Fazio15_Big Data Storage in the Cloud for Smart Environment Monitoring

 

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ScienceDirect  Procedia (2015)) 500 – 506  Procedia Computer Science 52 (2015

The 6th International  Conference on Ambient Systems, Networks and Technologies   (ANT 2015)

Big Data Storage in the Cloud for Smart Environment Monitoring M. Fazio∗, A. Celesti, A. Puliafito, M. Villari Univer Uni versit sityy of Mes Messin sina, a, C.da C.da Di Dio - San Sant’A t’Agat gata, a, Mes Messin sina a 981 98166, 66, Italy Italy

Abstract Monitoring activities detect changes in the environm environment ent and can be used for several purpose. To develop new advanced services for smart environments, data gathered during the monitoring need to be stored, processed and correlated to di ff erent erent pieces of  information that characterize or influence the environment itself. In this paper we propose a Cloud storage solution able to store huge amount of heterogeneous data, and provide them in a uniform way. To this aim, we adopt an hyrid architecture that couple Document and Object oriented strategies, in order to optimize data storage, querying and retrieval. In this paper, we present the architecture design and discuss some implementation details in the development of the architecture within a specific use case.

c  2015  TheAuthors. Authors. Published Elsevier © 2015 The Published by by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license (Peer-revi http://creativecommons.org/licenses/by-nc-nd/4.0/ ). http://creativecommons.org/licenses/by-nc-nd/4.0/  ). Program Chairs. Peer-review ew under responsibility of the Conference Peer-review under responsibility of the Conference Program Chairs Keywords:   Big Data; Storage system; Smart environment; Sensing; IoT; IoT; Cloud; SWE

1. Introdu Introduction ction The growing exploitation of smart environments and audio / v video ideo streams is causing a massive generation of complex and pervasiv pervasivee digital data. Sensing Sensing equipment and sensor networks networks are deployed deployed to monitor phenomena phenomena of  interest providing many heterogeneous measurements and multimedia data. Then, data are stored, shared and processed for several purposes, such as healthcare 1 , air quality monitoring 2 , and risk management 3 . For many many y years, ears, enterprise organizations have accumulated growing stores of data, running analytics on that data to gain value from large information sets, and developing applications to mange data exclusively. exclusively. However, However, a new trend is arising, where data production, information management and a nd application development are decoupled, thus giving to business companies diff eerent rent roles in the market. In such a scenario, flexible solutions to merge activities of vendors, manufacturers, service providers, and retailers are necessary. In this paper we focus the attention on data storage services, and we present a new storage architecture specifically aimed to monitoring activities in smart environment. In the Internet of Things (IoT) perspective, billions of physical sensors and devices are interconnected through the Internet to provide many heterogeneous, complex and unstructured data. Many e ff ort ort in the industry and in the research community have been focused on the storage of IoT data, in order to balance costs and performance for data maintenance and analysis 4 . Indeed, Indeed, the design of powerful powerful storage systems systems can e fficiently handle the requirements

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Maria Fazio. Tel.:  + 39-090-3977344 ; fax:  + 39-090-3977176.  E-mail address: [email protected]

1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/  http://creativecommons.org/licenses/by-nc-nd/4.0/ ). ). Peer-review under responsibility of the Conference Program Chairs doi:10.1016/j.procs.2015.05.023 doi: 10.1016/j.procs.2015.05.023

 

