Frieder Ganz

A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

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 characterize meaningful abstractions from the raw data. This meaningful abstractions then have to be presented in a human and/or machine-understandable 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. This 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.

A Knowledge-Based Approach for Real-Time IoT Data Stream Annotation and Processing

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.

Context-aware management for sensor networks

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.

Information Abstraction for Heterogeneous Real World Internet Data

Everyday around 2.5 quintillion bytes of data are created. There is also a growing trend toward integrating real world data into the Internet, which is provided by sensory devices, smart phones, GPS, and many other sources that capture and communicate real world data. The term Internet of Things (IoT) refers to billions of devices that produce and exchange data related to real world objects (i.e., Things). This paper focuses on how to optimize the data exchange between the sensory devices and applications in IoT and Cyber-Physical systems. In particular, a method to construct higher-level abstractions of data at local gateways is proposed. This will reduce the traffic load imposed on the communication networks that provide the real world data. The proposed method is based on an information processing algorithm where gateways analyze the data collected from the sensors and create higher level abstractions. We enhance the symbolic aggregate approximation (SAX) algorithm that is used as a building block of the abstraction creation framework, into an optimized version for sensor data, called sensor SAX. We extend the parsimonious covering theory that is usually used for medical purposes with a probabilistic parsimonious criterion in the temporal domain to infer abstractions based on time-dependent sensor data. The proposed method is analyzed and evaluated over a real world data set and the results are discussed in terms of the data size reduction, accuracy, and latency needed to create the abstractions.

Computing perception from sensor data

This paper describes a framework for perception creation from sensor data. We propose using data abstraction techniques, in particular Symbolic Aggregate Approximation (SAX), to analyse and create patterns from sensor data. The created patterns are then linked to semantic descriptions that define thematic, spatial and temporal features, providing highly granular abstract representation of the raw sensor data. This helps to reduce the size of the data that needs to be communicated from the sensor nodes to the gateways or highlevel processing components. We then discuss a method that uses abstract patterns created by SAX method and occurrences of different observations in a knowledge-based model to create perceptions from sensor data.

Automated Semantic Knowledge Acquisition From Sensor Data

The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended k- means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.

A Resource Mobility Scheme for Service-Continuity in the Internet of Things

In the Internet of Things (IoT) a large number of devices enable data communication and interaction between physical objects and the cyber world. An important feature of IoT is the possibility of having mobile objects equipped with sensing devices. In service-enabled IoT platforms, where data and interacting are provisioned as services, access and utilisation of these services are affected by the mobility of the resources that provide the data and services. In a reliable and dependable environment, service continuity is supported in scenarios where the IoT resources are mobile or can become unavailable due to handover delays, network disconnection or power outage. In this paper, we propose a resource mobility scheme with two operating modes - caching and tunnelling. We use these methods to enable applications to access the sensory data when the resources become temporarily unavailable. We have implemented a prototype for the proposed scheme in a mobile scenario. The evaluation results show a reduction of service loss in mobility scenarios by 30%.

Semantic sensor service networks

Sensors and sensor networks are fundamental instruments to acquire and communicate contextual information from the physical world. This information enables better understanding of the physical world for humans and supports creation of ambient intelligence for a wide range of applications in different domains such as smart cities, healthcare and intelligent transportation. To facilitate scalable and seamless access and management of the information obtained from large and heterogeneous sensor networks, we introduce the concept of Semantic Sensor Service Networks which brings the research on Semantic Sensor Network one-step further. The concept combines semantic modelling, knowledge management and service-oriented design to support sensor service access, discovery and composition. It defines a framework in which sensor services can collaborate and co-operate to support data integration from different sensor networks and service computing for complex business applications.

Multi-resolution data communication in wireless sensor networks

There is an increasing trend in using data collected by sensor devices to enable better understanding of the physical world for humans and support the creation of pervasive environments for a wide range of applications in different domains such as smart cities, and intelligent transportation. However, the deluge of data created and communicated and the low-processing capabilities of the used sensor devices lead to bottlenecks in the processing and interpreting of the data. We introduce a data reduction approach that submits high-granular data in times of high activity in the sensor readings and low-granular data in times of low activity. We consider and discuss different methods to measure activity in the data and modify the symbolic aggregate approximation algorithm that uses a fixed window length to adapt the length according to the data activity for ultimately less data communication between sensor node and sink/gateway. We evaluate our approach over real-world data sets and show that reduction of data size while maintaining the features of the data can be achieved.

A mediated gossiping mechanism for large-scale sensor networks

Gateways in sensor networks are used to relay, aggregate and communicate information from capillary networks to more capable (e.g. IP-based) networks. However Gateway-to-Gateway (G2G) communication to exchange and update information among the gateways in large-scale sensor networks for query processing, data fusion and other similar tasks has been less discussed in recent works. The requirements for large-scale sensor networks such as dynamic topology and update strategies to reduce the overall network load makes G2G communications an important aspect in the network design. In this paper, we introduce a mediated gossip-based G2G communication mechanism. The proposed solution leverages the publish/subscribe approach and uses high-level context assigned to publish/subscribe channels to enable the information discovery and G2G communications. Gateways store/aggregate sensor observation and measurement data according to specific context which is defined based on features such as spatial and temporal attributes, observed phenomena (i.e. feature of interest) and sensor device features. The gateways communicate with each other to exchange data and also to forward related queries for data aggregation in cases that the data should be aggregated from two different sources. The proposed solution also facilitates reliable sensor service provisioning by enabling gateways to communicate and/or forward requests to other gateways when a resource fails or a sensor node becomes unavailable. We compare our results to probabilistic gossiping algorithms and run benchmarks on different dynamic network topologies based on indicators such as number of sent messages and dissemination delay.