2015 IEEE First International Conference on Big Data Computing Service and Applications
Big Data Sensing and Service: A Tutorial Jerry Gao Computer Engineering Department San Jose State University, CA, USA Corresponding mail:
[email protected]
Lihui Lei School of Computer Science Shaanxi Normal University, Xi’an, China
[email protected]
As the advance of the Internet of Things (IoT), more machine-to-machine sensors and devices are connected to the Internet [1-3]. These sensors and devices generate sensor-based big data and bring new business opportunities and demands for creating and developing sensor-oriented big data infrastructures, platforms and analytics service applications. Big data sensing is becoming a new concept and next technology trend based on a connected sensor world because of IoT. It brings a strong impact on many sensor-oriented applications, including smart city, disaster control and monitor, healthcare services, and environment protection and climate change study.
Abstract — As the advance of the Internet of Things (IoT), more M2M sensors and devices are connected to the Internet. These sensors and devices generate sensor-based big data and bring new business opportunities and demands for creating and developing sensor-oriented big data infrastructures, platforms and analytics service applications. Big data sensing is becoming a new concept and next technology trend based on a connected sensor world because of IoT. It brings a strong impact on many sensor-oriented applications, including smart city, disaster control and monitor, healthcare services, and environment protection and climate change study. This paper is written as a tutorial paper by providing the informative concepts and taxonomy on big data sensing and services. The paper not only discusses the motivation, research scope, and features of big data sensing and services, but also exams the required services in big data sensing based on the state-of-the-art research work. Moreover, the paper discusses big data sensing challenges, issues, and needs.
Although there are many papers addressing different topics in IoT and sensor clouds, only a few papers focusing on big data sensing after this term has been coined by F. Giannotti’s group4 in 2012, according to our recent literature survey [5, 6]. People believe that this could be the next exciting wave following the wave of IoT. As the advances of cloud computing and big data service and applications, people have many questions about “big data sensing and services”. Here are typical ones:
Keywords—Big Data Sensing, Sensor-Based Big Data Analytics, Internet of Things, Sensor Cloud, Sensor Big Data
I. INTRODUCTION The Internet of Things (IoT) is changing the nature world we live into a global connected sensor world in which massive number of sensors and devices are connected to the Internet, and generate large-scale and massive amounts of sensororiented big data. According to Cisco1, the sensor market is going to be a $19 trillion market within the coming years, including a projected $2.9 trillion market for manufacturing.
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What is big data sensing? Why is it important to us? What kinds of big data sensing services and applications? What are big data sensing infrastructures, platforms, and services? What are the current advances and state-of-the-art research work in big data sensing and services? What are the underlying challenges and issues?
This paper is written to attempt to answer most of these questions. As a tutorial paper, it is written to provide the informative concepts and taxonomy on big data sensing, including its definitions, motivation, scope, and features. The paper exams the required services in big data sensing based on the state-of-the-art research work. Furthermore, it discusses big data sensing infrastructure and a reference framework supporting big data sensing and services.
Figure 1. Internet of Things Was Born” Between 2008 and 2009 2 [Source Cisco IBSG April 2011]
In 2013, International Data Corporation (IDC)3, the global IoT market stood at around $1.9 trillion, and around 90 percent of all IoT devices being installed in the world's developed regions. Based on IDC’s predication, the global IoT market is expected to grow by more than $5 trillion over the next six years, will research to $7.1 trillion by 2020. 1
Shui Yu School of Information Technology Deakin University, Australia
[email protected]
This paper is structured as follows. Section 2 covers the basic concepts about big data sensing and services, and discusses its major objectives, motivations, scope, benefits, as well as classified big data sensing services. Section 3 discusses different types of big data sensing services and their classifications based on the recent existing research work.
https://datafloq.com/read/sensor-data-internet-save-lot-money/88
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Dave Evans, “the Internet of Things – How the Next Evolution of the Internet is Changing Everything”, Cisco White Paper, April 2011.
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F. Giannotti, et al. (2012), “A planetary nervous system for social mining and collective awareness”, The European Physical Journal Special Topics, 214: pp. 49–75.