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of big data applications and Cloud computing is expected to play a significant role in IoT paradigm. Indeed, Cloud storage off eers rs huge amount of storage and processing capabilities in a scalable way 5 . Thus, we designed a monitoringoriented Cloud architecture for the storage of big data, that can be exploited for the development of application and services useful in many di ff erent erent applications for smart environments (e.g., smart cities, homeland security, disaster prevention, etc.). This paper analyzes Big Data issues arising from monitoring activities, and discusses di ff erent erent sotrage technologies that can be exploited to support diff eerent rent types of data, in order to optimize data storage, querying and retrieval. Our storage architecture couples both the Document and Object oriente Storage Systems approaches in Big Data storage, thus to providetoa benefit unique of solution able and to treat di ff eerent rent Moreover, it user, exploits Cloud computing technology scalability reliability. Fr information From om the pointsources. of view of the Cloud datathe gathered from the monitoring infrastructure are provide in a uniform way way,, that has been designed according to the Sensor Web Enablement (SWE) specifications defined by the Open Geospatial Consortium (OGC) 6 . The paper is organized organized as follows. follows. Section 2 describ describes es related works. Data features in smart environm environments ents are discussed in Section 3. In Section 4, we present our Cloud storage solution, discussing many design choices. A few implementation highlights are discussed in Section 5. Our conclusion are summarized in Section 6.

2. Related Works New Cloud infrastructures interacting with Sensors and Internet of Things (IoTs) are recently appearing in literaFull llyy Co Conn nnec ecte ted d Car  Car  system ture. A Cloud Platform useful useful for suppo supporting rting the Fu   system is presented by Dingo et al.   7 . The architecture is at very high level, in which telco and Cloud operators are included in the picture. A much more detailed Platform as a Service architecture is called CloudThings 8 . It represents a collection of Cloud services o ff ered ered by the IT market (i.e., Facebook, GAE,...), smart devices and embedded systems(i.e., Wiring, Sun SPOT, mbed, Arduino) and Cloud applications (Heroku, Paraimpu,...). The implementation shows all adopted solutions tailored for Smart Home scenarios, a real use case deployed in Oulu Finnish city. Cloud4Sensing 3 is a framework that integrates two diff eerent rent strategies for managing sensing resources in the Cloud and let the end-user free to choose which type of  Cloud service he needs. Specifically, the framework provides services according to a data-centric or a device-centric model:: the former is implemented model implemented as a PaaS (Platform (Platform as a Service) able to abstract abstract and store heterog heterogeneous eneous sensing / actuation actuation data that are provided to clients; the latter is implemented as a IaaS (Infrastructure as a Service) o ff eers rs 9 a sensing /  actuation   actuation infrastructure to the clients. Another high level level platform is able to integrate Wireless Sensor Networks with Cloud Computing. All these platforms present the same type of functionalities and elements. In our view,, for making real progress it is necessary to take into account interoperability among heterogeneous systems. view Cloud Computing Computing is also becoming becoming the basis for Big Data needs needs.. At the Infastructure Infastructure as a Service Service (IaaS) level, Big Data can leverage the Storage capabilities of Clouds, as well at the same time, it can rely on computation inside VMs 10 . Also Hado Hadoop, op, installed installed into VMs VMs,, is optimized for processin processing g Big Data. It is interesting interesting to see that VM instances and their configurations strongly aff ect ect this kind of processing. processing. Using Cloud Cloud resources in relation to Big Data task is a straightforward goal. Hadoop is the larger used opensource framework adopted for managing Big Data with Map / Reduce Reduce approach. Another example of Big Data processing in the Cloud is presented by Rao et al. 11 . In this work the computation framework used is Sailfish, a new Map / Reduce Reduce environment similar to Hadoop. Sailfish was conceive for improving the disk performance for large scale Map-Reduce computations. It tries to build network-wide data aggregation inside data centers and improve disk throughput. Big Data is driving the way of using algorithms and resources even in the Cloud. Big Data problem in e-health scenarios looks at NoSQL DBs as the key key solution toward the full development of IoT, and specifically they investigate on how to shift towards the Web of Things 12 . The work described in our paper is based on SWE, the standard of OGC that is currently looking to form the “Sensor Web for IoT” Standards Working Group 6 , able to explore opportunities to extend the SWE framework and to harmo harmonize nize it with existing open standards to accommodate accommodate Web-frien Web-friendly dly and efficient implementations of sen13 sor interfaces and sensor networks using the REST protocol . The problem problem to find an abstraction abstraction on sensing sensing data 14 representation was also identified from Ballarini et al. , where they analyzed the concepts of proximity, adjacency or containment. They even introduced introduced the contexts of data representation with diff erent erent dynamics. dynamics. They provided provided a globa globall model with a dynamic dynamic interoperability interoperability disregarding disregarding how the global view should be accomp accomplishe lished. d. Their

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Fig. 1. The Cloud storage service

decision-maker  is   is requested to process a huge amount of incoming data, but it is not clear how such a problem is practically addressed (i.e. scalability problems).

