Cluster-based Routing Algorithm Research Articles

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Improved smart energy-based routing approach for IoT networks in wireless sensor nodes

An emerging communication and network domain called the Internet of Things (IoT) uses nodes that function like actual real-world objects. Both moving and fixed nodes are possible for these. Maintaining node stability or improving inter-node connectivity is the main task for every IoT network. These networks are portable, leading to an overwhelming production of control overhead transmissions which can be reduced with the help of network clustering. In order to extend the network’s lifespan, it is therefore necessary to improve the power usage of sensor nodes. The goal of this research work is to improve network lifetime, increase throughput, reduce packet latency or packet loss, and even expand to addressing threatening nodes in order to cope with the issues of IoT-based sensor systems. The proposed Improved Smart Energy-Based Routing Protocol (ISERP), once combined with a wireless energy harvesting (WEH) unit, improves the node’s lifespan, the network’s quality of services (QoS) amid higher unequal congestion, and those aspects controlling its availability of energy. In the framework of distributed IoT, this protocol meets the standards of QoS through employing hardware-based link quality estimation and accomplishing very high efficiency in terms of energy in comparison with existing routing methods. The sink node chooses both the cluster head (CH) and cluster gateway (CG) by considering the estimation of its cost function. Simulation findings indicate that this proposed technique exhibits more network longevity along with lower consumption of energy compared with other existing cluster-based routing algorithms. Specifically, with the proposed ISERP, CHs use 33% less energy in comparison to existing techniques, and 40% of all nodes remain active until the end of the phase.

Hybrid Secure Cluster-Based Routing Algorithm for Enhanced Security and Efficiency in Mobile Ad Hoc Networks

ABSTRACT This research addresses critical gaps in Mobile Ad hoc Networks (MANETs) by proposing a hybrid secure cluster-based routing algorithm, focusing on enhancing network security, robustness, and reliability through multipath routing. Methodologically, the approach integrates Convolutional Neural Networks (CNN) for optimal path routing and Emperor Penguin Optimization (EPO) for clustering, introducing a novel combination for efficient cluster head selection. A novel contribution lies in the development of a prediction technique utilizing a trust assessment algorithm to calculate direct trust ratings at each node, incorporating fuzzy values between zero and one. Trust values are further influenced by node performance, adding a dynamic dimension to the trust evaluation process. Key novelties include the emphasis on energy efficiency, network longevity, remaining energy, security level, bandwidth, and packet delivery ratio as evaluation criteria. The proposed CNN-EPO model demonstrates superior results compared to traditional routing protocols, achieving a remarkable 95% energy efficiency, a heightened security level of 99%, and a throughput reaching up to 8 Mbps. Additionally, the Packet Delivery Ratio (PDR) attains close to 99% and routing overhead remains below 0.5, ensuring efficiency in challenging network scenarios with 50 adversaries. In summary, this research contributes a comprehensive solution to MANET challenges, introducing a novel hybrid routing algorithm, incorporating advanced methodologies for path optimization and clustering. These outcomes highlight how important the suggested strategy is to improve the existing state of the art in MANETs.

C4O: chain-based cooperative clustering using coati optimization algorithm in WSN

In order to provide sensing services to low-powered IoT devices, wireless sensor networks (WSNs) organize specialized transducers into networks. Energy usage is one of the most important design concerns in WSN because it is very hard to replace or recharge the batteries in sensor nodes. For an energy-constrained network, the clustering technique is crucial in preserving battery life. By strategically selecting a cluster head (CH), a network's load can be balanced, resulting in decreased energy usage and extended system life. Although clustering has been predominantly used in the literature, the concept of chain-based clustering has not yet been explored. As a result, in this paper, we employ a chain-based clustering architecture for data dissemination in the network. Furthermore, for CH selection, we employ the coati optimisation algorithm, which was recently proposed and has demonstrated significant improvement over other optimization algorithms. In this method, the parameters considered for selecting the CH are energy, node density, distance, and the network’s average energy. The simulation results show tremendous improvement over the competitive cluster-based routing algorithms in the context of network lifetime, stability period (first node dead), transmission rate, and the network's power reserves.

