Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Enabling Efficient Data Transmission in Wireless Sensor Networks-Based IoT Application

The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and exchanged.However,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques.Consequently,designing a lightweight secure data transmission scheme is becoming essential.In this article,we propose lightweight secure data transmission(LSDT)scheme for IoT environments.LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol.We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs.Security and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.

Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks

A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks(MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation and state feedback to effectively manipulate the spatiotemporal dynamics of malware propagation. The hybrid control can not only suppress the Turing instability caused by diffusion factor but can also adjust the occurrence of Hopf bifurcation induced by time delay. Numerical simulation results show that the hybrid control strategy can efficiently manipulate the transmission dynamics to achieve our expected desired properties, thus reducing the harm of malware propagation to MWSNs.

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.

A Practical Approach for Missing Wireless Sensor Networks Data Recovery

In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.

Optimization Model Proposal for Traffic Differentiation in Wireless Sensor Networks

Wireless sensor networks(WSNs)are characterized by heterogeneous traffic types(audio,video,data)and diverse application traffic requirements.This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs.The requirements for each class regarding sensitivity to QoS(Quality of Service)parameters,such as loss,delay,and jitter,are described.These classes encompass real-time and delay-tolerant traffic.Given that QoS evaluation is a multi-criteria decision-making problem,we employed the AHP(Analytical Hierarchy Process)method for multi-criteria optimization.As a result of this approach,we derived weight values for different traffic classes based on key QoS factors and requirements.These weights are assigned to individual traffic classes to determine transmission priority.This study provides a thorough comparative analysis of the proposed model against existing methods,demonstrating its superior performance across various traffic scenarios and its implications for future WSN applications.The results highlight the model’s adaptability and robustness in optimizing network resources under varying conditions,offering insights into practical deployments in real-world scenarios.Additionally,the paper includes an analysis of energy consumption,underscoring the trade-offs between QoS performance and energy efficiency.This study presents the development of a differentiated services model for heterogeneous traffic in wireless sensor networks,considering the appropriate QoS framework supported by experimental analyses.

Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower latency.However,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power.For this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this paper.Specifically,we use UAVs to assist the WSN in collecting data.In the current UAV-assisted WSN,the clustering and routing schemes are determined sequentially.However,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing.To efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together.In the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing scheme.Therefore,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution faster.Unnecessary point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes.To avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are indispensable.An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs.Compared with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks

Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based application.The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor nodes.The clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems.With this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted WSN.The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN environment.In addition,an effective multihop route planning approach was designed by the use of EHGS technique.The proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS algorithm.Moreover,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal routes.The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

PSAP-WSN:A Provably Secure Authentication Protocol for 5G-Based Wireless Sensor Networks

Nowadays,the widespread application of 5G has promoted rapid development in different areas,particularly in the Internet of Things(IoT),where 5G provides the advantages of higher data transfer rate,lower latency,and widespread connections.Wireless sensor networks(WSNs),which comprise various sensors,are crucial components of IoT.The main functions of WSN include providing users with real-time monitoring information,deploying regional information collection,and synchronizing with the Internet.Security in WSNs is becoming increasingly essential because of the across-the-board nature of wireless technology in many fields.Recently,Yu et al.proposed a user authentication protocol forWSN.However,their design is vulnerable to sensor capture and temporary information disclosure attacks.Thus,in this study,an improved protocol called PSAP-WSNis proposed.The security of PSAP-WSN is demonstrated by employing the ROR model,BAN logic,and ProVerif tool for the analysis.The experimental evaluation shows that our design is more efficient and suitable forWSN environments.

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns.Multiple factors such as weather,soil,water,and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems.A Multi-Agent System(MAS)has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks(WSNs)positioned in rice,cotton,cassava crops for knowledge discovery and decision making.The radial basis function network has been used for irrigation prediction.However,in recent work,the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud,which would affect the accuracy of decision making.To handle the above mentioned issues,an efficient method for irrigation prediction is used in this work.The factors considered for decision making are soil moisture,temperature,plant height,root depth.The above-mentioned data will be gathered from the sensors that are attached to the cropfield.Sensed data will be forwarded to the local server,where data encryption will be performed using Adaptive Elliptic Curve Cryptography(AECC).After the encryption process,the data will be forwarded to the cloud.Then the data stored in the cloud will be decrypted key before being given to the deci-sion-making module.Finally,the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decisionmaking module.Thefinal decision regarding the level of water required for cropfields would be taken.Based on this outcome,the water volve opening duration and the level of fertilizers required will be considered.Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey(USGS)database in terms of precision,accuracy,recall,and packet delivery ratio.

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.

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Wireless sensor networks(WSNs)encompass a massive set of sensor nodes,which are self-configurable,inexpensive,and compact.The sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt transceiver.For reducing energy consumption and lengthen lifetime of WSN,multihop routing protocols can be designed.This study develops an improved rat swarm optimization based energy aware multi-hop routing(IRSO-EAMHR)protocol for WSN.An important intention of the IRSO-EAMHR method is for determining optimal routes to base station(BS)in the clustered WSN.Primarily,a weighted clustering process is performed to group the nodes into clusters and select cluster heads(CHs).Next,the IRSO-EAMHR approach derives afitness function containing three input parameters(residual energy,dis-tance,and node degree)for routing process.The IRSO technique was designed by the integration of Levy movement concepts into the traditional RSO algorithm.The experimental result analysis of the IRSO-EAMHR technique is carried out and the outcomes are examined in various aspects.The simulation outcomes demonstrate the promising performance of the IRSO-EAMHR technique over the recent state of art approaches.

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.

Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing function.This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for WSN.The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN.To accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication.For the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust degree.The experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.