Clustering Protocol Research Articles

Improvement of life time for wireless body sensor networks using optimal clustering and routing protocol

Medical advancements are being made in order to extend the lifespan of mankind. In the medical field, the penetration of Wireless Sensor Networks (WSN) can aid doctors in diagnosing patients accurately and prescribing the medications accordingly. In recent times, several people have permanent implants such as face makers and it is threatening to life to keep altering this body enhancement as well as it is required to possess a system in place to improve the performance of the Wireless Body Sensors. Transmission loss and route loss are two important elements that will drag the battery energy and minimizes its life span. This research proposes optimal clustering and path selection protocol to enhance the lifetime of wireless body sensor networks. Initially, the data is collected from each body sensor through a clustering method called Glow-worm Swarm Optimization (GSO) and the Fruit-fly technique is applied to find the best path. Here, the cluster head is selected with the help of GSO that minimizes the energy consumption as well as enhances the lifetime of WBSN. Further, the best path is identified by the FFO using the fitness value that is measured within the nodes on the basis of the distance. Since hybrid technology is used here, the routing accomplished is shown to be better. The results reveal that the proposed model has improved the sensor life term (95 sec) while compared with other existing methods like PSO with FFO (78 sec), ACO with FFO (77 sec), GA with FFO (76 sec), and LEACH (68 sec) algorithm for 500 nodes.

A Centralized Energy-Efficient Clustering Protocol for Wireless Sensor Networks

Energy saving is one of the hot research topics in wireless sensor networks (WSNs), and a number of solutions have been proposed. Clustering protocols have become the widely used method to reduce energy consumption. However, the energy efficiency in existing clustering protocols is limited because of the excessive energy consumption in periodic reclustering. In this article, we propose a centralized energy-efficient clustering (CEEC) protocol, which includes two aspects of node energy balancing schemes, to enhance energy efficiency in WSNs. As for intracluster energy balancing, we employ the cluster head (CH)-rotation approach to balance the energy load among nodes in each cluster. With respect to intercluster energy balancing, we suggest four sequential algorithms to balance the energy consumption among clusters, and the main contributions are as follows: 1) a size-balanced cluster formation (SBCF) algorithm aims to partition the whole network into several small clusters of the same size; 2) a low-cost forecast-based maintenance mechanism is raised to help the base station (BS) maintain the total nodes’ energy information of each cluster in the network, and then, BS utilizes our introduced intercluster energy adjustment (ICEA) algorithm to reallocate a few cluster members in related clusters with low overhead; and 3) an energy-aware data forwarding (EDF) scheme is to maintain the nodes’ energy balance among clusters in intercluster communication. Simulation results show that our CEEC protocol outperforms the classical clustering mechanisms in terms of network lifetime, energy consumption, and network throughput.

A smart optimizer approach for clustering protocol in UAV-assisted IoT wireless networks

The Internet of Things (IoT) is a network in the socio-physical space that provides a platform for large-scale data collection. In this context, Wireless Sensor Nodes (WSN) and Unmanned Aerial Vehicles (UAVs) can play an essential role in reducing costs and ease of usage. One of the critical challenges of this network is energy consumption, the main part is related to the transmission unit. In this way, the Smart Optimizer Approach (SOA) to solving global optimization, engineering problems, and clustering in UAV-Assisted IoT Wireless Networks is presented. The SOA uses a Hybrid Vector-Based Operator (HVO), a Smart Selection Search Mode (S3M), and a Smart Re-Randomize Schema (SRS). Numerical comparison and discussions are done on CEC 2017 functions and benchmark functions and engineering problems. Comparison of performance with other algorithms shows the high generality of the SOA. Finally, this paper presented a new SOA-based Clustering protocol (SOAC) for large-scale UAV-Assisted IoT Wireless Networks. In this network, UAVs are used as Air Base Station (ABS). According to this large-scale network and the UAVs' movement, traditional protocols with high control message overhead aren't suitable. Clustering is presented with the aim of reducing overhead and increasing throughput by UAVs simultaneously. SOAC uses two new mechanisms, Assistant to the Cluster Head (ACH) and discretion license (DL), which have been introduced recently. The results of the simulations show the proposed clustering has a good improvement in stability, energy/load balancing, network lifetime, and throughput.

