调查云传感器集成

Chowdhury Chandrani雷
助理教授,MCA的部门。,SDET-Brainware Group of Institution, Barasat, Kolkata, West Bengal, India

文摘

现在无线传感器网络(WSN)在人类生活中已经成为一个无处不在的实体。人类无法到达那些地方。无线传感器网络的发展和应用成为一种不可战胜的潮流成各种工业、环境和商业领域。但是它有资源约束和设计约束。将云计算与网络,我们可以克服弱点的基础。本文简要的调查云集成传感器。这里主要是三个方面的传感器云一体化被认为即先生云数据库,不同的基于云的传感器数据共享平台和基于云的传感器数据处理方法。这里给出一个表总结的特性得到不同的基于云的传感器数据共享平台的支持。

关键字

传感器网络;云计算;传感器云;美洲狮;TinyDB;聪明的

介绍

无线传感器网络的发展和应用成为一种不可战胜的潮流成各种工业、环境和商业领域。一个典型的传感器网络由大量传感器节点代理一起监控区域和获取的数据环境。包含空间分布式系统,自动调整的传感器协同监测环境条件如声音、温度、压力、运动、振动、污染、等等[1],[2]。目前,网络正在利用在医疗等多个领域,军事目标跟踪和监测、自然灾害救济、危险环境勘探、地震感应等等。这些传感器提供各种有用的数据可用于监视和控制他们的环境部署。在传感器网络的数据量是巨大的,异构的和多维的。存储和处理这些数据,高数量的存储和计算能力是必需的。与传统网络不同,传感器网络有自己的设计和资源约束。资源约束包括有限的能源,通信范围、低带宽、有限的处理和存储在每个传感器节点。设计约束是应用程序依赖、基于监控环境[6]。

云计算已经进化成为未来一代计算范例[3]。NIST(美国国家标准与技术研究院)定义了云计算的概念如下:云计算是一个模型,使方便,按需网络访问共享池的可配置的计算资源(如网络、服务器、存储应用程序和服务),可以快速予以配置和发布通过最少的管理工作或服务提供者交互[4]。

云计算允许系统和用户使用平台即服务(PaaS)(例如操作系统(OSs)),基础设施即服务(IaaS)(如存储和服务器)和软件即服务(SAAS)(例如,应用程序级项目)等等以非常低的成本是由几个云提供商(如亚马逊、谷歌和微软)的基础上每使用支付服务[5]。云提供了一个巨大的计算能力和存储,我们可以将它与传感器网络集成到解决问题有限的计算能力和存储能力有限。传感器与云数据库系统集成,提供数据库云传感器网络。o消除计算约束的基础,云计算可以集成。部署传感器网络提供者特定即提供者定义自己的标准。云也集成了传感器网络提供一个通用的数据共享平台。本文调查了WSN提供集成的云数据库云传感器,基于云的WSN数据处理框架和基于云的大规模传感器网络数据共享平台。

本文组织如下。部分我给介绍。部分II, III和IV给出了三种方法的调查。最后第五部分总结了纸。

传感器云数据库方法

一个传感器网络产生大量的多维流数据固有的噪声和短暂的。传感器网络中的数据有spatio时间维度需要额外的处理与传统数据库系统[36]。在传感器网络,我们必须都实时数据(传感器读数)和存储数据(传感器元数据)处理即传感器数据库必须支持连续查询处理和一次性查询处理技术[34]。一个传感器网络空间分布和患有短程通信、存储和计算能力有限。所以管理这些数据我们需要轻量级分布式处理与传统网络重量级处理。在传感器网络作为数据在本质上是不确定的[36],我们必须管理缺失值和校准误差预处理。传感器网络生成大量的数据需要数据聚合。查询处理,近似查询处理技术是用来管理数据量[37]。我们可以用概率方法在传感器网络近似查询处理。一些传感器网络使用RFID标签来识别目的需要特殊权力[33]。 Data in the sensor network is considered as continuous stream which leads to data structuring for processing. Generally sensor network data are represented as XML. Sensor modelling language (SML) [7] is used to represent any physical sensor’s metadata like their type, accuracy, physical location and so forth. The nodes in sensor network are highly failure prone, so we must have fault tolerant and data recovery mechanism to protect data. As sensor network is used to monitor and control the different environmental situations and to avoid different disaster, time critical real time database will be suitable for this network. Further security and reliability is important for this type of distributed network. Finally data in sensor network are heterogeneous in terms of data requirement, application needs, types of deployment and types of data generated by sensor network [24]. So to judge the sensor database the parameters are:

•大量的多维分布的非均匀流数据处理。

•结构化数据和非结构化数据的处理。

•时空数据管理。

•连续和一次性查询处理。

•单查询和多查询优化技术。

•数据预处理支持管理数据的噪声和转瞬即逝。

•轻量级分布式处理考虑资源约束和能源效率。

•内置的容错和恢复机制。

•概率方法考虑近似查询处理传感器数据的支持。

•安全性和可靠性。

节约能源和减少不必要的,无关紧要的网内数据传输处理是必需的。有不同类型的数据库上可用的云可用于传感器网络。这个数据库可能keyvalue存储或关系或两者的结合。美洲狮,TinyDB和Apache Hadoop基于云数据库系统用于传感器网络。有三种类型的云存储服务即云数据库服务、云Blob存储、云存储表。表三云存储服务、云存储和云数据库服务更适合存储传感器数据。Blob存储不提供结构化的存储和查询功能所需处理传感器数据[13]。不同的基于云的数据管理系统框架是用于管理传感器数据。RDB-KV CloudDB是一个框架来管理大规模异构传感器数据流。这个框架结合关系数据库和键值存储的方法。 It supports global data distribution. For keyword based queries, a Global ObjectID B+-Tree (GOID B+-Tree) index is used. It focuses on spatio-temporal query processing and specify operator to process streaming data [9]. But this framework does not support data pre-processing, nested query processing, aggregated query processing and acquitional query processing. Google Bigtable and apache Map-Reduce parallel processing model are combined to use as a sensor database. But Bigtable is not distributed outside Google and are only accessed within Google App Engine [10]. Both Bigtable and Map-Reduce are combined to support streaming multidimensional processing of huge amount of sensor data [11], [12]. Apache Hadoop HDFS, HBase and Map-Reduce are combined to provide a sensor data storage and parallel processing framework in cloud. This system can store large datasets suitable for sensor network using block oriented architecture and ensure high throughput of data. Feature replication strategy is used for fault tolerance of database [13]. But Hadoop supports batch processing style while sensor network supports real time interactive processing. In Managing Wearable Sensor Data through Cloud Computing [14] authors propose an approach for data processing of sensors attached to patients. This approach uses java to develop an application that receives sensor data from patient body and stores them in Google public cloud. It also provides a visualization web application and APIs (Google charts). WSDL Web Services are used to access remote services and it helps different application to communicate with each other. This paper performs fault analysis, but does not consider QOS, data pre-processing and energy efficient data processing. It uses 3rd party Google public CLOUD SERVICE. Pachube [15] is a web service which provides a scalable infrastructure to manage real time sensor data. CLEVER [16] is a sensor data processing service which analyses and filters data from sensor networks and provides them as an input to other advanced services. It provides support for sensor provisioning. To provide cloud services it uses C-COMPUTING module for cloud data centre resource management, C-SENSOR module to add PAAS functionality for sensor network and C-STORAGE module to store necessary middleware data. CLEVER uses Sedna native XML database as distributed database and XMPP protocol to support interoperability. It uses distributed hierarchical clustering and provides special support of security management. In [17], authors propose a cloud based framework to collect ECG data from the sensors attached to the body of patients, store them and provides as input to different softwares which analyse and process this data. They use Amazon EC2 and Amazon S3 as IAAS. For PAAS they implement a new module called Dynamic Scalable Runtime with Aneka middleware to ensure QOS and provide different software to analyse, process and monitor patient health status as SAAS. This paper stresses the cost, scalability, dynamic load distribution and QOS of system. ThingSpeak is an open source platform available online to store and retrieve web based data. Users can store and create sensor-logging application in this platform. This platform is also used for sensor monitoring [18].

基于云的传感器数据共享平台

当我们设计一个传感器数据共享平台,我们必须确保交付的信息从WSN中间件在时间和尽快。传感器数据共享平台是用于访问传感器数据超出了专有的边界问题实现传感器网络,多个应用程序之间共享数据,集成不同的传感器网络和提供的WSN数据信息作为服务(IAS)在云。

现有的传感器数据共享平台:

在[19]提出一种技术集成工业传感器网络与云监控开发过程(例如核电站管理系统)。这种方法支持监测工艺参数阈值违反检查,协调生产地点的信息,维护过程的质量,感觉到参数作为服务,服务搜索和绑定。但是这种方法不考虑分布式处理、用户隔离,扩展网络、负载平衡和故障传感器的分析。另外一个EB-XML注册中心存储库和单一集成控制器将受单点故障。有一个系统[20]提供智能设备用户,一个基于云的无线传感器网络基础设施用于访问传感器数据和主机的计算任务。这个框架被设计用来提供传感器的信息和应用程序服务。作者使用虚拟化(虚拟机)来管理异构性和隐藏潜在的复杂性。它使用SENSORML语言传感器数据处理。它还提供了支持自动配置的传感器。谷歌公共云托管服务(Google App Engine)。 In [21] a publishersubscriber based framework is proposed to integrate sensor network with cloud based community to provide data and applications as a SAAS. It provides the following services

