Congestion-free Routing Research Articles

Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model

The emergence of the social internet of things (SIoT) network has brought forth distinctive challenges, including node mobility and varying densities, leading to congestion and hampered network efficiency. To overcome these issues, a congestion-free routing model for SIoT is proposed. This model combines the relationship-ordering points to identify the clustering structure (R-OPTICS) algorithm for intelligent node clustering based on relationships and ordering,along with a van emde boas tree (vEBT) for efficient path selection. R-OPTICS enables effective network management by clustering nodes appropriately. The model’s performance is evaluated using metrics such as Rand-Index (1.5765),Davies-Bouldin (-0.4305), and Silhouette Coefficient (1.71685) to assess average goodness values. vEBT identifies optimal paths between clusters, facilitating smart routing decisions. The primary objective of the model is to enhance network efficiency and alleviate congestion by intelligently routing data between clusters. Through extensive simulations, the proposed model outperforms existing routing methods, resulting in improved efficiency and congestion reduction. This congestion-free routing model presents a promising solution to address the unique challenges of SIoT networks, ensuring optimal performance and effective resource management.

A Statistical Analysis of Wireless Body Area Network using Clustering Based Routing Protocols

Recently, research on wireless body-area sensor networks (WBASN) or wireless body area networks (WBANs) has gained great importance in medical applications, and now plays an important role in patient monitoring. Clustering is the most important task in wireless sensor networks (WSNs) by data aggregation through each cluster head (CH). The development of technology has seen comfort in the domestic and professional life of an individual. However, such survival was not able to meet medical emergencies during the pandemic COVID-19 and other health surveillance scenarios. As a result, it is important to design an energy-efficient routing system for WBAN. Existing routing algorithms focus more on energy efficiency than security. A Secure Optimal Path-Routing (SOPR) protocol is proposed to identify and discover routes in WBAN that are secure against black-hole attacks. In this paper the sensor's wireless capability has been tested with a simulator to detect and transmit signals across a wide range of frequencies and to ensure that data is transferred successfully. CARF reduces the maximum work done by relay nodes in case of overloaded nodes and thereby increases the energy distribution in the network. The proposed CARF design reduces packet loss and time delay, thereby increasing network lifetime compared to other fuzzy and heuristic methods used for routing in WSNs. The proposed algorithm maintains more than 75% of the residual energy in the network at a minimum initial energy level of 1 J to enable data syncing. A SAC-TA approach for data aggregation in WSNs is presented. False injection attacks are identified based on traffic analysis at the time of the route discovery process. One-time key generation algorithm is introduced to eliminate malicious nodes from the network. The presented paper proposes a congestion-free routing framework for data transmission from source to sink nodes in WSNs. Congestion-aware routing mechanisms modeled using fuzzy sets (CARF) include operations such as establishing non-localized nodes, by which more paths can be created for traffic distribution, and predicting an optimal congestion.

Fine-Grained Boundary Conditions in Field-Based Routing

In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to tackle these network challenges. This routing approach draws inspiration from physics and employs a differential equation to model its behavior in finding efficient routes. Given the fundamental significance of boundary conditions in physics, where they play an essential role in shaping the solutions to the equation, exploring the impact of boundary conditions on field-based routing behavior within network domains becomes highly significant. However, despite their influence, the impact of boundary conditions has remained unexplored in existing studies on field-based routing. In this context, our work explores the boundary condition problem and introduces new advanced fine-grained boundary conditions for field-based routing. We demonstrate the superior performance of our proposed scheme, highlighting the substantial role of boundary conditions in network behavior. Our work holds significant value in that it explores the boundary condition problem, an aspect largely overlooked in previous research, and provides a viable solution, underscoring its crucial importance in routing enhancement.

DetSpace: Distributed congestion free routing for global deterministic network in mega-constellations

DetSpace: Distributed congestion free routing for global deterministic network in mega-constellations

A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

For the application of intelligent and green transportation systems (e.g., autonomous driving), traffic congestion is a severe challenge. So far, when traffic congestion is perceived for a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestion should be tackled with re-routing since the extra overhead (e.g., travel time, fuel consumption, and CO2 emission) caused by specific congestion might be lower than that of re-routing. Against this backdrop, a Prediction-based Route Guidance Method (PRGM) is proposed for intelligent and green transportation systems. To begin with, PRGM involves a novel hybrid and dynamic system architecture based on the collaboration of vehicle clusters and the cloud platform. Notably, a backup mechanism between adjacent cluster heads is designed to avoid the problem that the data might be lost during dynamic clustering. Furthermore, PRGM involves a novel traffic congestion control strategy, which is based on four procedures: perception about traffic congestion with three indexes (i.e., speed index, dense index, acceleration index); judgment about congestion type with four defined congestion types; prediction about congestion duration considering the formation of congestion (i.e., why and how the congestion is formed); route planning about vehicles considering congestion duration and the extra time overhead of re-routing. Simulations are performed, and they show that the proposed PRGM not only can perceive traffic congestion more precisely and timely but also can reduce the travel time, fuel consumption, and CO2 emission of vehicles.

