Wireless sensor nodes have the advantage of being low-cost,easily deployed and of good mobility.If deployed in an underground mine with existing underground transmission systems a wireless sensor network can improve t...Wireless sensor nodes have the advantage of being low-cost,easily deployed and of good mobility.If deployed in an underground mine with existing underground transmission systems a wireless sensor network can improve the collection of information.To get good transmission performance for 2.4 GHz wireless sensor nodes at the working face we calculated the reflection properties of electromagnetic waves from a flat metal plate.Using the cascade impedance method(CIM),we studied transmission attenuation and compared the results to actual tests.The results show that the effective transmission distance of 2.4 GHz wireless sensor nodes meets the stipulations of the ZigBee protocol.展开更多
The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to pred...The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.展开更多
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
Major consideration dimensions for the physical layer design of wireless sensor network （WSN） nodes is analyzed by comparing different wireless communication approaches, diverse mature standards, important radio fre...Major consideration dimensions for the physical layer design of wireless sensor network （WSN） nodes is analyzed by comparing different wireless communication approaches, diverse mature standards, important radio frequency （RF） parameters and various microcontroller unit （MCU） solutions. An implementation of the WSN node is presented with experimental results and a novel ＂one processor working at two frequencies＂ energy saving strategy. The lifetime estimation issue is analyzed with consideration to the periodical listen required by common WSN media access control （MAC） algorithms. It can be concluded that the startup time of the RF which determines the best sleep time ratio and the shortest backoff slot time of MAC, the RF frequency and modulation methods which determinate the RX and TX current, and the overall energy consumption of the dual frequency MCU SOC （ system on chip） are the most essential factors for the WSN node physical layer design.展开更多
Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization...Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data...Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.展开更多
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 facto...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.展开更多
The main parameter to be considered in the wireless sensor network is the amount of energy that is available in each sensor node. The lifetime of the sensor node (SN) depends on it. As the SNs are deployed in remote l...The main parameter to be considered in the wireless sensor network is the amount of energy that is available in each sensor node. The lifetime of the sensor node (SN) depends on it. As the SNs are deployed in remote locations, if the entire energy is consumed, it would be very difficult to replace or recharge the energy source immediately. Hence the energy consumed by each node is very important. If individual SNs send information directly to the base station (BS), then the availability of energy in such SN decreases very fast. This will lead to reduction in the life time of the SN. Instead, the SNs can send the data to the cluster head (CH), then the CH consolidates the received data. The CH sends it to the BS periodically. In this way, utilizing CH for sending the information to the BS increases the lifetime of the SN. The cluster head selection is very crucial in such networks. This paper proposes a novel fuzzy based BEENSIH protocol for CH selection.展开更多
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 (...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.展开更多
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 analytica...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.展开更多
Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location i...Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.展开更多
The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potenti...The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living （SHAAL）to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM （industrial, scientific, and medical） band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate （PRR） above 80% for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market.展开更多
Wireless sensor networks (WSNs) are fast evolving technology for collecting data in real time. Every wireless sensor network (WSN) is consisted of technical and software components which have to refer to the selected ...Wireless sensor networks (WSNs) are fast evolving technology for collecting data in real time. Every wireless sensor network (WSN) is consisted of technical and software components which have to refer to the selected application. The paper focuses on the selection of WSN components. The WSN will be situated in the center of Olomouc City (OWSN). It will focus on measurements of harmful air pollutants and selected basic meteorological elements. The criteria for selection of WSN components including the most important parameters will be chosen and the final evaluation of the option utility will be made on the basis of multicriteria decision making process.展开更多
