Routing Approach Research Articles

RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Ring-Split: Deadlock-Free Routing Algorithm for Circulant Networks-on-Chip.

This article considers the usage of circulant topologies as a promising deadlock-free topology for networks-on-chip (NoCs). A new high-level model, Newxim, for the exploration of NoCs with any topology is presented. Two methods for solving the problem of cyclic dependencies in circulant topologies, which limit their applications for NoCs due to the increased possibility of deadlocks, are proposed. The first method of dealing with deadlocks is universal and applicable to any topology; it is based on the idea of bypassing blocked sections of the network on an acyclic subnetwork. The second method-Ring-Split-takes into account the features of circulant topologies. The results of high-level modeling and comparison of the peak throughput of NoCs for circulant and mesh topologies using deadlock-free routing algorithms are presented. It was shown that a new approach for routing in circulants (compared to mesh topology) shows up to 59% better network throughput with a uniform distribution of network load.

Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol (DEAOA-MHRP) for WSN. The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN. To accomplish this, DEAOA-MHRP model initially integrates the concepts of Different Evolution (DE) and Arithmetic Optimization Algorithms (AOA) to improve convergence rate and solution quality. Besides, the inclusion of DE in traditional AOA helps in overcoming local optima problems. In addition, the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance. In order to ensure the energy efficient performance of DEAOA-MHRP model, a detailed comparative study was conducted and the results established its superior performance over recent approaches.

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes. The clustering and routing procedures assumed that Non-Polynomial (NP) hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems. With this motivation, this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning (BCEHGS-RP) scheme for IoT assisted WSN. The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supported WSN environment. In addition, an effective multihop route planning approach was designed by the use of EHGS technique. The proposed EHGS technique is derived from the concept of Hill Climbing strategy (HCS) and HGS algorithm. Moreover, a fitness function with two parameters namely residual energy (RE) and inter-cluster distance to elect optimal routes. The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes. Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

A Hybrid MFCM-PSO Approach for Tree-Based Multi-Hop Routing Using Modified Fuzzy C-Means in Wireless Sensor Network

A Hybrid MFCM-PSO Approach for Tree-Based Multi-Hop Routing Using Modified Fuzzy C-Means in Wireless Sensor Network

Fuzzy Logic-Based Approach for Location Identification and Routing in the Outdoor Environment

Fuzzy Logic-Based Approach for Location Identification and Routing in the Outdoor Environment

Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity. Disaster management in smart cities can be considered as a safety critical application. Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN. Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks. This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM). The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region. To achieve this, EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering (YSGF-C) technique to elect cluster heads (CHs) and organize clusters. In addition, enhanced cockroach swarm optimization (ECSO) based multihop routing (ECSO-MHR) approach was derived for optimal route selection. The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime. The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work. For examining the improved outcomes of the EAMCR-RTDM system, a wide range of simulations were performed and the extensive results are assessed in terms of different measures. The comparative outcomes highlighted the enhanced outcomes of the EAMCR-RTDM algorithm over the existing approaches.

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating the pathways using multiple criteria and analytics. We present a Group Adaptive Hybrid Routing Algorithm (GAHRA) for gathering portability, which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations. Based on this aspect, the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach, with the objectives of enhancing the output of MANET routing computation in each hub. Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

Internet of Things: Underwater routing based on user’s health status for smart diving

Technological advancements affect everyday life; they benefited our daily routines, habits, and activities. Underwater diving is one of the most interesting and attractive activities for tourists worldwide but could be risky and challenging. When paths are not clear, diving might take additional time and effort leading to some health problems. Thus, providing divers with proper direction information to surf underwater can be useful and helpful. Also, monitoring diverse health statuses and alerting them in case of any undesirable condition can increase their safety. Smart devices such as mobiles, watches, sensor devices, cellular networks along with the Internet of Things (IoT) can all provide location-based services. Such services can help in providing the best path for the divers and monitor their health status during diving. This paper proposes a new underwater routing approach, called Underwater Routing for Smart Diving “URSD”, which provides divers with routing information to visit underwater cultural or natural resources and monitors their health status during the diving period. The URSD approach was simulated and compared with the shortest path. Results showed that the URSD helped divers to route within paths that have a larger number of nodes, furthermore, it could enhance and improve divers experience and help them mitigate underwater risks.