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http://www.zdnet.com/article/internet-of-things-market-to-hit-7-1trillion-by-2020-idc/
978-1-4799-8128-1/15 $31.00 © 2015 IEEE DOI 10.1109/BigDataService.2015.45
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Section 4 focuses on the discussions of the infrastructures and architectures in big data sensing and proposes a reference infrastructure for big data sensing services. Finally, Section 5 presents the challenges and issues for future research. II. UNDERSTANDING OF BIG DATA SENSING AND SERVICES A. What is Big Data Sensing? Big data sensing is the unification of heterogeneous WSN data sources originating from various sensing domains into a uniform cloud storage platform for the purpose of provisioning, metering and analyzing the hence stored data. This conglomerate of such sensor network data stored on a cloud storage infrastructure can be referred to as big data sensing.
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Community big data sensing – It refers to a shared massive data sensing infrastructure for many organizations and enterprises to form a community environment to support the deployment and execution of diverse sensing data analytics applications based on big data collected from sensors. This shared community environment may be orchestrated and managed by the organizations or a third party and may exist on premise or off premise.
Business service models, which include different business services models for big data sensing and services. Details are discussed later.
Big Data Sensing (BDS) is an emergent paradigm which leverages big data computing & services, sensor cloud computing & services, and Sensors and Sensor Networking to provide on-demand sensor-oriented big data computing infrastructures, platforms, services, and SaaS applications. A BDS system usually is developed to provide users with on-demand, scalable, and tenant based big data analytics services for diverse domain applications. Typical applications include smart city control and monitor, environment protection and analysis, disaster evaluation and forecasting, medical healthcare monitor and response, etc.
Figure 2. Big Data Sensing and Service Rack
As shown in Figure 2, big data sensing and service rack includes four tiers. They are listed below. -
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Resource Pool, which includes four types of resources in the big data sensing resource pool. They are: sensors, sensor networks, computing and storage servers, internet and mobile networks, as well as sensor-oriented big data. Network infrastructure, which includes wireless networks and Internet, which provides network connectivity among sensors, and sensor networks, as well as data sensing and analytics service software. Deployment Models, which include four different deployment models: a) private big data sensing, b) public big data sensing, c) community big data sensing, and d) hybrid big data sensing. Their detailed definitions are listed below.
Figure 3 displays the essential features in big data sensing. They are listed below.
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Figure 3. Major Features of Big Data Sensing
Public big data sensing - It refers to a public and external data sensing infrastructure and environment, owned by a third-party as a data sensing service provider. In a public big data sensing infrastructure, various data sensor resources (sensors and sensor networks) are dynamically provisioned to form a virtual sensor cloud to support the deployment and execution of diverse data sensing analytic applications based on sensor-oriented big data in a pay-as-you-go billing model based on service-level-agreements (SLA). The authors in [10] introduced a method to reduce resource usage needed on sensor nodes. Private big data sensing - It refers to an internal data sensing infrastructure and environment, which is constructed based on massive private sensor networks for enterprises to host private computing resources, high risk data store, hide internal communications from public webbased services and applications, and connect to public clouds for global accesses.
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Big data sensing clouds – Different types of big sensor clouds will be deployed to provide diverse big data sensing services to various tenanted users based on diverse sensing networks and resources. Automatic data sensing prevision – This refers to automatic provision for big data sensing services, including sensor network resources and provision, sensing data provision, and sensing data service provision. Big data sensing security – Ensuring the security in big data sensing requires well-defined security polices, solutions, and technologies at the different levels in big data sensing service systems and environments. On-demand data sensing services – Diverse big data sensing services can be delivered to respond both ondemand and scheduled services. On-demand sensing services address the dynamic needs for big data sensing and analytics.
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Elastic data sensing scalability – This refers to the elastic scalability in a big data sensing infrastructure and environment by using load balance and scalability solutions to achieve the scalability in sensor resources, sensing data, and large-scale sensing service requests. Monitor & measurement and billing – This refers to the capability of monitoring and measurement for various big data sensing resources and services. Based on the resource usages and provided service, users will be charged in a pay-as-you-go billing model following pre-defined service level agreements Multi-tenanted accesses and services – This feature is critical to different application users to fulfill their diverse needs and expectations by providing cost-effective software upgrading and maintenance in big data sensing and analytics. Big sensing data collection and management – This provides basic sensor data collection, data store, data management, data search and accesses.
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B. Why Big Data Sensing and Services? There are a number of primary reasons to study and develop big data sensing and services. They include: -
C. The Research Scope of Big Data Sensing What is the scope of big data sensing and services? Figure 4 shows its major components and related subjects in big data sensing and services.
