Clustering Routing Research Articles

A WSN clustering routing method based on an improved WOA-optimized K-means clustering algorithm

To address the problem of transmission energy consumption and premature node death in wireless sensor networks (WSN), a novel routing strategy by IWOA that optimizes the K-means (KM) is proposed. WOA is improved by using it to optimize the KM algorithm, and more evenly distributed and suitable cluster centers are selected as the initial cluster heads (CH). In the CH election process, a CH election function is established, and the optimal CH under the influence of multiple factors is obtained. The simulation results have verified the feasibility of the proposed scheme in this paper.

Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering

Vehicular ad hoc networks (VANETs) are a rapidly evolving field at the intersection of intelligent transportation systems, emphasizing the need for a stable and scalable VANET topology to accommodate growing vehicular densities. The intricate challenge of route selection calls for advanced clustering protocols to bolster road safety and message routing. This research introduces a novel approach to intelligent clustering routing protocols, leveraging heuristic-based solutions built upon an enhanced ant colony optimizer (ACO) framework. The study unfolds in two stages: the creation of a dynamic search space model and the election of cluster heads (CHs). The innovative dynamic aware transmission range parallel Euclidean distance (DA-TRPED) technique establishes a dynamic search space using the parallel Euclidean distance (PED) concept. This approach evaluates vehicular nodes by estimating PED values, reducing the search process’s complexity. Subsequently, an intelligent cluster head is selected by enhancing the dynamic evaporation factor (DEF) within the ACO technique. The experimental validation of the DA-TRPED technique takes place in NS2 simulations, demonstrating superior performance compared to conventional ACO. This enhancement is evident in metrics such as packet delivery, packet drop, throughput, end-to-end delay, and the lifetime analysis of clustered nodes. The proposed approach holds promise for optimizing VANETs, enhancing their stability and scalability while promoting road safety and efficient message routing.

A Novel Hybridized Cluster-Based Geographical Opportunistic Routing Protocol for Effective Data Routing in Underwater Wireless Sensor Networks

Underwater wireless sensor nodes comprise hundreds to thousands of battery-operated sensor nodes with limited bandwidth. These networks are employed to transmit the data with enhanced quality of service (QoS). However, efficient data routing is the most challenging obstacle in many underwater applications. To solve the issues in underwater sensor nodes, the hybridized cluster-based geographical opportunistic routing protocol with distance vector establishment has been proposed to transmit the data efficiently. Primarily, the proposed methodology finds out the shortest path with minimal hop count whereas the void node can be updated with infinite hop count. Thereafter, the sleep/wake scheduling and waiting mechanism and periodic beaconing algorithm are incorporated into the proposed model to attain a higher packet delivery ratio with minimal energy consumption. This proper scheduling and optimal cluster routing enhance the continuous data transmission in underwater applications. The simulation result reveals that the proposed method achieves better energy efficiency and higher network lifetime when compared with the existing clustering methods.

Trust Score based Optimized Cluster Routing (TSOCR) approach for Enhancing the Lifetime of Wireless Sensor Networks

Energy efficiency is the most significant obstacle that Wireless Sensor Networks (WSN) must overcome, and the desire for solutions that maximize energy efficiency will never go away. There are a variety of methods that can be utilized to improve energy efficiency, with data transmission as the primary driver of maximum energy consumption. The transmission of data from the source to destination nodes uses more energy. When the transmission of data is handled better, the energy efficiency is improved and the lifetime of the network is increased. The purpose of this research is to propose an Trust Score based Optimized Cluster Routing (TSOCR) scheme for WSNs, which is based on Whale Optimization Algorithm (WOA). A total trust score is derived by combining the results of computing three distinct trust scores, such as the direct, indirect, and the most recent trust score. The path that has the highest trust score is chosen as the route and employed for data transmission. The effectiveness of the work is evaluated by looking at factors such as the rate of packet delivery, the latency, the amount of energy consumed and the lifetime of the network.

A Clustering Routing Algorithm Based on Improved Ant Colony Optimization for an Urban Rail Transit Ad Hoc Network

As traffic pressure increases, urban rail transit has attracted attention because of its high speed and growing capacity in recent years. However, the traditional vehicle–ground communication systems of urban rail transit have been unable to meet the increasing requirements for communication. In this paper, we introduce an ad hoc network for urban rail transit to improve communication reliability, reduce end-to-end latency, and improve throughput of vehicle–ground communication. A novel clustering algorithm based on improved ant colony optimization is presented, which integrates various variables to elect cluster heads and adopts a low-latency queuing strategy. In addition, the algorithm can also replace the cluster heads adaptively to optimize the communication quality. Through the simulation results, the proposed algorithm can reduce the packet loss rate by about 60%, reduce the delay by about 50%, and improve the throughput by about 40% compared with the classical clustering routing protocol.