Reputation-Based Opportunistic Routing Protocol Using Q-Learning for MANET Attacked by Malicious Nodes

Irrespective of whether the environment is wired or wireless, routing is an important challenge in networks. Since mobile ad hoc networks (MANETs) are flexible and decentralized wireless networks, routing is very difficult. Furthermore, malicious nodes existing in the MANET can damage the routing performance of the network. Recently, reinforcement learning has been proposed to address these problems. Being a reinforcement learning algorithm, the Q-learning mechanism is suitable for an opportunistic routing approach because it not only adapts to changing networks, but also mitigates the effect of malicious nodes on packet transmission. In this study, we propose a new reinforcement learning routing protocol for MANETs called reputation opportunistic routing based on Q-learning (RORQ). Using this protocol, which works based on game theory, a reputation system can detect and exclude malicious nodes in a network for efficient routing. Thus, our method can find a routing path more effectively in an environment attacked by malicious nodes. The simulation results showed that the proposed method could achieve superior routing performance compared with other state-of-the-art routing protocols. In addition, compared to other algorithms, the proposed method demonstrated gains of up to 55% in terms of packet loss, up to 82% in terms of average end-to-end delay, and up to 28% in terms of energy efficiency in the blackhole attack scenario and up to 73% in terms of packet loss, up to 35% in terms of average end-to-end delay, and up to 12% in terms of energy efficiency in the gray hole attack scenario.

A novel traffic controlling model for enhancing routing in mobile Ad-Hoc network

The advancement in the communication and services demand for better QoS, but in parallel the increase in network traffic demands for an effective and significant traffic controlling model to manage high network traffic and also reduce congestion. In case of MANET circumstances it is further difficult to control and manage due to high mobilizing nodes and limited resource restrictions. Even MANET’s dynamic mobility creates high maintenance overhead for effective communication. In the past most of the designed routing protocols utilize reactive routing mechanism, which are primary source driven. In case of a link failure and data failure it has to update the source node and source node only have to decide next course of action. This will generates an additional overhead in the network and may reduce throughput of the protocols. This paper aims to propose a novel Traffic Controlling Model (TCM) to efficiently manage the routing overhead in MANET. The TCM will handle heavy traffic, which is the main cause of network congestion and overhead. It first determines the network traffic and evaluates the probable Node Link Life (NLL) to recommend the TCM process. The analysis of the results generated through the experiment evaluation shows that the data routing has improved and substantiates the effectiveness of the proposal in compared to the existing routing approaches.

A Deep Learning-Based Routing Approach for Wireless Mesh Backbone Networks

Optimal routing decisions are key in communication network environments to minimize bottlenecks such as traffic congestion and limited bandwidth. Routing in wireless mesh backbone networks is the focus of this study given that they are popular particularly for providing broadband connectivity to a huge number of users accessing and transmitting multimedia data hence are susceptible to communication-oriented bottlenecks. Existing routing solutions are mainly optimistic approaches. These mainly depend on link states, distance and hop counts which present a routing generalization bottleneck especially in huge wireless mesh networks because it is difficult to get the entire footprint of the network. Simply put, it is very difficult to determine an optimal route in a huge wireless mesh network (WMN) with dynamic network conditions. Since deep learning has a strong generalization ability. In this paper, a deep learning-based routing approach is proposed with the goal of ensuring a defined optimal quality of service (QoS) in a WMN. In order to achieve the study purpose, a wireless mesh network simulation environment is built and a network data feature set is orchestrated to generate a data set used to train a Long short Term Memory (LSTM)-based deep learning model that estimates the route with the optimal QoS. The generated data set is validated by training other learning models including the Multilayer Perceptron (MLP), Logistic Regression (LR) and Random Forest (RF). Our results show that the routes selected by the LSTM-based model provides the best packet delivery ratio (PDR) and throughput. Our results further show that the learning models (MLP, LR, RF) also provide better PDR and throughput compared to the traditional Ad-hoc On-demand Distance Vector (AODV) routing protocol.