Increase and maximize the usage and sharing of sensor networks & sensor-oriented big data. Reduce the costs of sensor network management, monitor, and maintenance in a large-scale by leveraging sensor clouds. Provide a global connectivity to diverse senor networks and their data via sensor clouds Offer elastic scalable on-demand sensor-based big data analytics for multi-tenants. Provide easy and seamless access interface and connectivity to any sensor networks to obtain diverse sensor-based big data at anywhere and anytime. Provide cost-effective sensor-oriented big data collection, management, storage, as well as accesses. Provide easy and efficient sensor-oriented big data analytics supporting domain specific applications.
Big data sensing infrastructures and service solutions bring the following major benefits to sensor service vendors and stakeholders. -
Offer them a tenanted virtualized sensor cloud based infrastructure with massively connected sensors and sensor networks. Provide them a ready-to-use sensing data platform to build and deploy diverse sensing data applications. Give them a seamless connectivity channel to access large-scale sensor-based big data for data analytics. Furnish a sensing data analytics platform which allows them to conduct diverse big data analytics projects with provided and selectable data analytics models and solutions. Obtain on-demand data sensing and services in a pay-asyou-use business model. Allow them to work on a new data sensing workflow process with a great flexibility, which allows users to achieve automatic sensor data provision, collection, storing, and accessing. Extend their sensing data analytics solutions with a great selection on models, algorithms, and solutions to set up domain-specific sensing data analytics applications and services.
Figure 4 Big Data Sensing Infrastructure Rack and Scope -
Increase or even maximize the usage of sensors and sensor networks Easily reach out to public users and connect to data sensing application vendors Reduce the maintenance and upgrading costs by leveraging and outsourcing to sensor cloud service vendors Create diverse business models and opportunities to generate revenues from their sensors and sensor networks Use cost-effective and efficient ways to monitor and management sensor and senor networks resources Eliminate sensor data management and storage issues by leveraging cloud-based sensing big data platform solutions.
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For big data sensing users and application vendors, big data sensing and services brought them the following benefits.
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Big sensing resource tier – This includes four types of hardware resources: a) sensors and sensor networks, b) Internet and mobile networks, c) computing servers, and d) big data storage servers. Big sensing infrastructure tier – This refers to a cloudbased big sensing infrastructure that provide users a visualized sensor infrastructure based on four types of connected resources, including sensors/sensor networks, computing and storage servers, and internet and mobile networks. This infrastructure allows users to obtain ondemand services in sensor prevision & management, virtualization, monitoring and billing with high scalability and multi-tenancy. Big sensing service tier – This includes three types of services:
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Big sensing data services – These services provide sensor-based data prevision, collection, transmission, storage, management, query, and search. If a system is created to respond and delivery various big sensing data services as a service, then the system is known as big sensing data- as-a-service (BDaaS). Big sensing data platform services – When a service system is developed to support users to develop, validate, deploy, host, and maintain diverse big data sensing services, then the system is known as big sensing data service platform. Big sensing data analytics services – This refers to sensor-based data analytics applications and services that support diverse data learning & analysis, validation, and visualization using data mining and machine learning algorithms and solutions. When a big sensing data analytics platform can be developed to allow users to develop, deploy, host, and upgrade a big data sensing analytics service system.
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Big sensing application service tier – This refers to various types of sensor-oriented data application programs that support domain-specific sensor-oriented data mining, modeling, learning, analysis, decision making, and visualization.
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In addition the above, we need to pay special attentions to other technical topics, including business and cost models, green computing[18], resource prevision and virtualization [12][14], sensing security[4][8], service scalability, data mining and machine learning[16], and mlutitenancy. -
Automatic prevision and management – A sensor cloud provides the automatic sensor prevision and management capability to allow users to create a virtualized cloudbased sensor network infrastructure based on connected sensors and sensor networks in a sensor cloud resource pool. The related management service are provided too. Sensor/sensor network virtualization – In a sensor cloud, massive sensor and network connections, configuration, and virtualized grouping, classification, and management in a sensor cloud. On-demand sensor service – This feature allows a sensor cloud’s users to access diverse on-demand sensor and/or senor network services. Elastic scalability - This refers to elastic scalability a sensor cloud’s users to access diverse on-demand sensor and/or senor network services. Monitor and Measurement – All sensors and sensor networks in a sensor cloud are monitored and tracked for their usage, operations, management service, as well as their healthy status. Multi-tenanted management and accesses – Tenanted based sensor cloud services are delivered to users based on their desirable selections and customizations in sensor data collection, transferring and formatting. Secured sensor data collection and transmission– Although different sensors and sensor networks may have diverse data parameters and formats for data collections and transmissions, security policy and solutions must be in place to support a sensor cloud to meet different security requirements service vendors, sensor manufactures, as well as multi-tenanted users. SLA-based billing – All of provided sensor cloud services will charged by service vendors based on a sensor cloud’s service-level agreements. Pre-defined cost models and metrics are used to generate service bills.
