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.

A VMIMO-Based Cooperative Routing Algorithm for Maximizing Network Lifetime

Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the energy consumption problem and maximize the network lifetime, this paper proposes a Virtual Multiple Input Multiple Output based Cooperative Routing algorithm(VMIMOCR). VMIMOCR chooses cooperative relay nodes based on Virtual Multiple Input Multiple Output Model, and balances energy consumption by reasonable power allocation among transmitters, and decides the forwarding path finally. The experimental results show that VMIMOCR can improve network lifetime from 37% to 348% in the medium node density, compared with existing routing algorithms.

Design of maximizing homogeneous and heterogeneous clustered sensor network lifetime

Aimed at the problem of unbalanced energy existed in sensor networks, the clustered method is employed to enhance the efficient utilization of limited energy resources of the deployed sensor nodes. In this paper, we describe the network lifetime as a function of the communication and data aggregation energy consumption and analyze the lifetime of different transmission schemes in the homogeneous and heterogeneous sensor networks. The analysis carried out in this paper can provide the guidelines for network deployment and protocol design in the future applications.

Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks

In a wireless sensor network(WSN),data gathering is more effectually done with the clustering process.Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network.Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station.Moreover,existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes.Here,an improved clustering approach is anticipated to attain energy efficiency by implementingMapReduction for regulatingmapping and reducing complexity in routing mechanisms for eliminating redundancy and overlapping.In order to optimize the network performance,this work considers intelligent behaviors’to adapt with network changes and to introduce computational intelligence ability.In the proposed research,improved teaching learning based optimization is used to evaluate the coordinates of target nodes and nodes upgradation for determining energy consumption.Node upgradation is performed by integratingMap reduction to attain modification in Hop size of nodes.This variation reduces communication complexities.Therefore,network lifetime is increased,and redundancy is reduced.While comparingwith existing approaches here,sleep and wake-up nodes are considered for data transmission.The proposed algorithm clearly demonstrates 50%,16%&12%improvement in nodes lifetime,residual energy and throughput respectively compared to other models.Also it shows progressive improvement in reducing average waiting time,average queuing time and average energy utilization as 30%,20%and 46%respectively.Simulation has been done in NS-2 environment for distributed heterogeneous networks.

LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.

Wireless Sensor Network Lifetime Enhancement Using Modified Clustering and Scheduling Algorithm

Random distribution of sensor nodes in large scale network leads redundant nodes in the application field. Sensor nodes are with irreplaceable battery in nature, which drains the energy due to repeated collection of data and decreases network lifetime. Scheduling algorithms are the one way of addressing this issue. In proposed method, an optimized sleep scheduling used to enhance the network lifetime. While using the scheduling algorithm, the target coverage and data collection must be maintained throughout the network. In-network, aggregation method also used to remove the unwanted information in the collected data in level. Modified clustering algorithm highlights three cluster heads in each cluster which are separated by minimum distance between them. The simulation results show the 20% improvement in network lifetime, 25% improvement in throughput and 30% improvement in end to end delay.

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.

Data Aggregation Scheduling with Guaranteed Lifetime and Efficient Latency in Wireless Sensor Networks

In scenarios of real-time data collection of long-term deployed Wireless Sensor Networks （WSNs）, low-latency data collection with long net- work lifetime becomes a key issue. In this paper, we present a data aggregation scheduling with guaran- teed lifetime and efficient latency in WSNs. We first Construct a Guaranteed Lifetime Mininmm Ra- dius Data Aggregation Tree （GLMRDAT） which is conducive to reduce scheduling latency while pro- viding a guaranteed network lifetime, and then de-sign a Greedy Scheduling algorithM （GSM） based on finding the nmzximum independent set in conflict graph to schedule he transmission of nodes in the aggregation tree. Finally, simulations show that our proposed approach not only outperfonm the state-of-the-art solutions in terms of schedule latency, but also provides longer and guaranteed network lifetilre.

