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
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
The present study was conducted to evaluate the role of effective microbial supplementation to feed on the load of Salmonella in the mesenteric and sub-iliac lymph nodes of beef cattle. Bulls of Harer cattle breed man...The present study was conducted to evaluate the role of effective microbial supplementation to feed on the load of Salmonella in the mesenteric and sub-iliac lymph nodes of beef cattle. Bulls of Harer cattle breed managed at Chercher Oda-Bultum Farmers Union beef Farm were used as study subject. A total of 130 bulls were used using double blinded randomized controlled field trial based on parallel group design from January 2018 to July 2018. The study animals were randomly assigned to the treatment group (n = 100) and control group (n = 30). The feed of treatment group was mixed with EM at dose of 5 × 10<sup>10</sup> cfu/day/head and supplemented for 90, 100 and 115 days while that of the control group was mixed with molasses, which acts as placebo. Both the treatment and control were slaughtered and two lymph nodes were collected from each animal under strict sterile condition and processed for the isolation and identification of Salmonella using standard procedure. A significant (p = 0.001) reduction in the load of Salmonella was observed in the lymph node of treatment group as compared to the control group. The load of Salmonella was significantly affected by length of feeding period and age of bulls. This study indicated that effective microbial supplementation to bulls from Harar cattle reduces the load of Salmonella in the lymph node of beef cattle thereby potentially minimizing the economic and public health impacts of Salmonella infection.展开更多
传统边缘分布式存储系统中网络配置繁琐,优化网络所需的网络状态信息测量操作开销大,当终端设备对数据存储和检索的业务需求处于高峰时,会导致网络链路负载过重从而影响数据转发传输的性能。此外,现有分布式存储系统在进行数据的存储节...传统边缘分布式存储系统中网络配置繁琐,优化网络所需的网络状态信息测量操作开销大,当终端设备对数据存储和检索的业务需求处于高峰时,会导致网络链路负载过重从而影响数据转发传输的性能。此外,现有分布式存储系统在进行数据的存储节点选择时,只考虑了节点剩余存储空间,没有考虑网络状态和节点自身负载对系统存储性能的影响。为解决上述问题,设计和实现了一种基于软件定义网络(Software Defined Network,SDN)和无人机辅助的边缘分布式存储系统,利用SDN技术测量网络状态、网络节点自身负载和存储节点负载状态信息,通过无人机移动节点飞行到重负载网络节点的上方进行分流以平衡各条链路的流量负载;对于重负载网络节点和存储节点的选择,提出了一种基于多属性决策模型综合考虑网络状态和节点自身负载状态的节点选择算法,选择出重负载网络节点和合适的存储节点,然后通过对无人机的位置部署,实现网络链路流量的分流,平衡网络链路的流量负载。经实验测试,结果显示在无线Mesh网络拓扑中,所提无线边缘分布式存储系统的存储性能优于现有边缘分布式存储系统,存储时间明显缩短,在增加流量负载的情况下依然可以保持良好的存储性能,具有良好的负载均衡性能。展开更多
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.
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The present study was conducted to evaluate the role of effective microbial supplementation to feed on the load of Salmonella in the mesenteric and sub-iliac lymph nodes of beef cattle. Bulls of Harer cattle breed managed at Chercher Oda-Bultum Farmers Union beef Farm were used as study subject. A total of 130 bulls were used using double blinded randomized controlled field trial based on parallel group design from January 2018 to July 2018. The study animals were randomly assigned to the treatment group (n = 100) and control group (n = 30). The feed of treatment group was mixed with EM at dose of 5 × 10<sup>10</sup> cfu/day/head and supplemented for 90, 100 and 115 days while that of the control group was mixed with molasses, which acts as placebo. Both the treatment and control were slaughtered and two lymph nodes were collected from each animal under strict sterile condition and processed for the isolation and identification of Salmonella using standard procedure. A significant (p = 0.001) reduction in the load of Salmonella was observed in the lymph node of treatment group as compared to the control group. The load of Salmonella was significantly affected by length of feeding period and age of bulls. This study indicated that effective microbial supplementation to bulls from Harar cattle reduces the load of Salmonella in the lymph node of beef cattle thereby potentially minimizing the economic and public health impacts of Salmonella infection.
文摘传统边缘分布式存储系统中网络配置繁琐,优化网络所需的网络状态信息测量操作开销大,当终端设备对数据存储和检索的业务需求处于高峰时,会导致网络链路负载过重从而影响数据转发传输的性能。此外,现有分布式存储系统在进行数据的存储节点选择时,只考虑了节点剩余存储空间,没有考虑网络状态和节点自身负载对系统存储性能的影响。为解决上述问题,设计和实现了一种基于软件定义网络(Software Defined Network,SDN)和无人机辅助的边缘分布式存储系统,利用SDN技术测量网络状态、网络节点自身负载和存储节点负载状态信息,通过无人机移动节点飞行到重负载网络节点的上方进行分流以平衡各条链路的流量负载;对于重负载网络节点和存储节点的选择,提出了一种基于多属性决策模型综合考虑网络状态和节点自身负载状态的节点选择算法,选择出重负载网络节点和合适的存储节点,然后通过对无人机的位置部署,实现网络链路流量的分流,平衡网络链路的流量负载。经实验测试,结果显示在无线Mesh网络拓扑中,所提无线边缘分布式存储系统的存储性能优于现有边缘分布式存储系统,存储时间明显缩短,在增加流量负载的情况下依然可以保持良好的存储性能,具有良好的负载均衡性能。
