Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average exchanged message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.

The term Internet of Things (IoT) refers to the interaction and communication between billions of devices that produce and exchange data related to real world objects (i.e. Things). Extracting higher-level information from the raw sensory data captured by the devices and representing this data as machine-interpretable or human-understandable information has several interesting applications. Deriving raw data into higher-level information representations demands mechanisms to find, extract and characterise meaningful abstractions from the raw data. This meaningful abstractions then have to be presented in a human and/or machineunderstandable representation. However, the heterogeneity of the data originated from different sensor devices and application scenarios such as e-health, environmental monitoring and smart home applications and the dynamic nature of sensor data make it difficult to apply only one particular information processing technique to the underlying data. A considerable amount of methods from machine-learning, the semantic web, as well as pattern and data mining have been used to abstract from sensor observations to information representations. This paper provides a survey of the requirements and solutions and describes challenges in the area of information abstraction and presents an efficient workflow to extract meaningful information from raw sensor data based on the current state-of-the-art in this area. The paper also identifies research directions at the edge of information abstraction for sensor data. To ease the understanding of the abstraction workflow process, we introduce a software toolkit that implements the introduced techniques and motivates to apply them on various data sets.

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The wide field of wireless sensor networks requires that hundreds or even thousands of sensor nodes have to be maintained and configured. With the upcoming initatives such as Smart Home and Internet of Things, we need new mechanism to discover and manage this amount of sensors. In this paper, we describe a middleware architecture that uses context information of sensors to supply a plug-and-play gateway and resource management framework for heterogeneous sensor networks. Our main goals are to minimise the effort for network engineers to configure and maintain the network and supply a unified interface to access the underlying heterogeneous network. Based on the context information such as battery status, routing information, location and radio signal strength the gateway will configure and maintain the sensor network. The sensors are associated to nearby base stations using an approach that is adapted from the 802.11 WLAN association and negotiation mechanism to provide registration and connectivity services for the underlying sensor devices. This abstracted connection layer can be used to integrate the underlying sensor networks into high-level services and applications such as IP-based networks and Web services.