Cluster-based Routing Scheme Research Articles

Energy Efficient Cluster based Routing Scheme for WSN based IoT to Extend Network Lifetime

With the development and advancement of wireless sensor networks (WSN), the emergence of the Internet of Things (IoT) has achieved prominence in the modern era. With the increasing number of connected devices, WSN has become a key factor in the communication component of the IoT. In IoT-based WSN infrastructure, devices are equipped with intelligent sensors that sense the environment to collect data, process data, and deliver information to the sink or base station (BS). WSN-assisted IoT has become a key technology for various data-centric applications such as health care, smart cities, and the military. Sensor nodes in IoT devices are equipped with bound and irreplaceable batteries. An increased number of connected devices face serious issues of energy depletion, maintenance, and load balancing, which might result in device failure. Energy efficiency is considered a vital parameter in the design of an IoT based WSN, and this can be accomplished through clustering and multihop routing techniques. In this paper, we propose an energy-aware multihop routing scheme (EAMRS) for hierarchical cluster-based WSN-assisted IoT. EAMRS considers the improved low-energy adaptive clustering algorithm (I-LEACH) to select optimal cluster heads (CH). During data transmission, multihop routing is involved by considering routing metrics such as residual energy, distance to BS and optimal route choice to balance the energy load. However, conventional routing schemes fail to achieve the flexibility and adaptability prerequisites of load balancing mechanisms. EAMRS decreases computation overhead and restricts energy usage, resulting in a prolonged network lifetime.

Energy-efficient device-to-device communication in internet of things using hybrid optimization technique

<span lang="EN-US">Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime.</span>

Link quality and energy efficient optimal simplified cluster based routing scheme to enhance lifetime for wireless body area networks

Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable multi path data transfer. This work was executed in three phases. First, an optimal simplified clustering technique for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.

RVMF: RELIABLE ROUTING METHOD FOR VEHICULAR AD HOC NETWORKS USING MOTH-FLAME AND FIREFLY OPTIMIZATION ALGORITHMS

With the advancement of wireless communication technology, the intelligent transportation system (ITS) has attracted the attention of vehicle companies and academic researchers. Recently, vehicular ad hoc networks (VANETs) as a leading genuine technology have received serious attention as a kind of mobile ad hoc network (MANET) to ensure the safety of vehicles, drivers, and passengers. However, these networks face many challenges due to the mobility of vehicle nodes, wireless communication, and frequent topology changes. One of the crucial issues of these networks is a cluster-based routing scheme with the ability to provide quality of service (QoS) parameters. A clustering scheme is an appropriate method for increasing the scalability of VANETs. In a cluster-based routing scheme, the cluster head (CH) is responsible for receiving data from its member nodes, and aggregating and transferring data to the next CH node. On the other hand, providing a suitable clustering method is NP-hard problems and meta-heuristic algorithms are suitable for solving these problems. A scalable and reliable routing scheme is necessary and essential in VANETs. In this paper, a routing method based on the clustering technique is presented considering the moth-flame optimization (MFO) algorithm for clustering and the Firefly optimization algorithm (FoA) with a suitable fitness function for routing between CHs. The simulation of the proposed method with MATLAB software shows that the proposed RVMF method improves the parameters of packet delivery rate (PDR), latency, and throughput.

Floating Nodes Assisted Cluster-Based Routing for Efficient Data Collection in Underwater Acoustic Sensor Networks