MOCRAW: A Meta-heuristic Optimized Cluster head selection based Routing Algorithm for WSNs

In Wireless Sensor Networks (WSNs), sensors are deployed in a specific region to sense the environment’s physical parameters. After sensing, data is processed and sent to the base station through a given route. Sensing and transmitting nodes consume a lot of energy; hence nodes die quickly; therefore, hot spot problems occur. Henceforth, data transmission is done by a single route; thus, WSNs experience network overhead problems. Nowadays, the enhancement of the energy of WSNs remains a challenging issue. Alternatively, efficient processes such as routing or clustering may be improved. Dynamic cluster head selection can be considered an important decision approach for optimal path selection and saving energy. This paper proposes a Meta-heuristic Optimized Cluster head selection-based Routing algorithm for WSNs (MOCRAW) to minimize node’s energy consumption and fast data transmission. MOCRAW removes isolated nodes or hot-spot problems and provides loop-free routing with the help of the Dragonfly Algorithm (DA), wherein the decision is based on Local Search Optimization (LSO) and Global Search Optimization (GSO). This protocol exploits two sub-processes: the optimal Cluster Head Selection Algorithm (CHSA) and Route Search Algorithm (RSA). CHSA uses Energy Level Matrix (ELM). ELM is based on node density, residual energy, the distance between Cluster Head (CH) and Base Station (BS), and inter-cluster formation. The inter-cluster discovers the optimum path between source to destination in RSA by levy distribution. MOCRAW performance is compared with other clustering and routing protocols on parameters such as the number of alive nodes, delay, packet delivery ratio, and average energy consumption. Simulation-based findings exhibit that the proposed methodology surpasses its peers and competitors in terms of energy efficiency.

An efficient key validation mechanism with VANET in real-time cloud monitoring metrics to enhance cloud storage and security

A vehicle-specific network (VANET) in automotive networks is formed along the road unit. There are many challenges in quality management to avoid expensive RSUs and improve the quality management of VANET systems. Our proposed work proposes a new algorithmic programme for organizing the cluster structure and choosing cluster headers (CHs) compatible with VANET. Weighted Cluster Protocol (AWCP) teams nodes at random, so it reaches the best channel by optimizing network parameters. The proposed work with analysis of the AWCP EE-WOA model is an automotive network with selected speed and placement. VANET provides a list of vehicles that failed to interact in the preceding period. System administrators should routinely update the generated keys of all the systems' vehicles, and the system administrator should suspend or revoke the power of scenarios. This validation occurs in the cloud whenever the vehicle generates the best algorithm for identifying keys. The results show that the projected AWCP EE-WOA model outperforms the Weighted Cluster Protocol and AWCP Whale algorithmic program protocols in terms of improved encapsulation potency and movability with a maximum cluster efficiency of 96.84.

An Intelligent Energy-Efficient Clustering Technique to Maximize Wireless Sensor Network Lifetime

A wireless sensor network (WSN) is an environment monitoring network that collects and transmits data wirelessly to the base station. Due to their inability to be recharged or replaced, sensors face battery constraints. Considering how much energy is wasted by sensors in WSN, this is one of the most popular research topics in WSN. Protocols for improving energy efficiency have been developed to improve the network's energy dissipation and, ultimately, its lifetime. An energy-efficient and throughputenhancing clustering technique is presented in this paper, which is superior to existing protocols based on LEACH. A cluster head in LEACH elected from a cluster that includes nodes with small residual energy will lead to an early death of the network, which will adversely affect its efficiency. The proposed clustering technique, on the other hand, uses the remaining energy of the sensor to make the sensor a cluster head. The base station finds the shortest path between the cluster heads. Through this compound, power dissipation is reduced, which contributes to a longer network lifespan and higher throughput. According to simulations performed with the NS3 simulator, the proposed clustering technique achieves higher network lifetime and throughput compared to some recent clustering protocols, i.e. LEACH, BCE-LEACH, and MO-LEACH.

Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks

Recently, the wearable internet of things (WIoT) has brought a new dimension of connectivity to wireless body area networks (WBANs). For instance, the WIoT provides valuable functions, such as collecting, analyzing, and transmitting data for real-time health monitoring of medical services. However, designing a clustering and routing protocol is challenging because WIoT attributes are subject to high interference in dense deployment, social mobility, and limited energy. In a WIoT-enabled WBAN, cooperative control is the fundamental function of clustering and routing. Cluster-based routing protocols are preferred for WBAN because of their scalability. However, one-hop neighbor-based clustering approaches do not ensure connectivity and reachability owing to node mobility. The existing two-hop-based clustering protocols are mostly centralized-based approaches, which are unsuitable for dynamic WBANs. In this paper, we propose a distributed energy-efficient two-hop-based clustering and routing protocol (DECR) targeting WIoT-enabled WBAN. In DECR, in the cluster formation phase, each node obtains the information of its neighbor nodes within the two-hop range. We utilize the modified grey-wolf optimization algorithm for the cluster head (CH) selection and routing optimization. Node connectivity and residual energy were jointly considered when determining the CH in each cluster. We also developed an analytical model to determine the optimal number of clusters by considering intra- and inter-cluster transmission distances to reduce the overall transmission distance and number of transmissions. Finally, we proposed a routing algorithm to ensure energy-efficient packet delivery from CH to sink. Our simulation outcomes revealed that the proposed DECR significantly outperforms the existing clustering and routing protocols in various performance metrics.

Fuzzy clustering and energy resourceful routing protocol (FCER2P) for smart dust

Sensor nodes in smart dust are used to track and identify data that is being transferred to a sink. The accessibility of node energy poses a significant challenge for smart dust and may have an impact on the long-term viability of the network. As a result, constructing smart dust must take into account the need for algorithms and techniques that enable the most use of scarce resources, especially energy resources. For instance, routing algorithms are distinctive algorithms because they have a clear and direct connection with network longevity and energy. The offered routing techniques use clustering each round and single-hop data delivery to the sink. A Fuzzy Clustering and Energy Resourceful Routing Protocol (FCER2P) that reduces smart dust energy consumption and lengthens lifespan of the network is proposed in this research. Using a specified threshold, FCER2P proposes a new cluster-based fuzzy routing mechanism that can make use of clustering and multiple hop routing capabilities simultaneously. This research is innovative in that it adjusts multi-hop connectivity by anticipating the optimum intermediate node for aggregating and the sink, eliminates clustering each round while taking a fixed threshold into account, and eliminates clustering per round altogether. When choosing the intermediary node to employ, some fuzzy parameters, including residual energy, the number of neighbors, and the distance to the sink, are taken into account.

Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

Wireless Sensor Networks (WSN) play a vital role in several real-time applications ranging from military to civilian. Despite the benefits of WSN, energy efficiency becomes a major part of the challenging issue in WSN, which necessitate proper load balancing amongst the clusters and serves a wider monitoring region. The clustering technique for WSN has several benefits: lower delay, higher energy efficiency, and collision avoidance. But clustering protocol has several challenges. In a large-scale network, cluster-based protocols mainly adapt multi-hop routing to save energy, leading to hot spot problems. A hot spot problem becomes a problem where a cluster node nearer to the base station (BS) tends to drain the energy much quicker than other nodes because of the need to implement more transmission. This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems (JSOUCP-MHP) in WSN. The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search, persecution, and jumping skills to attack prey. The presented JSOUCP-MHP technique mainly resolves the hot spot issue for maximizing the network lifespan. The JSOUCP-MHP technique elects a proper set of cluster heads (CHs) using average residual energy (RE) to attain this. In addition, the JSOUCP-MHP technique determines the cluster sizes based on two measures, i.e., RE and distance to BS (DBS), showing the novelty of the work. The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy. The comparison study shows the significance of the JSOUCP-MHP technique over other models.

Adaptive Threshold Residual Energy‐Based Efficient Sensor Network Protocol (ATREEN)

Wireless sensor network (WSN) hierarchical (clustering) routing involves the gathering of sensor nodes to form clusters in which sensor node communicates to cluster head (CH). The CH is responsible for data aggregation and forwarding collected data toward base station (BS). Energy expenditure in usual protocols becomes unequal because of clusters having diverse lengths. In this paper, we propose another new enhanced clustering protocol called adaptive threshold residual energy‐based efficient sensor network protocol (ATREEN) for adaptive selection of CH in WSNs. In ATREEN, CHs are chosen by comparing threshold of node value with respect to distance between cluster member and CH, residual energy of sensor nodes, and position of sensor node with respect to BS. Simulation results show that ATREEN protocol extends network lifetime and stability with decline in energy dissipation compared to clustering protocols such as low‐energy adaptive clustering hierarchy (LEACH), power‐efficient gathering in sensor information systems (PEGASIS), and distributed energy‐efficient clustering (DEEC) scheme for heterogeneous WSNs.

