Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic st...Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.展开更多
Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmiss...Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.展开更多
With the growing popularity of wireless sensor networks, network stability has become a key area of current research. Different applications of wireless sensor networks demand stable sensing, coverage, and connectivit...With the growing popularity of wireless sensor networks, network stability has become a key area of current research. Different applications of wireless sensor networks demand stable sensing, coverage, and connectivity throughout their operational periods. In some cases, the death of just a single sensor node might disrupt the stability of the entire network. Therefore, a number of techniques have been proposed to improve the network stability. Clustering is one of the most commonly used techniques in this regard. Most clustering techniques assume the presence of high power sensor nodes called relay nodes and implicitly assume that these relay nodes serve as cluster heads in the network. This assumption may lead to faulty network behavior when any of the relay nodes becomes unavailable to its followers. Moreover, relay node based clustering techniques do not address the heterogeneity of sensor nodes in terms of their residual energies, which frequently occur during the operation of a network. To address these two issues, we present a novel clustering technique, Dynamic Clustering with Relay Nodes (DCRN), by considering the heterogeneity in residual battery capacity and by removing the assumption that relay nodes always serve as cluster-heads. We use an essence of the underlying mechanism of LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most popular clustering solutions for wireless sensor networks. In our work, we present four heuristics to increase network stability periods in terms of the time elapsed before the death of the first node in the network. Based on the proposed heuristics, we devise an algorithm for DCRN and formulate a mathematical model for its long-term rate of energy consumption. Further, we calculate the optimal percentage of relay nodes from our mathematical model. Finally, we verify the efficiency of DCRN and correctness of the mathematical model by exhaustive simulation results. Our simulation results reveal that DCRN enhances the network stability period by a significant margin in comparison to LEACH and its best-known variant.展开更多
Aiming at the problem that node load is rarely considered in existing clustering routing algorithm for Wireless Sensor Networks (WSNs), a dynamic clustering routing algorithm for WSN is presented in this paper called ...Aiming at the problem that node load is rarely considered in existing clustering routing algorithm for Wireless Sensor Networks (WSNs), a dynamic clustering routing algorithm for WSN is presented in this paper called DCRCL (Dynamic Clustering Routing Considering Load). This algorithm is comprised of three phases including cluster head (CH) selection, cluster setup and inter-cluster routing. First, the CHs are selected based on residual energy and node load. Then the non-CH nodes choose a cluster by comparing the cost function of its neighbor CHs. At last, each CH communicates with base station by using multi-hop communication. The simulation results show that comparing with the existing one, the techniques life cycle and date volume of the network are increased by 30.7 percent and 29.8 percent respectively by using the proposed algorithm DCRCL.展开更多
Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of th...Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.展开更多
In the wireless sensor networks(WSN),the sensor nodes have limited battery life and are deployed in hostile environments.It is very difficult to recharging or replacement of the batteries after deployment for the sens...In the wireless sensor networks(WSN),the sensor nodes have limited battery life and are deployed in hostile environments.It is very difficult to recharging or replacement of the batteries after deployment for the sensor nodes in inaccessible areas.Therefore,how to increase the network lifetime of the WSN is deserved to be studied.In this study,a WSN routing algorithm was proposed based on block clustering and springboard nodes to increase the network lifetime of the WSN.Firstly,by analyzing the influence of communication transmission distance on network energy consumption,block clustering was introduced to control node transmission distance in order to reduce total network energy consumption.In addition,a network transmission model was proposed based on springboard nodes and the advantages of network energy consumption of this model against multi-hop between clusters were analyzed.The simulation results show that,compared with the LEACH algorithm,EECPK-means algorithm and energy centroid clustering algorithm,the proposed routing algorithm effectively prolongs the network lifetime of WSN.展开更多