Exhaust research work has been carried out in the field of Terrestrial Wireless Sensor Networks (TWSN). Now, research penetration is moving toward Underwater Acoustic Sensor Networks (UASNs). The communication of UASNs has dependencies on acoustic instead of radio. Efficient data collection by the UASN seems a tedious job in comparison with TWSN due to the peculiar features of underwater communication. However, there is a way to accomplish efficient data collection metrics through the design of routing protocols by considering the unique features of UASN communications. In this context, we propose a novel scheme and we call it Floating Nodes assisted Cluster-Based Routing (FNCBR) scheme for efficient data collection in UASNs. In FNCBR, the network space is divided into cuboids to form clusters. Then, every cuboid is consigned with a floating node (FN) at the surface layer and two fixed cluster heads (CHs) are suspended at different depth levels. All CHs of cuboids are supposed to be connected with a floating node via a wired connection, while source nodes are haphazardly distributed in the whole network region. In FNCBR, source nodes are liable to send the sensed data either to the FN or to the nearest CH. The data collected by the CHs is moved towards the FNs, which further disseminate the data to the on-shore monitoring center via a Radio Frequency (RF) link. We conduct the simulations in Network Simulator (NS-3) to appraise the proposed FNCBR protocol with other clustering protocols (e.g., CVBF, BS-CVBF, and ANCRP) in terms of different performance metrics. After describing the results, a quantitative comparison table is also given. The FNCBR scheme is also evaluated in terms of multiple FN densities. The simulation outcomes justify that the proposed FNCBR scheme has given better results in all performance metrics than the baseline schemes.

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

Hybrid Marine predators optimization and improved particle swarm optimization-based optimal cluster routing in wireless sensor networks (WSNs)

Wireless Sensor Networks (WSNs) play an indispensable role in the lives of human beings in the fields of environment monitoring, manufacturing, education, agriculture etc., However, the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence. In this context, several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time. However, there still exists a room for improvement in Cluster Head (CH) selection based on the integration of critical parameters. The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant. In this paper, a hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-based Optimal Cluster Routing (MPO-IPSO-OCR) is proposed for ensuring both efficient CH selection and data transmission. The robust characteristic of MPOA is used in optimized CH selection, while improved PSO is used for determining the optimized route to ensure sink mobility. In specific, a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA. The high-speed ratio, unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point. The simulation investigation and statistical results confirm that the proposed MPO-IPSO-OCR is capable of improving the energy stability by 21.28%, prolonging network lifetime by 18.62% and offering maximum throughput by 16.79% when compared to the benchmarked cluster-based routing schemes.

An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks

Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%.

Energy efficient cluster-based routing scheme using type-2 fuzzy logic in underwater wireless sensor networks

Energy efficient cluster-based routing scheme using type-2 fuzzy logic in underwater wireless sensor networks

Stability-Delay Efficient Cluster-based Routing Protocol for VANET

Vehicular Ad hoc Network (VANET) can be used in safety applications to transfer information about some events (e.g. accidents) with minimum time. Sending this information is achieved by using a routing algorithm. A large number of cluster-based routing schemes were presented for VANET. Unfortunately, the mobility of vehicles in unexpected directions negatively affects the performance of these schemes, destroys the network links, and decreases the routes' stability. This problem leads to repeat route discovery and maintenance operations and, as a result increases the overhead and delay. Thus, they are not an optimal selection for safety applications. Moreover, the cluster-based policies need efficient clustering approaches, but the previous ones fail to enhance the stability of links and cluster heads. Moreover, they did not focus on the stability of gateways. Therefore, a new clustering formation approach that focuses on the stability of cluster head and gateway is proposed. It gives priority to the parked and stopped vehicles to be cluster heads and gateways. Moreover, a new cluster-based routing protocol to build optimal paths with minimum delay and maximum stability called CRDS is suggested for safety applications. CRDS depends on the suggested clustering approach and computes the optimal routes depending on a novel suggested optimization model. Several simulation scenarios with various mobility speeds and numbers of stopped and parked vehicles have been run. The results showed that CRDS is better than LRCA, PASRP and CVoEG according to network overhead, average end to end delay, path stability, cluster head stability, and packet delivery ratio.

A Connectivity-Based Clustering Scheme for Intelligent Vehicles

The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing scheme (hereinafter referred to as connectivity-based clustering), where link connectivity is used as a metric for cluster formation and cluster head (CH) selection. Link connectivity is a function of vehicle density and transmission range in the proposed connectivity-based clustering scheme. Moreover, we used a heuristic approach of spectral clustering for the optimal number of cluster formation. Lastly, an appropriate vehicle is selected as a CH based on the maximum Eigen-centrality score. The simulation results show that the suggested connectivity-based clustering scheme performs well in the optimal number of cluster selections, strongly connected (STC) route selection, and route request messages (RRMs) in the discovery of a particular path to the destination. Thus, we conclude that link connectivity and the heuristic approach of spectral clustering are valuable additions to existing routing schemes for high evolving networks.