A. SENSOR CLOUD INFRASTRUTURE-AS-A-SEVICE To fulfill diverse needs from different user groups and multi-tenanted users, it is necessary to provide virtualized sensor cloud infrastructure-as-a-service (known as SIaaS). SIaaS is a cloud-based sensing infrastructure service software that supports the provision, management, deployment, and hosting, as well as maintenance of diverse virtual sensor infrastructures for multi-tenanted sensor application users to deliver their on-demand services based on massively connected sensors and sensor networks using the pay-as-you-go billing model based on service-levelagreements (SLA). Its major objective is to increase the sharing and usage of massive sensors and sensor networks by reducing the management and maintenance costs.
Figure 5. Sensor Cloud Features III. BIG DATA SENSING SERVICES This section classifies and discusses different types of important big data sensing services based on existing research publications and the state-of-art practice. One of popular big data sensing services is to provide users with a virtualized sensing infrastructure as a service based on their sensing requests and requirements by leveraging a sensor cloud infrastructure. A Sensor Cloud is defined by [15] as “a unique sensor data storage, visualization and remote management platform that leverages powerful cloud computing technologies to provide excellent data scalability, rapid visualization, and user programmable analysis can be knows as a sensor cloudā.
Recently, there is a few of papers addressing sensor clouds and related issues. Authors in [12] proposed a methodology for integrating wireless sensor networks by leveraging cloud computing technology to form a sensor cloud. Figure 6 shows the high-level sensor cloud infrastructure and the associated workflow. It consists of several major functional components, including automatic provision, sensor control, monitor, web portal, as well as sensor and sensor network visualization.
As shown in Figure 5, the essential features of a sensor cloud include the followings.
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According to [13], this sensor cloud provides four functional services below.
Various sensor network owners can be connected to the sensor cloud infrastructure by through the registration service. They are allowed to update and maintain the underlying physical sensors in their sensor networks. Users are allowed prevision their virtualized sensor infrastructure. To cope with diverse sensor networks and support their connectivity, the sensor cloud in [16] allows users to create Service Templates (ST) or Virtual Groups by subscribing data for particular physical sensors, so users can easily add, remove or share diverse configurations of ST and sensors.
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Figure 6. A Sensor-Cloud Infrastructure [12] Another published sensor cloud infrastructure is given [12], as shown in Figure 8. The authors in this paper propose a sensor-cloud architecture service system for collecting, processing and storing sensor data originating from the physical sensor networks. Using the sensor cloud, users are allowed to collect sensor data from underlying wireless physical sensor networks, and store them in a data store through standard HTTP communications.
Acquire – This service directly upload aggregated wireless sensor data to a cloud’s data storages using costumed REST APIs. All access data transactions are encrypted with security solutions. Visualize - Visualize the previously uploaded data on a sensor cloud. Since there are different sorts of scattered physical sensors deployed, they propose a virtual sensor group in place for the clients to have the capacity to utilize sensors without agonizing over the locations and the details of physical sensor network. Monitor – This allows users to customized alerts to keep them informed about the data 24/7. E-mail and texting service can be enabled to push alerts to users. In addition, time-stamp and/or threshold trigging services can be linked to the stored sensor data. The authors in [11] gave an example to describe how to realize it. Analyze – This service helps users to analyze the preprocessed data using any open data analytics application service solutions. Users can define and customize interfaces to perform common operations such as smoothing and filtering of the data.
Since there are different kind of sensors scattered over the spatial area, the authors in [12] proposes the concept of virtual sensors and sensor groups so that users can prevision and use the sensors without worrying about their locations and the specifications. Using the sensor virtualization service, multiple users can freely share, control and use physical sensors via virtual sensors. Figure 9 shows an example. In a virtual sensor cloud, different users may have different ways to control a sensor through virtual sensors [9]. Similar to the previous work, this sensor cloud also provides some common sensing services, including publishing & brokerage, metering and monitoring, management and registration.