Mobile Ad hoc Networks (MANET) are infrastructure less networks which provide multi-hop wireless links between nodes. The main applications of MANET in real time environment are military and emergency areas where the ...Mobile Ad hoc Networks (MANET) are infrastructure less networks which provide multi-hop wireless links between nodes. The main applications of MANET in real time environment are military and emergency areas where the fixed infrastructure is not required. It is a temporary communication infrastructure network for quick communication with minimal configuration settings among the group of nodes. The security is one of the primary concerns in MANET. The malicious nodes in MANET environment degrade the performance of the network. In this paper, the nodes in MANET are grouped using back-off duration technique and further the malicious nodes are detected using this algorithm. The proposed clustering based malicious nodes detection in MANET achieves higher performance in terms of packet delivery ratio, latency and energy consumption. The proposed method achieves 89.35% of packet delivery ratio, 36.2 ms latency and 26.91 mJ of energy consumption.展开更多
Mobile Ad hoc Network (MANET) is a significant concept of wireless networks which comprises of thousands of nodes that are mobile as well as autonomous and they do not requires any existing network infrastructure. The...Mobile Ad hoc Network (MANET) is a significant concept of wireless networks which comprises of thousands of nodes that are mobile as well as autonomous and they do not requires any existing network infrastructure. The autonomous nodes can freely and randomly move within the network which can create temporary dynamic network and these networks can change their topology frequently. The security is the primary issue in MANET which degrades the network performance significantly. In this paper, cluster based malicious node detection methodology is proposed to detect and remove the malicious nodes. Each node within the cluster gets the cluster key from the cluster head and this key is used for the data transaction between cluster head and node. The cluster head checks this key for every data transaction from node and match with their cluster table. If match is valid, and then only it will recognize that this node is belongs to this cluster, otherwise it is decided as malicious node. This paper also discusses the detection of link failure due to the presence of malicious node by determining the gain of each link in the network. The performance of the proposed method is analyzed using packet delivery ratio, network life time, and throughput and energy consumption. The proposed malicious node detection system is compared with the conventional techniques as OEERP (Optimized energy efficient routing protocol), LEACH (Low energy adaptive clustering hierarchy), DRINA (Data routing for In-network aggregation) and BCDCP (Base station controlled dynamic clustering protocol).展开更多
In wireless sensor networks(WSNs), due to the limited battery power of the sensor nodes, the communication energy consumption is the main factor to affect the lifetime of the networks. A reasonable design of the commu...In wireless sensor networks(WSNs), due to the limited battery power of the sensor nodes, the communication energy consumption is the main factor to affect the lifetime of the networks. A reasonable design of the communication protocol can effectively reduce the energy consumption of the network system. Based on low-energy adaptive clustering hierarchy(LEACH), an improved LEACH protocol in WSNs is proposed. In order to optimize the cluster head(CH) election in the cluster setup phase, the improved LEACH takes into account a number of factors, including energy consumption of communication between nodes, remaining energy of the nodes,and the distance between nodes and base station(BS). In the steady phase, one-hop routing and multiple-hop routing are combined to transmit data between CHs to improve energy efficiency. The forward CH is selected as relay node according to the values of path cost. The simulation results show that the proposed algorithm performs better in balancing network energy consumption, and it can effectively improve the data transmission efficiency and prolong the network lifetime, as compared with LEACH, LEACH-C(LEACH-centralized) and NDAPSO-C(an adaptive clustering protocol based on improved particle swarm optimization) algorithms.展开更多
In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy ...In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.展开更多
The network performance and the unmanned aerial vehicle(UAV)number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange.The problem is a non-determinist...The network performance and the unmanned aerial vehicle(UAV)number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange.The problem is a non-deterministic polynomial hard(NP-hard)multi-objective optimization problem,instead of generating a Pareto solution,this work focuses on considering both objectives at the same level so as to achieve a balanced solution between them.Based on the property that agents connected to the same UAV are a cluster,two clustering-based algorithms,M-K-means(MKM)and modified fast search and find density of peaks(MFSFDP)methods,are first proposed.Since the former algorithm requires too much computational time and the latter one requires too many relays,an algorithm for the balanced network performance and relay number(BPN)is proposed by discretizing the area to avoid missing the optimal relay positions and defining a new local density function to reflect the network performance metric.Simulation results demonstrate that the proposed algorithms are feasible and effective.Comparisons between these algorithms show that the BPN algorithm uses fewer relay UAVs than the MFSFDP and classic set-covering based algorithm,and its computational time is far less than the MKM algorithm.展开更多
