Cluster Based Routing Protocol Research Articles

Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively.

Energy efficient multi-objective cluster-based routing protocol for WSN using Interval Type-2 Fuzzy Logic modified dingo optimization

Energy efficient multi-objective cluster-based routing protocol for WSN using Interval Type-2 Fuzzy Logic modified dingo optimization

Trust Enabled Cluster Aware Routing Protocol Design with Energy Considerations using Fuzzy Strategies

In this modern era, wireless communication technologies are getting high impact over network industries. Due to this drastic development of communication technologies, a network needs to be more precise with certain metric such as: energy consumption, throughput, trust management, performance and lifetime. These are all the primary concern while handling the transactions with wireless communication systems. In this paper, a new approach of network routing protocol is introduced, which will replace the issues found in existing routing protocols and provide good support while communication between entities. The proposed paper introduces a new routing protocol called Modified Adhoc On-Demand Distance Vector (MAODV), in which the classic AODV protocol supports mobile nodes with several impacts such as mobility, failure prevention, loss management and so on. However, the issues in industry still remains, so, that a new approach or routing protocol is required to overcome such issues. This is the concern to develop a new routing protocol for establishing communication between mobile nodes or wireless devices. The drastic development of communication devices requires some latest technologies to provide range free communication model with the assistance of internet support. A term, 'Internet of Things (IoT)', which rules the world now-a-days through its power of connectivity and establishing the bridge between communication devices around the globe without any range restrictions. In this paper, a cluster based routing strategy is designed with the help of fuzzy assistance. The cluster based routing protocols divides the range of communication into clusters and electing the cluster head based on the communication needs and provides a range free communication model with the hands of IoT support. This cluster based routing model provides support for both inter and intra-cluster communications, which all will be brief in further sections. The proposed model of wireless communication assures the following things such as Improved Energy Efficiency, reduced packet loss and delay, improved throughput ratio and lifetime over resulting scenario with the help of a new algorithm called Fuzzy Assisted Cluster enabled Routing Model (FACeRM) with the association of new routing protocol MAODV.

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

An Enhanced Energy Efficiency Routing for WSN based on Elephant Herding and Swarm Optimization Approaches

Energy utilization and inadequacy of sensor nodes are considered major drawbacks in wireless sensor networks (WSNs). This is because the sensor nodes use the battery for recharging energy. To overcome this issue WSN utilized a clustering-routing algorithm. This protocol divides the adjacent sensor nodes into separate clusters to choose a cluster head. Thus, the cluster head gathers information from all clusters and transmits it to the base station. In this article, the proposed method used cluster-based routing protocols to enhance energy efficiency and network lifetime. Moreover, this paper follows three stages to maximize energy efficiency. Initially, the clustering process is performed using dolphin swarm optimization (DSO), where a group of clusters is formed. Then the second stage is composed of cluster head selection among the group of clusters by elephant herding optimization (EHO) strategy. Finally, the collected data are necessary to forward to the base station for transferring the information. A specified path (routing) is selected by chicken swarm optimization (CSO). By using these algorithms, the network nodes support the balance of energy utilization. Experimental analysis proves when evaluated with existing methods the proposed technique has improved energy efficiency with an increase in network lifetime.

Secure Vehicle-to-Vehicle Communication Using Routing Protocol Based On Trust Authentication Secure Sugeno Fuzzy Inference System Scheme

Introduction: Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. Method: To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. objective: To overcome the drawbacks, this work proposed a new method based on Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Result: In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Conclusion: Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

Energy-efficient cluster-based routing protocol for heterogeneous wireless sensor network

Energy-efficient cluster-based routing protocol for heterogeneous wireless sensor network

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

Distance-Distributed Energy Efficient Clustering (D-DEEC) Routing Protocol for Wireless Sensor Network