Energy efficient clustering-based routing algorithm for internet of things

Routing process is one of the most critical processes in wireless sensor network (WSN). Due to WSN is mainly used in many applications in internet of things (IoT), routing algorithm can affect the performance of these applications. Thus, the usage of inefficient routing algorithm may lead to losing the data collected by sensors. Moreover, it will cause the sensors to waste energy. This paper proposes an energy efficient clustering-based routing algorithm that is based on tunicate swarm algorithm (TSA). TSAbased clustering algorithm selects the optimal cluster head by calculating the remaining energy, the distance to the base station (BS), the distance to each cluster member and balancing the load between the created clusters. TSAbased routing algorithm is used to create paths from cluster heads to the BS using relay nodes. The TSA-based routing algorithm creates the paths based on the path length, the count of relay nodes in the path, and the number of cluster members of each relay node. The result shows that the proposed algorithm is promising in respect of extending the lifetime of the network and conserving the energy.

Fuzzy based inference system with ensemble classification based intrusion detection system in MANET

Mobile ad hoc networks (MANET) have become one of the hottest research areas in computer science, including in military and civilian applications. Such applications have formed a variety of security threats, particularly in unattended environments. An Intrusion detection system (IDS) must be in place to ensure the security and reliability of MANET services. These IDS must be compatible with the characteristics of MANETs and competent in discovering the biggest number of potential security threats. In this work, a specialized dataset for MANET is implemented to identify and classify three types of Denial of Service (DoS) attacks: Blackhole, Grayhole and Flooding Attack. This work utilized a cluster-based routing algorithm (CBRA) in MANET.A simulation to gather data, then processed to create eight attributes for creating a specialized dataset using Java. Mamdani fuzzy-based inference system (MFIS) is used to create dataset labelling. Furthermore, an ensemble classification technique is trained on the dataset to discover and classify three types of attacks. The proposed ensemble classification has six base classifiers, namely, C4.5, Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM). The experimental results demonstrate that MFIS with the Ensemble classification technique enables an enhancing security in MANET’s by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET.

Analisis Performansi Algoritma LEACH-C pada Wireless Sensor Network

Wireless Sensor Network (WSN) is a wireless network infrastructure that requires sensor nodes to process information and communicate. Small sensor nodes generally use batteries as their energy source, causing sensor nodes to have limited power, therefore energy efficiency is needed to extend network life. The solution to overcome these problems is by using the clustering method. In this study the cluster-based routing algorithm used is LEACH-C which can function to allocate overall energy consumption between sensor nodes. In this study, LEACH-C divides the network into several clusters, each of which has a cluster head (CH). cluster head is performed at the base station based on the average energy size of all nodes. The research simulation uses a scenario of changing nodes 50, 100, and 150. The simulation results show that the number of nodes affects the value of energy consumption, the number of data packets received, and the number of packet losses.

Routing Overhead Aware Optimal Cluster based Routing Algorithm for IoT Network using Heuristic Technique

Globally, billion of devices in heterogeneous networks are interconnected by the Internet of Things (IoT). IoT applications require a centralized decision-making system due to the failure-prone connectivity and high latency of such a system. Low-latency communications are a primary characteristic of applications. Since IoT applications usually have small payload sizes, reducing communication overhead is crucial to improving energy efficiency. Researchers have proposed several methods to resolve the load balancing issue of IoT networks and reduce communication overhead. Although these techniques are not effective, in terms of high communication costs, end-to-end delay, packet loss ratio, throughput, and node lifetimes negatively impact network performance. In this paper, we propose a communication overhead aware optimal cluster-based (COOC) routing algorithm for IoT networks based on a hybrid heuristic technique. Using three benchmark algorithms, we form load-balanced clusters using k-means clustering, fuzzy logic, and genetic algorithm. In the next step, compute the rank of each node in a cluster using multiple design constraints, which are optimized by using the improved COOT bird optimum search algorithm (I-COOT). After that, we choose the cluster head (CH) according to the rank condition, thereby reducing the communication overhead in IoT networks. Additionally, we design chaotic golden search optimization algorithm (CGSO) for choosing the optimal best path between IoT nodes among multiple paths to ensure optimal data transfer from CHs. To conclude, we validate our proposed COOC routing algorithm against the different simulation scenarios and compare the results with existing state-of-the-art routing algorithms.

