Francois Carrez

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.

Designing IoT architecture(s): A European perspective

Internet of Things (IoT) domain has attracted a lot of interest over the last few years, to a large extent due to its applicability across a plethora of application domains. This variety of application domains resulted in a variety of requirements that IoT systems should comply with. Due to the heterogeneity of the domains, the requirements varied significantly, and demanding more or less complex systems with varied performance expectations. This situation affected the architecture design and resulted in a range of IoT architectures with not only varied set of components and functionalities, but also varied terminologies used. It resulted in limited interoperability between the systems which in turn hampered development of the complete domain. To address these issues, to ensure a common understanding by providing a framework catering for different applications and eventually enable reuse of the existing work across the domains, reference architectures are an appropriate tool. This paper presents an overview of the activities done in Europe towards definition of such a common framework together with how it is being used and a potential outlook for these efforts.

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.

A Domain Model for the Internet of Things

By bringing together the physical world of real objects with the virtual world of IT systems, the Internet of Things has the potential to significantly change both the enterprise world as well as society. However, the term is very much hyped and understood differently by different communities, especially because IoT is not a technology as such but represents the convergence of heterogeneous - often new - technologies pertaining to different engineering domains. What is needed in order to come to a common understanding is a domain model for the Internet of Things, defining the main concepts and their relationships, and serving as a common lexicon and taxonomy and thus as a basis for further scientific discourse and development of the Internet of Things. As we show, having such a domain model is also helpful in design of concrete IoT system architectures, as it provides a template and thus structures the analysis of use cases.

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.

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.

Contextual occupancy detection for smart office by pattern recognition of electricity consumption data

The advent of IoT has resulted in a trend towards more innovative and automated applications. In this regard, occupancy detection plays an important role in many smart building applications such as controlling heating, ventilation and air conditioning (HVAC) systems, monitoring systems and managing lighting systems. Most of the current techniques for detecting occupancy require multiple sensors fusion for attaining acceptable performance. These techniques come with an increased cost and incur extra expenses of installation and maintenance as well. All of these methods are intended to deal with only two states; when a user is present or absent and control the system accordingly. In this paper, we have proposed a non-intrusive approach to detect an occupancy state in a smart office using electricity consumption data and introduced a novel concept of third state as standby for dealing with situations when the user lefts his seat for small breaks. We demonstrated our approach using electricity data collected within our research centre and detected occupancy state with efficiency up to 94%. Furthermore, our solution does not require extra equipment or sensors to deploy for occupancy detection as smart energy meters are already being deployed in most of the smart buildings.

Distributing resource intensive mobile web services

One of the goals that can be achieved by providing adaptive web services from mobile hosts is to allow continuous service provisioning. However, there are limitations in terms of complexity and size of the services that may be executed on mobile hosts. In this paper, two steps are taken towards providing adaptive web services from resource limited mobile devices. The first step is to investigate mechanisms that facilitate distributing the execution of mobile web services; the main mechanisms are offloading and migration. The second step is to integrate these mechanisms with available web service architectures to produce an extended mobile web service framework. In this case we integrated them with both SOAP as well as REST. The paper describes the offloading and migration mechanisms as well as the implementation of a prototype that allows performance evaluation of both extended frameworks. To investigate the load and performance of the distributed services, the prototype implements resource intensive applications. The results presented show that basing distributed mobile-hosted services on REST is more suitable than using SOAP as underlying web service infrastructure.

Context-aware stream processing for distributed IoT applications

Most of the IoT applications are distributed in nature generating large data streams which have to be analyzed in near real-time. Solutions based on Complex Event Processing (CEP) have the potential to extract high-level knowledge from these data streams but the use of CEP for distributed IoT applications is still in early phase and involves many drawbacks. The manual setting of rules for CEP is one of the major drawback. These rules are based on threshold values and currently there are no automatic methods to find the optimized threshold values. In real-time dynamic IoT environments, the context of the application is always changing and the performance of current CEP solutions are not reliable for such scenarios. In this regard, we propose an automatic and context aware method based on clustering for finding optimized threshold values for CEP rules. We have developed a lightweight CEP called μCEP to run on low processing hardware which can update the rules on the run. We have demonstrated our approach using a real-world use case of Intelligent Transportation System (ITS) to detect congestion in near real-time.

Test-Enabled Architecture for IoT Service Creation and Provisioning

The information generated from the Internet of Things (IoT) potentially enables a better understanding of the physical world for humans and supports creation of ambient intelligence for a wide range of applications in different domains. A semantics-enabled service layer is a promising approach to facilitate seamless access and management of the information from the large, distributed and heterogeneous sources. This paper presents the efforts of the IoT.est project towards developing a framework for service creation and testing in an IoT environment. The architecture design extends the existing IoT reference architecture and enables a test-driven, semantics-based management of the entire service lifecycle. The validation of the architecture is shown though a dynamic test case generation and execution scenario.