针对低轨卫星高动态场景下多服务质量(Quality of Service,QoS)时变物联业务资源调度问题,提出了一种基于Lyapunov优化的窄带物联网(Narrow Band Internet of Things,NB-IoT)多业务资源切片动态管理方法。该方法综合考虑多物联业务QoS...针对低轨卫星高动态场景下多服务质量(Quality of Service,QoS)时变物联业务资源调度问题,提出了一种基于Lyapunov优化的窄带物联网(Narrow Band Internet of Things,NB-IoT)多业务资源切片动态管理方法。该方法综合考虑多物联业务QoS需求、不同QoS业务队列状态以及切片大小的动态划分,构建了资源切片动态管理的资源调度优化问题。基于Lyapunov优化理论将非凸的多时隙动态资源切片划分问题转化为单时隙多QoS业务资源切片配置问题,从而在动态业务场景下实现资源切片与多QoS业务队列之间的动态适配。仿真结果表明,与传统NB-IoT上行资源调度方法相比,所提方法在低轨高动态场景下能够显著提升时延确定性业务的QoS保障和吞吐量。展开更多
为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检...为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检测电路,在电平交叉发生后开启NS SAR ADC的转换。二阶无源噪声整形电路积分过程只在事件触发后发生,从而能够根据输入信号的活跃度动态调节整体功耗。在1.8 V 180 nm CMOS工艺、采样率为40 kS/s、过采样率(OSR)为20、带宽为1 kHz下对该ADC进行仿真验证,结果表明信噪失真比(SNDR)达到87 dB,电路功耗为2.70μW,心电图信号输入时功耗仅为0.79μW,相较于传统等间隔奈奎斯特采样ADC,采样点减少了73%,在处理生物医学信号时实现了约5∶1的数据压缩比,Schreier品质因数(FoMs)和Walden品质因数(FoMw)分别为172.6 dB和67.0 fJ/conv.step。展开更多
针对智能反射面(Intelligent Reflecting Surface,IRS)辅助的含窃听者的认知物联网(Cognitive Internet of Things,C-IoT)通信系统,提出了一种基于联合波束成型的保密率优化方案。在系统模型中,考虑了一个由发射机、主用户、次用户、窃...针对智能反射面(Intelligent Reflecting Surface,IRS)辅助的含窃听者的认知物联网(Cognitive Internet of Things,C-IoT)通信系统,提出了一种基于联合波束成型的保密率优化方案。在系统模型中,考虑了一个由发射机、主用户、次用户、窃听者和智能反射面组成的多输入单输出通信场景。基于该模型,构建保密率优化问题,即在发射机总功率约束、主用户端干扰功率约束以及智能反射面单位模约束的条件下,通过联合优化主被动波束成型,最大化系统的保密率(Secrecy Rate,SR)。在实现过程中,由于公式化的问题非凸,因此使用交替优化的方法将原始问题分解为两个子问题进行优化,即发射机波束成型矩阵的优化以及IRS相移矩阵优化。针对发射机波束成型的矩阵优化,使用半定松弛法与逐次凸逼近法。接着,使用丁克尔巴赫法与逐次凸逼近的方法对IRS的相移矩阵进行优化。仿真结果表明,在含有窃听者的多输入单输出系统中,引入智能反射面实现主被动波束成型的优化有效提高了系统的保密率。展开更多
Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compro...Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.展开更多
The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attentio...The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.展开更多