Weak node protection to maximize the lifetime of wirelesssensor networks

Wireless sensor networks （WSN） provide an approachto collecting distributed monitoring data and transmiting them tothe sink node. This paper proposes a WSN-based multi-hop networkinfrastructure, to increase network lifetime by optimizing therouting strategy. First, a network model is established, an operatingcontrol strategy is devised, and energy consumption characteristicsare analyzed. Second, a fast route-planning algorithm isproposed to obtain the original path that takes into account the remainingenergy of communicating nodes and the amount of energyconsumed in data transmission. Next, considering the amount ofenergy consumed by an individual node and the entire network,a criterion function is established to describe node performanceand to evaluate data transmission ability. Finally, a route optimizingalgorithm is proposed to increase network lifetime by adjusting thetransmission route in protection of the weak node （the node withlow transmission ability）. Simulation and comparison experimentalresults demonstrate the good performance of the proposed algorithmsto increase network lifetime.

Tradeoff between utility and lifetime in energy-constrained wireless sensor networks

We study the tradeoff between network utility and network lifetime using a cross-layer optimization approach. The tradeoff model in this paper is based on the framework of layering as optimization decomposition. Our tradeoff model is the first one that incorporates time slots allocation into this framework. By using Lagrangian dual decomposition method, we decompose the tradeoff model into two subproblems： routing problem at network layer and resource allocation problem at medium access control （MAC） layer. The interfaces between the layers are precisely the dual variables. A partially distributed algorithm is proposed to solve the nonlinear, convex, and separable tradeoff model. Numerical simulation results are presented to support our algorithm.

Fuzzy Based Adaptive Clustering to Improve the Lifetime of Wireless Sensor Network

The objective of the recently proposed fuzzy based hierarchical routing protocol F-SCH is to improve the lifetime of a Wireless Sensor Network. Though the performance of F-SCH is better than LEACH, the randomness in CH selection inhibits it from attaining enhanced lifetime. CBCH ensures maximum network lifetime when CH is close to the centroid of the cluster. However, for a widely distributed network, CBCH results in small sized clusters increasing the inter cluster communication cost. Hence, with an objective to enhance the network lifetime, a fuzzy based two-level hierarchical routing protocol is proposed. The novelty of the proposal lies in identification of appropriate parameters used in Cluster Head and Super Cluster Head selection. Experiments for different network scenarios are performed through both simulation and hardware to validate the proposal. The performance of the network is evaluated in terms of Node Death. The proposal is compared with F-SCH and the results reveal the efficacy of the proposal in enhancing the lifetime of network.

Adaptive Multi-Cost Routing Protocol to Enhance Lifetime for Wireless Body Area Network

Wireless Body Area Network(WBAN)technologies are emerging with extensive applications in several domains.Health is a fascinating domain of WBAN for smart monitoring of a patient’s condition.An important factor to consider in WBAN is a node’s lifetime.Improving the lifetime of nodes is critical to address many issues,such as utility and reliability.Existing routing protocols have addressed the energy conservation problem but considered only a few parameters,thus affecting their performance.Moreover,most of the existing schemes did not consider traffic prioritization which is critical in WBANs.In this paper,an adaptive multi-cost routing protocol is proposed with a multi-objective cost function considering minimum distance from sink,temperature of sensor nodes,priority of sensed data,and maximum residual energy on sensor nodes.The performance of the proposed protocol is compared with the existing schemes for the parameters:network lifetime,stability period,throughput,energy consumption,and path loss.It is evident from the obtained results that the proposed protocol improves network lifetime and stability period by 30%and 15%,respectively,as well as outperforms the existing protocols in terms of throughput,energy consumption,and path loss.