Wireless sensor networks have been identified as one of the most important technologies for the 21 st century.Recent advances in micro sensor fabrication technology and wireless communication technology enable the pra...Wireless sensor networks have been identified as one of the most important technologies for the 21 st century.Recent advances in micro sensor fabrication technology and wireless communication technology enable the practical deployment of large-scale,low-power,inexpensive sensor networks.Such an approach offers an advantage over traditional sensing methods in many ways:large-scale,dense deployment not only extends spatial coverage and achieves higher resolution,but also increases the system's fault-tolerance and robustness.Moreover,the ad-hoc nature of wireless sensor networks makes them even more attractive for military and other risk-associated applications,such as environmental observation and habitat monitoring.展开更多
This paper focuses on the key issues of information processing in the new sensing technology-electromagnetic induction tomography and depth theoretical study and experimental simulation have been conducted.In this stu...This paper focuses on the key issues of information processing in the new sensing technology-electromagnetic induction tomography and depth theoretical study and experimental simulation have been conducted.In this study,Labview is used to drive the data acquisition card to control the signal generation and acquisition,and Matlab is used to achieve algorithms such as Fast Fourier Transformation (FFT) algorithm,relevant law algorithm and the classical method algorithm.The simulation results show this software system enables successful digital phase identification and the phase difference resolution of 0.10 can be achieved,which is consistent with theoretical analysis.It can also be seen that software system based on Labview and Matlab is a successful method to identify the phase difference in magnetic induction tomography system,which can meet the measurement needs of sensor nodes,laying the basis for the further development of medical IoT study.展开更多
Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditi...Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.展开更多
Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years.Nodes must advance their energy use for expanding network ...Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years.Nodes must advance their energy use for expanding network lifetime.The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results.Due to different network resource constraints and malicious attacks,security assurance in wireless sensor networks has been a difficult task.The implementation of these features requires larger space due to distributed module.This research work proposes new sensor node architecture integrated with a self-testing core and cryptoprocessor to provide fault-free operation and secured data transmission.The proposed node architecture was designed using Verilog programming and implemented using the Xilinx ISE tool in the Spartan 3E environment.The proposed system supports the real-time application in the range of 33 nanoseconds.The obtained results have been compared with the existing Microcontroller-based system.The power consumption of the proposed system consumes only 3.9 mW,and it is only 24%percentage of AT mega-based node architecture.展开更多
基金Project 60774090 supported by the National Natural Science Foundation of China
文摘Wireless sensor nodes have the advantage of being low-cost,easily deployed and of good mobility.If deployed in an underground mine with existing underground transmission systems a wireless sensor network can improve the collection of information.To get good transmission performance for 2.4 GHz wireless sensor nodes at the working face we calculated the reflection properties of electromagnetic waves from a flat metal plate.Using the cascade impedance method(CIM),we studied transmission attenuation and compared the results to actual tests.The results show that the effective transmission distance of 2.4 GHz wireless sensor nodes meets the stipulations of the ZigBee protocol.
基金supported by Taif University Researchers supporting Project number(TURSP-2020/347),Taif University,Taif,Saudi Arabia.
文摘The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.
文摘Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
基金The National High Technology Research and Deve-lopment Program of China (863Program) (No.2003AA143040).
文摘Major consideration dimensions for the physical layer design of wireless sensor network （WSN） nodes is analyzed by comparing different wireless communication approaches, diverse mature standards, important radio frequency （RF） parameters and various microcontroller unit （MCU） solutions. An implementation of the WSN node is presented with experimental results and a novel ＂one processor working at two frequencies＂ energy saving strategy. The lifetime estimation issue is analyzed with consideration to the periodical listen required by common WSN media access control （MAC） algorithms. It can be concluded that the startup time of the RF which determines the best sleep time ratio and the shortest backoff slot time of MAC, the RF frequency and modulation methods which determinate the RX and TX current, and the overall energy consumption of the dual frequency MCU SOC （ system on chip） are the most essential factors for the WSN node physical layer design.
基金appreciation to King Saud University for funding this research through the Researchers Supporting Program number(RSPD2024R918),King Saud University,Riyadh,Saudi Arabia.
文摘Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金supported in part by National Basic Research Program of China(973 Program)(2010CB731803)National Natural Science Foundation of China(61375105)+2 种基金China Postdoctoral Science Foundation Funded Project(2015M570235)Youth Foundation of Hebei Educational Committee(QN2015187)Science Foundation of Yanshan University(B832,14LGA010)
文摘Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.
基金supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:HI21C1831)the Soonchunhyang University Research Fund.
文摘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.
