A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.

Design and Implementation of the Sense Egypt Platform for Real-Time Analysis of IoT Data Streams

Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.

IoT感知的业务微流程建模

基于物联大数据赋能的业务流程能够更快更准地感知物理世界并及时做出响应的需求突现,提出一种物联网(Internet of Things,IoT)感知的业务微流程建模方法。首先,以单个IoT对象为中心建模,融合MAPE-K(monitor,analysis,plan,execution and knowledge base,MAPE-K)模型思想,将IoT对象实例生命周期的行为状态与微流程实例状态一一映射,实现对单个IoT对象的环形自动监控和调节;其次,基于从IoT传感设备获取的数据,定义基于SASE+语言的业务规则,提取对业务流程有意义的业务事件,避免了无关事件对宏流程的干扰;最后,通过设计一个微流程建模工具原型系统,结合真实案例分析,验证了提出建模方法的有效性,实现了业务流程与IoT实时流式感知数据的结合,并显著减少了宏流程需要处理的业务事件数量。

基于NB-IoT的智慧路灯控制器设计

随着智慧城市建设步伐的加快,各大城市必然要对城市路灯照明系统进行节能化和智能化升级。文章针对传统路灯能耗大、人力维护成本高、智能化程度低等问题,设计一种基于NB-IoT的智慧路灯控制器。单灯控制器以微控制器STM32为控制核心,通过NB-IoT通信模组实现路灯与控制中心的组网,从而实现路灯的远程开关及调光控制、路灯数据采集、故障定位等功能。该路灯控制器以实际LED路灯进行测试验证,基于NB-IoT路灯控制器能够满足路灯照明系统的节能化和智能化以及集中管理的需求。

A Deep Neural Network Coordination Model for Electric Heating and Cooling Loads Based on IoT Data

As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time optimization,extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads.In this paper,a deep neural network coor-dination model for electric heating and cooling loads based on the situation awareness(SA)of thermostatically controlled loads(TCLs)is proposed.First,a sliding window is used to adaptively preprocess the IoT node data with uncertainty.According to personal thermal comfort(PTC)and peak shaving contribution(PSC),a dynamic model for loads is proposed;meanwhile,personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs.Then,a deep Q-network(DQN)-based approach,using the thermal comfort and electricity cost as the comprehensive reward function,is proposed to solve the sequential decision problem.Finally,the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads,by using UEPIoT intelligent dispatching system data.The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.

Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

IoT Based Greenhouse Real-Time Data Acquisition and Visualization through Message Queuing Telemetry Transfer (MQTT) Protocol

One of the most indispensable needs of life is food and its worldwide availability endorsement has made agriculture an essential sector in recent years. As the technology evolved, the need to maintain a good and suitable climate in the greenhouse became imperative to ensure that the indoor plants are more productive hence the agriculture sector was not left behind. That notwithstanding, the introduction and deployment of IoT technology in agriculture solves many problems and increases crop production. This paper focuses mainly on the deployment of the Internet of Things (IoT) in acquiring real- time data of environmental parameters in the greenhouse. Various IoT technologies that can be applicable in greenhouse monitoring system was presented and in the proposed model, a method is developed to send the air temperature and humidity data obtained by the DHT11 sensor to the cloud using an ESP8266-based NodeMCU and firstly to the cloud platform Thing- Speak, and then to Adafruit.IO in which MQTT protocol was used for the reception of sensor data to the application layer referred as Human-Machine Interface. The system has been completely implemented in an actual prototype, allowing the acquiring of data and the publisher/subscriber concept used for communication. The data is published with a broker’s aid, which is responsible for transferring messages to the intended clients based on topic choice. Lastly, the functionality testing of MQTT was carried out and the results showed that the messages are successfully published.

基于NB-IoT的智慧燃气物联网系统框架设计与研究

在数字经济发展的浪潮中,窄带物联网(NB-IoT)等物联网(IoT)技术在燃气行业的应用较为广泛,但普遍存在兼容性问题。如果物联网终端通信平台要兼容不同厂家、不同类型、不同协议的燃气设备,并监控不同数据点、处理不同业务需求,其难度非常大,尤其要兼容将来的新增设备更是难上加难。为了减少系统二次开发,节约人力、财力、物力,对物联网系统框架进行了研究,设计了物联网应用控制平台、物联网终端通信平台和物联网终端设备三级新型框架。在某集团公司物联网系统中,采用独特的数据传输格式,对不同设备、不同协议、不同业务、不同加密算法进行无差别化数据编组,采用统一格式减少数据交互次数,解决了系统对不同设备的兼容性、安全性和数据传输效率问题,实现了物联网通信的安全、可靠、灵活、高效。该研究有助于燃气行业的信息化、数字化发展。