A wireless sensor network (WSN) composed of many tiny devices that rely on energy efficient routing protocols for extension of their lifetime. Many cluster-based routing protocols have been proposed based on heterogeneity in recent times. Indeed, these protocols are aiming at achieving energy efficiency, throughputs and better lifetime of the networks. However, two important factors that could have helped some of these protocols to achieve the above-mentioned aims are really missing. Factors such as the distance and average distance between nodes and the Base Station (BS) in selecting a cluster head needed to be considered. These were the major challenges that were identified in the Distributed Energy Efficient Clustering (DEEC) protocol after careful study. As a result, the throughputs and the lifetime of the scheme were affected. In this paper, a reviewed hierarchy-based heterogeneous routing protocol called Distance-DEEC (D-DEEC) is proposed to enhance the DEEC protocol. The new algorithm took into account the residual energy, distance of the individual nodes and average distance of all the nodes from the BS in selecting the Cluster Heads (CHs). This has allowed the protocol to select a cluster head that has high residual energy, is closer to the BS and at the same time not too far from its neighbours. The scheme also employed the sleep and awake approach to reduce energy dissipation. The technique allows the Base BS to calculate the maximum energy of distant nodes and determines when such nodes can transmit their report based on a given threshold energy, Eth . The performance of the proposed algorithm was evaluated using MatLab R2018a and the outcomes showed that, D-DEEC protocol outperformed TDEEC in terms of energy consumption, throughputs and the network lifetime.

A Q-learning-based smart clustering routing method in flying Ad Hoc networks

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly.

EEM-CRP: Energy-Efficient Meta-Heuristic Cluster-Based Routing Protocol for WSNs

EEM-CRP: Energy-Efficient Meta-Heuristic Cluster-Based Routing Protocol for WSNs

A high-scalability and low-latency cluster-based routing protocol in time-sensitive WSNs using genetic algorithm

Wireless Sensor Networks (WSNs) play a critical role in data transmission and reception across a multitude of applications. A primary challenge in WSNs is managing scalability and latency due to the deployment of numerous sensor nodes, each with limited memory and energy resources. Moreover, understanding the randomness in node distribution across the WSN is complex. A commonly employed technique to address this issue is clustering, which groups distributed nodes within the network. A node with high energy, preferably located near the cluster's center, is selected as the cluster head to enhance latency. This paper primarily focuses on developing an efficient routing protocol to improve the scalability of WSNs. An ideal routing protocol should adapt to network topology changes. In this proposal, a Genetic Algorithm-based routing mechanism, the Cluster-based Genetic Routing Protocol (CGRP), is established. CGRP is particularly suitable for highly distributed and rapidly expanding networks, performing well even with an increase in workload. A mathematical evaluation is conducted to elucidate the scalability of the network. To demonstrate CGRP's efficiency, it was compared with various conventional state-of-the-art routing algorithms. Experimental results indicate that CGRP outperforms these algorithms, achieving lower latency and proving robust in highly scalable WSNs.

Gaussian Harris Hawks Optimizer (GHHO) Based Cluster Head Selection (CHS) And Enhanced Energy Harvesting Clustering (EEHC) Protocol for WSN

Designing routing protocols for Energy Harvesting- Wireless Sensor Network (EH-WSN) has two key challenges like improving harvested energy utilization and examination harvested energy utilization. However, the clustering and routing phases of the clustering-based routing protocols employed in EH-WSN need a lot of message overhead. Cluster Head Selection (CHS) is a crucial component of clustering that improves energy efficiency. An Enhanced Energy Harvesting Clustering (EEHC) protocol with two phases for cluster setup and data transmission is presented in this research. Sensor nodes are grouped into clusters during cluster construction, and CHS using the Gaussian Harris Hawks Optimizer (GHHO) algorithm is used for data transmission. Hawks chasing behaviours serve as the search agent in the GHHO algorithm, while prey serves as the ideal CH position. Data from each cluster node is collected by CHS, which then sends the aggregated data to the base station. Following CHS, connecting Cluster Member (CM) to a suitable CH is done using a variety of factors like energy, distance to neighbours, distance to the Base Station (BS), node density, and Signal-to-Noise Ratio (SNR). Each CM awakens during its designated working time slot during the DT stage, and transfers the data it has gathered towards the CH of the same cluster. A mobile sink is employed to enhance network performance and relay transmission distance is changed to match the amount of gathered energy. Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Network Lifetime, and average delay are metrics used to measure the results of clustering methods.