A Survey of Chain Cluster-Based Routing Protocols in Wireless Sensor Network

Over a period, Wireless sensor networks (WSNs) have turned out to be continuously increasingly appealing and discovered the way into a wide assortment of uses and also frameworks in view of their relatively inexpensive, self-sustaining ability, and their detecting capacity in harsh environment. It is a collection of hubs composed onto a system. Based on their routing architecture they are generally separated into two classes: Hierarchical routing topology and Flat routing topology. In a flat routing topology every nodes will have the similar use and they perform similar task presented by the network. Nodes present in hierarchical topology carry out unique tasks and they are normally formed into clusters in WSNs. In this paper, a detailed study is made on chain cluster-based routing schemes for WSNs and few comparisons made by considering performance features such as Fault tolerance, scalability and topology Strengths and limitations of each scheme and the same is presented.

Clustering Algorithm for Deployment of Independent Heterogeneous Wireless Sensor Network

Wireless Sensor Network (WSN) is the main element of modern Industrial Internet of Things system in event monitoring. However, WSN is unable to respond for longer life due to the limited battery backup. So, it is hard to monitor the event of interest for longer span of time with deployed WSN. This can be resolved by utilizing energy efficient design approach for data communication. Cluster based routing scheme could be the most prominent design approach for the said option. Nevertheless, on the cost of certain design constraints. Although, most of the real-life applications are designed based on clustering scheme. But, it utilizes random node deployment strategy to support assumptions of prethrown nodes in test field. Similarly, uniform node deployment is also being demanded in some specific applications. Thus, there is a high need to design and implement clustering protocol that can work satisfactorily independent of the node deployment. This paper presents an innovative design approach with Clustering Algorithm for Deployment of Independent Heterogeneous Wireless Sensor Network. In proposed approach cluster head (CH) is selected based on index modeling with available node attributes. Proposed design ensures selected CH offers better node degree and intra-connectivity inside the network. Here, we try to minimize the efforts of CH and cluster member such that energy efficiency of each individual node get extend to a high level. Hence, it improves stability period, lifetime and throughput of the network. Simulation of proposed approach outperforms the existing LEACH, SEP and DEEC by more than 1.3 times.

Software defined networking approach based efficient routing in multihop and relay surveillance using Lion Optimization algorithm

Wireless Networks (WN) are widely employed in numerous real time applications in almost all the domains such as healthcare, remote monitoring, object tracking, security and surveillance based applications etc. The inexpensive nature of wireless sensors make it so popular and the major issue being faced by WN is the energy efficiency. The existing literature presents numerous solutions to guarantee energy efficiency, however the optimal energy efficient solutions are still in demand. Software Defined Networking (SDN) provide a hopeful resolution in bendy supervision WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based supervision in structure of WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand This paper proposes a hierarchical cluster based routing scheme by employing Lion Optimization (LO) algorithm. The sensor nodes are clustered by means of LO algorithm and routes are established to transmit data. This performance of this work is analysed in terms of packet delivery rate, average latency, energy consumption and network lifetime. The proposed approach proves better results when compared to the existing approaches. Finally Quality of Services (QoS) of network is increased with SDN based routing optimization of proposed approach.

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

An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks.

A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods still have some drawbacks such as uneven distribution of cluster heads (CH) and unbalanced energy consumption. Recently, much attention has been paid to intelligent clustering methods based on machine learning to solve the above issues. In this paper, an affinity propagation-based self-adaptive (APSA) clustering method is presented. The advantage of K-medoids, which is a traditional machine learning algorithm, is combined with the affinity propagation (AP) method to achieve more reasonable clustering performance. AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids. Then the modified K-medoids is utilized to form the topology of the network by iteration. The presented method effectively avoids the weakness of the traditional K-medoids in aspects of the homogeneous clustering and convergence rate. Simulation results show that the proposed algorithm outperforms some latest work such as the unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H), the low-energy adaptive clustering hierarchy using affinity propagation (LEACH-AP) algorithm, and the energy degree distance unequal clustering (EDDUCA) algorithm.

