Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks

Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic algorithms.This adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par with the competitive CH selection schemes under different number of data transmission rounds.The statistical analysis of the proposed HSWOA-DCP scheme also confirmed its energy stability with respect to ANOVA test.

Energy Efficient Clustering Protocol to Enhance Network Lifetime in Wireless Sensor Networks

In this paper,the energy conservation in the ununiform clustered network field is proposed.The fundamental reason behind the methodology is that in the process of CH election,nodes Competition Radius(CR)task is based on not just the space between nodes and their Residual Energy(RE),which is utilized in Energy-Aware Distributed Unequal Clustering(EADUC)protocol but also a third-degree factor,i.e.,the nearby multi-hop node count.In contrast,a third-factor nearby nodes count is also used.This surrounding data is taken into account in the clustering feature to increase the network’s life span.The proposed method,known as Energy Conscious Scattered Asymmetric Clustering(ECSAC),self-controls the nodes’energy utilization for equal allotment and un-equal delivery.Besides,extra attention is agreed to energy consumption in the communication process by applying a timeslot-based backtracking algorithm for increasing the network’s lifetime.The proposed methodology reduces the clustering overhead and node communication energy consumption to extend the network’s lifetime.Our suggested method’s results are investigated against the classical techniques using the lifetime of the network,RE,alive hop count and energy consumption during transmission as the performance metric.

An unequal clustering routing protocal for wireless sensor networks based on genetic algorithm

The imbalance of energy consumption in wireless sensor networks(WSNs)easily results in the“hot spot”problem that the sensor nodes in a particular area die due to fast energy consumption.In order to solve the“hot spot”problem in WSNs,we propose an unequal clustering routing algorithm based on genetic algorithm(UCR-GA).In the cluster head election phase,the fitness function is constructed based on the residual energy,density and distance between nodes and base station,and the appropriate node is selected as the cluster head.In the data transmission phase,the cluster head selects single-hop or multi-hop communication mode according to the distance to the base station.After we comprehensively consider the residual energy of the cluster head and its communication energy consumption with the base station,an appropriate relay node is selected.The designed protocal is simulated under energy homogeneous and energy heterogeneity conditions,and the results show that the proposed routing protocal can effectively balance energy consumption,prolong the life cycle of network,and is appicable to heterogeneous networks.

A Distributed Dynamic Clustering Algorithm for Wireless Sensor Networks

This paper proposes a distributed dynamic k-medoid clustering algorithm for wireless sensor networks （WSNs）, DDKCAWSN. Different from node-clustering algorithms and protocols for WSNs, the algorithm focuses on clustering data in the network. By sending the sink clustered data instead of practical ones, the algorithm can greatly reduce the size and the time of data communication, and further save the energy of the nodes in the network and prolong the system lifetime. Moreover, the algorithm improves the accuracy of the clustered data dynamically by updating the clusters periodically such as each day. Simulation results demonstrate the effectiveness of our approach for different metrics.

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 JSOUCPMHP 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 JSOUCPMHP technique over other models.

Wireless sensor networks routing algorithm based on block clustering and springboard nodes

In the wireless sensor networks(WSN),the sensor nodes have limited battery life and are deployed in hostile environments.It is very difficult to recharging or replacement of the batteries after deployment for the sensor nodes in inaccessible areas.Therefore,how to increase the network lifetime of the WSN is deserved to be studied.In this study,a WSN routing algorithm was proposed based on block clustering and springboard nodes to increase the network lifetime of the WSN.Firstly,by analyzing the influence of communication transmission distance on network energy consumption,block clustering was introduced to control node transmission distance in order to reduce total network energy consumption.In addition,a network transmission model was proposed based on springboard nodes and the advantages of network energy consumption of this model against multi-hop between clusters were analyzed.The simulation results show that,compared with the LEACH algorithm,EECPK-means algorithm and energy centroid clustering algorithm,the proposed routing algorithm effectively prolongs the network lifetime of WSN.

An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of networks.The clustering process takes sensors’location and residual energy into consideration to elect suitable cluster heads.Furthermore,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself.Related experimental results display that the presented method can avoid long distance communicate between sensor nodes.Furthermore,this algorithm reduces energy consumption effectively and improves package delivery rate.

A Power- and Coverage-aware Clustering Scheme for Wireless Sensor Networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.

Energy efficient clustering algorithm based on neighbors for wireless sensor networks

In this paper, an energy efficient clustering algorithm based on neighbors （EECABN） for wireless sensor networks is proposed. In the algorithm, an optimized weight of nodes is introduced to determine the priority of clustering procedure. As improvement, the weight is a measurement of energy and degree as usual, and even associates with distance from neighbors, distance to the sink node, and other factors. To prevent the low energy nodes being exhausted with energy, the strong nodes should have more opportunities to act as cluster heads during the clustering procedure. The simulation results show that the algorithm can effectively prolong whole the network lifetime. Especially at the early stage that some nodes in the network begin to die, the process can be postponed by using the algorithm.