3. Big Data Issue in Smar Smartt Envir Environments onments The Cloud storage solution we present provides data access and query capabilities to several heterogeneous data sources. It allows users to express their needs in terms of type of measurement, time interval, geolocalization of data, etc., and to receive data according to a uniform format. Before presenting our solution, we need to present the main issues that need to be addressed in monitoring data management, thus to better explain our main design strategies. Monitoring infrastructures in smart environments belong to di ff erent erent tenants tenants spread on a worldwide area. There are are several  several possible models that lead tenants to share their data over the Cloud. For example, the tenants provides data as open sensing data through the web. In this case, the Cloud storage provider is interested in integrating such type of  data in its system; or the tenant is at the same time both resource provider and consumer, and it exploits the Cloud to extend his physical infrastructure by means of the Cloud virtual infrastructure; otherwise, the Cloud storage provider and the tenant company make commercial agreements. The The type type of agre agreem emen entt be betw tween een te tena nant ntss of moni monito tori ring ng in infr fras astru tructu ctures res and and Clou Cloud d st stor orag agee prov provid ider erss is ou outt of the the sc scop opee of this paper, but we want to highlight that, in a such a complex scenario, data coming from monitoring infrastructures are very heterogeneous. We can roughly classify such data in two main types: 1) Observations: measurements of physical or compos composed ed phenomena performed by sensin sensing g devices. Observations can be expressed by tuples (key value) and stored in text file forwarded across the network; 2) Objects: multimedia contents (e.g., audio, image, video and animation) recorded by information content processing devices 15 . The meaning of ”Big Data” today deals with very large unstructured data sets (PetaByte of data 16 ), that need of  rapid analytics with answers provided in seconds. Howev However, er, strategies to manage Big Data strongly depend on the specific type of data. Observations can generate Big Data because monitoring activities in a wide geographical area produce several tuples in short time interval. Thus, in long periods (days, months, years) a huge amount of data need ,

 

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to be structured and stored. Observations can be made av available ailable through Documents, where they are encapsulated in a standardized internal format. An eff ective ective Document-Oriented Storage System (DO-SS) (e.g., MongoDB, Cassandra, CouchDB,...) indexes the contents of each document in order to make an easily retrieval of them. Moreover, a great deal of publishing is done in HTML, XML, JSON, or systems that can at least export or convert to those. Objects can originate files with big size, but Big Data issues arise not only from the volume of Objects, but also with respect to their heterogeneou heterogeneouss nature. Indeed, Indeed, di ff erent erent types of queries can be executed to find an Object in a storage system according according to the specific type of data. An Object-Ori Object-Oriented ented Storage Storage System (OO-SS) (e.g., AWS S3, SWIFT, Kinetic,...) combines storage capabilities (e.g.,transparently p persistent ersistent data, concurrency control, data recovery, recovery , associative queries,...) withset object-oriented programming langu language age capabilities. approaches mainly rely on metadata, an extensible of attributes describing the Object. OO-SS explicitlyTraditional separates file metadata from data to support additional capabilities and typical formats used for extracted metadata are XML, YAML and JSON. The JSON.  The information schema associated to an Object depends on the specific OO-SS, but, usually, it is strictly related to the features of Object itself (e.g., image size, type of compression, video duration, image resolution,...) and not to the the context where the Object has been generated. In this paper we propose an hybrid storage system that exploit both Document- and Object-oriented storage strategies to optimize data management tasks. It is deployed into a Cloud environment able to o ff er er a transparent storage  to the end users, which do not have kno knowledge wledge of the di ff erent erent technologies involved, but just access data services to services through RESTful API. Moreover, exploiting Cloud technologies means implementing a distributed and scalable service in a reliable infrastructure. We present our Cloud storage system in detail in the next section.

