Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)

Big data has been emerging as a new approach in utilizing large datasets to optimize complex system operations. Big data is fueled with Internet-of-Things (IoT) services that generate immense sensory data from numerous sensors and devices. While most current research focus of big data is on machine learning and resource management design, the economic modeling and analysis have been largely overlooked. This paper thus investigates the big data market model and optimal pricing scheme. We first study the utility of data from the data science perspective, i.e., using the machine learning methods. We then introduce the market model and develop an optimal pricing scheme afterward. The case study shows clearly the suitability of the proposed data utility functions. The numerical examples demonstrate that big data and IoT service provider can achieve the maximum profit through the proposed market model

Converging Future Internet, “Things”, and Big Data: An Specification Following NovaGenesis Model

The convergence of Internet of “things” (IoT) with big data platforms and cloud computing is already happening. However, the vast majority, if not all the proposals are based on the current Internet technologies. The convergence of IoT, big data and cloud in “clean slate” architectures is an unexplored topic. In this article, we discuss this convergence considering the viewpoint of a “clean slate” proposal called NovaGenesis. We specify a set of NovaGenesis services to publish sensor device’s data in distributed hash tables employing selfverifying addresses and contract-based trust network formation. IoT devices capabilities and configurations are exposed to software-controllers, which control their operational parameters. The specification covers how the “things” sensed information are subscribed by a big data service and injected in Spark big data platform, allowing NovaGenesis services to subscribe data analytics from Spark. Future work include implementation of the proposed specifications and further investigation of NovaGenesis services performance and scalability

IoT mashups : From IoT big data to IoT big service

Internet of Things (IoT) addresses the challenge to provide a transparent access to a huge number of IoT resources that can be either physical devices or just data resources. Moreover, because of the large number of resource-constrained devices and the dynamic nature of IoT environments, integrating the resulted data becomes a non trivial task. We believe that the use of mashups, a way to compose new services from existing ones, can be a solution to the above challenge if each resource exposes its functionalities as a Web service. In the IoT environment, this will constitute a Web of Things where mashups development will take advantage of the connected physical world. The huge amounts of IoT-generated data from physical devices and data sources, called IoT Big Data, requires new design solutions to speed up data processing, scale up with the data volume and improve data adaptability. Besides existing techniques for IoT data collection, filtering, and analytics, we present in this article a mashup oriented model, called IoT Big Services, for provisioning data-centric IoT services in the context of IOT mashups. These IoT services are organized in tree structure where each node, called an IoT Big Service, acts as an integrator that collects data from lower level, processes them and delivers the results to higher level in the architecture

New Facets of Semantic Interoperability: Adding JSON - JSON-LD Transformation Functionality to the BIG IoT API

The BIG IoT project focuses on easy integration of the IoT data and services offered by existing IoT platforms and services based on semantic technologies. To enable IoT applications to consume data and services provided by heterogeneous systems and different stakeholders, a common set of ontologies and an RDF triple store with querying functionalities are used in the service discovery phase. The subsequent communication between such applications or services (as consumer) and the provider can be optimized as well. The process of automatically transforming JSON-serialized responses from an IoT data provider to linked data format is discussed and the benefits of this automation are explored in this poster contribution

CIRUS: an elastic cloud-based framework for Ubilytics

International audienceThe Internet of Things (IoT) has become a reality with the availability of chatty embedded devices. The huge amount of data generated by things must be analysed with models and technologies of the " Big Data An-alytics " , deployed on Cloud platforms. The CIRUS project aims to deliver a generic and elastic cloud-based framework for Ubilytics (ubiquitous big data analytics). The CIRUS framework collects and analyses IoT data for Machine to Machine services using Component-off-the-Shelves (COTS) such as IoT gateways, Message brokers or Message-as-a-Service providers and Big Data analytics platforms deployed and reconfigured dynamically with Roboconf. In this paper, we demonstrate and evaluate the genericity and elasticity of CIRUS with the deployment of an Ubilytics use case using a real dataset based on records originating from a practical source

The role of big data in smart city

The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the existing communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model that can manage big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data

Coop-DAAB : cooperative attribute based data aggregation for Internet of Things applications

The deployment of IoT devices is gaining an expanding interest in our daily life. Indeed, IoT networks consist in interconnecting several smart and resource constrained devices to enable advanced services. Security management in IoT is a big challenge as personal data are shared by a huge number of distributed services and devices. In this paper, we propose a Cooperative Data Aggregation solution based on a novel use of Attribute Based signcryption scheme (Coop - DAAB). Coop - DAAB consists in distributing data signcryption operation between different participating entities (i.e., IoT devices). Indeed, each IoT device encrypts and signs in only one step the collected data with respect to a selected sub-predicate of a general access predicate before forwarding to an aggregating entity. This latter is able to aggregate and decrypt collected data if a sufficient number of IoT devices cooperates without learning any personal information about each participating device. Thanks to the use of an attribute based signcryption scheme, authenticity of data collected by IoT devices is proved while protecting them from any unauthorized access

Data Analysis, Analytics in Internet of Things and BigData

The Internet-of-Things (IoT) is gradually being established as the new computing paradigm, which is bound to change the ways of our everyday working and living. IoT emphasizes the interconnection of virtually all types of physical objects (e.g., cell phones, wearables, smart meters, sensors, coffee machines and more) towards enabling them to exchange data and services among themselves, while also interacting with humans as well. Few years following the introduction of the IoT concept, significant hype was generated as a result of the proliferating number of IoT-enabled devices, which (according to many projections) are expected to amount to several billion in the next years. During recent years, this hype has been turning to reality, as a wave of IoT applications with significant social and economic has been emerging. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorization of analytic approaches and propose a layered taxonomy from IoT data to analytics. IoT data analysis is an integral element of any non- trivial IoT system. Nevertheless, IoT analytics are still in their infancy, as IoT data still remain largely unexploited