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.

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.

Advances in Triboelectric Nanogenerators for Blue Energy Harvesting and Marine Environmental Monitoring

Blue energy,which includes rainfall,tidal current,wave,and water-flow energy,is a promising renewable resource,although its exploitation is limited by current technologies and thus remains low.This form of energy is mainly harvested by electromagnetic generators(EMGs),which generate electricity via Lorenz force-driven electron flows.Triboelectric nano genera tors(TENGs)and TENG networks exhibit superiority over EMGs in low-frequency and high-entropy energy harvesting as a new approach for blue energy harvesting.A TENG produces electrical outputs by adopting the mechanism of Maxwell’s displacement current.To date,a series of research efforts have been made to optimize the structure and performance of TENGs for effective blue energy harvesting and marine environmental applications.Despite the great progress that has been achieved in the use of TENGs in this context so far,continuous exploration is required in energy conversion,device durability,power management,and environmental applications.This review reports on advances in TENGs for blue energy harvesting and marine environmental monitoring.It introduces the theoretical foundations of TENGs and discusses advanced TENG prototypes for blue energy harvesting,including TENG structures that function in freestanding and contact-separation modes.Performance enhancement strategies for TENGs intended for blue energy harvesting are also summarized.Finally,marine environmental applications of TENGs based on blue energy harvesting are discussed.

Comparison of Energy Harvesting Systems for Wireless Sensor Networks

Wireless sensor networks (WSNs) offer an attractive solution to many environmental,security,and process monitoring problems.However,one barrier to their fuller adoption is the need to supply electrical power over extended periods of time without the need for dedicated wiring.Energy harvesting provides a potential solution to this problem in many applications.This paper reviews the characteristics and energy requirements of typical sensor network nodes,assesses a range of potential ambient energy sources,and outlines the characteristics of a wide range of energy conversion devices.It then proposes a method to compare these diverse sources and conversion mechanisms in terms of their normalised power density.

Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor Networks

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

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.

Bidirectional secondary transmissions with energy harvesting in cognitive wireless sensor networks

To the existing spectrum sharing schemes in wireless-powered cognitive wireless sensor networks,the protocols are limited to either separate the primary and the secondary transmission or allow the secondary user to transmit signals in a time slot when it forwards the primary signal.In order to address this limitation,a novel cooperative spectrum sharing scheme is proposed,where the secondary transmission is multiplexed with both the primary transmission and the relay transmission.Specifically,the process of transmission is on a three-phase time-switching relaying basis.In the first phase,a cognitive sensor node SU1 scavenges energy from the primary transmission.In the second phase,another sensor node SU2 and primary transmitter simultaneously transmit signals to the SU1.In the third phase,the node SU1 can assist the primary transmission to acquire the opportunity of spectrum sharing.Joint decoding and interference cancellation technique is adopted at the receivers to retrieve the desired signals.We further derive the closed-form expressions for the outage probabilities of both the primary and secondary systems.Moreover,we address optimization of energy harvesting duration and power allocation coefficient strategy under performance criteria.An effective algorithm is then presented to solve the optimization problem.Simulation results demonstrate that with the optimized solutions,the sensor nodes with the proposed cooperative spectrum sharing scheme can utilize the spectrum in a more efficient manner without deteriorating the performance of the primary transmission,as compared with the existing one-directional scheme in the literature.

Distributed Algorithms for Maximizing Lifetime in Clustered Wireless Sensor Networks Using Energy-harvesting Relay Nodes

Motivated by recent developments in wireless sensor networks(WSNs),we present distributed clustering algorithms for maximizing the lifetime of WSNs,that is,the duration until the first node dies.We study the joint problem of prolonging network lifetime by introducing clustering techniques and energy-harvesting(EH)nodes.First,we propose a distributed clustering algorithm for maximizing the lifetime of clustered WSN,which includes EH nodes,serving as relay nodes for cluster heads(CHs).Second,graph-based and LP-based EH-CH matching algorithms are proposed which serve as benchmark algorithms.Extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources.By grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the organization.Nevertheless,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively.In this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based IoT.Three consecutive segments are suggested.For the covering of adjusted clusters,an ideal number of clusters is determined first.Then,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor hubs.Cluster heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster members.Specifically,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long time.The assessment results demonstrate that the IEPA performs better than existing conventions.