RFID-based Production Data Analysis in an IoT-enabled Smart Job-shop

Under industry 4.0, internet of things(IoT), especially radio frequency identification(RFID) technology, has been widely applied in manufacturing environment. This technology can bring convenience to production control and production transparency. Meanwhile, it generates increasing production data that are sometimes discrete, uncorrelated, and hard-to-use. Thus,an efficient analysis method is needed to utilize the invaluable data. This work provides an RFID-based production data analysis method for production control in Io T-enabled smart job-shops.The physical configuration and operation logic of Io T-enabled smart job-shop production are firstly described. Based on that,an RFID-based production data model is built to formalize and correlate the heterogeneous production data. Then, an eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution. Furthermore, a near big data approach is used to excavate hidden information and knowledge from the historical production data. A demonstrative case is studied to verify the feasibility of the proposed model and methods. It is expected that our work will provide a different insight into the RFIDbased production data analysis.

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

基于LoRa和NB-IoT的温室大棚数据采集系统网关设计

温室大棚环境参数检测系统在使用WiFi,Bluetooth,GPRS,ZigBee,4G等通信技术时,要么存在通信距离短,要么存在功耗高的缺点,而LoRa和NB-IoT刚好解决了这一看似矛盾的问题。文章应用LoRa和NB-IoT无线通信技术,设计了温室大棚数据采集系统网关,实现了接收数据采集终端上报的数据并转发到OceanConnect物联网平台,推送到应用服务器,并详细介绍了网关的软硬件设计。通过对这两种无线通信技术的组合,在一定范围和特定场景下能够降低无线通信的功耗和费用,具有一定的实用价值。

基于NB-IOT的农作物大棚监测系统研究

为了解决农作物大棚种植环境复杂、通信差、监管不严等问题,借助NB-IOT无线通信技术,设计了一种农作物大棚监测系统。该系统分为环境数据采集、物联网云平台、监测中心3大部分,主要工作原理:通过分布在大棚内的终端采集设备,采集对应的空气温湿度、光照强度、二氧化碳浓度及土壤湿度等环境数据,发送给主处理器STM32F103C8T6;对数据进行初步处理后,借助串口传送给BC95型NB-IOT通信模块,由通信模块上传至Ocean Connect DEV物联网云平台,用户可通过上位机监测中心实时监测大棚内的环境数据及设备状态。为此,提出该系统的总体设计方案,对其硬件和软件进行了详细的设计,并通过上位机进行测试,证明了该方案的可行性。

基于虚拟网关的交通基础设施监测点位数据接入方法

针对交通基础设施点位监测数据来源多样、结构各异、协议不一的特点,分析具体接入需求,提出一种基于Netty架构的交通基础设施监测点位数据接入虚拟网关。阐述网关接入监测点位的配置方法及集群分配策略,定义超文本传输协议(HTTP)、传输控制协议(TCP)、用户数据报协议(UDP)下的数据传输格式,设计一种数据传输消息的编码和校验机制。在真实监测数据样本增强的基础上,利用分布式消息模拟工具对虚拟网关性能进行测试,结果表明,该虚拟网关实现了多点位多协议的交通基础设施监测数据统一接入,每亿条数据接入时间、存储时间分别达到8.14 s和9.75 s,平均数据溯源时间为2.96 s,具有亿级规模点位监测数据的接入能力,可为交通基础设施数字化监测的研究和应用提供理论支撑。

The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN

The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.

NB-IoT智能水表在供水企业产销差管理中的应用

以NB-IoT技术为主流的智能水表具有低功耗、广覆盖、大连接、低成本等优势。文章就供水企业利用NB-IoT智能水表的海量数据从计量效率、抄表准确性、用水监控、数据分析等各方面降低产销差为主题,通过供水企业P公司的诸多案例,阐述了如何利用NB-IoT智能水表帮助供水企业定位有收益水量的用户并制定换表策略以提升售水水量,准确抄见水表并及时提示水表故障,及时发现用户违章用水、内装漏水及正反转等异常用水行为,以及通过NB-IoT智能水表的海量数据动态制定产销差战略目标、为产销差目标任务优先级别排序等。结果可为供水企业更好地利用NB-IoT智能水表精细化、科学性地做好产销差管理工作指明方向。

