智慧城市的应用体系架构研究

世界各国都在探索城市发展的新模式,智慧城市是当今世界发达国家在推进产业和城市信息化进程中的前沿理念和探索实践,是对互联网技术、传感器技术、智能信息处理等技术的高度集成。智慧城市的体系结构包括基础设施建设、信息资源开发与利用体系以及智慧应用体系等,其中智慧应用体系与城市的发展以及百姓的生活息息相关,包括智慧交通、智慧医疗、智慧能源、智慧公共管理等。     

Robust state estimation for wireless sensor networks with data‐driven communication

Robust state estimation problem for wireless sensor networks composed of multiple remote sensor nodes and a fusion node is investigated subject to a limitation on the communication rate. An analytical robust fusion estimator based on a data‐driven transmission strategy is derived to save the sensor energy consumption and reduce the network traffic congestion. The conditions guaranteeing the uniform boundedness of estimation errors of the robust fusion estimator are investigated. Numerical simulations are provided to show the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.     

物联网关键技术研究

针对近年来人们广泛关注的物联网,分析讨论了制约物联网发展应用的关键技术,包括感知技术、网络通信技术、数据融合与智能技术,以及云计算等。这些技术将对物联网的发展和应用乃至人类生活、工业发展、科技进步起到至关重要的促进作用。     

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.     

基于大数据的智慧林业系统设计

With the development of 5G, big data, cloud computing, IOT, mobile Internet and other information technologies of new generation, the design of forestry information system establishes a new mode of forestry development integrating three-dimensional perception, collaborative management and internal and external services by means of perception, IOT and intelligence. Intelligent forestry system is a comprehensive information management system integrating forest management, intelligent forestry business management, green maintenance management and emergency command and dispatching management. Based on the specific project practice, this paper puts forward the overall framework design scheme and management platform design scheme of intelligent forest- ry.     

针对高速数据流的大规模数据实时处理方法

以实时传感数据和历史感知数据为基础的各类计算需求逐渐成为当前物联网应用建设中的关键,如何实现基于高速数据流和大规模历史数据的实时计算成为数据处理领域的新挑战.现有批处理方式的MapReduce大规模数据处理技术难以满足此类计算的实时要求.文中结合城市车辆数据的实时采集与处理应用,在理论和实践分析的基础上,提出了一种针对高速数据流的大规模数据实时处理方法,并对方法中的本地阶段化流水线、中间结果缓存等关键技术瓶颈进行了改进.其中,根据系统参数控制阶段化流水线,使CPU得到了充分、有效利用;通过改造内外存数据结构、读写策略和替换算法,优化了本地中间结果的高并发读写性能.实验表明,上述方法可以显著提升大规模历史数据上数据流处理的实时性和可伸缩性.     

Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedingly flexible manner during the second stage. Extensive simulation has been done and results are evaluated to point out the strength of the proposed work, while dynamically varying the specific requirements of IoT applications. We observe that the work proposed is path-breaking the prevailing methods, where a specific routing protocol is employed throughout the period of time. It’s clearly shown that the proposed, Low-cost Context-Aware Protocol Switching (LCAPS) scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.     

IoT‐fog‐cloud based architecture for smart systems: Prototypes of autism and COVID‐19 monitoring systems

Making resources closer to the user might facilitate the integration of new technologies such as edge, fog, cloud computing, and big data. However, this brings many challenges shall be overridden when distributing a real‐time stream processing, executing multiapplication in a safe multitenant environment, and orchestrating and managing the services and resources into a hybrid fog/cloud federation. In this article, first, we propose a business process model and notation (BPMN) extension to enable the Internet of Things (IoT)‐aware business process (BP) modeling. The proposed extension takes into consideration the heterogeneous IoT and non‐IoT resources, resource capacities, quality of service constraints, and so forth. Second, we present a new IoT‐fog‐cloud based architecture, which (i) supports the distributed inter and intralayer communication as well as the real‐time stream processing in order to treat immediately IoT data and improve the entire system reliability, (ii) enables the multiapplication execution within a multitenancy architecture using the single sign‐on technique to guarantee the data integrity within a multitenancy environment, and (iii) relies on the orchestration and federation management services for deploying BP into the appropriate fog and/or cloud resources. Third, we model, by using the proposed BPMN 2.0 extension, smart autistic child and coronavirus disease 2019 monitoring systems. Then we propose the prototypes for these two smart systems in order to carry out a set of extensive experiments illustrating the efficiency and effectiveness of our work.     

MaD‐WiSe: a distributed stream management system for wireless sensor networks

Wireless sensor networks (WSN) are composed of several sensors having limited memory, processing power, communication bandwidth, and energy, which cooperate in performing a given task. The use of the database paradigm has emerged in the last few years as a viable solution to manage data in such a context. In this paper we present the MaD‐WiSe system, a distributed query processing framework that moves the processing of the query into the network. MaD‐WiSe reconsiders various aspects related to database system design and it reinterprets them according to the WSN constraints and requirements. In particular it considers the aspects related to the definition of a query language to formalize the queries, a stream model to manage data acquired by the sensors, a query algebra to define the operators that actually perform the query, and energy efficiency and query optimization strategies for saving energy. Copyright © 2010 John Wiley & Sons, Ltd.     

RIoTBench: An IoT benchmark for distributed stream processing systems

The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed‐loop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed stream processing systems (DSPS) hosted in cloud data centers are becoming the vital engine for real‐time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we propose RIoTBench , a real‐time IoT benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as modular microbenchmarks. Further, we define four IoT application benchmarks composed from these tasks based on common patterns of data preprocessing, statistical summarization, and predictive analytics that are intrinsic to the closed‐loop IoT decision‐making life cycle. These are coupled with four stream workloads sourced from real IoT observations on smart cities and smart health, with peak streams rates that range from 500 to 10 000 messages/second from up to 3 million sensors. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public cloud and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, by IoT practitioners to evaluate DSPS platforms, and even reused within IoT solutions.     

基于云计算的日光温室群物联网服务平台

以建设智能化日光温室物联网为目标,提出日光温室群物联网服务平台设计方案。该平台包括感知操作层、采集控制层、组网传输层、门户服务层和后台云支撑层5个层次,实现了温室群的数据存储、管理控制和云数据分析等功能。设计面向日光温室生产动态过程的实时云预警技术及云分析建模系统,提高日光温室生产的精细化作业水平。应用结果表明,该平台能扩大日光温室的管理规模,降低物联网系统建设和运行成本,提高日光温室群物联网的大数据存储和数据分析能力,并且具有良好的可扩展性、安全性和稳定性,在农业信息化领域有较好的推广前景。     

基于线性回归的无线传感器网络分布式数据采集优化策略

事件监测是无线传感器网络中最重要的应用之一,部署在监测区域内的传感器节点通过对感知数据信息的采集、处理和传输等基本操作完成具体的监测任务,在各种操作中,节点之间的数据传输是最消耗能量的.为了减少节点之间的通信数据量,达到降低网络能耗和延长网络生命周期的目的,该文提出了一种能量高效的基于线性回归的无线传感器网络分布式数据采集优化策略,通过应用线性回归分析方法构建感知数据模型,保持感知数据的特征,使节点仅传输回归模型的参数信息,代替传输实际监测的感知数据信息.仿真实验结果表明,文中提出的数据采集优化策略能通过较小的通信量有效地实现事件监测区域感知数据的预测和估计,降低网络的总能量消耗,延长网络的生命周期.