A comparative study of energy-efficient clustering protocols for WSN-internet-of-things

Recent research predicts many internet-connected technologies, including mobile phones and devices that collect data and operate in exterior settings. WSNs provide flexibility, reliability, and intelligence to the internet of things (IoT). WSNs often use sensors with limited power and memory. The biggest issue in these systems is the high energy depletion during data transmission from the origin node to the destination nodes, which reduces node longevity. Because node batteries cannot be replaced, WSN-IoT energy usage is a big design challenge. Thus, WSN-IoT clustering protocol (CP) improves energy efficiency and network lifetime. Many researchers have created energy-efficient CPs (EECPs) for WSN-IoT energy conservation. Such protocols cluster nodes by residual energy, distance, etc. to maximise energy production. This study covers various WSN-IoT EECPs. It also tabulates protocol benefits, drawbacks, and simulation efficiency. Several protocols were simulated to evaluate network longevity, residual energy, throughput, packet delivery ratio (PDR), and stability. Finally, WSN-IoT systems can be made more energy efficient and last longer.

Neural Network Clustering and Swarm Intelligence-Based Routing Protocol for Wireless Sensor Networks: A Machine Learning Perspective.

With no requirement for an established network infrastructure, wireless sensor networks (WSNs) are well suited for applications that call for quick network deployment. Military training and emergency rescue operations are two prominent uses of WSNs. The individual network nodes must carry out routing and intrusion detection because there is no predetermined routing or intrusion detection in a wireless network. WSNs can only manage a certain volume of data, and doing so requires a significant amount of energy to process, transmit, and receive. Since sensors have a modest energy source and a constrained bandwidth, they cannot transmit all of their data to a base station for processing and analysis. Therefore, machine learning (ML) techniques are needed for WSNs to facilitate data transmission. Other current solutions have drawbacks as well, such as being less reliable, more susceptible to environmental changes, converging more slowly, and having shorter network lifetimes. This study addressed problems with wireless sensor networks and devised an efficient clustering and routing algorithm based on machine learning. Results from simulations demonstrate that the proposed system beats previous state-of-the-art models on a variety of metrics, including accuracy, specificity, and sensitivity (0.93, 0.93, and 0.92 respectively).

Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network (WSN) includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications. It comprises a massive set of nodes with restricted energy and processing abilities. Energy dissipation is a major concern involved in the design of WSN. Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms. In order to design an energy aware cluster-based route planning scheme, this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing (HBAC-AVOR) protocol for WSN. The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way. The presented HBAC-AVOR model follows a two stage process. At the initial stage, the HBAC technique is exploited to choose an optimal set of cluster heads (CHs) utilizing a fitness function involving many input parameters. Next, the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN. A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol. On comparing with existing techniques, the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Fuzzy Clustering and Routing Protocol With Rules Tuned by Improved Particle Swarm Optimization for Wireless Sensor Networks

Fuzzy Clustering and Routing Protocol With Rules Tuned by Improved Particle Swarm Optimization for Wireless Sensor Networks

A comparative study of energy-efficient clustering protocols for WSN-internet-of-things

A comparative study of energy-efficient clustering protocols for WSN-internet-of-things

Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

A Wireless Sensor Network (WSN) becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears impracticable. Concerning the energy production of the nodes, WSN has major issues that may influence the stability of the system. As a result, constructing WSN requires devising protocols and standards that make the most use of constrained capacity, especially the energy resources. WSN faces some issues with increased power utilization and an on going development due to the uneven energy usage between the nodes. Clustering has proven to be a more effective strategy in this series. In the proposed work, a hybrid method is used for reducing the energy consumption among CHs. A Fuzzy Logic-based clustering protocol FLUC (unequally clustered) and Fuzzy Clustering with Energy-Efficient Routing Protocol (FCERP) are used. A Fuzzy Clustering with Energy Efficient Routing Protocol (FCERP) reduces the WSN power usage and increases the lifespan of the network. FCERP has created a novel cluster-based fuzzy routing mechanism that uses a limit value to combine the clustering and multi-hop routing capabilities. The technique creates uneven groups by using fuzzy logic with a competitive range to choose the Cluster Head (CH). The input variables include the distance of the nodes from the ground station, concentrations, and remaining energy. The proposed FLUC-FCERP reduces the power usage and improves the lifetime of the network compared with the existing algorithms.

