) and Location Aided Routing (LAR) perform when forwarding TCP traffic among the parked vehicles that form a VFC infrastructure in an urban street parking scenario. In order to reflect an urban street parking scenario, we consider a traffic mobility traces that are generated using SUMO in our simulation. To the best of our knowledge, this work is the first effort to understand how vehicle density, vehicle speed and parking duration can influence TCP in an urban street parking scenario when packet forwarding decision is made using proactive, reactive and geographic routing protocols. In our performance evaluation, positive results are observed on the influence of parking duration in parked vehicles as TCP performance in all routing protocols increases with longer parking duration. However, variable speed in parked vehicles and moving vehicles in an urban street parking scenario may not have significant influence on TCP performance, especially in case of reactive and proactive routing protocols. Further, our findings reveal that vehicle density in a VFC infrastructure can noticeably influence TCP performance. Towards the end of the paper, we delineate some important future research issues in order to improve routing performance in a street-parked vehicle based VFC infrastructure.

Improved routing in MANET with optimized multi path routing fine tuned with hybrid modeling

Routing in MANET is very challenging due to movement of nodes. Predicting the node position and routing based on predicted positions helps to establish routing path with much longevity. Most predictions approaches are based on the past locations of the node. In this work a node location prediction based on the temporal and spatial characteristics with respect to its neighborhood is applied to estimate the probable locations using a hybrid model. A multi path routing protocol based on estimated probability locations with path diversion at necessary places along path is proposed for improved routing performance without larger packet overhead.

Dynamic Multi Constraint Routing Based Efficient Data Transmission in MANET for Improved Sink Point Streaming

The mobile adhoc network (Manet) has been identified as keen networking scenario in modern internet world. The most networking solutions have been enabled to access through mobile devices. The physical characteristics of mobile nodes keep changing the topology of network at each second. However, achieving higher streaming performance is most important in point of quality of service. There exist numerous techniques to route data packets between the source and sink/destination nodes, but suffer with poor performance. To overcome the deficiency, a dynamic multi constraint routing algorithm has been presented in this paper. The method considers different parameters like energy, lifetime, traffic, mobility speed, direction and hop count in route selection. According to the above mentioned parameters, the multi constraint algorithm estimates streaming support score (SSS) for any route to perform routing of packets. The algorithm improved routing performance of routing and increases the streaming rate in Manet.