Figure 7. Sensor-Cloud – An Interaction View[12]
Figure 9. Virtual Sensor Cloud and Sensors/Sensor Groups The connectivity in sensor clouds is a very challenge issue due to the fact that many sensors and sensor networks are supported with different network protocols and technologies. One interesting solution is to use a gateway as an open interface to diverse connectivity. It includes two parts.
Figure 8. Sensor Cloud Architecture [http://www.comp.nus.edu.sg/~urbandb/details.html]
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Figure 10 OPENAPI Gateway Interfaces [14] -
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Common Interface, which plays as a common interface for the gateway that connects to different sensor networks. The gateway receives the raw data from information from the correspondence ports. Data Processor, which retrieves data packets from the gateway and processes it accordingly.
Table 1. Big Data Sensing Services Service Type
Service Descriptions
User subscription services
Public/private/enterprise user registration Multi-tenanted user service registration Sensor/sensor network owner registration Sensor/sensor network connection service
Figure 10 shows a good example, known as OPENAPI gateway interface [14]. It relies on relational database storage to access the sensor metadata and store the sensor data received from the sources. Its major advantage is providing a uniformed connectivity channel between a sensor cloud and diverse sensor networks. Achieving this requires two types of data formats describing sensor data below.
Resource services
Mobile sensor/network owner registration Mobile sensor/network connection service Provision/allocation/management service Virtual sensor/network registration
Virtualization services
Virtual sensor/network configuration/set-up Virtual sensor/network specification/grouping Sensing data collection services
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Sensor Archive Data Format (SADF): defines request and response data formats for accessing values stored in a WSN data archive. Sensor Information Data Format (SIDF): describes methods and data formats for subscribing and delivering real-time measurement data.
Sensing data formatting/processing service Data sensing services
Sensing data transmission services Sensing data migration/synchronization service Sensing data validation and QoS services
Monitor & metering Services
Monitor data sensing service transactions
B. SENSING DATA SERVICE AS A SERVICE (SDaaS) Business and billing services for data sensing
SDaaS refers to an online-based sensor data service system that supports and delivers various sensor data collection, storage, management, and transmission services based on an underlying sensor cloud infrastructure and environment. Its major objective is to provide tenanted based users and applications with a centralized seamless interface to deal with sensor-based big data without knowing the details of underlying sensor networks and sensor clouds. These data services could be delivered to respond dynamic on-demand requests from users in a pay-as-you-use billing model. Table 1 shows a classification of big data sensing services.
Monitor sensor/sensor network healthy statues Monitor virtualized sensors/sensor networks User service account and charging models Service billing based on agreements and cost models
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Collected big sensor data storage services Big sensor data storage and management
Big sensor data search and query access services Big data administration management (such as back-up and replication) Sensor/sensor network access security control
Data sensing control
security
Secured sensor data collection and transmission Tenanted data sensing security policy control
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Stakeholders and users
There are different types data sensing stakeholders and users. They include: sensor/sensor network owners, government agencies, business enterprises, pubic users, and application systems. Figure 11 shows the interaction between various stakeholders and represents the data flows between them. Interaction between smart device developers and industrial domain is crucial for feeding information to the corporate open data platform. Industry will be responsible for sensing and actuating infrastructure, but benefit from the collected sensing data. Industrial need will also push smart sensor vendors to create new and better sensors wiith effiicient and green solutions.
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Data sensing services
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Service Model
For big data sensing services, cloud computing and wireless sensor networks forms two major services. In [14], authors proposed a concept service model shown in Figure 11. Some entities are explained below.
Developing big data sensing services must consider the following sensing data attributes. •
Data transmission mode – There are two types of data transmission models: a) the synchronized-mode, in which sensor data are always connected and sensor data are collected dynamically in real-time for data transmissions; or b) the asynchronized mode, in which sensor data are collected in asynchronously. Data transmission protocols – Diverse data communication protocols are deployed and used for sensor data communications, including wireless internet, WiFi, Bluetooth, 3G/4G, and etc.
Figure 12. Service Life Cycle [17]
Figure 11 System Consumers & Data Flow in data Sensing -
Sensing data transmission: Similar to wireless sensor networking, big data sensing must support data transmission between sensors/sensor networks and backend servers and data stores.
