https://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=1986&context=knoesis

Ontology paper: The SSN ontology of the W3C semantic sensor network incubator group

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

The Semantic Web 10

The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.

https://repositorio.fei.edu.br/bitstream/FEI/997/1/RI_997.pdf

Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture

Smart City represents one of the most promising, prominent and challenging Internet of Things (IoT) applications. In the last few years, indeed, the smart city concept has played an important role in academic and industry fields, with the development and deployment of various middleware platforms and IoT-based infrastructures. However, this expansion has followed distinct approaches creating, therefore, a fragmented scenario, in which different IoT ecosystems are not able to communicate between them. To fill this gap, there is a need to re-visit the smart city IoT semantic and offer a global common approach. To this purpose, this paper browses the semantic annotation of the sensors in the cloud, and innovative services can be implemented and considered by bridging Cloud and Internet of Things. Things-like semantic will be considered to perform the aggregation of heterogeneous resources by defining the Cloud of Things (CoT) paradigm. We survey the smart city vision, providing information on the main requirements and highlighting the benefits of integrating different IoT ecosystems within the cloud under this new CoT vision. This paper also discusses relevant challenges in this research area.

/pdf/towards-a-smart-city-based-on-cloud-of-things-a-survey-on-5et9vdg08i.pdf

Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms

The developed world is awash with sensors. However, they are typically locked into unimodal closed systems. To unleash their full potential, access to sensors should be opened such that their data and services can be integrated with data and services available in other information systems, facilitating novel applications and services that are based on the state of the real world. We describe our vision and architecture of a Semantic Web of Things: a service infrastructure that makes the deployment and use of semantic applications involving Internet-connected sensors almost as easy as building, searching, and reading a web page today.

SPITFIRE: toward a semantic web of things

We present the platform-independent Wiselib RDF Provider for embedded IoT devices such as sensor nodes. It enables the devices to act as semantic data providers. They can describe themselves, including their services, sensors, and capabilities, by means of RDF documents. Used in a protocol stack that provides Internet connectivity (6LowPAN) and a service layer (CoAP), a sensor can autoconfigure itself, connect to the Internet, and provide Linked Data without manual intervention. We introduce Streaming HDT, a lightweight serialization format for RDF documents that allows for transmitting compressed documents with minimal effort for the encoding; this is tailored for typical IoT applications where the embedded devices are often senders and seldom receivers of complete documents.

/pdf/rdf-provisioning-for-the-internet-of-things-12hohxf3pm.pdf

RDF provisioning for the Internet of Things

Current surveys and forecast predict that the number of wireless devices is going to increase tremendously. These wireless devices can be computers of all kinds, notebooks, netbooks, Smartphones and sensor nodes that evolve into real-world scenarios forming a “Real-World-Internet” in the future. In our work we focus on the Future Internet with small battery driven devices forming the “Internet of Things”. In recent networking research, testbeds gain more and more attention, especially in the context of Future Internet and wireless sensor networks (WSNs). This development stems from the fact that simulations and even emulations are not considered sufficient for the deployment of new technologies as they often lack realism. Experimental research on testbeds is a promising alternative that can help to close the gap. The deployment of testbeds is challenging and user and operator requirements need to be considered carefully. Therefore, the goal is to design an architecture that allows operators of WSN testbeds to offer numerous users access to their testbeds in a standardized flexible way that matches these requirements. In this paper we first identify some of the requirements, then introduce the architecture and general concepts of our WISEBED approach and show how this architecture meets the requirements of both groups. We give an overview of existing WISEBED-compatible WSN testbeds that can be used for experimentation today. Main focus in this paper compared to previous work is to address the perspective of both users and operators on how to experiment or respectively operate a WSN testbed based on WISEBED technology.

Using and operating wireless sensor network testbeds with WISEBED

We present a sensor network testbed that monitors a hallway. It consists of 120 load sensors and 29 passive infrared sensors (PIRs), connected to 30 wireless sensor nodes. There are also 29 LEDs and speakers installed, operating as actuators, and enabling a direct interaction between the testbed and passers-by. Beyond that, the network is heterogeneous, consisting of three different circuit boards---each with its specific responsibility. The design of the load sensors is of extremely low cost compared to industrial solutions and easily transferred to other settings. The network is used for in-network data processing algorithms, offering possibilities to develop, for instance, distributed target-tracking algorithms. Special features of our installation are highly correlated sensor data and the availability of miscellaneous sensor types.

Hallway Monitoring: Distributed Data Processing with Wireless Sensor Networks

We present a sensor network testbed that monitors a hallway. It consists of 120 load sensors and 29 passive infrared sensors (PIRs), connected to 30 wireless sensor nodes. There are also 29 LEDs and speakers installed, operating as actuators, and enabling a direct interaction between the testbed and passers-by. Beyond that, the network is heterogeneous, consisting of three different circuit boards--each with its specific responsibility. The design of the load sensors is of extremely low cost compared to industrial solutions and easily transferred to other settings. The network is used for in-network data processing algorithms, offering possibilities to develop, for instance, distributed target-tracking algorithms. Special features of our installation are highly correlated sensor data and the availability of miscellaneous sensor types.

/pdf/hallway-monitoring-distributed-data-processing-with-wireless-2al6b8d3g0.pdf

Max Pagel

Papers

SPITFIRE: toward a semantic web of things

RDF provisioning for the Internet of Things

Using and operating wireless sensor network testbeds with WISEBED

Hallway Monitoring: Distributed Data Processing with Wireless Sensor Networks

Hallway monitoring: distributed data processing with wireless sensor networks