北斗卫星在智慧城市建设中的应用评述

在“数字城市”的概念后提出了“智慧城市”,用来解决或减轻城市化和经济转型所造成的社会问题。随着我国北斗卫星组网日益完善,以及物联网、云计算等技术的快速发展,使城市运行管理更加智慧化提供了可能性。通过智慧交通、智慧减灾、智慧社区等北斗项目积极探索“智慧城市”的建设,使北斗真正融入百姓生活,逐渐建立我国北斗在社会中的置信度、认可度。对北斗卫星顺利投入我国全方位、多维度应用领域具有重要意义,以便更好服务于城市建设和管理,进一步推进城市和谐发展。     

A framework based on sparse representation model for time series prediction in smart city

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.     

智慧城市中隐私保护性广播加密算法

现代化城市公共部门和市民社交网络会产生大量的数据,这些海量数据的使用和处理主要依赖现代化信息通信技术和网络技术。为了保护用户隐私和数据安全,在数据传输过程中采用加密算法对数据进行加密,广播加密是多用户环境下最有效的方法。传统算法中,基于身份的广播加密密文可以广播到一组接收方,接收方的身份包含在密文中,当多个接收方解密密文时会泄露其他用户的身份信息。为了保护接收方用户之间的身份隐私,提出一种基于身份的隐私保护性广播加密算法,实现了接收方用户之间的匿名性。此外,考虑了如何从匿名广播的密文中撤销指定目标的接收者,根据数据访问控制策略决定用户的数据访问权限,为用户提供密文撤销操作,撤销过程不泄露明文和接收者的身份信息。在随机预言模型下,基于BDH困难性问题证明了该算法的安全性,并通过实际数据集的仿真实验验证了算法的有效性和可行性。     

物联网技术在智慧城市建设中的应用

通过分析物联网的概念及特点,总结了物联网技术在智慧城市建设中的核心应用。同时对城市智慧交通、智慧医疗、智能电网、智慧物流、智慧水利、智慧旅游、智慧校园等方面的物联网应用进行了描述。     

Analysis and assessment of a knowledge based smart city architecture providing service APIs

The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs.Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.     

边缘计算和云计算在智慧城市建设中的应用

文章主要对边缘计算、云计算的定义和特点及智慧城市的概念进行简单阐释,结合城市建设情况,分析边缘计算和云计算在智慧城市建设中应用的必要性,并提出二者的具体应用,希望充分挖掘边缘、云计算技术的优势,在二者相互补充的过程中实现智慧城市的建设,为实际智慧城市建设工作提供参考依据。     

基于情境感知技术的智慧城市服务系统研究

随着城市规模越来越复杂,全国各级政府都在进行城市物联网和信息化建设,目前虽然搭建了互联骨干网和部署了大量的传感器,收集了众多的城市行为数据,但落后的信息管理模式难以体现信息价值,将信息体现在服务提升之中,造成信息资源的极大浪费。因此,将情境感知技术引入到智慧城市服务的应用之中,构建了一个基于情境感知的城市服务系统,并通过情境信息采集、情境信息推理和服务配置模型等关键技术,实时感知城市内的情境需求,从而提供智能化的业务服务组合。最后通过一社区智慧街道管理系统来验证本文所设计的系统效果。     

A smart healthcare reward model for resource allocation in smart city

Today, cities face many significant challenges, and the smart city concept is a promising means to address typical traditional city problems. The wireless e-health technologies is an evolving topic in the area of telemedicine nowadays. Mobile telecommunication and the use of multimedia technologies are the core of providing better access to healthcare personnel on the move. These technologies provide equal access to medical information and expert care leading to a better and a more efficient use of resources. Mobile and Fog computing technologies can also cope with many challenges in smart healthcare resources of mobility, scalability, efficiency, and reliability. Optimal healthcare systems are particularly critical in cities, due to the highly concentrated populations. This high population increases the potential for harm and damage in the case of negligence or improper treatment. This can lead to infections and disease outbreaks, which could become epidemic situations and require containment, which is very costly. Motivated by the need for better usage and management of healthcare resources, which is crucial for reliable healthcare delivery, this paper introduces a model that can provide improved delivery and utilization of resources. The quality reward-based model was developed to study and react to the satisfaction factors of healthcare systems, and proposes an optimization-based algorithm called the Maximum Reward Algorithm (MRA), that enhances the use and delivery of healthcare resources. The algorithm has been tested with multiple experiments and simulations, and has proved that it can provide reliability, efficiency and achieves 50.1% to 77.2% performance improvement.