针对电力物联网环境下配电变压器状态监测面临的低功耗、可靠传输及数据融合等技术难题,设计基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的配电变压器状态监测系统。该系统采用省电模式(Power Saving Mode,PSM)休眠控制策...针对电力物联网环境下配电变压器状态监测面临的低功耗、可靠传输及数据融合等技术难题,设计基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的配电变压器状态监测系统。该系统采用省电模式(Power Saving Mode,PSM)休眠控制策略降低终端能耗,利用受限应用协议(Constrained Application Protocol,CoAP)及重传机制保障数据可靠传输,并引入加权融合计算实现多源异构数据的协同诊断。研究结果表明,该系统能够有效满足配电变压器长期稳定运行与精准状态评估的需求。展开更多
针对智慧电厂汽轮机振动监测中的强电磁干扰、部署困难及带宽瓶颈等挑战,提出了相应的窄带物联网(Narrow Band-Internet of Things,NB-IoT)通信技术应用方案。该方案采用授权频段规避干扰,部署无线终端简化布线,利用独立子载波优化数据...针对智慧电厂汽轮机振动监测中的强电磁干扰、部署困难及带宽瓶颈等挑战,提出了相应的窄带物联网(Narrow Band-Internet of Things,NB-IoT)通信技术应用方案。该方案采用授权频段规避干扰,部署无线终端简化布线,利用独立子载波优化数据汇聚,构建了稳定高效的监测体系。应用案例分析表明,NB-IoT通信技术能显著提升振动数据传输的可靠性与实时性。展开更多
高压电缆接头局部放电监测是电力系统状态检修的重要基础。分析窄带物联网(Narrow Band Internet of Things,NB-IoT)技术的特点,设计基于NB-IoT的高压电缆接头局部放电远程监测系统,围绕放电信号获取、终端节点构建、数据远程传输与运...高压电缆接头局部放电监测是电力系统状态检修的重要基础。分析窄带物联网(Narrow Band Internet of Things,NB-IoT)技术的特点,设计基于NB-IoT的高压电缆接头局部放电远程监测系统,围绕放电信号获取、终端节点构建、数据远程传输与运行状态监控形成完整实现方案,并结合系统性能测试验证其在放电识别、数据上报与运行时长方面的工程适用性,为高压电缆接头在线监测与运维管理提供技术支撑。展开更多
针对智慧仓储场景中多径/非视距无线传输(Non Line of Sight,NLOS)无线传输干扰导致定位精度低、终端续航短、部署成本高三大瓶颈,提出窄带物联网(Narrow Band Internet of Things,NB-IoT)与超宽带(Ultra Wide Band,UWB)融合的定位算法...针对智慧仓储场景中多径/非视距无线传输(Non Line of Sight,NLOS)无线传输干扰导致定位精度低、终端续航短、部署成本高三大瓶颈,提出窄带物联网(Narrow Band Internet of Things,NB-IoT)与超宽带(Ultra Wide Band,UWB)融合的定位算法改进方案。首先,构建时延-能量联合约束的加权最小二乘(Weighted Least Squares,WLS)与扩展卡尔曼滤波(Extended Kalman Filter,EKF)联合模型,引入信道状态信息(Channel State Information,CSI)能量熵权重与动态遮挡因子抑制干扰,提升复杂环境下的定位稳定性;其次,设计加权信号质量评估+自适应同步周期的动态功耗管理策略,有效减少模式切换振荡与无效能耗;最后,搭建射频(Radio Frequency,RF)+压电+光伏多源能量回收架构,结合动态电压调整(Dynamic Voltage Scaling,DVS)优化功率分配,延长终端续航。仿真验证结果表明:在金属货架区、NLOS货物堆放区、自动导向车(Automated Guided Vehicle,AGV)动态通道区,改进方案(粒子滤波优化版)的平均定位误差分别降至0.30、0.40、0.28 m,较传统UWB-飞行时间(Time Of Flight,TOF)方案降低75%~81%;终端平均功耗降低42%,启用多源能量回收架构后续航延长至60个月;以100 000 m2仓储场景测算,部署成本较NB-IoT+UWB基础融合方案降低30%。该方案适配常规及冷链、化工等极端仓储场景,为智慧仓储定位的工程化落地提供了技术支撑。展开更多