In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most...In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.展开更多
The purpose of sensing the environment and geographical positions,device monitoring,and information gathering are accomplished using Wireless Sensor Network(WSN),which is a non-dependent device consisting of a distinc...The purpose of sensing the environment and geographical positions,device monitoring,and information gathering are accomplished using Wireless Sensor Network(WSN),which is a non-dependent device consisting of a distinct collection of Sensor Node(SN).Thus,a clustering based on Energy Efficient(EE),one of the most crucial processes performed in WSN with distinct environments,is utilized.In order to efficiently manage energy allocation during sensing and communication,the present research on managing energy efficiency is performed on the basis of distributed algorithm.Multiples of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of the focus of EE methods on energy harvesting and minimum Energy Consumption(EC).The three stages of performance are proposed in this research work.At the outset,during routing and Route Searching Time(RST)with fluctuating node density and PKTs,EC is reduced by the Hybrid Energy-based Multi-User Routing(HEMUR)model proposed in this work.Energy efficiency and an ideal route for various SNs with distinct PKTs in WSN are obtained by this model.By utilizing the Approximation Algorithm(AA),the Bregman Tensor Approximation Clustering(BTAC)is applied to improve the Route Path Selection(RPS)efficiency for Data Packet Transmission(DPT)at the Sink Node(SkN).The enhanced Network Throughput Rate(NTR)and low DPT Delay are provided by BTAC.To MAX the Clustering Efficiency(CE)and minimize the EC,the Energy Effective Distributed Multi-hop Clustering(GISEDC)method based on Generalized Iterative Scaling is implemented.The Multi-User Routing(MUR)is used by the HEMUR model to enhance the EC by 20%during routing.When compared with other advanced techniques,the Average Energy Per Packet(AEPP)is enhanced by 39%with the application of proportional fairness with Boltzmann Distribution(BD).The Gaussian Fast Linear Combinations(GFLC)with AA are applied by BTAC method with an enhanced Communication Overhead(COH)for an increase in performance by 19%and minimize the DPT delay by 23%.When compared with the rest of the advanced techniques,CE is enhanced by 8%and EC by 27%with the application of GISEDC method.展开更多
One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques ...One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques that have been using to minimize sensor nodes’ energy consumption during operation. In this paper, A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (ANCAEE) has been proposed. The algorithm achieves good performance in terms of minimizing energy consumption during data transmission and energy consumptions are distributed uniformly among all nodes. ANCAEE uses a new method of clusters formation and election of cluster heads. The algorithm ensures that a node transmits its data to the cluster head with a single hop transmission and cluster heads forward their data to the base station with multi-hop transmissions. Simulation results show that our approach consumes less energy and effectively extends network utilization.展开更多
A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associat...A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.展开更多
In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-ti...In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-tion for Cluster Head and Gateway Selection(NQCAFFFOCHGS)has the best network performance because it uses the Improved Weighted Clustering Algo-rithm(IWCA)to cluster the network and the FFO algorithm,which uses fuzzy-based network metrics to select the best CH and entryway.However,the major drawback of the fuzzy system was to appropriately select the membership func-tions.Also,the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway.When learning fuzzy sets,this algorithm employs a new Continuous Action-set Learning Automata(CALA)approach that correctly modifies and chooses the fuzzy membership functions.Despite the fact that it extends the network’s lifespan,it does not assist in the detection of defective nodes in the routing route.Because of this,a new Fault Tolerance(NQCAEFFFOCHGS-FT)mechanism based on the Distributed Connectivity Restoration(DCR)mechanism is proposed,which allows the net-work to self-heal as a consequence of the algorithm’s self-healing capacity.Because of the way this method is designed,node failures may be utilised to rebuild the network topology via the use of cascaded node moves.Founded on the fractional network information and topologic overhead related with each node,the DCR is suggested as an alternative to the DCR.When compared to the NQCAFFFOCHGS algorithm,the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay,energy consumption,Packet Loss Ratio(PLR),Normalized Routing Overhead(NRO),and Balanced Load Index(BLI).展开更多
In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization...In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.展开更多
For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but faul...For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.展开更多
Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caus...Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41771537)Fundamental Research Funds for the Central Universities
文摘Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.