Two-Level Linear Clustering Protocol Based on Wireless Sensor Networks

Aiming at the defects of the nodes in the low energy adaptive clustering hierarchy （LEACH） protocol, such as high energy consumption and uneven energy consumption, a two-level linear clustering protocol is built. The protocol improves the way of the nodes distribution at random. The terminal nodes which have not been a two-level cluster head in the cluster can compete with the principle of equivalent possibility, and on the basis of the rest energy of nodes the two-level cluster head is selected at last. The single hop within the cluster and single hop or multiple hops between clusters are used. Simulation experiment results show that the performance of the two-level linear clustering protocol applied to the Hexi corridor agricultural field is superior to that of the LEACH protocol in the survival time of network nodes, the ratio of success, and the remaining energy of network nodes.

Energy-balanced clustering protocol for data gathering in wireless sensor networks with unbalanced traffic load

Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering protocol (DSCP) was proposed to solve the data gathering problem in this scenario.In DSCP,a node evaluates the potential lifetime of the network (from its local point of view) assuming that it acts as the cluster head,and claims to be a tentative cluster head if it maximizes the potential lifetime.When evaluating the potential lifetime of the network,a node considers not only its remaining energy,but also other factors including its traffic load,the number of its neighbors,and the traffic loads of its neighbors.A tentative cluster head becomes a final cluster head with a probability inversely proportional to the number of tentative cluster heads that cover its neighbors.The protocol can terminate in O(n/lg n) steps,and its total message complexity is O(n2/lg n).Simulation results show that DSCP can effectively prolong the lifetime of the network in multi-hop networks with unbalanced traffic load.Compared with EECT,the network lifetime is prolonged by 56.6% in average.

MST-BASED CLUSTERING TOPOLOGY CONTROL ALGORITHM FOR WIRELESS SENSOR NETWORKS

In this paper, we propose a novel clustering topology control algorithm named Minimum Spanning Tree (MST)-based Clustering Topology Control (MCTC) for Wireless Sensor Networks (WSNs), which uses a hybrid approach to adjust sensor nodes' transmission power in two-tiered hi- erarchical WSNs. MCTC algorithm employs a one-hop Maximum Energy & Minimum Distance (MEMD) clustering algorithm to decide clustering status. Each cluster exchanges information between its own Cluster Members (CMs) locally and then deliveries information to the Cluster Head (CH). Moreover, CHs exchange information between CH and CH and afterwards transmits aggregated in- formation to the base station finally. The intra-cluster topology control scheme uses MST to decide CMs' transmission radius, similarly, the inter-cluster topology control scheme applies MST to decide CHs' transmission radius. Since the intra-cluster topology control is a full distributed approach and the inter-cluster topology control is a pure centralized approach performed by the base station, therefore, MCTC algorithm belongs to one kind of hybrid clustering topology control algorithms and can obtain scalability topology and strong connectivity guarantees simultaneously. As a result, the network topology will be reduced by MCTC algorithm so that network energy efficiency will be improved. The simulation results verify that MCTC outperforms traditional topology control schemes such as LMST, DRNG and MEMD at the aspects of average node's degree, average node's power radius and network lifetime, respectively.

An Iterative Clustering-Based Localization Algorithm for Wireless Sensor Networks

In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

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.

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 opti-mal set of cluster heads(CHs)utilizing afitness 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.

Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,etc.The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments.Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes.Therefore,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network.In this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster management.The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions.To achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence rate.In addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios.The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.

A Clustering-tree Topology Control Based on the Energy Forecast for Heterogeneous Wireless Sensor Networks

How to design an energy-efficient algorithm to maximize the network lifetime in complicated scenarios is a critical problem for heterogeneous wireless sensor networks (HWSN). In this paper, a clustering-tree topology control algorithm based on the energy forecast (CTEF) is proposed for saving energy and ensuring network load balancing, while considering the link quality, packet loss rate, etc. In CTEF, the average energy of the network is accurately predicted per round (the lifetime of the network is denoted by rounds) in terms of the difference between the ideal and actual average residual energy using central limit theorem and normal distribution mechanism, simultaneously. On this basis, cluster heads are selected by cost function (including the energy, link quality and packet loss rate) and their distance. The non-cluster heads are determined to join the cluster through the energy, distance and link quality. Furthermore, several non-cluster heads in each cluster are chosen as the relay nodes for transmitting data through multi-hop communication to decrease the load of each cluster-head and prolong the lifetime of the network. The simulation results show the efficiency of CTEF. Compared with low-energy adaptive clustering hierarchy (LEACH), energy dissipation forecast and clustering management (EDFCM) and efficient and dynamic clustering scheme (EDCS) protocols, CTEF has longer network lifetime and receives more data packets at base station. © 2014 Chinese Association of Automation.

A Novel Energy Aware Clustering Technique for Routing in Wireless Sensor Networks

Cluster-based architectures are one of the most practical solutions in order to cope with the requirements of large-scale wireless sensor networks (WSN). Cluster-head election problem is one of the basic QoS requirements of WSNs, yet this problem has not been sufficiently explored in the context of cluster-based sensor networks. Specifically, it is not known how to select the best candidates for the cluster head roles. In this paper, we investigate the cluster head election problem, specifically concentrating on applications where the energy of full network is the main requirement, and we propose a new approach to exploit efficiently the network energy, by reducing the energy consumed for cluster forming.