The Internet of Things (IoT) envisions that objects may be connected to the Internet, producing and consuming data in real-time. Today, numerous middleware platforms are available to facilitate the communication with these objects. Unfortunately, the interoperability of these platforms is very limited because it requires to "manually" connect the services proposed by each platform. One key design goal for our contribution is not to build yet another middleware, but rather to augment the functionalities of existing systems via an extension to support their integration into a network of heterogeneous IoT hubs. The extension includes a RESTful API to manipulate the basic component of our extension, the IoT feeds. The IoT feeds allow the platform's owner to dynamically marshal the IoT features connected to the platform, as well as the data that they produce. Furthermore, the feeds enable the owner to manage and control the data flows before connecting them to his applications. Subsequently, these feeds may also be published to meta-hubs in order to expose them to third parties. We evaluated an implementation our extension for Android systems to show the feasibility of managing the data flows using the RESTful API on this platform.
The EveryAware platform @cite_8 provides an extendable data concept that could be use to enhance the possibilities of sharing and fusing data feeds. The platform is a centralized solution and was the only one focusing on a fine-granularity of data visibility. In our opinion, fine-grained data visibility is essential but is not yet well covered by current IoT solutions. In addition, it provides flexible and extendable data models.
@cite_8
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In the context of the Internet of Things, an increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common device-centric approach limit the flexibility of these platforms: there are no means to incorporate people and their subjective impressions about the collected data. In order to build the Internet of Things and People and ultimately the Internet of Everything, we aim at providing an extendable concept of data which allows to enrich existing data points with any kind of additional information. This enables us to gain semantic and user specific context by attaching subjective data to objective values. For this end we support data ranging from text-based formats like JSON to images and video footage. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform. The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This Internet-of-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster. Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions. Pervasive computing and Internet of Things (IoTs) paradigms have created a huge potential for new business. To fully realize this potential, there is a need for a common way to abstract the heterogeneity of devices so that their functionality can be represented as a virtual computing platform. To this end, we present novel semantic level interoperability architecture for pervasive computing and IoTs. There are two main principles in the proposed architecture. First, information and capabilities of devices are represented with semantic web knowledge representation technologies and interaction with devices and the physical world is achieved by accessing and modifying their virtual representations. Second, global IoT is divided into numerous local smart spaces managed by a semantic information broker (SIB) that provides a means to monitor and update the virtual representation of the physical world. An integral part of the architecture is a resolution infrastructure that provides a means to resolve the network address of a SIB either using a physical object identifier as a pointer to information or by searching SIBs matching a specification represented with SPARQL. We present several reference implementations and applications that we have developed to evaluate the architecture in practice. The evaluation also includes performance studies that, together with the applications, demonstrate the suitability of the architecture to real-life IoT scenarios. In addition, to validate that the proposed architecture conforms to the common IoT-A architecture reference model (ARM), we map the central components of the architecture to the IoT-ARM.