Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.

A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs

As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.

An Optimal Cluster Head and Gateway Node Selection with Fault Tolerance

In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-tion for Cluster Head and Gateway Selection(NQCAFFFOCHGS)has the best network performance because it uses the Improved Weighted Clustering Algo-rithm(IWCA)to cluster the network and the FFO algorithm,which uses fuzzy-based network metrics to select the best CH and entryway.However,the major drawback of the fuzzy system was to appropriately select the membership func-tions.Also,the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway.When learning fuzzy sets,this algorithm employs a new Continuous Action-set Learning Automata(CALA)approach that correctly modifies and chooses the fuzzy membership functions.Despite the fact that it extends the network’s lifespan,it does not assist in the detection of defective nodes in the routing route.Because of this,a new Fault Tolerance(NQCAEFFFOCHGS-FT)mechanism based on the Distributed Connectivity Restoration(DCR)mechanism is proposed,which allows the net-work to self-heal as a consequence of the algorithm’s self-healing capacity.Because of the way this method is designed,node failures may be utilised to rebuild the network topology via the use of cascaded node moves.Founded on the fractional network information and topologic overhead related with each node,the DCR is suggested as an alternative to the DCR.When compared to the NQCAFFFOCHGS algorithm,the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay,energy consumption,Packet Loss Ratio(PLR),Normalized Routing Overhead(NRO),and Balanced Load Index(BLI).

Adaptive Route Sink Relocation Using Cluster Head Chain Cycling Model in WSN

Wireless Sensor Networks(WSN)have revolutionized the processes involved in industrial communication.However,the most important challenge faced by WSN sensors is the presence of limited energy.Multiple research inves-tigations have been conducted so far on how to prolong the energy in WSN.This phenomenon is a result of inability of the network to have battery powered-sensor terminal.Energy-efficient routing on packetflow is a parallel phenomenon to delay nature,whereas the primary energy gets wasted as a result of WSN holes.Energy holes are present in the vicinity of sink and it is an important efficient-routing protocol for WSNs.In order to solve the issues discussed above,an energy-efficient routing protocol is proposed in this study named as Adaptive Route Decision Sink Relocation Protocol using Cluster Head Chain Cycling approach(ARDSR-CHC2H).The proposed method aims at improved communica-tion at sink-inviting routes.At this point,Cluster Head Node(CHN)is selected,since it consumes low energy and permits one node to communicate with others in two groups.The main purpose of the proposed model is to reduce energy con-sumption and define new interchange technology.A comparison of simulation results demonstrates that the proposed algorithm achieved low cluster creation time,better network error and high Packet Delivery Rate with less network failure.

Dynamic Cluster Head for Lifetime Efficiency in WSN

Saving energy and increasing network lifetime are significant challenges in wireless sensor networks （WSNs）. In this paper, we propose a mechanism to distribute the responsibility of cluster-heads among the wireless sensor nodes in the same cluster based on the ZigBee standard, which is the latest WSN standard. ZigBee supports ad hoc on-demand vector （AODV） and cluster-tree routing protocols in its routing layer. However, none of these protocols considers the energy level of the nodes in the network establishing process or in the data routing process. The cluster-tree routing protocol supports single or multi-cluster networks. However, each single cluster in the multi-cluster network has only one node acting as a cluster head. These cluster-heads are fixed in each cluster during the network lifetime. Consequently, using these cluster-heads will cause them to die quickly, and the entire linked nodes to these cluster-heads will be disconnected from the main network. Therefore, the proposed technique to distribute the role of the cluster head among the wireless sensor nodes in the same cluster is vital to increase the lifetime of the network. Our proposed technique is better in terms of performance than the original structure of these protocols. It has increased the lifetime of the wireless sensor nodes, and increased the lifetime of the WSN by around 50% of the original network lifetime.

Quantitative Survivability Analysis Using Probability Model Checking: A Study of Cluster-Based Vehicle Networks with Dual Cluster Heads

As an important part of future 5G wireless networks,a vehicular network demands safety,reliability and connectivity.In this context,networking survivability is usually considered an important metric to evaluate network performance.In this paper,we propose a survivability model for vehicle communication networking based on dual cluster heads,wherein a backup cluster head(CH)will be activated if the primary CH fails,thereby effectively enhancing the network lifetime.Additionally,we introduce a software rejuvenation strategy for the prime CH to further improve the survivability of the entire network.Using the Probabilistic Symbolic Model Checker(PRISM),we verify and discuss the proposed survivability model via numerical simulations.The results show that network survivability can be effectively improved by introducing an additional CH and further enhanced by adopting the software rejuvenation technique.