A Combined Dual Leader and Relay Node Selection for Markov Cluster Based WSN Routing Protocol

The major challenge in Wireless Sensor Networks (WSNs) is to increase the node’s lifespan and decrease energy utilization. To avoid this issue, many Clustering Routing Protocols (CRPs) have been developed, where Cluster Head (CH) in each cluster accumulates the data from each other node and transfers it to the sink through Relay Nodes (RNs). But both CHs and RNs dissipate more energy to aggregate and transfer data. As a result, it is vital to choose the appropriate CHs and RNs concurrently to reduce energy utilization. Hence, this article proposes a Weighted Markov Clustering with Dual Leader and Relay node Selection based CRP (WMCL-DLRS-CRP) in WSNs. This protocol aims to lessen energy dissipation during inter- and intra-cluster communication. Initially, a Markov Clustering (MCL) algorithm is applied by the sink to create nodes into clusters based on a threshold distance. Then, a dual leader selection scheme is proposed to elect dual CHs in each cluster according to the node weighting factor that considers the node’s remaining energy, the distance between CHs and sink, the distance among all nodes, and abundance. Also, an RN selection scheme is proposed to choose the appropriate RNs based on a new Predicted Transmission Rate (PTR) factor. Moreover, the elected RNs transfer the data from the CHs to the sink, resulting in a tradeoff between the node’s energy utilization and lifetime. At last, extensive simulations illustrate that the WMCL-DLRS-CRP achieves better network performance compared to the existing protocols.

A Nonuniform Clustering Routing Algorithm Based on a Virtual Gravitational Potential Field in Underwater Acoustic Sensor Network

Abstract-Due to the harsh deployment environment of the underwater coustic sensor networks (UASNs), a reliable and energy-saving routing algorithm has always been an important challenge and a hot topic. A Non-uniform Clustering (NC) algorithm is designed first in which clusters are genetated according to different node densities. Based on NC, the backbone of the underwater acoustic sensor network is formed in UASNs. To guarantee the reliability of data transmission of the backbone network, a Non-uniform Clustering Routing algorithm based on Virtual Gravitational Potential Field (NC_RVGPF) is proposed. This algorithm (1) establishes a virtual gravitational potential field model to allow data transmission by three-dimensional underwater nodes, (2) designs the virtual gravitational potential energy by combining the transmission distance between the nodes, the residual energy of the nodes, and other parameters, and (3) selects the path of the highest average potential energy as being the optimal path of data transmission. The simulation results show that compared with the classical routing algorithm, NC_RVGPF algorithm has higher transmission efficiency, less energy consumption, and can more effectively extend the network’s lifetime.

Fault-Tolerant Routing for Wireless Sensor Networks using a Gaussian Network

Abstract: Wireless Sensor Networks (WSNs) are utilised for sensing various physical and environmental variables like temperature, pressure, motion, and pollution. A component of a network to continue operating even when some sensor nodes fail is regarded as fault tolerance in wireless sensor networks. Due to battery life deadlines and the challenges associated with charging or replacing failing nodes, the deployment of sensor nodes in the target area is done in a dense manner to maximise coverage and connection. This paper implements a hybrid fault-tolerant routing methodology to address the problem of fault tolerance in hierarchical topology for wireless sensor networks (WSNs). The network location is partitioned into small square grids, with a Gaussian integer serving as each grid's cluster head. A Gaussian network is created through communicating these cluster heads together. Discovering the Gaussian network's shortest path and multi-path routing, the project implements a hybrid Fault-tolerant Clustering routing protocol based on Gaussian network for Wireless Sensor Network (FCGW). The purpose of FCGW is to improve fault tolerance and reduce energy consumption for Wireless Sensor Networks. Experimental results using MATLAB shows that FCGW protocol has high data reliability. In addition, the FCGW protocol consumes lesser energy in the network compared to other protocols namely FT-LEACH, HEED and PSO-UFC

Optimized clustering routing framework to maintain the optimal energy status in the wsn mobile cloud environment

Optimized clustering routing framework to maintain the optimal energy status in the wsn mobile cloud environment

FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

SummaryThe internet of things (IoT) has recently become extremely important in our lives. With the advancement in IoT, wireless sensor networks (WSNs) have gained much importance. In WSNs, the routing of clusters has gained more interest. However, there is still a problem with the hotspot. One way to address this issue is uneven clustering. In these, nodes closer to the base station have the smallest cluster size. It decreases the relay pressure of the nodes which are close to the base station. In this paper, the unequal clustering routing based on fuzzy logic and bat algorithm is proposed, that is, FUCBR. Relative intra‐cluster costs and relative inter‐cluster costs are used in a very innovative manner. Fuzzy logic is used to create unequal clusters while the bat algorithm is used for multi‐hop routing. Unequal clustering minimizes the energy utilization for the member of the clusters. A novel probability technique is implemented in the cluster‐forming process to allow cluster members to determine which cluster they wish to join. The bat algorithm's objective function is carefully chosen to optimize the cluster heads' energy usage. After simulation, it was discovered that the proposed algorithm utilizes less energy and extends the network lifespan. The simulation results show that FUCBR's performance is 30% more improved than fuzzy‐based unequal clustering algorithm, genetic algorithm based unequal clustering and routing protocol, and grey wolf optimizer‐C algorithms. It is suitable for networks that require a long life.

PMLC- Predictions of Mobility and Transmission in a Lane-Based Cluster VANET Validated on Machine Learning

VANET refers to a massive network system, to communicate with each vehicle or infrastructure a precision protocol, an advanced view and routing system is required. This means of communication should be appropriate for all kind of vehicles. In this proposed PMLC protocol, which was built on cluster routing in a lane-based road environment. The network requires optimal solutions to form the cluster and choose its leader. All road environment characteristics are chosen, and multilayer estimations are generated to obtain specific deviations and variations, which are calculated based on data transfer and vehicle movement, and exact values are found using the machine learning system. The neural network processes the inputs, selects the required leader, and sends the data to the destination. At the end of this explanation, the execution of this protocol is depicted graphically.

Load Balancing in Compute Clusters With Delayed Feedback

Load balancing arises as a fundamental problem, underlying the dimensioning and operation of many computing and communication systems, such as job routing in data center clusters, multipath communication, Big Data and queueing systems. In essence, the decision-making agent maps each arriving job to one of the possibly heterogeneous servers while aiming at an optimization goal such as load balancing, low average delay or low loss rate. One main difficulty in finding optimal load balancing policies here is that the agent only partially observes the impact of its decisions, e.g., through the delayed acknowledgements of the served jobs. In this paper, we provide a partially observable (PO) model that captures the load balancing decisions in parallel buffered systems under limited information of delayed acknowledgements. We present a simulation model for this PO system to find a load balancing policy in real-time using a scalable Monte Carlo tree search algorithm. We numerically show that the resulting policy outperforms other limited information load balancing strategies such as variants of Join-the-Most-Observations and has comparable performance to full information strategies like: Join-the-Shortest-Queue, Join-the-Shortest-Queue(d) and Shortest-Expected-Delay. Finally, we show that our approach can optimise the real-time parallel processing by using network data provided by Kaggle.

Priority based energy efficient hybrid cluster routing protocol for underwater wireless sensor network

<p class="Affiliation"><span lang="EN-US">A little change in the environment that goes unnoticed in an underwater communication network might lead to calamity. A little alteration in the environment must also be adequately analyzed in order to deal with a potential crisis. A priority-based routing protocol is required to ensure that the vital data perceived by the sensor about the environment changes. The priority-based routing system guarantees that vital data packets are delivered at a quicker pace to the destination or base station for further processing. In this work, we present a priority-based routing protocol based on the energy efficient hybrid cluster routing protocol (EEHRCP) algorithm. The suggested approach keeps two distinct queues for lower and higher priority data packets. In order to ensure that these packets get at their destination without any information loss and at a quicker rate, all of the crucial sensed data is passed through a higher priority queue. Test findings show that the suggested technique increases throughput, delivery percentage, and reduces latency for the crucial data packets.</span></p>

OQ‐IICA: Optimal QoS‐aware intra‐inter cluster data aggregation technique for IoT‐assisted WSNs using hybrid optimization techniques

SummaryThe wireless sensor network (WSN) contains sensor nodes for understanding, communicating, and storing battery capacity data. Data collection can be defined as the process that base stations use to eliminate unwanted transmissions and provide mixed information. This improves energy efficiency and extends the life of low‐energy WSNs enabled by IoT (IoT‐WSNs). In this article, we propose an optimal QoS‐aware intra‐inter cluster data aggregation technique for WSNs using hybrid optimization techniques (OQ‐IICA). First, we introduce a modified bowerbird optimization (MBO) algorithm for balanced clustering which improves energy efficiency. Second, we develop a multi‐objective seagull optimization‐based decision‐making (MSO‐DM) algorithm to estimate the CH of clusters in the network. Next, we introduce a teacher‐inspired cappuccino search algorithm to ensure the quality of data transfer between nodes by learning through internal and cluster routing. Finally, the proposed OQ‐IICA algorithm compares the latest technologies in ICA, Leach, and Leach‐C power consumption, latency, throughput, latency, number of live nodes, routing, and grid length.