4. Hybrid St Storage orage System in the Cloud Our Cloud architecture is shown in Figure 1. It gathers data from many heterogeneous Monitoring Infrastructures (MIs) and decouples the functionalities of the Storage Systems in managing di ff erent erent types of data. Thus, it includes instances of both a DO-SS and OO-SS deployed in the Cloud virtual infrastructure, that are used according to well defined rules, in order to o ff er er an hybrid storage solution efficient and versatile. Data from MIs are collected through the  Data Gathering Interface. It is a plug-in based interface able to interact with diff erent erent information systems and communication communication technologies. All the collected data (both Observation Observationss and Objects) are managed by the Data Manager , that is in charge to abstract data, enrich data with geolocalized information, select the best storage technology for the specific type of data and, finally, insert data in the storage system. The  Identity Manager  and  and  Access Control components implements security functionalities to manage users accounts and set polices to access data and services. Authorized users access data through RESTful API.

4.1. 4.1. Stor Storag agee Syst System em:: the the Da Data ta Mana Manag ger 

The Data Manager  component  component in the Cloud architecture is responsible re sponsible for collecting data coming from the MI The main functionalities of the Data Manager  are:  are: 1) data abstraction and 2) data enrichment. The data abstraction task of the  Data Manager   is is necessary to overcome issues related to the heterogeneity of  data. It abstracts information on both monitoring devices and sensed data, providing a uniform semantic description of them. Abstracted entities interact each others and represent the real world, where things (e.g., monitoring device) observe other things (e.g., monitoring data). The Sensor W Web eb Enablement (SWE) initiative of the Open Geospatial Consortium (OGC) has taken important early ear ly steps towards enabling web-based discovery discovery,, exchanging and processing sensing information. It defines service interfaces which enable an interoperable usage of sensor resources by defining standardized service interfaces. SWE services hides the heterogeneity of an underlying sensor network, its commu communinication details and various hardware components, from the applications built on top of it. In this paper, we specifically refer to SWE standards to characterize data stored in the Cloud. Even if SWE has been designed to describe Observations in a sensing environment, we adopt its semantic also to treat Objects, and to optimize optimize querying and retrieval retrieval tasks. Indeed, Indeed, traditional traditional OO-SS rely on metad metadata. ata. To fulfill monitoring purposes, it is necessary to relate the Object with the environment and, most of all, abstract information according to the SWE specifications in order to provide a seamless querying interface towards end users. To this aim,

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data enrichment functionalities allow to extend the information schema of each object with context-aware metadata compliant with SWE specifications. 4. 4.2. 2. Data Data Publ Publis ishi hing ng