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing optimization. By using distributed data transmission modification, an adaptive hierarchical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical clustering-based routing protocol is presented in terms of balancing node energy consumption. The Multi-Swarm optimization (MSO) based Genetic Algorithms are proposed to select an efficient Cluster Head (CH). It also improves the network’s longevity and optimizes the routing. As a result of the study’s findings, the proposed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan computation reduces average packet loss, and end-to-end delay.

Efficient Clustering Based Routing for Energy Management in Wireless Sensor Network-Assisted Internet of Things

Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR’s performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life.

An optimal cluster-based routing algorithm for lifetime maximization of Internet of Things

Edge computing for Internet of Things (IoT) is a promising framework that can help small devices such as low-powered sensor nodes to accomplish complex computational tasks. The limited power supply of sensor nodes is one of their major limitations. A successful approach to improve the network lifetime and the overall scalability of the IoT supported wireless sensor networks (WSNs) is clustering. However, in a clustered IoT supported WSNs, some of the Cluster Heads (CHs) bear more traffic load than the others and therefore die sooner leading to decrease the network lifetime. To overcome this problem and maximize the network lifetime, the load of the CHs must be balanced. This research work suggests a new clustering method to balance the traffic load imposed on the cluster heads in IoT supported WSNs. The proposed clustering method uses a 1.2-approximation algorithm. In addition, we introduce an energy-aware routing algorithm for transmitting data packets from the CHs to their destination. The proposed routing algorithm distributes the communication load of the data packets among more nodes near the destination by a proper segmentation of the area. The simulation results show that the proposed clustering and routing algorithms in addition to being practical for large-scale IoT supported WSNs, cause the network to have a better performance compared to other similar algorithms.

A Novel Protein Sequence Alignment-Based Patch Similarity Estimation for Two-Level Data Aggregation in WMSNs

In the digital era, massive growth in Wireless Multimedia Sensor Networks (WMSNs) can be used in various applications such as environmental and industrial monitoring, disaster management. WMSN comprises many low-power camera sensor nodes that may have overlapping field of view for enhancing the accuracy of the monitored environment. Hence, huge volume of redundant data which make it inefficient in terms of network lifetime and energy. It needs to establish a tremendous suitable approach to communicate images over WMSN that handle critical factors such as maximize QoS and minimize energy consumption efficiently. There is a need for energy efficient image processing and transmission over WMSN. So aggregating multi-view images using cluster based distributed routing approaches with QoS constraints have received significant research interest. The Distributed Two-Layer Cluster framework is used in this paper for transmitting aggregated data by local cluster head (LCH) and master cluster head from various clusters to base station in multi-hop basis. This aggregated data are generated from multi-view images captured by correlated camera sensor nodes of local cluster (LC) and master cluster (MC) using first level data aggregation (FLDA) and second level data aggregation (SLDA) algorithms respectively. In FLDA, less complex novel protein sequence alignment based patch matching algorithm is used to reduce inter-view redundancy of multi-view images. Then, this first level aggregated data transmission of LC is reduced using Varying Bit Encoding based on Arithmetic Operations algorithm by combining multi-view images together and achieves its compression ratio of about 2.54. In SLDA, Higher-Order SVD is used to aggregate FLDA data from LCH for reducing MC transmission rate and achieves its compression ratio of about 9.1. The comparison of network performance results with existing cluster based routing algorithms exhibit the improvement of the proposed system in terms of performance metrices such as energy consumption, packet delivery ratio, end-to-end delay and network lifetime. This results show that PSA based TLDA performs better and average energy consumption of processing and transmitting multi-view images per round is considerably minimized to 0.320 J. Thus the proposed system prolongs the network lifetime by 35%, 6% when compared with DFRP and GRAD-CORR based TLDA in terms of number of rounds.