A light weight efficient cluster based routing model for mobile ad hoc networks (LWECM)

In recent few decades mobile ad hoc network have gained attention among researchers due to its extemporized nature. Routing of packets in these networks is very challenging task due to the mobility of nodes and dependency on small battery to remains active in the network. Flat routing schemes impose the great control overhead over network and perform poorly when number of terminals is increases. Cluster based routing schemes for MANETs has evolved an important alternative as it allows the scalability of network and better utilization of resources. In many clustering schemes a cluster contains a special node called cluster-head. Cluster-head act as a coordinator in its cluster. Since the cluster-head has to perform additional tasks in compare to other nodes in the network therefore it becomes over loaded and drains its battery more rapidly than other nodes in network. In this work “A Light Weight Efficient Cluster based routing Model for Mobile Ad hoc Networks” (LWEC) is proposed in which the life of cluster-head is prolonged by reducing the unnecessary load on cluster-head.

Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks

In large-area wireless sensor networks with hierarchical cluster-based routing protocols, the average number of clusters, k, and the transmission range for the control messages, R, significantly affect the network lifespan. We analyze energy consumption in depth as a function of ( k , R ) , taking into account the energy dissipation of cluster head nodes and the member nodes, separately. To achieve joint optimization of ( k o p t , R o p t ) , we adopt derivative-free Nelder–Mead Simplex method. Computer simulations have shown that our approach effectively reduces energy consumption of sensor nodes in the process of clustering and data transmission in large-area sensor fields. Our optimization can be applied to existing cluster-based routing schemes to maximize their energy efficiency.

Two-Level Cluster Based Routing Scheme for 5G V2X Communication

Efficient data dissemination is a big challenge for both IEEE 802.11p (MAC contentions problem) and cellular vehicle-to-everything communications (limited bandwidth in the high dense network). In this paper, a two-level clustering scheme is proposed for efficient data dissemination in 5G V2X communications. In the proposed protocol, level-1 cluster heads (L1CHs) are selected by a fuzzy logic algorithm using three factors, i.e., relative velocity factor, ${k}$ -connectivity factor, and link reliability factor. The slide link vehicle-to-vehicle (V2V) or the Third Generation Partnership Project V2V overcomes the MAC contention problem in L1CHs selections. Next, the level-2 cluster heads are selected by an improved Q-learning aiming to reduce the number of iterations in the gateway selection to LTE base station. The proposed scheme is evaluated under different network conditions showing that our protocol achieves good results compared to the existing schemes.

An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks

In wireless sensor networks (WSNs), clustering can significantly reduce energy dissipation of nodes, and also increase communication load of cluster heads. When multi-hop communication model is adopted in clustering, “energy hole” problem may occur due to unbalanced energy consumption among cluster heads. Recently, many multi-hop clustering protocols have been proposed to solve this problem. And the main way is using unequal clustering to control the size of clusters. However, many of these protocols are about homogeneous networks and few are about heterogeneous networks. In this paper, we present an unequal cluster-based routing scheme for WSNs with multi-level energy heterogeneity called UCR-H. The sensor field is partitioned into a number of equal-size rectangular units. We first calculate the number of clusters in each unit by balancing energy consumption among the cluster heads in different units. And then we find the optimal number of units by minimizing the total energy consumption of inter-cluster forwarding. Finally, the size of clusters in each unit is elaborately designed based on node’s energy level and the number of clusters in this unit. And a threshold is also designed to avoid excessive punishment to the nodes with higher energy level. Simulation results show that our scheme effectively mitigates the “energy hole” problem and achieves an obvious improvement on the network lifetime.