Network Lifetime Global Optimization for Multi-Source and Single-Sink Topology in Wireless Sensor Networks

The multi-source and single-sink(MSSS) topology in wireless sensor networks(WSNs) is defined as a network topology,where all of nodes can gather,receive and transmit data to the sink.In energy-constrained WSNs with such a topology,the joint optimal design in the physical,medium access control(MAC) and network layers is considered for network lifetime maximization(NLM).The problem of integrating multi-layer information to compute NLM,which involves routing flow,link schedule and transmission power,is formulated as a nonlinear optimization problem.Specially under time division multiple access(TDMA) scheme,this problem can be transformed into a convex optimization problem.To solve it analytically we make use of the property that local optimization is global optimization in convex problem.This allows us to exploit the Karush-Kuhn-Tucker (KKT) optimality conditions to solve it and obtain analytical solution expression,i.e.,the globally optimal network lifetime(NL).NL is derived as a function of number of nodes,their initial energy and data rate arrived at them. Based on the analysis of analytical approach,it takes the influence of data rates,link access and routing method over NLM into account.Moreover,the globally optimal transmission schemes are achieved by solution set during analytical approach and applied to algorithms in TDMA-based WSNs aiming at NLM on OMNeT++ to compare with other suboptimal schemes.

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.

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.

Energy Based Random Repeat Trust Computation in Delay Tolerant Network

As the use of mobile devices continues to rise,trust administration will significantly improve security in routing the guaranteed quality of service(QoS)supply in Mobile Ad Hoc Networks(MANET)due to the mobility of the nodes.There is no continuance of network communication between nodes in a delay-tolerant network(DTN).DTN is designed to complete recurring connections between nodes.This approach proposes a dynamic source routing protocol(DSR)based on a feed-forward neural network(FFNN)and energybased random repetition trust calculation in DTN.If another node is looking for a node that swerved off of its path in this situation,routing will fail since it won’t recognize it.However,in the suggested strategy,nodes do not stray from their pathways for routing.It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route,based on their pattern of mobility.The EBRRTC-DTN algorithm(Energy based random repeat trust computation)is based on the time that has passed since nodes last encountered the destination node.Compared to other existing techniques,simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node,which improves routing performance,increases the number of delivered messages,and decreases delivery delay.Therefore,the suggested method is better at providing better QoS(Quality of Service)and increasing network lifetime,tolerating network system latency.

Wireless Network Security Using Load Balanced Mobile Sink Technique

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Sensor deployment strategy for chain-type wireless underground mine sensor network

Wireless sensor networks (WSNs) are very important for monitoring underground mine safety. Sensor node deployment affects the performances of WSNs. In our study, a chain-type wireless underground mine sensor network (CWUMSN) is first pre- sented. A CWUMSN can monitor the environment and locate miners in underground mines. The lowest density deployment strate- gies of cluster head nodes are discussed theoretically. We prove that the lifetime of CWUMSN with a non-uniform deployment strategy is longer than with a uniform deployment strategy. Secondly, we present the algorithm of non-uniform lowest density de- ployment of cluster head nodes. Next, we propose a dynamic choice algorithm of cluster head nodes for CWUMSN which can im- prove the adaptability of networks. Our experiments of CWUMSN with both non-uniform lowest density and uniform lowest den- sity deployments are simulated. The results show that the lifetime of CWUMSN with non-uniform lowest density deployment is almost 2.5 times as long as that of the uniform lowest density deployment. This work provides a new deployment strategy for wire- less underground mine sensor networks and then effectively promotes the application of wireless sensor networks to underground mines.

Node deployment strategy optimization for wireless sensor network with mobile base station

The optimization of network performance in a movement-assisted data gathering scheme was studied by analyzing the energy consumption of wireless sensor network with node uniform distribution. A theoretically analytical method for avoiding energy hole was proposed. It is proved that if the densities of sensor nodes working at the same time are alternate between dormancy and work with non-uniform node distribution. The efficiency of network can increase by several times and the residual energy of network is nearly zero when the network lifetime ends.