Trusted energy aware cluster based routing using fuzzy logic for WSN in IoT

WSN plays a major role in the design of IoT system. In today’s internet era IoT integrates the digital devices, sensing equipment and computing devices for data sensing, gathering and communicate the data to the Base station via the optimal path. WSN, owing to the characteristics such as energy constrained and untrustworthy environment makes them to face many challenges which may affect the performance and QoS of the network. Thus, in WSN based IoT both security and energy efficiency are considered as herculean design challenges and requires important concern for the enhancement of network life time. Hence, to address these problems in this paper a novel secure energy aware cluster based routing algorithm named Trusted Energy Efficient Fuzzy logic based clustering Algorithm (TEEFCA) has been proposed. This algorithm consists of two major objectives. Firstly, the trustworthy nodes are identified, which may act as candidate nodes for cluster based routing. Secondly, the fuzzy inference system is employed under the two circumstances namely selection of optimal Cluster Leader (CL) and cluster formation process by considering the following three parameters such as (i) node’s Residual Energy level (ii) Cluster Density (iii) Distance Node BS. From, the experiment outcomes implemented using MATLAB it have been proved that TEEFCA shows significant improvement in terms of power conservation, network stability and lifetime when compared to the existing cluster aware routing approaches.

Modified LEACH and Fuzzy C-Means Based Clustering Protocol for Wireless Sensor Networks

Wireless Sensor Networks formed by grouping of small batteries equipped sensor nodes, faces the major issue in optimization of energy when designing the routing protocols. Clustering based routing algorithm proved energy efficiency in Wireless Sensor Networks (WSNs), in which appropriate cluster formation and cluster heads (CHs) selection is crucial for energy saving. Thus, this paper proposes an approach which uses fuzzy C-Means (FCM) in cluster formation and uses the three parameters for cluster head selection: the new energy parameter considered threshold value, the residual energy of each node and the distance from node to base station (BS). FCM algorithm creates uniform clusters in the network area and reduces the distances between each node and their corresponding cluster head node. The proposed clustering protocol can give longer network lifetime when it is implemented in MATLAB simulation environment and compared to LEACH. Simulation results tell that the proposed protocol overcome the performance of LEACH protocol in terms of saving energy and reducing dead nodes to prolong the network lifetime.

Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN

In wireless sensor networks (WSNs), energy optimization and the provision of security are the major design challenges. Since the wireless sensor devices are energy constrained, the issue of high energy consumption by the malicious nodes must be addressed well in order to enhance the network performance by making increased network lifetime, reduced energy consumption and delay. In the past, many researchers worked in the provision of new techniques for providing improved security to WSN in order to enhance the reliability in the routing process. However, most of the existing routing techniques are not able to achieve the required security through the use of intelligent techniques for safeguarding the sensor nodes from malicious attacks. In order to address these problems, a new fuzzy temporal clustering-based secured communication model with trust analysis and outlier detection has been developed in this research work. For this purpose, a new fuzzy temporal rule-based cluster-based routing algorithm with trust modelling and outlier detection for monitoring the nodes participating in the communication has been proposed. In addition, a fuzzy temporal rule- and distance-based outlier detection algorithm is also proposed in this paper for distinguishing the malicious nodes from other nodes within each cluster of the network and has been used in the secured routing algorithm. The proposed secure routing algorithm uses the temporal reasoning tasks of explanation-based learning and prediction as well as spatial constraints for making efficient routing decisions through the application of trust and key management techniques for performing effective authentication of nodes and thereby isolating the malicious nodes from communication through outlier detection. By applying these two proposed algorithms for communication in the proposed work, it is proved through experiments that the proposed secure routing algorithm and the outlier detection algorithm are able to perform secured and reliable routing through genuine cluster head nodes more effectively. Moreover, these two algorithms provide improved quality of service with respect to the reliability of communication, packet delivery ratio, reduction in end-to-end delay and reduced energy consumption.

DiCDU: distributed clustering with decreased uncovered nodes for WSNs

This study proposes a distributed cluster-based routing algorithm, distributed clustering with decreased uncovered nodes (DiCDU), alleviating the uncovered node problem occurring after the election of cluster heads. For the purpose of increasing the performance of the system in terms of the lifetime of the network, an energy-based cluster head election without requiring any extra control message is used. Moreover, an energy saving mode is proposed, in which the nodes perform static clustering controlled by predefined thresholds to conserve batteries. Extensive simulations are conducted for different values of cluster radius and the number of nodes and proposed schema is compared with similar methods, including a time-based cluster-head selection algorithm for low-energy adaptive clustering hierarchy, a clustering algorithm based on time delay, and regional energy aware clustering with the isolated nodes, for the death of the first, the half and the last nodes with the number of uncovered nodes. The results show that DiCDU outperforms compared approaches for all values of cluster radius and the number of nodes.