为提高铁路供配电系统故障检测的及时性和精准性,基于窄带物联网(Narrow Band Internet of Things,NB-IoT)通信技术,提出了铁路供配电系统故障数据识别技术,并构建了完整的数据采集、传输和识别模型系统。研究结果表明,该技术在故障识...为提高铁路供配电系统故障检测的及时性和精准性,基于窄带物联网(Narrow Band Internet of Things,NB-IoT)通信技术,提出了铁路供配电系统故障数据识别技术,并构建了完整的数据采集、传输和识别模型系统。研究结果表明,该技术在故障识别准确性和响应速度上表现突出,为复杂环境下铁路供配电系统的稳定运行提供了有效支持。展开更多
针对台区智能融合终端通信现存接入冲突、传输效率低、资源利用不均及能耗过高问题,提出基于窄带物联网(Narrow Band-Internet of Things,NB-IoT)的通信优化方案。该方案包括台区终端分级接入控制、上行数据报文聚合、空口资源动态调度...针对台区智能融合终端通信现存接入冲突、传输效率低、资源利用不均及能耗过高问题,提出基于窄带物联网(Narrow Band-Internet of Things,NB-IoT)的通信优化方案。该方案包括台区终端分级接入控制、上行数据报文聚合、空口资源动态调度及终端能耗自适应调控等设计。开展实验验证方案有效性,实验结果表明,该优化方案可显著提升台区终端通信性能,为台区智能融合终端通信优化提供可靠的技术支撑。展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
文摘针对低轨卫星高动态场景下多服务质量(Quality of Service,QoS)时变物联业务资源调度问题,提出了一种基于Lyapunov优化的窄带物联网(Narrow Band Internet of Things,NB-IoT)多业务资源切片动态管理方法。该方法综合考虑多物联业务QoS需求、不同QoS业务队列状态以及切片大小的动态划分,构建了资源切片动态管理的资源调度优化问题。基于Lyapunov优化理论将非凸的多时隙动态资源切片划分问题转化为单时隙多QoS业务资源切片配置问题,从而在动态业务场景下实现资源切片与多QoS业务队列之间的动态适配。仿真结果表明,与传统NB-IoT上行资源调度方法相比,所提方法在低轨高动态场景下能够显著提升时延确定性业务的QoS保障和吞吐量。
文摘为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检测电路,在电平交叉发生后开启NS SAR ADC的转换。二阶无源噪声整形电路积分过程只在事件触发后发生,从而能够根据输入信号的活跃度动态调节整体功耗。在1.8 V 180 nm CMOS工艺、采样率为40 kS/s、过采样率(OSR)为20、带宽为1 kHz下对该ADC进行仿真验证,结果表明信噪失真比(SNDR)达到87 dB,电路功耗为2.70μW,心电图信号输入时功耗仅为0.79μW,相较于传统等间隔奈奎斯特采样ADC,采样点减少了73%,在处理生物医学信号时实现了约5∶1的数据压缩比,Schreier品质因数(FoMs)和Walden品质因数(FoMw)分别为172.6 dB和67.0 fJ/conv.step。
文摘针对智能反射面(Intelligent Reflecting Surface,IRS)辅助的含窃听者的认知物联网(Cognitive Internet of Things,C-IoT)通信系统,提出了一种基于联合波束成型的保密率优化方案。在系统模型中,考虑了一个由发射机、主用户、次用户、窃听者和智能反射面组成的多输入单输出通信场景。基于该模型,构建保密率优化问题,即在发射机总功率约束、主用户端干扰功率约束以及智能反射面单位模约束的条件下,通过联合优化主被动波束成型,最大化系统的保密率(Secrecy Rate,SR)。在实现过程中,由于公式化的问题非凸,因此使用交替优化的方法将原始问题分解为两个子问题进行优化,即发射机波束成型矩阵的优化以及IRS相移矩阵优化。针对发射机波束成型的矩阵优化,使用半定松弛法与逐次凸逼近法。接着,使用丁克尔巴赫法与逐次凸逼近的方法对IRS的相移矩阵进行优化。仿真结果表明,在含有窃听者的多输入单输出系统中,引入智能反射面实现主被动波束成型的优化有效提高了系统的保密率。
文摘Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.
文摘The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.
