Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links.Particularly,some of the nodes in IoT are mobile and dynamic in nature.Hence maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing.In such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of service.Due to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the nodes.The existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the routing.Link stability helps us to define whether the node is within or out of a coverage range.This paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and throughput.In this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability value.Among the existing routes,the sink node will choose the optimal route which is having less link stability value.Highly stable link is determined by evaluating link stability value using distance and velocity.Residual-energy of the node is estimated using the current energy and the consumed energy.Consumed energy is estimated using transmitted power and the received power.Available bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

Energy Aware Clustering with Medical Data Classification Model in IoT Environment

With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer processes.However,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy costs.This paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research process.The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers.These sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for analysis.The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center environment.Through the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors.The model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power costs.Furthermore,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and AlexNet.The NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark algorithms.These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems.The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers.

Application of Internet of Things in the Health Sector:Toward Minimizing Energy Consumption

The Internet of Things(IoT)is currently reflected in the increase in the number of connected objects,that is,devices with their own identity and computing and communication capacities.IoT is recognized as one of the most critical areas for future technologies,gaining worldwide attention.It applies to many areas,where it has achieved success,such as healthcare,where a patient is monitored using nodes and lightweight sensors.However,the powerful functions of IoT in the medical field are based on communication,analysis,processing,and management of data autonomously without any manual intervention,which presents many difficulties,such as energy consumption.However,these issues significantly slow down the development and rapid deployment of this technology.The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading.The distance between nodes is one of the factors influencing energy consumption.In this article,we have proposed direct communication between nodes to avoid collision domains,which will help reduce data retransmission.The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.

Harnessing Crowdsourced Data and Prevalent Technologies for Atmospheric Research

The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields,including agriculture, transportation, energy, public health and safety, and more. Understanding the basic processes in each of these fields relies greatly on progress being made in conceptual, observational and technological approaches. However,existing instruments for environmental observations are often limited as a result of technical and practical constraints. Current technologies, including remote sensing systems and ground-level measuring means, may suffer from obstacles such as low spatial representativity or a lack of precision when measuring near ground-level. These constraints often limit the ability to carry out extensive meteorological observations and, as a result, the capacity to deepen the existing understanding of atmospheric phenomena and processes. Multi-system informatics and sensing technology have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies create unprecedented data streams with extraordinary levels of coverage and immediacy, providing a growing opportunity to complement traditional observation techniques using the large volumes of data created. Commercial microwave links that comprise the data transfer infrastructure of cellular communication networks are an example of these types of systems. This viewpoint letter briefly reviews various works on the subject and presents aspects concerning the added value that may be obtained as a result of the integration of these new means, which are becoming available for the first time in this era, for studying and monitoring atmospheric phenomena.

基于数字孪生与元宇宙技术的能源互联网态势感知系统论方法研究(一):概念、挑战与研究框架

态势感知是能源互联网的核心任务,为运管调控各项决策提供重要辅助信息。能源互联网的复杂定性对其态势感知任务提出严峻挑战——受其所辖单元日益增长的规模、种类、主动性、耦合性以及外界环境等不确定因素的影响,能源互联网其复杂性与日俱增。系统复杂性所衍生的一些问题超出经典简化论的讨论范畴,亟需一种新的认知手段。该文通过关联数据、数据科学、物理系统与具体应用,提出能源互联网数字孪生系统;进一步,延伸出一种新型数据驱动的态势感知方法论,即数字孪生态势感知(digitaltwinsituation awareness,DT-SA)。其核心思想是将真实世界的固有难题转化到虚拟空间,继而借助复杂系统理论和大数据分析等工具予以解决,具体涉及孪生体建模、分析和认知等核心环节。此外,该文也讨论元宇宙技术对孪生体互联的增强作用,以及相关的科学问题。该系列研究有益于推进能源系统领域和数据科学领域的交叉融合,对于企业数字化转型、数字城市建设等具备参考价值。