A novel chain‐based clustering for green communication in wireless sensor network

SummaryThe rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting in recent decades. However, the resource‐constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain‐based Energy‐Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state‐of‐the‐art routing protocols.

Secure Cluster-based Routing using TCSA and Hybrid Security Algorithm for WSN

Wireless Sensor Network (WSN) is operated as a medium to connect the physical and information network of Internet-of-Things (IoT). Energy and trust are two key factors that assist reliable communication over the WSN-IoT. Secure data transmission is considered a challenging task during multipath routing over the WSN-IoT. To address the aforementioned issue, secure routing is developed over the WSN-IoT. In this paper, the Trust-based Crow Search Algorithm (TCSA) is developed to identify the Secure Cluster Heads (SCHs) and secure paths over the network. Further, data security while broadcasting the data packets is enhanced by developing the Hybrid Security Algorithm (HSA). This HSA is a combination of the Advanced Encryption Standard (AES) and Hill Cipher Algorithm (HCA). Therefore, the developed TCSA-HSA avoids malicious nodes during communication which helps to improve data delivery and energy consumption. The performance of the TCSA-HSA method is analyzed using Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, End to End Delay (EED), and throughput. The existing methods namely Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) and Secure and Energy-aware Heuristic-based Routing (SEHR) are used to evaluate the TCSA-HSA performances. The PDR of TCSA-HSA for 100 nodes is 99.7449%, which is high when compared to the OP-MDCRP and SEHR.

An Extensive Analysis of Wireless Sensor Network Clustering Routing Methods Based on LEACH

The creation and use of sensor networks has piqued a lot of interest. Remote sensor network is an assortment of countless little hubs which goes about as switches too. The limited power supply that these nodes carry is non-replaceable and non-rechargeable, making energy consumption a significant issue. In order to extend the network's lifespan, energy conservation is a crucial issue. The choice of routing method is crucial to regulating energy consumption because the sensor nodes also function as routers. Energy Efficient Routing in Wireless Sensor Networks is the subject of this paper's analysis and research, which also includes a description of the wireless sensor network framework.

Data Fusion Algorithm For Wireless Sensor Networks Based On Cluster Routing And Deep Learning

In order to make full use of node energy and extend the network’s lifetime, a data fusion algorithm for wireless sensor networks is proposed in this paper. The algorithm improves the traditional clustering routing protocol and introduces a deep learning model to build the data fusion algorithm based on the optimized clustering architecture. When optimizing cluster routing protocol, the cluster head is selected by comprehensively considering such factors as node density, residual energy of nodes and the reasonable cluster size. The deep learning model and classification model are introduced in the data fusion process. The optimal parameters of the network are obtained by training the deep learning model of the sink node, then the sink nodes transfer these parameters to the corresponding sensor nodes. The cluster head extracts, classifies and fuses similar features of the data. The nodes in the cluster use the deep learning model to fit the collected raw data. Simulation results show that the data fusion algorithm proposed in this paper can reduce the redundant data transmitted to the sink node of wireless sensor networks, reduce the network energy consumption and prolong the network’s life. The overall performance of the network is improved.

Energy‐efficient multipath clustering with load balancing routing protocol for wireless multimedia sensor networks

AbstractWireless multimedia sensor networks (WMSNs) has the capability of collecting scalar information, as well as multi‐media events like moving object tracking and traffic fatalities. Because energy in WMSNs is a constrained resource, several researchers offered cluster routing approaches, and most protocols generally do not offer an average resource usage of the network that is balanced and minimised. To address this issue, this paper proposes an Energy‐Efficient Multipath Clustering with Load Balancing Routing Protocol for WMSNs (EEMCL) to prolong the network lifetime. The suggested protocol, which segments the network into layers of clusters, would be implemented using multi‐hop. Sensing data from the sensor is transmitted to the sink by the main cluster heads in each layer, cooperating with the cluster heads in the upper layers. The proposed protocol improves energy dissipation, network life‐times, and network stability according to simulation results when compared with SEP, SEP‐E, and SEPFL protocols. The last node dead for the proposed protocol is at round 5833, 4027 for SEPFL, 2828 for SEP‐E, and 2325 for SEP.

GM-WOA: a hybrid energy efficient cluster routing technique for SDN-enabled WSNs

GM-WOA: a hybrid energy efficient cluster routing technique for SDN-enabled WSNs