Two-Tier Architecture for Congestion-Free Routing in Manet Using a Hybrid Optimization Algorithm

Two-Tier Architecture for Congestion-Free Routing in Manet Using a Hybrid Optimization Algorithm

Cross Layer based Congestion Free Route Selection in Vehicular Ad Hoc Networks

For creating a mobile network, the moving cars consider as nodes in the Vehicular Ad-Hoc Networks (VANETs). Each participating car is turned into a wireless router in the VANETs that allows the connecting and creating a network. To improve the comfort and safety of driving of automotive users, the vehicular environment system develops in the vehicular environment systems using the wireless access. The channel congestion causes the degradation of quality of service in such cases with higher vehicle density. The real-time and reliable communication is required for various safety applications of VANETs. The dense traffic network has included one of the major challenges as avoiding the communication channels’ degradation. To provide the network with efficient operation, most of the studies are recommended to use the appropriate congestion control methods. It’s important to note that many congestion control mechanisms are not implemented for event-driven real-time safety messages. Based on the congestion probability approach estimation, CFRS-CP-Congestion free route selection is introduced for minimizing the total number of data flow packets that passing through the congested nodes. At each node, the congestion probability is estimated using the proposed technique of CFRS-CP based on link quality, MAC overhead, neighbour density & vehicle velocity. Then, the estimated congestion probability is used for route assessment. The estimated probability value is appended to the control packets for comparison. All the available routes are assessed based on the estimated congestion probability which results in congestion free routing path for every round of data communication. The simulation results prove that the proposed method decreases end to end delay by 32% and improves PDR up to 30% and throughput up to 45% compared to the existing protocols.

A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle

In the Internet of Things (IoT), the advancement of the new modern era of Internet-off driving, known as the Internet of Vehicles (IoV), is aimed at the Intelligent Transportation System (ITS) to improve road safety and traffic. Therefore, IoV is better for traffic management. But perhaps, the main challenges in this type of network are that timely decision to make adequate decisions by a driver under certain conditions of rapid change in topology, high vehicular mobility, and frequent link failures is hard to improve road safety. Therefore, an optimized and congestion-free route is required to collect real-time data from vehicles. Thus, some of the latest bioinspired optimization routing algorithms in the IoV environment are presented in this study. Hence, monitoring speed limits, pollution checks, and emergency responses to traffic accidents should also be considered while performing vehicle routing to avoid traffic problems. In the previous decades, many route-directing protocols for the IoV environment have been proposed that can handle the requirements of reliability and security. But such routing of protocols suffers from high complexity and scalability limitations in big-scale networks, routing overlays, etc. Therefore, their strengths, weaknesses, and critical characteristics are compared using various criteria for such algorithms. Then, a suggestion is made to propose a prescribed combined model of a multimodular bioinspired approach to IoV routing. Finally, the main future directions of research in this sector are highlighted.

Congestion-free routing based on a hybrid meta-heuristic algorithm to provide an effective routing protocol by analyzing the significant impacts of QoS parameters in a dynamic VANET environment

Abstract A definitive form of Mobile Ad-hoc Network (MANET) is the Vehicular Ad-hoc Network (VANET), which delivers data communication using wireless transmission with the collection of vehicular nodes in a vehicular environment. The main goal of this VANET is to improve the Quality of Service (QoS) which is road safety, environmental kindliness, and logistics in intelligent transport. Moreover, mobility and routing play an important challenge for information interchange to the process in the VANET environment. In developing countries, smart cities are encountering problematic traffic congestion. Furthermore, in cluster communications, stable cluster formation and maintenance with the guarantying QoS has always remained a great challenge. In order to overcome the above-mentioned issue, this article proposes a congestion-free routing process ensuring QOS parameters. The main objective of the proposed scheme is to analyze the impact of QOS parameters while choosing the best routing path in Vehicular communication. The scheme also introduces the Hybrid-Harris Hawks Black Widow optimization-based VANET (H-HHBW-VANET) algorithm to improve QoS for routing between nodes. This algorithm differs from other algorithms in terms of optimal solution generation, which makes it more efficient. The proposed work is implemented in the working platform of NS2 to analyze the parameters such as Drop, Delay, Energy Consumption, Throughput, and Fairness Index. The simulation results provide better QOS service in terms of low energy consumption with higher throughput and low delay.