文摘针对电力物联网环境下配电变压器状态监测面临的低功耗、可靠传输及数据融合等技术难题,设计基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的配电变压器状态监测系统。该系统采用省电模式(Power Saving Mode,PSM)休眠控制策略降低终端能耗,利用受限应用协议(Constrained Application Protocol,CoAP)及重传机制保障数据可靠传输,并引入加权融合计算实现多源异构数据的协同诊断。研究结果表明,该系统能够有效满足配电变压器长期稳定运行与精准状态评估的需求。
文摘针对智慧电厂汽轮机振动监测中的强电磁干扰、部署困难及带宽瓶颈等挑战,提出了相应的窄带物联网(Narrow Band-Internet of Things,NB-IoT)通信技术应用方案。该方案采用授权频段规避干扰,部署无线终端简化布线,利用独立子载波优化数据汇聚,构建了稳定高效的监测体系。应用案例分析表明,NB-IoT通信技术能显著提升振动数据传输的可靠性与实时性。
文摘高压电缆接头局部放电监测是电力系统状态检修的重要基础。分析窄带物联网(Narrow Band Internet of Things,NB-IoT)技术的特点,设计基于NB-IoT的高压电缆接头局部放电远程监测系统,围绕放电信号获取、终端节点构建、数据远程传输与运行状态监控形成完整实现方案,并结合系统性能测试验证其在放电识别、数据上报与运行时长方面的工程适用性,为高压电缆接头在线监测与运维管理提供技术支撑。
文摘针对智慧仓储场景中多径/非视距无线传输(Non Line of Sight,NLOS)无线传输干扰导致定位精度低、终端续航短、部署成本高三大瓶颈,提出窄带物联网(Narrow Band Internet of Things,NB-IoT)与超宽带(Ultra Wide Band,UWB)融合的定位算法改进方案。首先,构建时延-能量联合约束的加权最小二乘(Weighted Least Squares,WLS)与扩展卡尔曼滤波(Extended Kalman Filter,EKF)联合模型,引入信道状态信息(Channel State Information,CSI)能量熵权重与动态遮挡因子抑制干扰,提升复杂环境下的定位稳定性;其次,设计加权信号质量评估+自适应同步周期的动态功耗管理策略,有效减少模式切换振荡与无效能耗;最后,搭建射频(Radio Frequency,RF)+压电+光伏多源能量回收架构,结合动态电压调整(Dynamic Voltage Scaling,DVS)优化功率分配,延长终端续航。仿真验证结果表明:在金属货架区、NLOS货物堆放区、自动导向车(Automated Guided Vehicle,AGV)动态通道区,改进方案(粒子滤波优化版)的平均定位误差分别降至0.30、0.40、0.28 m,较传统UWB-飞行时间(Time Of Flight,TOF)方案降低75%~81%;终端平均功耗降低42%,启用多源能量回收架构后续航延长至60个月;以100 000 m2仓储场景测算,部署成本较NB-IoT+UWB基础融合方案降低30%。该方案适配常规及冷链、化工等极端仓储场景,为智慧仓储定位的工程化落地提供了技术支撑。
文摘为提高铁路供配电系统故障检测的及时性和精准性,基于窄带物联网(Narrow Band Internet of Things,NB-IoT)通信技术,提出了铁路供配电系统故障数据识别技术,并构建了完整的数据采集、传输和识别模型系统。研究结果表明,该技术在故障识别准确性和响应速度上表现突出,为复杂环境下铁路供配电系统的稳定运行提供了有效支持。
文摘针对台区智能融合终端通信现存接入冲突、传输效率低、资源利用不均及能耗过高问题,提出基于窄带物联网(Narrow Band-Internet of Things,NB-IoT)的通信优化方案。该方案包括台区终端分级接入控制、上行数据报文聚合、空口资源动态调度及终端能耗自适应调控等设计。开展实验验证方案有效性,实验结果表明,该优化方案可显著提升台区终端通信性能,为台区智能融合终端通信优化提供可靠的技术支撑。
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