•传感器数据交付使用基于内容的发布-订阅系统。

•事件匹配算法称为统计组索引匹配(SGIM)来匹配合适消费者发布事件。

•与其他云提供商合作由签证官根据动态协作使资源可用。

它主要考虑数据交付。此外它还提供多用户支持,出版商用户的组密钥管理和动态添加。内脏大神经[22]是一个中间件进行动态集成和管理生产的传感器网络和数据流。它主要是点对点架构和支持QOS方面的系统。VFSN:虚拟联合传感器网络[23]用于在多个用户之间共享传感器数据和隐藏实际的资源。主要服务是传感器数据共享。这个框架是一个点对点架构使用PIAX水槽节点在哪里担任同伴。作为一个整体,这是一个覆盖网络。它使用服务器虚拟化,网络虚拟化提供功能可扩展性和资源安全。在网络处理传感器数据被用来降低数据传输。 This paper considers load distribution strategy. IEEE 1888 is used as data transmission protocol. It also specifies its operation. SenseWeb [24] is designed to provide common platform to share sensor data and build different application to manipulate data. But to control and manage all the activities it uses a centralized coordinator system which leads to single point of failure. In [25] authors propose a platform to develop a platform independent, adaptive, service centred application development for sensor networks. This platform helps end-users to develop their applications. Applications can be migrated. To support application migradiblity authors uses mobile script. Service binding is done by execution engine where authors implements most of the things as a service and include less functionality. So this paper mainly gives focus on application migration and addictiveness. It also considers multiple user support, adding new service and service provisioning. Though by run time binding we can achieve portability but execution time also increases which is not desirable in sensor network. Again, some execution are moved to sensor nodes which may increases power consumption. If multiple user access a single component then a strongly concurrency control system is required which is not considered in this paper.

基于云的传感器数据处理方法

传感器网络资源约束。我们不能使用重量级数据处理方法在传感器网络。但巨大的异构传感器数据流要求高度复杂的处理。将传感器网络与云计算可以解决挑战参与传感器网络数据处理。一个基于云的传感器数据处理系统可以处理的数据在两个方面——第一个传感器网络数据并将数据转发给云处理[26],[31]其次云数据处理系统的第一个过程查询,查询和远期细分为传感器网络。在响应所需的查询只传感器节点发送数据[32]。

现有的基于云平台的传感器数据处理:

不同作者使用不同的云框架在传感器网络数据处理。CLOUDVIEW是一个框架,用于存储、处理和分析大量的机器保养从大量的传感器收集的数据嵌入到工业机器在云计算环境中[26]。这个混合(离线和在线)系统使用分布式Hadoop和使用映射-规约模式进行存储和并行处理传感器数据流的时间系列。系统包括数据收集(数据聚合、预处理、过滤、存储),案例基础创建、更新和维护,功能降低,特征提取和故障预测使用CBR(案例推理基础)和粗糙集理论。但问题是Hadoop支持批处理风格。所以问题可能出现交互式实时处理。在[27]作者专注于保持传感器的隔离和一致性财产事务。一些作者使用Apache Hadoop和使用映射-规约模式处理半结构化和非结构化的分布式大规模传感器数据[28]。在[29]作者提出一个多层次、灵活的数据处理模型的传感器网络。在此系统中,协调和每个传感器网络网关用于收集和存储传感器数据。 They provide local storage facility. Different sensor networks are connected to a local server which provides local storage backup and computing functionality. Local server provides uniform data access interface whose specification are stored at cloud and used by clients. This system uses different databases (SQLite, TinyDB, MySQL and MongoDB). In [30] authors integrate ERP and sensor network to minimize resource usage of senor networks. They use processing and prediction model of cloud based ERP system to process data collected from sensor network, generate some decision and forwards this decision/control information back to sensor network to turn the sensors on/off and adjust their transmission frequency to reconfigure the sensor network. But the problem is sensor networks transmit all the data which increases power and energy consumption.

传感器网络查询处理框架考虑:

设计一个云框架,传感器数据处理,我们必须考虑以下几点

•系统支持存储或历史系列传感器数据处理以及实时流媒体数据处理。在这种情况下,我们可以考虑关系数据库或谷歌大桌子的方法存储结构化数据和Apache Hadoop /谷歌进行非结构化或半结构化的流数据处理。

•系统必须支持一次,连续的,定期查询处理。传感器网络是动态变化的查询优化计划必须适应传统数据库中而不是静态的。

•支持系统定义和用户定义的分组聚合,减少数据传输。

•明确支持的数据预处理(尤其是不确定性或差异管理)。

•近似查询处理与容忍一定程度的错误。

•系统必须支持acquitional查询处理,需求收集存储数据查询处理和查询。

•系统可以支持使用处理过的数据资源管理系统。

•明确故障管理、数据一致性管理和负载平衡。

•框架还包括能耗监控和网络易读性问题。

结论和未来的工作

集成云计算的基础上可以解决资源约束问题的基础。在本文中,我们专注于三个领域即云传感器数据库,基于云的传感器网络数据共享平台和基于云的传感器数据处理方法。我们也给出一个比较不同的基于云的传感器网络数据处理方法表最后给点设计基于云的传感器网络数据处理方法。在未来我们将考虑其他地方可以集成传感器网络和云计算。

表乍一看

表1

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