基于PLC+4G的病案库房远程巡查机器人研发

研发一款远程视频巡查库房中病案霉变痕迹、虫害痕迹及库房渗水痕迹的机器人。机器人采用四轮行走形式,2个后轮由2台步进电机分别驱动。机器人上安装垂直升降丝杆,其滑块上安装水平伸缩丝杆,一台筒形病案检查摄像机安装在水平丝杆滑块上,2根丝杆均由步进电机驱动,另有2台360°球形摄像机保证机器人行走无盲区。设计用户手机端HMI,点击HMI上按钮,通过物联网-4G-4G网关数据链向机器人上的PLC发出指令,控制机器人把病案检查摄像机移动到检查位置。3台摄像机视频通过另一条4G-物联网数据链传到用户电脑屏幕显示。实验验证远程控制系统及信号传输数据链切实可行,可在电脑上远程视频察看库房病案存储状况,确保病案安全。

移动清洁能源中心应用模式及运营策略研究

本研究深入探讨移动清洁能源中心在能源转型中的应用与运营策略,发现其结合物联网和大数据技术能有效提升运营效率。政策激励和环保教育对清洁能源发展至关重要。本文提出标准化的运营模式和策略,包括成本优化、技术升级等,旨在提供定制化解决方案,支持城市可持续能源系统建设。

物联网开放环境多源异构传感数据低能耗采集

物联网开放环境存在大规模异构数据,增加了传感数据采集的能耗,且容易导致采集误差。为此,提出了物联网开放环境多源异构传感数据低能耗采集。利用数据采集时间、负载均衡度和数据采集过程中的能量损耗为评价函数。并在此基础上构建数据采集分配模型,之后布置相关传感网络节点,收集数据信息。构建传感器节点优化部署模型,优化网络信道分配,降低数据采集能耗,以此达到低能耗数据采集的目的。实验验证表明,使用该方法对数据实施采集,当丢包率达到15%时,数据采集误差为0.03 m。平均能量消耗为5.24 J,平均节点存活数量为141个,平均采集数据量为2.18×10~3 bit,该方法优化了数据采集误差和网络信道的分配性能,有效降低了传感数据的采集能耗。

基于物联网的能源监测系统设计与研究

以能源绿色发展为背景,以工业企业能耗及环保的数据要素为载体,研究并设计了一个能源监测系统。从系统架构设计、硬件搭建设计、软件架构设计、功能模块设计及实现和数据分析等方面进行了详细的阐述。该系统可以广泛应用在各大工业企业,对水、电、煤等各类能源消耗进行数据分析、挖掘和评估,实现对企业能源供给及数据的实时采集、监测与统计,帮助企业掌握各类能耗指标的使用规律,实现精细化管理,满足终端企业用户的现代化、智能化能耗管理模式,提升能源的节能监测水平,从而提高企业的经济效益,对推动发展绿色能源建设具有深远意义。

基于DA多重插补法和电力物联网的电能数据缺失修复方法

针对电力物联网中电能数据量过多,缺失电能数据修复难度较大的问题,研究基于DA多重插补法和电力物联网的电能数据缺失修复方法。电力物联网利用感知层的电能数据采集终端采集电能数据,所采集电能数据利用通信层传送至应用层,应用层的电能数据缺失修复模块,利用EM插补算法计算电能数据缺失值的初始插补值;将所获取的电能数据插补值作为DA多重插补法的初始值,DA多重插补法利用局部加权回归模型,通过调整电能数据缺失值的预测误差,获取最终电能数据缺失修复结果。实验结果表明,该方法修复电力物联网电能数据的观测误差方差低于0.2,对于短期电能数据与长期电能数据,均具有良好的修复结果。

基于物联网和云计算技术的空调节能管控平台研究

空调系统能耗在建筑能耗中占据较大比重,随着铁路的快速发展,铁路车站及办公区的空调使用数量日益增加,其节能降耗潜力明显。基于物联网和云计算技术的深度融合开展空调系统节能管控平台研究,实现空调的实时监测、远程控制、智能开关机、人体感应、实时监测温湿度及耗电量等功能,达到了智能互联、智慧节能的目的。

数智驱动的“双控”智慧管理体系研究

针对当前能源管理中面临的精准控碳存在局限、能源消费总量管理缺乏弹性、能耗双控欠缺差别化管理等问题,依托数智技术,通过开展双控系统规划、全周期能耗数据治理、能耗策略智能优化等方面的研究,提出了一种能耗“双控”智慧管理体系的架构。旨在提供一种能实现能源供需精准匹配、能耗数据协同共享,以及能源消耗智能预测的智慧化能源管理思路。