Congestion-Aware Rectilinear Steiner Tree Construction Using PB-SAT

In this paper, Pseudo-Boolean Satisfiability (PB-SAT)-based congestion reduction and overflow minimization techniques are introduced for constructing congestion-aware rectilinear Steiner trees for global routing in IC design. Since congestion in different routing channels is a big issue in routing nets, various congestion minimization approaches have been proposed by many researchers. However, the increasing complexity of integrated circuits and the reduction of their size make the congestion problem more complicated. The proposed method can produce a congestion-free routing solution by generating a suitable rectilinear Steiner tree for circuit nets. To reduce the complexity of the problem, the nets are solved individually using a Minimum Bounding Box (MBB)-based heuristic technique. The algorithm is validated by utilizing the well-known ISPD 98 global routing benchmarks. Experimental results prove that the methodology is capable of routing most of the nets within the given routing capacity satisfying all the routing parameters. The results also show that the algorithm can produce congestion-aware Steiner trees efficiently within a nominal time-bound.

Energy Efficient Data Distribution on Cloud with Optimal Routing Path Based Congestion Control in WSN Environment

Presently, Wireless Sensor Network (WSN) is the extremely standard services utilized in commercial in addition to industrial applications, owing to its technical development in the embedded computing devices’ processor, communication, along with less-power usage. Moreover, in the WSN’s advantages, the sensor data’s energy-efficient distribution onto the cloud data centre is amid the core challenges caused by higher data loads and also congestion. Aimed at overcoming these problems, this work proffers an energy-efficient Data Packet (DP) distribution protocol comprising an optimal routing path centred congestion control technique in the WSN environment. This system not just ponders the energy-efficient DP distribution nevertheless increments the sensor’s lifetime by choosing the Cluster Head (CH) centred on the sensor’s residual energy, packet loss factor, data forwarding rate, and minimal distance. Next, the numerous routing paths prevalent in inter-cluster and intra-clusters are detected utilizing the QLA2ODV method; the congested nodes are detected as of these paths utilizing LM-NN classifier; if congestion happens, the QLA2ODV distributes the DP onto the Base Station (BS) via the optimum congestion-free routing path. The optimal routing paths were elected as of the congestion-free paths utilizing the WFCSO method. Past obtaining the sensor data, the BS transmits the DPs onto the Fog Nodes (FNs). At last, the sensor data are effectively distributed onto the cloud data centres grounded on the data’s features and cloud features utilizing the BD-SBO technique. The experiential outcomes determine the proposed system’s effectiveness.

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling.

Optimal Routing based Load Balanced Congestion Control using MAODV in WANET Environment

A decentralized sort of network that can allow the nodes to communicate with them lacking any central controller is Wireless Ad hoc Networks (WANET). Network Congestions can befall on account of nodes' restricted Bandwidth (BW) together with dynamic topology. Network Congestions brings about data loss as it makes the Data Packets (DP) be dropped on the network. Therefore, in order to lessen Network Congestions, it is necessary to model Congestion Control (CC) systems aimed at the WANET. Thus, this paper offers an optimal routing centered CC scheme utilizing the Modified Ad hoc on-demand Distances Vector (MAODV) Routing Protocol (RP) aimed at the WANET. Here, primarily, the Source Node (SN) together with Destination Nodes (DN) is initialized, and after that, the MAODV discovers the multiple routing paths. Subsequently, Stochastic Gradients Descent Deep Learning Neural Network (SGD-DLNN) identifies the Congestion Status (CS) of every node in the discovered paths. In addition, the MAODV allocates the traffic over the optimum congestion-free routing path if congestion befalls. The Levy Flight Based Black Widow Optimization (LF-BWO) algorithm chooses the optimal routing paths as of congestion-free paths. Centered upon path lifetime, residual energy, link cost, together with path distance, this algorithm enhances the Data Transmission (DT) performance by means of discovering a path. The experimentation’s outcomes are rendered to exhibit the proposed RP’s effectiveness.

Congestion aware Adaptive Reverse Routing Strategy for Improving QoS in WSN

General purpose routing techniques for a WSN cannot be capable to give adequate functioning for various communication. This creates low adaptively provided with a general purpose routing technique ensuing in several connections reaching extremely congested. Ant Colony Optimization-Based Fault-Aware Routing (ACO-FAR) is used to balance the load in WSN. In this scheme, the node can calculate the present routes as well as deviation packets via a least congested path [17]. However, this scheme lacking the efficient awareness of bandwidth distribution, guaranteed quality of service in the WSN. To overcome these problems in this paper, Congestion aware Adaptive Reverse Routing strategy (CARS) for Improving Quality of Service (QoS) in WSN is proposed. It is a highly adaptive congestion free routing algorithms that also allocate traffic more equally and reduce network congestion. Reverse routing is used for diminishes the energy utilization and avoid the multiple retransmission in the network. Average delay as well as throughput is the two parameters that are utilized to calculate the performance of routing strategy. Simulation results demonstrate that throughput 36.76% enhanced dramatically whereas preserving 40.31%lesser latency performance.