Mlora-CBF: efficient cluster-based routing protocol against resource allocation using modified location routing algorithm with cluster-based flooding

Mlora-CBF: efficient cluster-based routing protocol against resource allocation using modified location routing algorithm with cluster-based flooding

Cybersecurity threats mitigation in Internet of Vehicles communication system using reliable clustering and routing

Security and privacy while considering vehicular networks are important research topics. Appropriate clustering algorithms enable long-distance communications in highly dynamic Internet of Vehicles (IoV) systems. IoV system QoS requires constant, low-cost clustering. However, to prevent malicious behaviors, sensitive vehicle networks need an access control system for data security and privacy. The existing solutions suffered from higher complexity and complete reliability. Thus, the main objective of this paper is to propose a lightweight routing technique to express network access control policies to ensure higher reliability with minimum latency and overhead. The Trust Aware Clustering-based Routing Protocol (TACR) solves IoV communication security and reliability issues while reducing computational costs and latency. TACR functionality relies on trust-management methods for optimal Cluster Head (CH) and relay (data forwarder) selection. Each car's direct and indirect trust scores are calculated during clustering. The fitness function of the Ant Colony Optimisation (ACO) algorithm uses each vehicle's hybrid trust value. The ACO algorithm chooses stable vehicles for the best CH. In the routing phase, we select forwarding relays and apply trust-based access control rules to ensure safe and reliable data transmission. Fuzzy logic selects the optimal relay node and sets access control rules based on trust evaluation. The trust criteria consider each vehicle's expertise, knowledge, and suggestions throughout. The simulation findings reveal that the TACR protocol outperforms state-of-the-art protocols significantly. Average throughput and PDR are increased by 14.17 % and 14.34 % using TACR, respectively. Average delay and communication overhead were reduced by 33.84 % and 14.28 % using the TACR protocol, respectively.

An Efficient Routing Awareness Based Scheduling Approach in Energy Harvesting Wireless Sensor Networks

Wireless sensor networks (WSNs) have emerged as a promising technology for various Internet of Things (IoT)-based sensing applications. Generally, sensor nodes are powered by non-rechargeable and low-capacity batteries; they only last for a small amount of time. Due to their dispersed nature, sensor nodes cannot have their batteries replaced or maintained locally. The energy harvesting (EH) concept shows commitment as a method for addressing the current energy shortage. Another problem is the intra-energy control of various network load functions, including coverage preservation, routing, clustering, etc. This study proposes an ERAS (efficient routing awareness scheduling) algorithm to address the abovementioned issues. The ERAS method employs a solar harvesting system as a hierarchical clustering-based routing protocol. The presented scheme dynamically alters nodes between states and adjusts to new states based on the insides of sensed data packets. Synchronization-based scheduling ensures proper coordination between the cluster head (CH) and cluster members (CMs). It selects the CH based on both remaining and harvested energy and the number of active nodes to improve throughput. Surprisingly proposed EASR algorithm performs efficiently while considering various performance matrices, including network lifetime, packet delivery ratio, energy consumption, and network sustainability. Extensive simulation results exhibit the network lifetime of the proposed ERAS technique (79.8%) compared to ERA, SEED and CREST methods as 67.3%, 73.8%, and 75.2%, respectively.