     

震后过渡阶段应急物流系统的定位运输路线安排问题研究

针对震后过渡阶段中,回收救援物资与保护灾区环境的问题,在满足灾区民众基本生活需求的前提下,提出了一个正逆向结合的应急物流设施定位-运输路线安排问题(LRP)模型.首先,结合回收物资可分批运输的特点,建立以应急系统耗费总时间最小为目标函数的数学模型;然后,利用两阶段启发式算法对模型进行求解;最后,算例分析验证了模型和算法的可行性.实验结果表明,与传统单向LRP模型相比,所提方法的目标函数值减少了51%.所提模型能够有效提高应急物流系统运行效率,并为应急管理部门提供辅助决策支持.     

杭州＂智慧城管＂建设探究

“智慧城管”是一项系统工程、惠民工程,是智慧城市建设的重要组成部分。建设“智慧城管”是全面建设小康社会、加强社会管理、实施新型城市化战略的要求。文章探讨了杭州“智慧城管”建设的战略定位,分析了杭州推进“智慧城管”建设的核心要素,以利于推动杭州“智慧城管”的进一步发展。     

Visual data security and management for smart cities

This paper proposes a method to manage and utilize image and video data effectively in a smart city. Applying digital watermarking techniques, a framework for visual data security and management for smart cities is presented. In the framework, a reversible fragile or semi-fragile watermark embedded into the visual data is used to ensure trusted acquisition. Moreover, reversible metadata watermarks carrying information such as identification and other properties data is used to assist visual data management. A solution for tracing users on a large scale is presented using reversible watermarking.     

A multi-objective programming model for scheduling emergency medicine residents

Scheduling emergency medicine residents (EMRs) is a complex task, which considers a large number of rules (often conflicting) related to various aspects such as limits on the number of consecutive work hours, number of day and night shifts that should be worked by each resident, resident staffing requirements according to seniority levels for the day and night shifts, restrictions on the number of consecutive day and night shifts assigned, vacation periods, weekend off requests, and fair distribution of responsibilities among the residents. Emergency rooms (ERs) are stressful workplaces, and in addition shift work is well-known to be more demanding than regular daytime work. For this reason, preparing schedules that suit the working rules for EMRs is especially important for reducing the negative impact on shift workers physiologically, psychologically, and socially. In this paper, we present a goal programming (GP) model that accommodates both hard and soft constraints for a monthly planning horizon. The hard constraints should be adhered to strictly, whereas the soft constraints can be violated when necessary. The relative importance values of the soft constraints have been computed by the analytical hierarchy process (AHP), which are used as coefficients of the deviations from the soft constraints in the objective function. The model has been tested in the ER of a major local university hospital. The main conclusions of the study are that problems of realistic size can be solved quickly and the generated schedules have very high qualities compared to the manually prepared schedules, which require a lot of effort and time from the chief resident who is responsible for this duty.     

针对延迟容忍用户的智能电网需求响应

针对用电请求可被延迟响应的设备,首先将调度这些设备的用电请求响应问题建立成随机优化模型.然后利用Lyapunov优化技术设计一种需求响应算法来实时计算设备用电请求何时被响应及被舍弃的用电请求量,从而降低用户们的电费支出及由于电能供不应求或电网承载超限而被舍弃的部分用电请求对他们造成的影响.进一步通过理论分析,当可再生能源发电量、用电请求量及市场电价服从独立同分布时,建立设备的用电请求被响应的最大等待时间与代价函数值之间的权衡关系.最后,仿真实验验证了理论分析结果的有效性.     