基金The authors are grateful to the Raytheon Chair for Systems Engineering for funding.
文摘Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.
文摘With the growing popularity of wireless sensor networks, network stability has become a key area of current research. Different applications of wireless sensor networks demand stable sensing, coverage, and connectivity throughout their operational periods. In some cases, the death of just a single sensor node might disrupt the stability of the entire network. Therefore, a number of techniques have been proposed to improve the network stability. Clustering is one of the most commonly used techniques in this regard. Most clustering techniques assume the presence of high power sensor nodes called relay nodes and implicitly assume that these relay nodes serve as cluster heads in the network. This assumption may lead to faulty network behavior when any of the relay nodes becomes unavailable to its followers. Moreover, relay node based clustering techniques do not address the heterogeneity of sensor nodes in terms of their residual energies, which frequently occur during the operation of a network. To address these two issues, we present a novel clustering technique, Dynamic Clustering with Relay Nodes (DCRN), by considering the heterogeneity in residual battery capacity and by removing the assumption that relay nodes always serve as cluster-heads. We use an essence of the underlying mechanism of LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most popular clustering solutions for wireless sensor networks. In our work, we present four heuristics to increase network stability periods in terms of the time elapsed before the death of the first node in the network. Based on the proposed heuristics, we devise an algorithm for DCRN and formulate a mathematical model for its long-term rate of energy consumption. Further, we calculate the optimal percentage of relay nodes from our mathematical model. Finally, we verify the efficiency of DCRN and correctness of the mathematical model by exhaustive simulation results. Our simulation results reveal that DCRN enhances the network stability period by a significant margin in comparison to LEACH and its best-known variant.
文摘Aiming at the problem that node load is rarely considered in existing clustering routing algorithm for Wireless Sensor Networks (WSNs), a dynamic clustering routing algorithm for WSN is presented in this paper called DCRCL (Dynamic Clustering Routing Considering Load). This algorithm is comprised of three phases including cluster head (CH) selection, cluster setup and inter-cluster routing. First, the CHs are selected based on residual energy and node load. Then the non-CH nodes choose a cluster by comparing the cost function of its neighbor CHs. At last, each CH communicates with base station by using multi-hop communication. The simulation results show that comparing with the existing one, the techniques life cycle and date volume of the network are increased by 30.7 percent and 29.8 percent respectively by using the proposed algorithm DCRCL.
文摘Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.
基金National Natural Science Foundation of China(Nos.61661025,61661026)。
文摘In the wireless sensor networks(WSN),the sensor nodes have limited battery life and are deployed in hostile environments.It is very difficult to recharging or replacement of the batteries after deployment for the sensor nodes in inaccessible areas.Therefore,how to increase the network lifetime of the WSN is deserved to be studied.In this study,a WSN routing algorithm was proposed based on block clustering and springboard nodes to increase the network lifetime of the WSN.Firstly,by analyzing the influence of communication transmission distance on network energy consumption,block clustering was introduced to control node transmission distance in order to reduce total network energy consumption.In addition,a network transmission model was proposed based on springboard nodes and the advantages of network energy consumption of this model against multi-hop between clusters were analyzed.The simulation results show that,compared with the LEACH algorithm,EECPK-means algorithm and energy centroid clustering algorithm,the proposed routing algorithm effectively prolongs the network lifetime of WSN.
文摘Mobile Ad hoc Networks (MANET) are infrastructure less networks which provide multi-hop wireless links between nodes. The main applications of MANET in real time environment are military and emergency areas where the fixed infrastructure is not required. It is a temporary communication infrastructure network for quick communication with minimal configuration settings among the group of nodes. The security is one of the primary concerns in MANET. The malicious nodes in MANET environment degrade the performance of the network. In this paper, the nodes in MANET are grouped using back-off duration technique and further the malicious nodes are detected using this algorithm. The proposed clustering based malicious nodes detection in MANET achieves higher performance in terms of packet delivery ratio, latency and energy consumption. The proposed method achieves 89.35% of packet delivery ratio, 36.2 ms latency and 26.91 mJ of energy consumption.