C-QoS-AOMDV: A cluster based QoS aware multipath routing protocol for MANET using hybrid soft computing techniques

Mobile ad hoc networks (MANETs) favour flexible routing protocols because a single route exceeds certain limits. It has many advantages over one-way routing approaches, including high productivity, low end latency, high energy efficiency, and long network life expectancy. As MANET terminals are powered by batteries, energy efficiency is a crucial requirement for many applications. C-QoS-AOMDV is a cluster-based QoS-aware multipath routing protocol for MANET that uses hybrid soft computing techniques to further improve QoS. The main contributions of proposed C-QoS-AOMDV routing protocol are given as follows. In C-QoS-AOMDV routing protocol, we present a modified bird mating optimization (MBMO) clustering technique which reduces the total energy consumption of the network. We compute cluster head (CH) based on the multiple constrains for data transmission between cluster members to corresponding CH node. Then, the mine blast optimization (MBO) algorithm is used to optimize multiple constrains to compute trust degree and maximum trust degree to select CH. Third, we illustrate hierarchical decision tree based deep neural network (HDT-DNN) which is used to compute the next intermediate node for further data transfer between one CH to another CH. Finally, the proposed C-QoS-AOMDV routing protocol is implemented in Network Simulator (NS-3) and the results are compared to the cutting-edge technology protocols AOMDV, AOMDV-QoS, and GE-QoS-AOMDV.

Geocast Routing Protocols for Vehicular Ad-hoc Networks: A Survey

Geocast routing is considered to be advantageous in VANETs, as most of the safety applications are location-based and are relevant to a particular geographical area rather than individual vehicles. Hence, the geocast routing approach where data packets are delivered to a specific geographic area or zone of relevance has become an important research area among researchers and academicians. This article surveys the existing geocast routing protocols for the vehicular environment and compares them qualitatively based on various parameters. The pros and cons of each routing protocol are discussed. Certain directions for future research related to geocast routing protocols are also presented.

Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking

Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock's power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA's High Rate Delay Tolerant Networking (HDTN) project.

Efficient Route Planning Using Temporal Reliance of Link Quality for Highway IoV Traffic Environment

Intermittently connected vehicular networks, terrain of the highway, and high mobility of the vehicles are the main critical constraints of highway IoV (Internet of Vehicles) traffic environment. These cause GPS outage problem and the existence of short-lived wireless mobile links that reduce the performance of designed routing approaches. Nevertheless, geographic routing has attracted a lot of attention from researchers as a potential means of accurate and efficient information delivery. Various distance-based routing protocols have been proposed in the literature, with an emphasis on restricting the forwarding area to the next forwarding vehicle. Many of these protocols have issues with significant one-hop link disconnection, long end-to-end delays, and low throughput even at normal vehicle speeds in high-vehicular-density environments due to frequently interrupted wireless links. In this paper, an efficient geocast routing (EGR) approach for highway IoV–traffic environment considering the shadowing fading condition is proposed. In EGR, a geometrical localization for GPS outage problem and a temporal link quality estimation model considering underlying vehicular movement have been proposed. Geocast routing to select a next forwarding vehicle from forward region by utilizing temporal link quality is proposed for four different scenarios. To evaluate the effectiveness and scalability of EGR, a comparative performance evaluation based on simulations has been performed. It is clear from the analysis of the results that EGR performs better than state-of-the-art approaches in highway traffic environment in terms of handling the problem of wireless communication link breakage and throughput, as well as ensuring the faster delivery of the messages.

A Cluster-Tree-Based Secure Routing Protocol Using Dragonfly Algorithm (DA) in the Internet of Things (IoT) for Smart Agriculture

The Internet of Things defines a global and comprehensive network whose task is to monitor and control the physical world by collecting, processing, and analyzing data sensed by IoT devices. This network has succeeded in various areas, and one of its most important applications is in smart agriculture because there are many demands for producing high-quality foodstuff in the world. These demands need new production schemes in the agriculture area. In IoT, communication security is essential due to the extensive heterogeneity of IoT devices. In this paper, a cluster-tree-based secure routing approach using the dragonfly algorithm (CTSRD) is proposed for IoT. The proposed scheme presents a distributed and lightweight trust mechanism called weighted trust (W-Trust). W-Trust reduces the trust value corresponding to malicious nodes based on a penalty coefficient to isolate this node in the network. Furthermore, it improves the trust value of honest IoT devices based on a reward coefficient. Additionally, CTSRD introduces a trust-based clustering process called T-Clustering. In this clustering process, cluster head nodes (CHs) are selected among honest IoT nodes. Finally, CTSRD establishes a routing tree based on the dragonfly algorithm (DA) between CHs. This tree is called DA-Tree. To evaluate the quality of the routing tree, a new fitness function is provided in CTSRD. DA-Tree finds a secure, stable, and optimal routing tree to balance the consumed energy and boost the network lifetime. CTSRD is compared with EEMSR and E-BEENISH with regard to the network lifetime, consumed energy, and packet delivery rate. This comparison shows that our scheme can uniformly distribute the consumed energy in IoT and improves the energy consumption and network lifetime. However, it has a slightly lower packet delivery rate than EEMSR.