智慧灌溉大数据管理平台设计与应用

以粗放型为主的漫灌方式浪费水资源,探索高效节水农业灌溉意义重大。本研究研制了集数据采集、数据管理、数据可视化、分析决策和远程管理为一体的智慧灌溉大数据管理平台,旨在以数字化管理与智能化控制方式提升水资源利用效率。平台利用嵌入式、物联网、互联网、3S(GIS、GPS、RS)等技术,采取“1+1+N”的设计模式,构建作物需水预报模型与灌溉决策模型,基于B/S构架和Java语言,设计了1个灌溉数据中心、1个灌溉数据管理系统、4个应用系统,打造了智慧灌溉大数据管理平台。平台在河北、河南、山东、江苏等地区设有多个示范基地,汇聚了8个科研小组、24个试验基地的有关多种作物的生长、灌溉、气象、土壤等数据,平均采集数据18829条/天,帮助管理人员摸清家底;集成了多个团队的软件系统和62套物联网设备,及时、定量地呈现了农作物生长状况及环境状态,实现了农作区动态监测,助力生产决策;生成农作物需水预报和灌溉决策方案,完成了远程灌溉目标,并且经过实地试验,验证了自动灌溉的有效性,将灌溉水有效利用系数最高提升了31%以上;形成了大田产业、温室中心、数字科研和区域灌溉等不同专题可视化布局,满足多种农业场景应用需求。为农业生产和跨区域管理提供了便捷工具,为农业灌溉数字化系统搭建和应用提供了参考。

基于物联感知数据和张量融合的电力变压器绕组绝缘劣化评估方法

针对目前缺乏依托在线物联感知数据的电力变压器绕组绝缘劣化评估方法的问题,该文考虑电、热、机械劣化因素对电力变压器绕组绝缘的损伤累积效应,提出基于物联感知数据和张量融合的电力变压器绕组绝缘劣化评估方法。首先,研究电、热、机械因素对绕组绝缘的累积损伤机理,依托电压、电流、温度、局部放电物联感知数据构建变压器绕组绝缘的电性能、热性能和机械性能劣化损伤指标;然后,构建三种劣化损伤指标的特征张量,基于张量融合对三种劣化损伤指标进行特征融合,提取劣化损伤指标间的高维劣化特征关联信息;最后,采用基于自组织映射网络(SOM)的最小量化误差方法构建绕组绝缘的综合劣化评估指标,实现对绕组绝缘劣化状态的评估。该文通过多种评价准则评价构建综合劣化指标,算例结果表明,所提方法能准确评估变压器绕组绝缘的真实劣化程度。

基于区块链和代理重加密的医疗物联网数据安全管理系统

目的:基于可扩展区块链网络和代理重加密机制,建立医疗IoT数据管理系统,以保障物联网(IoT)智能医疗系统的数据传输安全。方法:将物联网与区块链相结合,通过分布式架构提高远程监测数据的安全性和隐私性。在物联网设备的注册和验证阶段,采用零知识证明(ZKP)方法确保数据的完整性。将代理重加密技术与区块链相结合以存储哈希数据,确保系统的安全性,并通过智能合约的设计实现了访问控制。为实现区块链的可扩展性,使用基于星际文件系统(IPFS)的离线数据库来存储数据。此外,为提高数据存储效率,提出了改进工作量证明(PoW)共识机制。结果:所建立的系统可有效抵御各种类型的攻击。IoT节点数量为40个时,系统总注册时间小于100 s,不同事务数量下的区块创建和访问时间均小于200 s。当事务数量为25个时,所提改进PoW算法的交易挖掘时间为0.001 s,优于原PoW算法的0.0024 s。结论:基于区块链和代理重加密技术的数据管理系统,能够确保医疗数据的机密性、完整性和隐私性,且效率优于传统的区块链方法,有助于提高IoT背景下医疗数据的安全管理。

Hadoop平台下粒子滤波结合改进ABC算法的IoT大数据特征选择方法

针对现有物联网大数据特征选择算法计算效率低下、可扩展性不高的问题,提出一种基于改进人工蜂群(ABC)选择特征的系统架构,该架构包含四层体系,可以高效地聚合有效数据,剔除不需要的数据。整个系统是基于Hadoop平台、MapReduce以及改进ABC算法的。改进ABC算法用于选择特征,而MapReduce则由并行算法支持,该算法可高效处理大数据集。该系统使用MapReduce工具实现,并利用粒子滤波来消除噪声。将提出的算法与同类方法进行比较,并通过使用十个不同的数据集对效率、准确性和吞吐量进行评估。结果表明,相比其他几种较新的算法,提出的算法在选择特征时更具可扩展性和高效性。

大数据网络及相关技术在学生违纪问题方面的应用(英文)

校园安全与学生校内违纪问题近年来关注热度逐渐上升,随着数智时代的到来,应对和防范学生违纪问题的措施越来越多,当前广泛使用的视频监控和校园一卡通等相关技术有一定的局限性,无法有效地防范违纪问题的发生,因此,通过三种场景将物联网与可安装在校园内的传感器连接起来,及时收集和处理数据,实现低能耗、广覆盖、低成本的数据处理并将数据上传到云端,通过MapReduce在云端进行数据处理,根据不同的违纪行为产生预警,使大数据技术与校园霸凌、旷课、吸烟等违纪识别相结合,为学校工作人员监控学生记录和安全问题提供有价值的信息。