Application of nature inspired optimization algorithm on hierarchal LEACH protocol for improved performance

In today’s scenario Wireless Sensor Network is playing a dynamic role with the advancement in IOT for mission critical applications due to the fact that these applications are either event driven or time driven applications. Sensor network comprises of battery operated tiny sensors for sensing any change in the environment since the battery life is limited so is the sensor life. One solution to overcome this problem is the use of clustering protocol. A lot of optimization techniques are employed for performance improvement of the hierarchal LEACH routing protocol. This paper explores the three types of Foraging behavior for cluster head selection which means the process to search for food or the energy of the node so that it can be elected as CH. A fitness function is defined relating to residual energy, CH distance to Base station, position of base station. Theses algorithm progresses on the basis of the energy of sensor node in the deployed network. Here the higher energy of node is analogous to food for which the search goes on; in doing so the best CH is selected. The algorithm uses the best location of the node. The overall performance of the network is improved in terms of Energy consumption; packet delivery ratio metrics the results are compared with the existing LEACH protocol.

An improved communication strategy in vehicular ad hoc networks: adaptive game theoretic modelling approach

The vehicular ad hoc network, a special type of MANET, is adopted for a better transportation system which can help to avoid the hazardous conditions and infotainment application while moving at high speed. Due to high mobility of these networks, various challenges arise, such as connectivity issues, link break and frequent disconnection, which leads towards a degraded communication performance. So, conventional short range communication techniques are not able to support high mobility. However, several clustering schemes were introduced for improving the network performance. But, existing VANET clustering protocols do not show desired performance and thus to overcome the issues of existing approaches, we present an end-to-end routing mechanism where first we apply a novel approach for clustering using game theory and later, relay the application of node selection process, which helps to improve the communication by reducing the packet drop. Finally, link reliability computation matrix is computed by applying probability distribution function according to the velocity of vehicle for establishing the connection between vehicles. Compared with state-of-art techniques, the proposed approach shows improved performance in terms of QoS metrics.

Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

Recently, energy harvesting wireless sensor networks (EHWSN) have increased significant attention among research communities. By harvesting energy from the neighboring environment, the sensors in EHWSN resolve the energy constraint problem and offers lengthened network lifetime. Clustering is one of the proficient ways for accomplishing even improved lifetime in EHWSN. The clustering process intends to appropriately elect the cluster heads (CHs) and construct clusters. Though several models are available in the literature, it is still needed to accomplish energy efficiency and security in EHWSN. In this view, this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks (CROC-SEHWSN) model. The presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in EHWSN. The CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization (RO) algorithm. Besides, the CROC-SEHWSN model derives a fitness function (FF) involving seven distinct parameters connected to WSN. To accomplish security, trust factor and link quality metrics are considered in the FF. The design of RO algorithm for secure clustering process shows the novelty of the work. In order to demonstrate the enhanced performance of the CROC-SEHWSN approach, a wide range of simulations are carried out and the outcomes are inspected in distinct aspects. The experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.

Certain Investigation on Improved Cluster Protocol with Trust security for Wireless Sensor Networks

Immense development of Micro Electro Mechanical Systems (MEMS) made an incredible advancement in wireless technology. The Wireless Sensor Network (WSN) has created many opportunities for the development of various applications in the fields of military, research, medical, engineering, etc. In this research article, a novel trust-based energy-aware clustering protocol is proposed. The clustering algorithm concentrates on reducing the time spent on cluster formation, controlling redundant data forwarding, and prolonging the network's lifespan. In this model, clustered nodes are classified into three levels like Cluster heads (CH), secondary CHs, and sensor nodes (SN) are used to sense the environmental changes and report to the Base Station (BS). An extension of the lifetime of a WSN is possible by the use of secure multi-hop routing with an aggregation technique to forward data from a cluster to the BS. Compared to relevant works on clustering with the routing protocol, the simulation result showed improved energy efficiency and network lifetime.