NO2-sensing proprieties of WS2/WO3 heterostructures obtained by hydrothermal treatment of tungsten oxide seed materials

This work reports on the preparation of WS2/WO3 heterostructures for the development of nitrogen dioxide (NO2) sensors via a novel wet-chemical hydrothermal route approach. Different tungsten oxides were used as seed materials, and the heterostructure were formed using thioacetamide as a source of sulfur during a second hydrothermal treatment step. Structural and morphological characterizations demonstrated that the heterostructures can be described as WS2 nanosheets deposited over the tungsten oxide phase. Compared against the respective tungsten oxide seed materials, the heterostructures exhibited significant enhancement of the sensor response to NO2. High sensor signal to a 5 ppm exposure level was observed for the WS2/WO3 microplates at a working temperature of 200 °C, with good selectivity against reducing gases and a sub 2 ppm lower detection limit. To explain the enhancement of the sensor signal, we proposed an electronic sensitization mechanism based on the electronic barriers introduced by the p-n semiconductor junctions. Overall, this work introduces a low-cost, low-temperature, versatile wet-chemical synthetic route for the fabrication of transition metal dichalcogenide-metal oxide heterostructures with potential applications for the development of sensors capable of detecting NO2 at ppm levels.

Equilibrium Allocation Approaches of Quantum Key Resources With Security Levels in QKD-Enabled Optical Data Center Networks

In this article, the network performance of equilibrium allocation of quantum key resources was investigated in quantum key distribution (QKD)-enabled optical data center networks. To effectively use quantum key resources, we propose three novel efficient load balancing routing, wavelength, and time-slot assignment (LB-RWTA) approaches, including LB-RWTA with flexible security level (LB-RWTA-FSL), LB-RWTA with specific security level (LB-RWTA-SSL), and LB-RWTA without security level (LB-RWTA-NSL). Particularly, the proposed LB-RWTA-FSL approach is uniquely featured by adaptive security level classification (ASLC) and load balancing (LB), aiming to demarcate the security level (SL) and reduce the overall network congestion. We introduce an existing routing, wavelength, and time-slot assignment (E-RWTA) approach without SL, called E-RWTA, for comparison. Simulation results show the effectiveness of our proposed approaches in terms of much higher quantum key resource efficiency and thus much higher network security performance than the state-of-the-art quantum key resource allocation approaches compared with the E-RWTA approach in QKD-enabled optical data center networks.

Prototyping using multi-FPGA platform: A novel and complete flow

Multi-FPGA prototyping platforms have seen tremendous popularity in recent times. This is because of the fact that they offer significant advantages like better speed and real world testing experience as compared to other prototyping techniques like simulation and emulation-based prototyping. However, a major challenge hampering the rapid evolution of multi-FPGA platforms is the absence of a complete and efficient back-end academic flow. Some commercial flows exist. But they are expensive and platform dependent. There exist some academic solutions as well. But most of them offer partial solutions only. In this work, we present a novel back-end flow for multi-FPGA prototyping. The major contribution of this flow is that it offers complete prototyping experience and all the tools used in this flow are either free for academia or developed by the academia. We perform extensive experimentation and exploration using the proposed flow. For experimentation, we use a set of fourteen mono- and multi-cluster benchmarks. These benchmarks are passed through all the steps of multi-FPGA prototyping. During exploration, we put special emphasis on two important steps of back-end flow. One step is partitioning and the other is routing. For partitioning, we explore two different approaches namely hierarchical and multilevel approach. For routing, we explore two routing approaches namely bi-terminal and multi-terminal routing. Experimental results show that multilevel approach gives 12.5% better cut-net results for mono-cluster benchmarks while hierarchical approach gives 13% better cut-net results for multi-cluster benchmarks. Furthermore, the comparison of bi- and multi-terminal routing approach shows that the latter gives, on average, 11.3% better frequency results as compared to the former.