基于数据采集及预测智能优化节能系统

本系统旨在通过先进的数据采集技术,全面、准确地收集能源使用相关的数据。利用大数据分析和预测模型,对能源消耗模式进行深入洞察和精准预测。基于这些数据和预测结果,系统采用智能优化算法,动态调整能源供应和使用策略,以实现显著的节能效果。例如,在工业生产中,根据设备运行数据和生产计划预测能源需求,优化设备的开关机时间和运行功率;在建筑领域,依据室内外环境数据和人员活动规律,自动调控照明、空调等系统的运行状态。该系统具有广泛的应用前景,可有效降低能源成本,减少碳排放,推动可持续发展。

基于物联网的能源企业大数据存储技术研究

针对能源企业在大数据存储技术方面存在的高延迟、数据处理效率低等问题,文中提出了一种基于物联网边缘计算的解决方案。该方案旨在通过在数据源附近实施边缘计算,优化能源企业的大数据存储和处理流程。该方法将数据处理任务从远程云中心转移到网络边缘,不仅降低了数据传输延迟,还提高了数据处理的效率。实验结果显示,相较于传统的中心化数据存储方法,该方案在处理能源数据时表现出更高的效率和更快的响应速度。该结果为能源企业提供了一种高效的大数据存储技术方案,也有助于优化能源数据的管理和分析流程,同时提升整体数据处理性能。

绿色建筑智能化监控平台的研究——数字孪生技术的应用

数字孪生技术在建筑智能化监控中的应用主要体现在运营与管理上,通过建立与真实世界相同的数字副本,使实体建筑与其相应的数字模型相连,实现对各种运营和管理全过程的监测、分析和管控。通过对比虚拟模型和实际建筑的能源数据,找出能源利用效率低下的环节,并提出优化建议。数字孪生技术在楼宇安全管理方面的应用具有广泛前景和潜力,通过实时监测与预警、设备健康管理、能耗优化管理、安全风险分析、应急预案演练、人员行为监控、智能决策支持和数据集成与分析等手段,可以显著提高建筑楼宇的安全管理水平,保障人们的生命财产安全。

基于节能与先进技术的制药行业灯塔工厂研究

科技的迅猛进步与医药行业市场竞争的日趋激烈,已迫使传统制药工厂寻求转型升级之道。在这一背景下,智慧灯塔制药工厂作为一种创新解决方案应运而生。该方案融合了节能内核、机器人及自动化物流系统、BIM可视化设计施工运维一体化数据库平台、SCADA及物联网数据采集技术、生产工艺仿真技术以及数据孪生技术等众多前沿科技。本文旨在详细剖析这些技术在智慧灯塔制药工厂规划与实施过程中的关键作用和应用价值,以期为制药行业的可持续发展提供新的视角和思路。

5G通信与泛在电力物联网的融合:应用分析与研究展望

泛在电力物联网是物联网在电力行业的一种具体表现形式,是互联互通的电力网与通信网深度融合的产物,是实现能源互联网的重要举措。第五代移动通信(5G通信)因具有高带宽、低时延、低功耗等优势,受到各行各业青睐,也将于泛在电力物联网深度融合。基于此,探讨了5G通信技术与泛在电力物联网的深度融合,深入分析了5G通信在泛在电力物联网中的应用场景,总结了5G通信支撑泛在电力物联网关键技术;鉴于未来5G通信网基站密集,能耗可观,进一步还研究了泛在电力物联网下5G通信网的能量管理机制,以及两者产能用能的协调互动;最后,对未来5G通信与泛在电力物联网的融合研究进行了展望。

一种低耗能的数据融合隐私保护算法

物联网中的隐私保护是实际应用中要解决的关键问题之一,作为物联网组成部分的无线传感器网络,希望在进行精确数据融合的同时,又能保护个人的隐私.文中提出了一种新的低能耗无线传感器网络数据融合隐私保护算法ESPART.一方面算法依靠数据融合树型结构本身的特性,减少数据通信量;另一方面算法分配随机时间片,避免碰撞.同时限制串通数据范围,降低数据丢失对精确度的影响.仿真结果显示,相比于SMART算法,ESPART可以在有效保护数据隐私的前提下,花费与TAG算法相同的时间和较少的数据通信量,得到精确的数据融合结果.