文摘Mobile Ad hoc Network (MANET) is a significant concept of wireless networks which comprises of thousands of nodes that are mobile as well as autonomous and they do not requires any existing network infrastructure. The autonomous nodes can freely and randomly move within the network which can create temporary dynamic network and these networks can change their topology frequently. The security is the primary issue in MANET which degrades the network performance significantly. In this paper, cluster based malicious node detection methodology is proposed to detect and remove the malicious nodes. Each node within the cluster gets the cluster key from the cluster head and this key is used for the data transaction between cluster head and node. The cluster head checks this key for every data transaction from node and match with their cluster table. If match is valid, and then only it will recognize that this node is belongs to this cluster, otherwise it is decided as malicious node. This paper also discusses the detection of link failure due to the presence of malicious node by determining the gain of each link in the network. The performance of the proposed method is analyzed using packet delivery ratio, network life time, and throughput and energy consumption. The proposed malicious node detection system is compared with the conventional techniques as OEERP (Optimized energy efficient routing protocol), LEACH (Low energy adaptive clustering hierarchy), DRINA (Data routing for In-network aggregation) and BCDCP (Base station controlled dynamic clustering protocol).
基金the National Natural Science Foundation of China(No.61673259)the International Exchanges and Cooperation Projects of Shanghai Science and Technology Committee(No.15220721800)
文摘In wireless sensor networks(WSNs), due to the limited battery power of the sensor nodes, the communication energy consumption is the main factor to affect the lifetime of the networks. A reasonable design of the communication protocol can effectively reduce the energy consumption of the network system. Based on low-energy adaptive clustering hierarchy(LEACH), an improved LEACH protocol in WSNs is proposed. In order to optimize the cluster head(CH) election in the cluster setup phase, the improved LEACH takes into account a number of factors, including energy consumption of communication between nodes, remaining energy of the nodes,and the distance between nodes and base station(BS). In the steady phase, one-hop routing and multiple-hop routing are combined to transmit data between CHs to improve energy efficiency. The forward CH is selected as relay node according to the values of path cost. The simulation results show that the proposed algorithm performs better in balancing network energy consumption, and it can effectively improve the data transmission efficiency and prolong the network lifetime, as compared with LEACH, LEACH-C(LEACH-centralized) and NDAPSO-C(an adaptive clustering protocol based on improved particle swarm optimization) algorithms.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.
基金the National Natural Science Foundation of China(61573285)。
文摘The network performance and the unmanned aerial vehicle(UAV)number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange.The problem is a non-deterministic polynomial hard(NP-hard)multi-objective optimization problem,instead of generating a Pareto solution,this work focuses on considering both objectives at the same level so as to achieve a balanced solution between them.Based on the property that agents connected to the same UAV are a cluster,two clustering-based algorithms,M-K-means(MKM)and modified fast search and find density of peaks(MFSFDP)methods,are first proposed.Since the former algorithm requires too much computational time and the latter one requires too many relays,an algorithm for the balanced network performance and relay number(BPN)is proposed by discretizing the area to avoid missing the optimal relay positions and defining a new local density function to reflect the network performance metric.Simulation results demonstrate that the proposed algorithms are feasible and effective.Comparisons between these algorithms show that the BPN algorithm uses fewer relay UAVs than the MFSFDP and classic set-covering based algorithm,and its computational time is far less than the MKM algorithm.
基金supported in part by the Key Program of National Natural Science Foundation of China(Grant No.60873244,60973110,61003307)the Beijing Municipal Natural Science Foundation(Grant No.4102059)
文摘In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.
基金The authors are grateful to the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabia.