PMCAR: proactive mobility and congestion aware route prediction mechanism in IoMT for delay sensitive medical applications to ensure reliability in COVID-19 pandemic situation

PurposeInclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption.Design/methodology/approachIn this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service.FindingsThe results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications.Originality/valueThe computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.

Congestion Free Routing Mechanism for IoT-Enabled Wireless Sensor Networks for Smart Healthcare Applications

Recently, the Internet of Things (IoT) topology has used to collect physical, physiological, vital signs of patients in consumer-centric e-health or consumer' wellness care services. In such healthcare systems, varieties of medical sensors are attached to the patients to collect vital signs from those who are under observation. The data gathering process in IoT-enabled Wireless Sensor Network (WSN) suffers from the congestion problem. The effect of this translates on missing packets, a decrease of reliability and throughput degradation in IoT-enabled WSN. This article proposes a distributed congestion control algorithm for IoT-enabled WSNs to effectively resolve the congestion for healthcare applications. The proposed scheme alleviates congestion by a priority-based data routing strategy. Furthermore, this article presents a priority queue based scheduling scheme for better reliability. We analyze the properties of the proposed congestion control mechanism mathematically and validate its performance through extensive simulation and real-life experiments. The application of this work can be used to an early warning system in detecting abnormal heart rate, blood pressure, ECG, EMG in the hospital/home care environment to the state-of-art diagnosis.

An improved congestion-aware routing mechanism in sensor networks using fuzzy rule sets

Congestion-free routing of packets across a Wireless Sensor Network (WSN) is an important issue to be addressed for quick response applications such as disaster management, healthcare systems, traffic control, etc. The existing routing algorithms have been developed by mainly considering hop-count leading to a gap in addressing congestion problem in the routing process. Congestion during routing leads to increase in packet drops, increased energy consumption and delay in delivery of packets at the sink node. Therefore, it is necessary to propose an optimal congestion aware routing mechanism that considers network parameters such as congestion level and energy dissipation in addition to hop count factor. Hence in this paper, a traffic prudent framework called Congestion Aware Routing using Fuzzy Rule sets (CARF) has been proposed for handling excess traffic conditions by identifying the non-localized node paths, there after adding them to the existing localized node paths and selecting a more reliable and a congestion alleviated path to sink node using fuzzy rule prediction. This results in reducing packet drops and increasing energy utilization. The proposed framework CARF is organized into two operational segments namely 1) Multiple Path Identification by positioning non-localized nodes and 2) Congestion Mitigated routing of data packets to sink node. First operation employs the Positioning of a Non-Localized node algorithm that is used to compute the unknown coordinates of a sensor node thereby utilizing them to form more packet transmission paths in addition to the existing paths. The Point In Which Side (PIWS) hop count-based geometric method is utilized here for finding non-localized nodes in the sensor network. Second operation uses an Enhanced Fuzzy-based Congestion Mitigation (ECFM) algorithm for estimation of congestion level in nodes using fuzzy rule sets by considering incoming data packets per second, bandwidth size and path reliability. This CARF framework has been simulated using NS-2 testbed. Depending on the two major network characteristics such as path reliability and congestion level, a network path is chosen for routing data packets. From the experiments conducted in this work, it is proved that the proposed CARF mainly alleviates congestion, besides reducing energy cost and interference such as shadowing and attenuation.

Congestion Free Routing in MANET based on Node Reliability Supported AODV

Congestion Free Routing in MANET based on Node Reliability Supported AODV

Performance Evaluation of Network Gateway Design for NoC based System on FPGA Platform

Network on Chip (NoC) is an emerging interconnect solution with reliable and scalable features over the System on Chip (SoC) and helps to overcome the drawbacks of bus-based interconnection in SoC. The multiple cores or other networks have a boundary which is limited to communicate with devices, which are directly connected to it. To communicate with these multiple cores outside the boundary, the NOC requires the gateway functionality. In this manuscript, a cost-effective Network Gateway (NG) model is designed, and also the interconnection of a network gateway with multiple cores are connected to the NoC based system is prototyped on Artix-7 FPGA. The NG mainly consists of Serializer and deserializer for transmitting and receiving the data packets with proper synchronization, temporary register to hold the network data, electronic crossbar switch is connected with multiple cores which are controlled by switch controller. The NG with the Router and different sizes of NoC based system is designed using congestion-free adaptive-XY routing. The implementation results and performance evaluation are analyzed for NG based NoC in terms of average Latency and maximum Throughput for different Packet Injection Ratio (PIR). The proposed Network gateway achieves low latency and high throughput in NoC based systems for different PIR.

Intelligent Beaconing Principle based Congestion Free Routing in Vanet

Intelligent Beaconing Principle based Congestion Free Routing in Vanet