Data sensing mode: There are two ways supporting data sensing operation modes: a) real-time dynamic and b) static batch operation modes. Data sensing frequency and size: These are important parameters for data sensing users as well as data processing and storage. Managing and supporting diverse data sensing frequencies and sizes for multi-tenant users require special handling in data collection, and data storage as well as sensor virtualization. Sensor data content and formatting: Sensor data could be collected and presented in various formats: a) structured, b) unstructured, and c) semi-formatted. This suggests that different kinds of big database technologies and processing solutions must be carefully selected and integrated. Diverse sensor data types: In a sensor cloud, different sensors and sensor networks usually are set-up and connected at its physical level. These sensors and sensor networks could use different data types. Segregating and supporting diverse sensor data and related types is very important and critical in big data sensing services.
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Service template, each of which represents a collection of the same type of sensor resources and devices; Service instance, which refers to wireless sensor network service. Each template stands for one type of service instances; Service provider, who creates and defines service templates; Service requester, who requests and uses different services through templates; Template repository, which stores and hosts diverse service templates.
This model focuses on the service availability for end users. Each service provider offers the service templates as a service catalog to service requesters, and allows them configure their services based their needs. As a result, a service instance will be generated for each request according to the selected service template. This service approach provides a good advantage to meet diverse service needs from users. Later, the authors in [17] present their extension work for additional services.
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Data storage services
data service applications. The diverse on-demand service requests from tenanted users will be responded and processed using a pay-as-you-use billing model.
Sensor-oriented data services is another essential services in big data sensing since it must connected to massive sensors and sensor networks in its physical level and support sensor data storage and access with big data technology and infrastructure. The challenge arises when diverse sensor networks are connected and deployed due to the following reasons: -
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WikiSensing 5 could be considered as an example. WikiSensng is a sensor data management platform developed by the Discovery Sciences Group at the Department of Computing of Imperial College London funded by the Digital City Exchange and Elastic Sensor Networks grants. WikiSensing runs on top of IC Cloud, the group's cloud computing infrastructure.
Different sensor networks may use different communication protocols, data formats, and data structures. Diverse users may have different requirements for data sensing services in data collection schedules, formats, communications, the target big sensing data storage should be able to store polymorphic sensor data stream on a sensor cloud infrastructure securely. In [7], the authors proposed a distributed storage solution for the polymorphism sensor data stream in a cloud. It has threelevel storage architecture: o
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Wikisensing is built using the Concinnity platform (in Figure 13) to address the collaborative big data sensing challenge using a crowd-sourcing approach, in which sensor networking vendors or service providers can register, connect, and manage big data sensing services in an easy workflowbased process. This platform concentrates on sensor data collection, fusion management, and analysis. In addition, Wikisensing aids crowdsourcing of data at the same time collating metadata to deal with its disparity and providing tools to assess its trustworthiness. Similarly, the workflow system is designed to address the multi-scale challenges whilst allowing integrated consumption of data to aid model based decision making.
The preliminary data feed layer is responsible for the storage and dynamic update of the sensor data streams (or the intermediate results). The secondary data process layer is responsible for the storage and dynamic update of the final processing results. The final data persist layer is responsible for the storage and additional update of the historical sensor data.
In their approach, a central storage scheduling module controls and synchronizes all the three layers and utilizes preprovided instructions and rules to keep the data consistency. -
Service Charging and Billing
In big data sensing, sensor data are the valuable resources to users. It implements diverse cost models for business service billing. A typical cost model must be computed based on the following parameters: -
Involved location based sensor cloud resources (no. of sensors/sensor clouds) Sensor data collection volumes, frequencies, scales and resolutions Data storage sizes, media types, and data management and administration services Data access meters, frequencies, and volumes
Figure 13. The Concinnity Platform As shown in Figure 13, the Concinnity platform consists of three parts: -
Clearly, well-defined cost metrics should be provided to support big data sensing services using a subscription model or a pay-as-you-go business model.
