A low-power environmental monitoring system based on WSN technology is proposed to effectively monitor the environmental status and ensure the healthy growth of greenhouse crops in the greenhouse. The system performs ...A low-power environmental monitoring system based on WSN technology is proposed to effectively monitor the environmental status and ensure the healthy growth of greenhouse crops in the greenhouse. The system performs dynamic mon- itoring on the environmental data of temperature, humidity, illumination, soil tempera- ture and humidity of the greenhouse, and it reduces the energy consumption by us- ing solar energy and lithium battery as the power supply mode and dynamic power management algorithm combined with improved routing protocol. Stable and reliable, the system could effectively monitor the key environmental factors in the green- house, making it of certain promotion value.展开更多
In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA)...In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis.展开更多
Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income.The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity....Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income.The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity.In this study,a Wireless Sensor Networks(“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning(DRL)technology in carrying out prediction tasks based on three classifications:“optimal,”“sub-optimal,”or“not-optimal”conditions based on three parameters including humidity,temperature,and soil moisture.The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.A value function-based will be employed to perform DRL model called deep Q-network(DQN)which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior.The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%.Meanwhile,the results of comparative accuracy model experiments of the proposed DQN,individual Q-learning,uniform coverage(UC),and NaÏe Bayes classifier(NBC)are 97.60%,95.30%,96.50%,and 92.30%,respectively.From the results of the comparative experiment,it can be seen that the proposed DQN used in the study has themost optimal accuracy.Testing with 22 test scenarios for“optimal,”“sub-optimal,”and“not-optimal”conditions was carried out to ensure the system runs well in the real-world data.The accuracy percentage which is generated from the real-world data reaches 95.45%.Fromthe resultsof the cost analysis,the systemcanprovide a low-cost systemcomparedtothe conventional system.展开更多
Aiming at the tourism informationization is difficult to meet the needs of passengers and massive data processing changes day by day, this paper puts forward a solution based on WSN wisdom travel. The scheme will be t...Aiming at the tourism informationization is difficult to meet the needs of passengers and massive data processing changes day by day, this paper puts forward a solution based on WSN wisdom travel. The scheme will be the tourism resources virtualization to frame a wisdom tourism cloud platform based on cloud computing IAAS, PAAS and SAAS model, and use cloud computing and open source software OpenStack, Hadoop architecture design. The purpose enable the tourism resources of wisdom navel cloud system into one unified management, unified sales, unified treatment and unified service. Thus the system implement the tourism industry cluster and the collaborative development of tourism resources, improve the information processing capacity and reliability.展开更多
Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.T...Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.展开更多
The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment e...The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment environment puts higher requirements on the search space,computational cost,and optimization efficiency of the algorithms.For this reason,a slime mould algorithm called SCA-SMA is proposed to solve the above problem.In SCA-SMA,a reverse Sobol sequence is used to initialize the population to increase the population diversity and improve the probability of approaching the optimal solution.To better balance local exploitation and global exploration,a dynamic selection of sine cosine update mechanism is proposed:using an optimal position selection mechanism in the global exploration phase to avoid local optima,and integrating the sine cosine algorithm in the local exploitation phase to improve the mucilage position update method,enrich the optimization search process and enhance the development capability of the algorithm.Finally,an adaptive mutation strategy can be proposed to increase the search range of the algorithm and motivate SCA-SMA to explore more promising regions.To evaluate the performance of the algorithm,SCA-SMA is experimentally validated in five different aspects.The results show that SCA-SMA is significantly competitive compared to advanced MAs.In particular,in facing the WSN node coverage problem,SCA-SMA has more obvious advantages in both average coverage and optimal coverage,which makes it possible to fully utilize the sensing range of each sensor node,while avoiding the waste of resources and the generation of monitoring blind zones.展开更多
基金Supported by the Fund for Independent Innovation of Agricultural Sciences in Jiangsu Province(CX(14)2108&CX(13)5066)~~
文摘A low-power environmental monitoring system based on WSN technology is proposed to effectively monitor the environmental status and ensure the healthy growth of greenhouse crops in the greenhouse. The system performs dynamic mon- itoring on the environmental data of temperature, humidity, illumination, soil tempera- ture and humidity of the greenhouse, and it reduces the energy consumption by us- ing solar energy and lithium battery as the power supply mode and dynamic power management algorithm combined with improved routing protocol. Stable and reliable, the system could effectively monitor the key environmental factors in the green- house, making it of certain promotion value.