A Fuzzy Based Energy Efficient Clustering Routing Protocol in Underwater Sensor Networks

The provision of proper routing methods in wireless sensor networks is important due to the sensors' limited hardware and software resources. Some important metrics should be achieved with the use of an efficient routing algorithm, such as low packet loss, improved quality of service, and low energy consumption. Clustering-based routing algorithms have a more efficient performance in the case of link breakages, compared to the other table-based methods. Therefore, a new clustering-based routing algorithm is proposed in this paper that takes the sensors' energy limitation into consideration. The proposed method is designed based on a three-step fuzzy logic. The steps are used to determine the cluster head node and to discover and select a suitable route. In the proposed fuzzy system, the best selection is done based on the existing real-time information. Simulation results show that the proposed method results in a 7% reduction in the network energy consumption simultaneous with a higher packet delivery ratio up to 4% in comparison with IDACB, as the basic algorithm.

Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks

In the past few decades, Energy Efficiency (EE) has been a significant challenge in Wireless Sensor Networks (WSNs). WSN requires reduced transmission delay and higher throughput with high quality services, it further pays much attention in increased energy consumption to improve the network lifetime. To collect and transmit data Clustering based routing algorithm is considered as an effective way. Cluster Head (CH) acts as an essential role in network connectivity and perform data transmission and data aggregation, where the energy consumption is superior to non-CH nodes. Conventional clustering approaches attempts to cluster nodes of same size. Moreover, owing to randomly distributed node distribution, a cluster with equal nodes is not an obvious possibility to reduce the energy consumption. To resolve this issue, this paper provides a novel, Balanced-Imbalanced Cluster Algorithm (B-IBCA) with a Stabilized Boltzmann Approach (SBA) that attempts to balance the energy dissipation across uneven clusters in WSNs. BIBCA utilizes stabilizing logic to maintain the consistency of energy consumption among sensor nodes’. So as to handle the changing topological characteristics of sensor nodes, this stability based Boltzmann estimation algorithm allocates proper radius amongst the sensor nodes. The simulation shows that the proposed B-IBCA outperforms effectually over other approaches in terms of energy efficiency, lifetime, network stability, average residual energy and so on.

An energy efficient authentication scheme based on hierarchical IBDS and EIBDS in grid-based wireless sensor networks

Wireless sensor network is a peculiar kind of ad hoc network, consists of hundreds of tiny, resource constrained as sensor nodes. Clustering is a demanding task in such environment mainly due to the unique constraints such as energy efficiency and dynamic topology. In this paper, a novel energy efficient cluster-based routing algorithm is proposed on which hierarchical identity-based digital signature (IBDS) and enhanced-identity-based digital signature (EIBDS) scheme is concerning in grid-based wireless sensor networks. Firstly we form clusters using multi-parameters-based type-2 fuzzy logic algorithm. This paper proposes an improved ant colony optimisation algorithm, which optimises the energy consumption on data transfer in a WSN. Each node in a sensor network is authenticated using elliptic curve cryptography (ECC). After a set of simulation tests on NS-3 simulator, our proposed work achieves good performance for various metrics.

Performance Review of Selected Topology-Aware Routing Strategies for Clustering Sensor Networks

In this paper, cluster-based routing (CBR) protocols for addressing issues pertinent to energy consumption, network lifespan, resource allocation and network coverage are reviewed. The paper presents an indepth performance analysis and critical review of selected CBR algorithms. The study is domain-specific and simulation-based with emphasis on the tripartite trade-off between coverage, connectivity and lifespan. The rigorous statistical analysis of selected CBR schemes was also presented. Network simulation was conducted with Java-based Atarraya discrete-event simulation toolkit while statistical analysis was carried out using MATLAB. It was observed that the Periodic, Event-Driven and Query-Based Routing (PEQ) schemes performs better than Low-Energy Adaptive Clustering Hierarchy (LEACH), Threshold-Sensitive Energy-Efficient Sensor Network (TEEN) and Geographic Adaptive Fidelity (GAF) in terms of network lifespan, energy consumption and network throughput.Keywords: Wireless sensor network, Hierarchical topologies, Cluster-based routing, Statistical analysis, Network simulation