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C. DATA SENSING PLATFORM SERVICES Another type of data sensing services is known as data sensing platform-as-a-service (DSPaaS). It refers to a service platform that provides users with necessary development models and tools as well as hosting environment to allow them to define, develop, deployment, and hosting sensor-oriented
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A sensor data store, which provides collaborative data storage for schema-less sensor data with ontology support; A workflow engine, which provides hierarchical workflows for data sensing analysis with plug-in architectures; Application development environment, which supports data sensing collaboration, data/model integration, as well as publication services for data sensing applications. http://wikisensing.org/Documentation/documentation.html
sensor networks are not new issues [4, 8]. In big data sensing, security issues must be examined in two different areas. One of them is sensor cloud security, which needs hardware resource-based security management (such as IAM), access control, and secured communication protocols for underlying sensor networks, cloud networking, big data storages, and computing servers. The other is the privacy concern and security requirements in big data sensing. According to our recent interviews with IoT users in different domains (i.e. Environmental and Smart City IoTs), there are two special security and privacy challenges and needs in big data sensing. First, there are diverse sensor information privacy policies from government agency, enterprises, and location-based regions. This demands tenant-based security policies and solutions which must support easy configuration and customization for users. Next, sensorbased data may need to be stored and encrypted for security needs. This requires new encryption and decryption approaches to support large-scale secured data searches, accesses, re-encryptions and decryptions. -Multi-tenant on-demand big data sensing services – Like cloud computing, multi-tenancy is a distinct feature in sensor clouds and big data sensing services. Multi-tenant users will have diverse requests and needs in the following perspectives.
Table 2. Analytics Services for Big Sensing Data Big data analytics services
On-demand/scheduled big data analysis On-demand/scheduled big data mining On-demand/scheduled machine learning for decision making services Big sensor data visualization
Software defined big data analytics services
Software defined big data modeling & support Software defined big data analytics processes and workflows Software configured data analytics algorithms and solutions Software configured big data visualization
Big data security services
Big data encryption and decryption services Big data oriented security policy service Big data program-oriented access control Big data tenant-based user access control Big data secured store/search/query/access
Big data QoS services
Big data quality validation services Big data model check services Quality control and validation for big data analytics solutions QoS control and validation service for big data decision makings
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IV. CHALLENGES, ISSUES, AND NEEDS With the fast advance of IoT, big data sensing will drive the next big wave in providing diverse on-demand services based on large-scale big data collected from massively connected and managed sensors and sensor networks. o
Here are major challenges and issues based on our observations. -
Engagement and connectivity to diverse sensor networks – A sensor cloud requires the connectivity and engagement of different sensor network vendors and service carriers. This will be a very challenge task which requires good business models, incentive policies, and government support. As given in Wikisensin.org, a crowdsourcing approach could be one alternative to achieve this.
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Lack of standardization – Today, the standardization issue has been raised in the internet of thing community. It is clear that it is very difficult to expect diverse sensor and sensor network vendors to agree on the same standardization on sensor data formats and communication messages. However, it is possible to provide certain standardized data sensing gateways and unified interfaces to support the connectivity between sensor networks and sensor clouds.
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Tenanted based sensor resource virtualization – This implies that each tenant is allowed to virtualize desirable sensors/sensor networks on an underlying sensor cloud as its data collection resources in a static or dynamic approach. This demands new and smart sensor resource virtualization solutions to consider big data sensing features and requirements, such as regional sensor resources, sensor types, and security and privacy policies. Tenanted based data collection and storage requirements – Different enterprises may have their desirable data collection time-intervals and schedules, transmission protocols, data storage locations and formats, even though they are stored in heterogeneous storages and locations. In addition, a user may face with multi-resolution scenario in a sensor cloud because sensor data are collected based on a diverse scale. Hence, a sensor data may be collected on a high level will not match when it is combined with sensor data that is collected on a microscopic level. Hence, certain standardization solution must be provided for resolving these multi-resolution and multi-scale conflicts.
-QoS big data sensing and analytics services – There are many open questions regarding to quality of services in big data sensing and analytics services because there are lack of QoS standards, processes, methods, and tools for big data analytics and sensing. One of them is sensor data quality. Since all of sensor data are generated from disparate sensor sources, they may have faulty
Lack of cost-effective security and privacy solutions in big data sensing – Security attacks against wireless
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[10]M. Baktashmotlagh, A. Bigdeli, and B. C. Lovell, “Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs” , in Proceedings of the 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 455–460, 2011.
calibrations or data tampering. This requires certain data quality services for collected sensor data, including filtering out data communication errors. The other is the quality service for validating big data analytics results, including the quality assurance of big data sensing based decisions, predictions, and recommendations.
[11]K. Lee, D. Murray, D. Hughes, and W. Joosen, “Extending sensor networks into the Cloud using Amazon web services”, in Proceedings of the 1st IEEE International Conference on Networked Embedded Systems for Enterprise Applications, pp. 1–7, 2010.
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