Fig. 2. Processing of data for storing

In the last year, we have widely investigated OGC-SWE specifications, especially to integrate monitoring environments in the Cloud and expose data to end-users. In this paper we focus the attention on data storage issues and, hence, we propose a new solution to organize and manage data. To this aim, we refer ref er to two specific SWE standards, that are the Sensor Observ Observation ation Service (SOS) and the SAS (Sensor Alert Service). Specifically Specifically,, the SOS standard standard discuss interfaces for requesting, filtering and retrieving Observations and sensor system information, whereas the SAS standard describe interfaces for publishing and subscribing Observationss coming from sensors. As shown in SOS S Ag Agen ent  t  and SAS Ag Agen ent  t , that implements respectively the Figure 2,the  Data Manager  contains   contains to agents, the SO   and the SAS SOS and SAS specifications. As pointed by the SWE guideline, they are specifically designed to manage ObservaAgent  nt  supports tions. In particular, partic ular, the SOS Age  supports all the functionalities for f or describing sensors and Observations, abstracting them in a well defined format and gathering measurements from MIs. Informations are then exposed following the specifications of the SWE SensorML and Observation and Measurements (O&M) standards. In particular, SensorML provides prov ides models and XML schemas schemas for describing describing sensor systems and processes, processes, and O&M prov provides ides models and XML schema for encoding Observations and measurements from a sensing environment. SAS Ag Agen ent  t  is The main task of the SAS   is to provide a platform to meet the requirements of Cloud users, which need environmental information to develop advanced services. It provides data according to the publish-subscribe model. Each type of Observation (characterized by a specific observed phenomena in a well defined MI) is identified by a PublicationID, and all the Observation are provided to users by publishing a SWE-S SWE-SAS AS Publish Publish document,  that is an  document, that XML document including one ore more Observations related to the same PublicationID. Objects can not be expressed through SWE files, but only the related metadata can be organized according to te SWE specifications to describe the content of the Object. Thus, the  Metadata Processing Agent  acts   acts to enrich the Object with geolocalized geolocalized information information (e.g., time and place of acquisition acquisition,, tenant, tenant, expiration expiration time...). time...). Such geolocalized geolocalized SAS S Agen Agent  t  that information are provided to the SA   that stores them ito the DO-SS. After the data enrichment process, the  Data Manager  component  component uploads the Object into the OO-SS. Thus, data Objects are splitted, in order to optimize the storage, querying and retrieval tasks: the metadata description is stored into the DO-SS, whereas the Object is stored into the OO-SS. From the point of view of the end user, queries for data are always submitted to the DO-SS. Since data are related to monitoring services, queries perform gelocalized and time oriented requests. The user submits his / her her request to  system and the related information is retrieved by the DO-SS. If the requested content is an Object, the retrieval the the system process will also provide the hook to access it into the OO-SS.

 

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5. Use case case:: the SIGMA Project Project The Sensor Integrated System in Cloud environment for the Advanced Multi-risk Management (SIGMA) is an Italian National Operative Program (PON) project aiming to acquire, integrate and compute heterogeneous data, from various sensor networks (weather, seismic, volcanic, water, rain, car and marine traffic, environmental, etc.), in order to manage risky situation in both the industrial production process and in the territory. For example in the industry field, analyzing data coming from both several ICT equipments and the surrounding environment, it may be possible to control the production processes; considering the territory, analyzing data coming from sensors able to detect traffic congestion in a given area, it may be possible to provide useful information to the population and relevant authorities, in order to optimize routes or manage social events or natural disasters. The SIGMA architecture architecture has five layers. At the lowest layer there are di ff erent erent sensor sensor networks. networks. Some of them are are already  already installed on territory, such as the SIAS network that consists of a series of weather stations to support the agriculture industry, the Water Water Observatory that consists of a series of hydrometric stations and rainfall to support the design of water projects, and the INGV networks for monitoring seismic and volcanic activities in Sicily, Italy. SIGMA integrates the existing networks, for multiparameter monitoring of sensitive areas and increased hydrological, hydro geological, geological, seismic, volcanic land risk, and integration with other networks such as that for car and val traffic monitoring with GPS and GSM systems. At the second layer, the architecture holds virtualized and disna naval tributed resources provided by a Cloud computing framework. This layer is based on CLEVER, a flexible framework  for inter-Cloud communications and event notification 17 . IItt includes specific components for virtual virtual infrastructure set up and management, sensing environment environment integration and data retrieval and storage. The advantages of the framework come from the fact that it will provide computation and flexible storage capacity with enhanced performance, thus facilitating the integration of unstructured networks that make available large amounts of data to be stored and processed. proces sed. At the third layer, there is the Middleware, Middleware, an intermediate intermediate software layer that, through a series of interfaces, gathers data from various heterogeneous networks, standardizing them and making it available at Business Intelligence. At the forth layer, the Business Intelligence components are responsible to process data, implementing the actual business logic of the architecture. At this level, through a series of algorithms, many complex problems are solved and the results are supporting the industrial plant or territory monitoring and management activities. The highest level of architecture is finally represented by the Application layer that takes care to create interfaces for user interaction with the system (e.g. Functional Centers, Operating Rooms, etc ...).