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing capabilities.In WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the sensors.So,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN.In this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for WSN.The presented model involves a two-stage process such as clustering and data aggregation.Initially,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster construction.Besides,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at CHs.Finally,the aggregated data is transmitted to the BS where it achieves energy efficiency.The experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of BS.The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

Cluster Head Selection Algorithm for UAV Assisted Clustered IoT Network Utilizing Blockchain

To guarantee the security of Internet of Things(IoT)devices,the blockchain tech⁃nology is often applied to clustered IoT networks.However,cluster heads(CHs)need to un⁃dertake additional control tasks.For battery-powered IoT devices,the conventional CH se⁃lection algorithm is limited.Based on the above problem,an unmanned aerial vehicle(UAV)network assisted clustered IoT system is proposed,and a corresponding UAV CH se⁃lection algorithm is designed.In this scheme,UAVs are selected as CHs to serve IoT clus⁃ters.The proposed CH selection algorithm considers the maximal transmit power,residual energy and distance information of UAVs,which can greatly extend the working life of IoT clusters.Through Monte Carlo simulation,the key performance indexes of the system,in⁃cluding energy consumption,average secrecy rate and the maximal number of data packets received by the base station(BS),are evaluated.The simulation results show that the pro⁃posed algorithm has great advantages compared with the existing CH selection algorithms.

A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head.The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network.The proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster head.The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.

AN EFFICIENT UE CLUSTER HEAD SELECTION ALGORITHM IN WIRELESS SENSOR NETWORKS AND CELLULAR NETWORKS

Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energy efficient clustering algorithms for WSN, but less research has been concerned in the mobile User Equipment (UE) acting as a Cluster Head (CH) for data transmission between cellular networks and WSNs. In this paper, we propose a cellular-assisted UE CH selection algorithm for the WSN, which considers several parameters to choose the optimal UE gateway CH. We analyze the energy cost of data transmission from a sensor node to the next node or gateway and calculate the whole system energy cost for a WSN. Simulation results show that better system performance, in terms of system energy cost and WSNs life time, can be achieved by using interactive optimization with cellular networks.

Effective Cluster Head Substitution Leach Protocol for Wireless Communication

In recent years, the demand for Wireless Sensor Network (WSN) in smart farming has had a tremendous increase in demand for its efficiency. Wireless sensor networks have very many nodes, and it is of no use when the battery dies. This is why there are several routing protocols being take into consideration to cub this problem. In this paper, in order to increase the heterogeneity and energy levels of the network, the M-LEACH protocol is proposed. The key aim of the Leach protocol is to prolong the existence of wireless sensor network by lowering the energy consumption needed for Cluster Head creation and maintenance, the proposed algorithm instructs a node to use high power amplification as it acts as the Cluster heads, and low power amplification when it becomes a Cluster Member, in the next stage. Finally, for better effectiveness, M-LEACH employs hard and soft threshold systems. Since it eliminates collisions and reduces the packet drop ratio for other signals, the M-LEACH protocol proposed works better than the Leach protocol.

Adaptive Cluster Head Selection Scheme for High Mobility Based IEEE 802.15.6 Wireless Body Area Networks

Due to the development in the field of Wireless Sensor Networks (WSNs), its major application, Wireless Body Area Network (WBAN) has presently become a major area of interest for the developers and researchers. Efficient sensor nodes data collection is the key feature of any effective wireless body area network. Prioritizing nodes and cluster head selection schemes plays an important role in WBAN. Human body exhibits postural mobility which affects distances and connections between different sensor nodes. In this context, we propose maximum consensus based cluster head selection scheme, which allows cluster head selection by using Link State. Nodal priority through transmission power is also introduced to make WBAN more effective. This scheme results in reduced mean power consumption and also reduces network delay. A comparison with IEEE 802.15.6 based CSMA/CA protocol with different locations of cluster head is presented in this paper. These results show that our proposed scheme outperforms Random Cluster head selection, Fixed Cluster head at head, Foot and Belly positions in terms of mean power consumption, network delay, network throughput and bandwidth efficiency.

A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs

In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.

CEER:Cooperative Energy-Efficient Routing Mechanism for Underwater Wireless Sensor Networks Using Clusters

Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol.Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node.All routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange operation.Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs.The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation.This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network lifespan.This paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)algorithm.CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process.LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to 100.To evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming packets.The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.

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.

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Wireless sensor networks(WSNs)are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data.WSNs use sensor nodes(SNs)to collect and transmit data.However,the power supplied by the sensor network is restricted.Thus,SNs must store energy as often as to extend the lifespan of the network.In the proposed study,effective clustering and longer network lifetimes are achieved using mul-ti-swarm optimization(MSO)and game theory based on locust search(LS-II).In this research,MSO is used to improve the optimum routing,while the LS-II approach is employed to specify the number of cluster heads(CHs)and select the best ones.After the CHs are identified,the other sensor components are allo-cated to the closest CHs to them.A game theory-based energy-efficient clustering approach is applied to WSNs.Here each SN is considered a player in the game.The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest.The proposed multi-swarm with energy-efficient game theory on locust search(MSGE-LS)efficiently selects CHs,minimizes energy consumption,and improves the lifetime of networks.The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters,average energy consumption,lifespan extension,reduction in average packet loss,and end-to-end delay.