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

The amount of needed control messages in wireless sensor networks(WSN)is affected by the storage strategy of detected events.Because broadcasting superfluous control messages consumes excess energy,the network lifespan can be extended if the quantity of control messages is decreased.In this study,an optimized storage technique having low control overhead for tracking the objects in WSN is introduced.The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List(OBLL).Whenever an object passes over the sensor area,the recognizing sensor can immediately produce an OBLL along the object’s route.To retrieve the entire information,the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures.Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.

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.

Efficient Energy and Delay Reduction Model for Wireless Sensor Networks

In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.

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.

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst themselves.There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by WSNs.There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data.Towards the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efficient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artificial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy utilization.There is avoidance of the local optima problem at the time of solution space search in this proposed technique.Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Power Allocation for Energy Harvesting in Wireless Body Area Networks

Wireless Body Area Networks(WBANs) are expected to achieve high reliable communications among a large number of sensors.The outage probability can be used to measure the reliability of the WBAN.In this paper,we optimize the outage probability with the harvested energy as constraints.Firstly,the optimal transmit power of the sensor is obtained while considering a single link between an access point(AP) located on the waist and a sensor attached on the wrist over the Rayleigh fading channel.Secondly,an optimization problem is formed to minimize the outage probability.Finally,we convert the non-convex optimization problem into convex solved by the Lagrange multiplier method.Simulations show that the optimization problem is solvable.The outage probability is optimized by performing power allocation at the sensor.And our proposed algorithm achieves minimizing the outage probability when the sensor uses energy harvesting.We also demonstrate that the average outage probability is reduced with the increase of the harvested energy.

Combined spatial-temporal energy harvesting and relay selection for cognitive wireless powered networks

In order to improve the Energy Efficiency(EE)and spectrum utilization of Cognitive Wireless Powered Networks(CWPNs),a combined spatial-temporal Energy Harvesting(EH)and relay selection scheme is proposed.In the proposed scheme,for protecting the Primary User(PU),a two-layer guard zone is set outside the PU based on the outage probability threshold of the PU.Moreover,to increase the energy of the CWPNs,the EH zone in the two-layer guard zone allows the Secondary Users(SUs)to spatially harvest energy from the Radio Frequency(RF)signals of temporally active PUs.To improve the utilization of the PU spectrum,the guard zone outside the EH zone allows for the constrained power transmission of SUs.Moreover,the relay selection transmission is designed in the transmission zone of the SU to improve the EE of the CWPNs.In addition to the EE of the CWPNs,the outage probabilities of the SU and PU are derived.The results reveal that the setting of a two-layer guard zone can effectively reduce the outage probability of the PU and improve the EE of CWPNs.Furthermore,the relay selection transmission decreases the outage probabilities of the SUs.

An Optimal Lifetime Utility Routing for 5G and Energy-Harvesting Wireless Networks

Harvesting energy from environmental sources such as solar and wind can mitigate or solve the limited-energy problem in wireless sensor networks. In this paper, we propose an energy-harvest-aware route-selection method that incorporates harvest availability properties and energy storage capacity limits into the routing decisions. The harvest-aware routing problem is formulated as a lin- ear program with a utility-based objective function that balances the two conflicting routing objectives of maximum total and maxi- mum minimum residual network energy. The simulation results show that doing so achieves a longer network lifetime, defined as the time-to-first-node-death in the network. Additionally, most existing energy-harvesting routing algorithms route each traffic flow independently from each other. The LP formulation allows for a joint optimization of multiple trafic flows. Better residual energy statistics are also achieved by such joint consideration compared to independent optimization of each commodity.

Cross Layered MAC Design for RF Energy Harvesting Sensor Network

The main research objective in wireless sensor networks (WSN) domain is to develop algorithms and protocols to ensure minimal energy consumption with maximum network lifetime. In this paper, we propose a novel design for energy harvesting sensor node and cross-layered MAC protocol using three adjacent layers (Physical, MAC and Network) to economize energy for WSN. The basic idea behind our protocol is to re-energize the neighboring nodes using the radio frequency (RF) energy transmitted by the active nodes. This can be achieved by designing new energy harvesting sensor node and redesigning the MAC protocol. The results show that the proposed cross layer CL_EHSN improves the life time of the WSN by 40%.