5.1 5.1. Big Da Data ta Sto Storage in SIGM SIGMA A

The Cloud storage system presented in this paper has been impemented to fulfill the requirements of data management at the layer two-three of the SIGMA architecture. In particular, the SIGMA Cloud platform uses MongoDB as DO-SS for the storage of all information information coming coming from the monitoring monitoring system systems. s. MongoDB MongoDB is an open source source document-oriented DB, able to organize data in JSON-style documents with dynamic scheme (called MongoDB BSON documents), making the integration of data with applications easier and fast. Collections in MongoDB stores data coming from diff erent erent sensor networks and monitoring environments. e nvironments. To integrate the subsystem for data collection with the storage subsystem, it is necessary to use a software module (parser) which operates a fast format conversion of SWE to BSON before permanently storing such data in MongoDB. The result of this operation is a flat representation of data organized according to the logic SWE, but exposed as BSON documents. We have implemented implemented the OO-SS by using Swift. Swift is a widely-used widely-used and popular popular object storage syst system em provided prov ided under the Apache 2 open source license. license. A key reason why Swift serves so well for highly-a highly-avai vailable, lable, unstructured unstru ctured applic application ation data is that its design, design, just like Amazon S3, incorporates incorporates even eventual tual consistency consistency.. In Swift, objects are protected by storing multiple copies of data so that if one node fails, the data can be retrieved from another node. Even if multiple nodes fail, data will remain available to the user. Swifts design for eventual consistency means that there is a guarantee that the system will eventually become consistent and have the most up-to-date version of  data for all copies of the data but still provide availability to data should hardware fail. This design makes it ideal when performance and scalability are critical, particularly for massive, highly distributed infrastructures with lots of  unstructured data serving global sites.

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The developed storage service expose Rest API to access data. Specifically we used the Mongodb REST server written in Java and based on Jetty web server 18 . 6. Conclus Conclusions ions The paper deals with big data storage issues due to monitoring monitoring activities activities in smart environme environments. nts. In particular, particular, we have discussed what is the meaning of ”big data” in smart environment and we have identified the most suitable technologies to store diff erent erent types of data. Then, we have proposed a new storage solution that integrate di ff erent erent types of storage technologies, that are Document and object oriented storage system, in order to optimize performance in data sotrage, querying and retrieval. r etrieval. The solution we proposed exploits the Cloud thus to benefit of scalability and a nd reliability. We have have provided a detailed description of our Cloud storage architecture, giving many indications on our design choices. choices. Also, we provided provided some hint on the e ff ective ective implementation of the storage architecture within the SIGMA project, an is an Italian National Operative Program project aimed to the monitoring of industrial production process and the territory ter ritory.. Acknowledgements The research was partially supported by the PON 2007-2013 SIGMA project and by the POR FESR Sicilia 20072013 SIMONE project. The author authorss wou would ld like like to tha thank nk Giusep Giuseppe pe Tricomi ricomi and Antoni Antonio o Gallett Galletta, a, for their their valua valuable ble eff ort o rt in the de devel velopm opment ent of the system prototype. References 1. E.-M. Fong, W W.-Y .-Y.. Chung, Mobile cloud-computing-based healthcare service by noncontact ecg monitoring, Sensors 13 (12) (2013) 16451– 16473. 2. X. Chen, Y Y.. Zheng, Y. Chen, Q. Jin, W. W. Sun, E. Chang, W.-Y. W.-Y. Ma, Indoor air quality monitoring system for smart buildings, in: UbiComp 2014, ACM, 2014. 3. M. Fazio, A. 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