基金supported in part by Thailand Science Research and Innovation(TSRI)and National Research Council of Thailand(NRCT)via International Research Network Program(IRN61W0006)Thailand+1 种基金by Khon Kaen University,ThailandDuy Tan University,Vietnam。
文摘In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis.
基金supported by the Department of Electrical Engineering at the National Chin-Yi University of Technology。
文摘Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income.The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity.In this study,a Wireless Sensor Networks(“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning(DRL)technology in carrying out prediction tasks based on three classifications:“optimal,”“sub-optimal,”or“not-optimal”conditions based on three parameters including humidity,temperature,and soil moisture.The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.A value function-based will be employed to perform DRL model called deep Q-network(DQN)which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior.The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%.Meanwhile,the results of comparative accuracy model experiments of the proposed DQN,individual Q-learning,uniform coverage(UC),and NaÏe Bayes classifier(NBC)are 97.60%,95.30%,96.50%,and 92.30%,respectively.From the results of the comparative experiment,it can be seen that the proposed DQN used in the study has themost optimal accuracy.Testing with 22 test scenarios for“optimal,”“sub-optimal,”and“not-optimal”conditions was carried out to ensure the system runs well in the real-world data.The accuracy percentage which is generated from the real-world data reaches 95.45%.Fromthe resultsof the cost analysis,the systemcanprovide a low-cost systemcomparedtothe conventional system.
文摘Aiming at the tourism informationization is difficult to meet the needs of passengers and massive data processing changes day by day, this paper puts forward a solution based on WSN wisdom travel. The scheme will be the tourism resources virtualization to frame a wisdom tourism cloud platform based on cloud computing IAAS, PAAS and SAAS model, and use cloud computing and open source software OpenStack, Hadoop architecture design. The purpose enable the tourism resources of wisdom navel cloud system into one unified management, unified sales, unified treatment and unified service. Thus the system implement the tourism industry cluster and the collaborative development of tourism resources, improve the information processing capacity and reliability.
基金supported by the National Natural Science Foundation of China under Grant No.62461041Natural Science Foundation of Jiangxi Province under Grant No.20224BAB212016 and No.20242BA B25068China Scholarship Council under Grant No.202106825021.
文摘Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.
基金supported by special project of the National Natural Science Foundation of China[No.42027806]special Fund of the National Natural Science Foundation of China[No.42041006]+3 种基金the National Key Research and Development Program Project of China[No.2018YFC1504705]the Key Program of the National Natural Science Foundation of China[No.61731015]the major instrument,the project of Natural Science Foundation in Shaanxi Province[No.2018JM6029]the Key Research and Development Program of Shaanxi[No.2022GY-331].
文摘The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment environment puts higher requirements on the search space,computational cost,and optimization efficiency of the algorithms.For this reason,a slime mould algorithm called SCA-SMA is proposed to solve the above problem.In SCA-SMA,a reverse Sobol sequence is used to initialize the population to increase the population diversity and improve the probability of approaching the optimal solution.To better balance local exploitation and global exploration,a dynamic selection of sine cosine update mechanism is proposed:using an optimal position selection mechanism in the global exploration phase to avoid local optima,and integrating the sine cosine algorithm in the local exploitation phase to improve the mucilage position update method,enrich the optimization search process and enhance the development capability of the algorithm.Finally,an adaptive mutation strategy can be proposed to increase the search range of the algorithm and motivate SCA-SMA to explore more promising regions.To evaluate the performance of the algorithm,SCA-SMA is experimentally validated in five different aspects.The results show that SCA-SMA is significantly competitive compared to advanced MAs.In particular,in facing the WSN node coverage problem,SCA-SMA has more obvious advantages in both average coverage and optimal coverage,which makes it possible to fully utilize the sensing range of each sensor node,while avoiding the waste of resources and the generation of monitoring blind zones.
