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.

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.

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.

A Novel Improved Artificial Bee Colony and Blockchain-Based Secure Clustering Routing Scheme for FANET

Flying Ad hoc Network(FANET)has drawn significant consideration due to its rapid advancements and extensive use in civil applications.However,the characteristics of FANET including high mobility,limited resources,and distributed nature,have posed a new challenge to develop a secure and ef-ficient routing scheme for FANET.To overcome these challenges,this paper proposes a novel cluster based secure routing scheme,which aims to solve the routing and data security problem of FANET.In this scheme,the optimal cluster head selection is based on residual energy,online time,reputation,blockchain transactions,mobility,and connectivity by using Improved Artificial Bee Colony Optimization(IABC).The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities.Further,a lightweight blockchain consensus algorithm,AI-Proof of Witness Consensus Algorithm(AI-PoWCA)is proposed,which utilizes the optimal cluster head for mining.In AI-PoWCA,the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51%attack.Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90%packet delivery ratio,lowest end-to-end delay,highest throughput,resilience against security attacks,and superior in block processing time.

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

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 Optimizationbased 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 MPOIPSO-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.

Relay-assisted wireless energy harvesting for multihop clustered IoT network

In large-scale networks such as the Internet of Things(IoT),devices seek multihop communication for longdistance communications,which considerably impacts their power exhaustion.Hence,this study proposes an energy harvesting-enabled,relay-based communication in multihop clustered IoT networks in a bid to conserve the battery power in multihop IoT networks.Initially,this study proposes an efficient,hierarchical clustering mechanism in which entire IoT devices are clustered into two types:the closest cluster(CC)and remote clusters(RCs).Additionally,Euclidean distance is employed for the CC and fuzzy c-means for the RCs.Next,for cluster head(CH)selection,this study models a fitness function based on two metrics,namely residual energy and distance(device-to-device distance and device-to-sink distance).After CH selection,the entire clustered network is partitioned into several layers,after which a relay selection mechanism is applied.For every CH of the upper layer,we assign a few lower-layer CHs to function as relays.The relay selection mechanism is applied only for the devices in the RCs,while for devices in the CC,the CH functions as a relay.Finally,several simulation experiments are conducted to validate the proposed method’s performance.The results show the method’s superiority in terms of energy efficiency and optimal number of relays in comparison with the state-of-the-art methods.

Stability-aware Energy Efficient Clustering Protocol in WSN using Opposition-based Elephant Herding Optimisation

The main intent of this paper is to implement the stability-aware energy-efficient clustering protocol in WSN.This paper plans to derive a multi-objective function with the constraints like energy,distance,delay,stability period,and intents to attain the objective by developing a new well-performing meta-heuristic algorithm called Opposition-based Elephant Herding Optimisa-tion(O-EHO).The objective function diminishes the energy consumption of sensor nodes by optimum selection of cluster heads that leads to maintain the energy balance between the nor-mal nodes.In this way,there is a remarkable enhancement in the performance parameters such as throughput,stability period,and network lifetime.It is proved that the network lifetime is enhanced by the stability period and thus it is considered as the most significant parameter.The experimental analysis proves the competitive performance of the proposed model over other heuristic methods.