A multi-server queueing model with retrial connection arrivals as a model for optimisation of the traffic control

We consider a multi-server queueing model with a finite buffer and requests arriving in connections. The number of requests in a connection is random and unknown at the connection initiation instant. Requests, which belong to the connection, arrive in accordance with a Poisson process. Admission of connections to the system is regulated by means of so-called tokens. The pool of tokens is finite. If a connection arrives and there are no tokens available, it leaves the system forever or joins the orbit and retries for access later on. The steady-state distribution of the system is analysed. The problem of the throughput maximisation under the constraint that the request loss probability does not exceed a predefined value is numerically solved. The effect of the retrial intensity, correlation and variation in the arrival process and the probability to leave the system if tokens are not available is numerically highlighted.     

智能电网弹性响应时间业务需求的接入控制

考虑智能电网多种类型业务需求的接入控制, 通过利用响应时间的弹性来平缓业务负荷的波动, 使得电网运行的长期平均代价最小. 针对业务需求和用户行为的随机分布特性, 建立连续时间Markov 控制过程的系统分析模型; 结合性能势基于样本轨道的估计, 提出一种基于仿真的策略迭代优化算法, 有效缓解了系统大状态空间导致的维数灾问题, 具有较快的收敛速度和良好的应用效果. 仿真实验结果验证了所提出方法的有效性.

     

A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support

In this paper, a distance-based group decision-making (GDM) methodology is proposed to solve unconventional multi-person multi-criteria emergency decision-making problems. In this model, some decision-makers are first identified to formulate a group decision-making framework. Then a standard multi-criteria decision-making (MCDM) process is performed on specific decision-making problems and different decision results are obtained from different decision-makers. Finally, these different decision results are aggregated into a group consensus to support the final decision-making. For illustration and verification purposes, a numerical example and a practical unconventional emergency decision case are presented. Experimental results obtained demonstrate that the proposed distance-based multi-criteria GDM methodology can improve decision-making objectivity and emergency management effectiveness.     

A fuzzy multi-objective covering-based vehicle location model for emergency services

Timeliness is one of the most important objectives that reflect the quality of emergency services such as ambulance and firefighting systems. To provide timeliness, system administrators may increase the number of service vehicles available. Unfortunately, increasing the number of vehicles is generally impossible due to capital constraints. In such a case, the efficient deployment of emergency service vehicles becomes a crucial issue. In this paper, a multi-objective covering-based emergency vehicle location model is proposed. The objectives considered in the model are maximization of the population covered by one vehicle, maximization of the population with backup coverage and increasing the service level by minimizing the total travel distance from locations at a distance bigger than a prespecified distance standard for all zones. Model applications with different solution approaches such as lexicographic linear programming and fuzzy goal programming (FGP) are provided through numerical illustrations to demonstrate the applicability of the model. Numerical results indicate that the model generates satisfactory solutions at an acceptable achievement level of desired goals.     

Towards a data‐driven IoT software architecture for smart city utilities

The Internet of things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart cities are a practical manifestation of IoT, with the goal of efficient, reliable, and safe delivery of city utilities like water, power, and transport to residents, through their intelligent management. A data‐driven IoT software platform is essential for realizing manageable and sustainable smart utilities and for novel applications to be developed upon them. Here, we propose such service‐oriented software architecture to address 2 key operational activities in a smart utility: the IoT fabric for resource management and the data and application platform for decision‐making. Our design uses Open Web standards and evolving network protocols, cloud and edge resources, and streaming big data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus‐scale IoT testbed at the Indian Institute of Science, Bangalore and present our experiences. Our architecture is scalable to a township or city while also generalizable to other smart utility domains. Our experiences serve as a template for other similar efforts, particularly in emerging markets and highlight the gaps and opportunities for a data‐driven IoT software architecture for smart cities.