Neural Network-based Dynamic Clustering Model for Energy Efficient Data Uploading and Downloading in Green Vehicular Ad-hoc Networks

Green VANET is an emerging field of research that spurs interest in energy consumption management for the development of smart cities. It also presents a unique variety of research problems for developing trustworthy and scalable routing protocols as vehicles are susceptible to the restrictions of road geometry and the barriers which limit networking capabilities in urban environments. Clustering is a process of assembling vehicle nodes to create a powerful and effective network infrastructure. According to recent studies, clustering-based routing algorithms in green VANET may significantly improve networking effectiveness and lower infrastructure costs. However, sometimes the vehicle nodes are unaware of their OBU energy consumption, which causes network execution issues and topological alterations. In such instances, the energy consumption of onboard sensors becomes a major problem in the clustering-based routing protocol. Hence, this paper proposes a self-organizing map neural network (SOMNN) based dynamic clustering model to identify energy-efficient nodes from each cluster for vehicular data uploading and downloading applications. The simulation results demonstrate that the proposed model solves network lifetime issues and provides superior network effectiveness with enhanced communication stability. The suggested dynamic clustering model reduces network energy consumption by 26% and 18% in comparison to k – means (KM) and fuzzy c – means (FCM) based clustering model.

A novel cluster-based routing protocol for WSN-enabled IoT using water-cycle algorithm

A novel cluster-based routing protocol for WSN-enabled IoT using water-cycle algorithm

A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management

City zones have become increasingly overcrowded due to the extensive population widening ratio and the swift relocation of people from villages. The traffic monitoring process is a significant issue in these areas due to the massive traffic flow on the roads. This research proposed a cluster-based improved authentication and communication protocol for an Intelligent Transportation System in Vehicular AdHoc Networks (VANETs). Our primary objective is to optimize resource sharing in vehicular communication. We enhanced the reliability, scalability, and stability of fast-moving VANETs by introducing cluster-based routing protocols for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. We used a third-party certification authority for vehicle authentication to address security and privacy concerns. Our proposed protocol support minimizes the end-to-end (E2E) delay and route request and reduces link failure. Our protocol’s leading yield includes throughput enhancement, TCP Socket Initialization time minimization, TCP handshake response speedup, and DNS lookup improvement. The protocols are centered on short-range peer-to-peer (P2P) wireless communication in a 400 m radius cluster. They include innovative P2P wireless communications on VANET using minimized resources. The proposed protocols deliver a secure authentication mechanism with a securely generated vehicle authentication key provided by a certification authority. Furthermore, we have developed RESTful APIs in vehicular communication for implementation purposes and also offered and implemented algorithms for resource sharing regarding V2V and V2I communication. Ultimately, we evaluated the performance of our experiments.

An efficient Clustered IoT (CIoT) routing protocol and control overhead minimization in IoT network

Internet of Things (IoT) is a novel networking communication field in which the nodes act as physical objects. These nodes might be stationary or mobile. The majority of networking communication problems occur due to nodes’ mobility. Due to the mobility of IoT networks, excessive control overhead messages are generated. These messages are generated due to the frequent exchange of some control information, such as device identity, location information, device mobility, device direction, and others. In general, network clustering helps to alleviate the problem of generating high control overhead messages. Therefore, many proposed or enhanced Cluster-Based Routing (CBR) protocols address the shortcoming of traditional CBR protocols. In CBR protocols, each cluster selects only one node based on some parameters, and this node is called a LEaDeR (LEDR). The LEDR acts as a coordinator for its associated cluster. Furthermore, the rest of the nodes located in the cluster are called Smart DEVices (SDEV). A problem occurs when the SDEVs and the LEDR exchange their control overhead messages with each other. The exchanging of these messages utilizes the available network resources. This article proposes a Clustered IoT (CIoT) routing protocol, the CIoT routing protocol applies in a grid clustered topology. The proposed CIoT protocol aims to efficiently propagate messages between any pair of devices. The CIoT protocol selects only one route among a set of candidate routes, and the selected route is verified and evaluated based on some constraints. Also, this article proposes a Predictive Generated Hello Messages Algorithm (PGHMA). The PGHMA aims to minimize the generated control overhead messages produced by SDEVs and LEDRs. In comparison to other CBR protocols, simulation results demonstrate the effectiveness of the CIoT algorithms by increasing network throughput by 78% and decreasing end-to-end latency by 12.5%. Also, the PGHMA algorithm significantly outperforms other algorithms in terms of reducing the number of generated HELLO messages by 5.9%. Finally, we implement and evaluate our proposed algorithms by comparing them to other related algorithms published in the literature.