文摘The main parameter to be considered in the wireless sensor network is the amount of energy that is available in each sensor node. The lifetime of the sensor node (SN) depends on it. As the SNs are deployed in remote locations, if the entire energy is consumed, it would be very difficult to replace or recharge the energy source immediately. Hence the energy consumed by each node is very important. If individual SNs send information directly to the base station (BS), then the availability of energy in such SN decreases very fast. This will lead to reduction in the life time of the SN. Instead, the SNs can send the data to the cluster head (CH), then the CH consolidates the received data. The CH sends it to the BS periodically. In this way, utilizing CH for sending the information to the BS increases the lifetime of the SN. The cluster head selection is very crucial in such networks. This paper proposes a novel fuzzy based BEENSIH protocol for CH selection.
基金Project 20070411065 supported by the China Postdoctoral Science Foundation
文摘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.
基金Project(60873081)supported by the National Natural Science Foundation of ChinaProject(NCET-10-0787)supported by Program for New Century Excellent Talents in UniversityProject(11JJ1012)supported by the Natural Science Foundation of Hunan Province,China
文摘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.
文摘Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.
基金supported by the Ministry of Higher Education,Malaysia under Grant No.R.J130000.7823.4L626
文摘The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living （SHAAL）to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM （industrial, scientific, and medical） band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate （PRR） above 80% for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market.
基金support of the European Social Fund and the state budget of the Czech Republic(Project No.CZ.1.07/2.3.00/20.017)support of the Internal Grant Agency of PalackýUniversity in Olomouc(Project No.PrF 2013 024).
文摘Wireless sensor networks (WSNs) are fast evolving technology for collecting data in real time. Every wireless sensor network (WSN) is consisted of technical and software components which have to refer to the selected application. The paper focuses on the selection of WSN components. The WSN will be situated in the center of Olomouc City (OWSN). It will focus on measurements of harmful air pollutants and selected basic meteorological elements. The criteria for selection of WSN components including the most important parameters will be chosen and the final evaluation of the option utility will be made on the basis of multicriteria decision making process.
文摘Wireless sensor networks have been identified as one of the most important technologies for the 21 st century.Recent advances in micro sensor fabrication technology and wireless communication technology enable the practical deployment of large-scale,low-power,inexpensive sensor networks.Such an approach offers an advantage over traditional sensing methods in many ways:large-scale,dense deployment not only extends spatial coverage and achieves higher resolution,but also increases the system's fault-tolerance and robustness.Moreover,the ad-hoc nature of wireless sensor networks makes them even more attractive for military and other risk-associated applications,such as environmental observation and habitat monitoring.
文摘This paper focuses on the key issues of information processing in the new sensing technology-electromagnetic induction tomography and depth theoretical study and experimental simulation have been conducted.In this study,Labview is used to drive the data acquisition card to control the signal generation and acquisition,and Matlab is used to achieve algorithms such as Fast Fourier Transformation (FFT) algorithm,relevant law algorithm and the classical method algorithm.The simulation results show this software system enables successful digital phase identification and the phase difference resolution of 0.10 can be achieved,which is consistent with theoretical analysis.It can also be seen that software system based on Labview and Matlab is a successful method to identify the phase difference in magnetic induction tomography system,which can meet the measurement needs of sensor nodes,laying the basis for the further development of medical IoT study.
基金This paper is supported by the NCAIRF 079 project fund.The project is funded by National Center of Artificial Intelligence.
文摘Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.
文摘Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years.Nodes must advance their energy use for expanding network lifetime.The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results.Due to different network resource constraints and malicious attacks,security assurance in wireless sensor networks has been a difficult task.The implementation of these features requires larger space due to distributed module.This research work proposes new sensor node architecture integrated with a self-testing core and cryptoprocessor to provide fault-free operation and secured data transmission.The proposed node architecture was designed using Verilog programming and implemented using the Xilinx ISE tool in the Spartan 3E environment.The proposed system supports the real-time application in the range of 33 nanoseconds.The obtained results have been compared with the existing Microcontroller-based system.The power consumption of the proposed system consumes only 3.9 mW,and it is only 24%percentage of AT mega-based node architecture.