文摘The purpose of sensing the environment and geographical positions,device monitoring,and information gathering are accomplished using Wireless Sensor Network(WSN),which is a non-dependent device consisting of a distinct collection of Sensor Node(SN).Thus,a clustering based on Energy Efficient(EE),one of the most crucial processes performed in WSN with distinct environments,is utilized.In order to efficiently manage energy allocation during sensing and communication,the present research on managing energy efficiency is performed on the basis of distributed algorithm.Multiples of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of the focus of EE methods on energy harvesting and minimum Energy Consumption(EC).The three stages of performance are proposed in this research work.At the outset,during routing and Route Searching Time(RST)with fluctuating node density and PKTs,EC is reduced by the Hybrid Energy-based Multi-User Routing(HEMUR)model proposed in this work.Energy efficiency and an ideal route for various SNs with distinct PKTs in WSN are obtained by this model.By utilizing the Approximation Algorithm(AA),the Bregman Tensor Approximation Clustering(BTAC)is applied to improve the Route Path Selection(RPS)efficiency for Data Packet Transmission(DPT)at the Sink Node(SkN).The enhanced Network Throughput Rate(NTR)and low DPT Delay are provided by BTAC.To MAX the Clustering Efficiency(CE)and minimize the EC,the Energy Effective Distributed Multi-hop Clustering(GISEDC)method based on Generalized Iterative Scaling is implemented.The Multi-User Routing(MUR)is used by the HEMUR model to enhance the EC by 20%during routing.When compared with other advanced techniques,the Average Energy Per Packet(AEPP)is enhanced by 39%with the application of proportional fairness with Boltzmann Distribution(BD).The Gaussian Fast Linear Combinations(GFLC)with AA are applied by BTAC method with an enhanced Communication Overhead(COH)for an increase in performance by 19%and minimize the DPT delay by 23%.When compared with the rest of the advanced techniques,CE is enhanced by 8%and EC by 27%with the application of GISEDC method.
文摘One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques that have been using to minimize sensor nodes’ energy consumption during operation. In this paper, A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (ANCAEE) has been proposed. The algorithm achieves good performance in terms of minimizing energy consumption during data transmission and energy consumptions are distributed uniformly among all nodes. ANCAEE uses a new method of clusters formation and election of cluster heads. The algorithm ensures that a node transmits its data to the cluster head with a single hop transmission and cluster heads forward their data to the base station with multi-hop transmissions. Simulation results show that our approach consumes less energy and effectively extends network utilization.
文摘A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.
文摘In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-tion for Cluster Head and Gateway Selection(NQCAFFFOCHGS)has the best network performance because it uses the Improved Weighted Clustering Algo-rithm(IWCA)to cluster the network and the FFO algorithm,which uses fuzzy-based network metrics to select the best CH and entryway.However,the major drawback of the fuzzy system was to appropriately select the membership func-tions.Also,the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway.When learning fuzzy sets,this algorithm employs a new Continuous Action-set Learning Automata(CALA)approach that correctly modifies and chooses the fuzzy membership functions.Despite the fact that it extends the network’s lifespan,it does not assist in the detection of defective nodes in the routing route.Because of this,a new Fault Tolerance(NQCAEFFFOCHGS-FT)mechanism based on the Distributed Connectivity Restoration(DCR)mechanism is proposed,which allows the net-work to self-heal as a consequence of the algorithm’s self-healing capacity.Because of the way this method is designed,node failures may be utilised to rebuild the network topology via the use of cascaded node moves.Founded on the fractional network information and topologic overhead related with each node,the DCR is suggested as an alternative to the DCR.When compared to the NQCAFFFOCHGS algorithm,the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay,energy consumption,Packet Loss Ratio(PLR),Normalized Routing Overhead(NRO),and Balanced Load Index(BLI).
基金supported by Ulsan Metropolitan City-ETRI joint cooperation project[21AS1600,Development of intelligent technology for key industriesautonomous human-mobile-space autonomous collaboration intelligence technology].
文摘In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/239),Taif University,Taif,Saudi Arabia.
文摘For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.
基金Supported by 2021 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Series Course Teaching Team(PPJH202102JXTD)2022 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Engineering(PPJHKCSZ-2022301)+1 种基金2023 Zhanjiang Science and Technology Bureau Project:Design and Simulation of Zhanjiang Mangrove Wetland Monitoring Network System(2023B01017)2022 Zhanjiang University of Science and Technology Quality Engineering Project:Audiovisual Language Teaching and Research Office(ZLGC202203).
文摘Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
