Muhammad Intizar Ali

CityPulse: Large Scale Data Analytics Framework for Smart Cities

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on peoples everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications

With the recent advancement of the Internet of Things (IoT), it is now possible to process a large number of sensor data streams using different large-scale IoT platforms. These IoT frameworks are used to collect, process and analyse data streams in real-time and facilitate provision of smart solutions designed to provide decision support. Existing IoT-based solutions are mainly domain-dependent, providing stream processing and analytics focusing on specific areas (smart cities, healthcare etc.). In the context of agri-food industry, a variety of external parameters belonging to different domains (e.g. weather conditions, regulations etc.) have a major influence over the food supply chain, while flexible and adaptive IoT frameworks, essential to truly realize the concept of smart farming, are currently inexistent. In this paper, we propose Agri-IoT, a semantic framework for IoT-based smart farming applications, which supports reasoning over various heterogeneous sensor data streams in real-time. Agri-IoT can integrate multiple cross-domain data streams, providing a complete semantic processing pipeline, offering a common framework for smart farming applications. Agri-IoT supports large-scale data analytics and event detection, ensuring seamless interoperability among sensors, services, processes, operations, farmers and other relevant actors, including online information sources and linked open datasets and streams available on the Web.

Real-time data analytics and event detection for IoT-enabled communication systems

Abstract Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference. Our solution exploits semantics to go beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data. It provides flexible and efficient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge, bridging the gap between IoT and online Enterprise Communication Systems. We document the technologies used for acquisition and semantic enrichment of sensor data, continuous semantic query processing for integration and filtering, as well as stream reasoning for decision support. Our main contributions are the following, (i) we define and deploy a semantic processing pipeline for IoT-enabled Communication Systems, which builds upon existing systems for semantic data acquisition, continuous query processing and stream reasoning, detailing the implementation of each component of our framework; (ii) we present a rich semantic information model for representing and linking IoT data, social data and personal data in the Enterprise Communication scenario, by reusing and extending existing standard semantic models; (iii) we define and develop an expressive stream reasoning component as part of our framework, based on continuous query processing and non-monotonic reasoning for semantic streams, (iv) we conduct experiments to comparatively evaluate the performance of our data acquisition and semantic annotation layer based on OpenIoT, and the performance of our expressive reasoning layer in the scenario of Enterprise Communication.

Sensor-based Linked Open Rules (S-LOR): An Automated Rule Discovery Approach for IoT Applications and its use in Smart Cities

This paper introduces an automated rule discovery approach for IoT device data (S-LOR: Sensor-based Linked Open Rules) and its use in smart cities. S-LOR is built following Linked Open Data (LOD) standards and provides support for semantics-based mechanisms to share, reuse and execute logical rules for interpreting data produced by IoT systems. S-LOR follows LOD principles for data re-usability, semantics-based reasoning and interoperability. In this paper, S-LOR main capability is demonstrated in the context of enabling semantics-based reasoning mechanisms and tools according to application-demand and user requirements. S-LOR (i) supports an automated interpretation of IoT data by executing rules, and (ii) allows an automated rule discovery interface. The implemented S-LOR mechanism can automatically process and interpret data from IoT devices by using rule-based discovery paradigm. Its extension called Linked Open Reasoning (LOR) enables and encourages re-usability of reasoning mechanisms and tools for different IoT smart city applications. The use cases described in this paper fits in the domain of smart city applications within Internet of Things deployed systems.

On Using the Intelligent Edge for IoT Analytics

This article presents a flexible architecture for Internet of Things (IoT) data analytics using the concept of fog computing. The authors identify different actors and their roles in order to design adaptive IoT data analytics solutions. The presented approach can be used to effectively design robust IoT applications that require a tradeoff between cloud- and edge-based computing depending on dynamic application requirements. The potential use cases of this technology can be found in scenarios such as smart cities, security surveillance, and smart manufacturing, where the quality of user experience is important.

SWoTSuite: A Toolkit for Prototyping End-to-End Semantic Web of Things Applications

In this demonstration, we present our Semantic Web of Things~(SWoT) prototyping toolkit called SWoTSuite. It is a set of tools supporting an easy and fast prototyping of end-to-end SWoT applications. SWoTSuite facilitates - (i) automation of application development life-cycle, (ii) reducing the amount of time and effort required for developing WoT applications and (iii) an easy integration of semantic web technologies within WoT applications.

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards smart cities solutions that can leverage this rich source of streaming data to gather knowledge that can be used to solve domain-specific problems. A key challenge that needs to be faced in this respect is the ability to automatically discover and integrate heterogeneous sensor data streams on the fly for applications to use them. To provide a domain-independent platform and take full benefits from semantic technologies, in this paper we present an Automated Complex Event Implementation System (ACEIS), which serves as a middleware between sensor data streams and smart city applications. ACEIS not only automatically discovers and composes IoT streams in urban infrastructures for users’ requirements expressed as complex event requests, but also automatically generates stream queries in order to detect the requested complex events, bridging the gap between high-level application users and low-level information sources. We also demonstrate the use of ACEIS in a smart travel planner scenario using real-world sensor devices and datasets.

QoS-aware adaptation for complex event service

Smart City applications often use event processing techniques to detect coarse-grained events and situations from fine-grained events of the physical and social world. They operate in dynamic environments in which the properties of underlying resources and streams need to be constantly updated according to changes and events in the real world (e.g. sensor readings, network availability, weather conditions, and temperature). In most of the existing solutions matchmaking between the requirements expressed by event consumers and available event providers is carried out at design-time. This approach is often far from optimal and its deficiencies become even more obvious in smart city scenarios due to their inherently dynamic stream properties. In this paper we discuss a solution for quality-aware adaptive complex event processing using a service-oriented approach. We detail the automatic adaption strategies and evaluate them in a smart city scenario with both real and synthesised datasets.

Do “Web of Things platforms” truly follow the Web of Things?

The Web of Things (WoT) aspires to bring interoperability at the application layer, on top of the Internet of Things. Many state of the art platforms and frameworks claim to support the WoT, following its principles towards the seamless integration of heterogeneous physical devices and real-world services at the web. But do these platforms truly comply to the concepts of the WoT or only follow some of its characteristics? Do designers understand the WoT when claiming that their products follow the WoT specifications? This paper lists the main elements of the WoT, as defined by pioneering works in the field and examines 26 popular platforms and frameworks, aiming to shed light on how the WoT is understood and applied, both in academia and commerce.

Semantic Web Meets Internet of Things and Web of Things: [2nd Edition]

An ever growing interest and wide adoption of Internet of Things (IoT) and Web technologies are unleashing a true potential of designing a broad range of high-quality consumer applications. Smart cities, smart buildings, and e-health are among various application domains which are currently benefiting and will continue to benefit from IoT and Web technologies in a foreseeable future. Similarly, semantic technologies have proven their effectiveness in various domains and a few among multiple challenges which semantic Web technologies are addressing are to (i) mitigate heterogeneity by providing semantic inter-operability, (ii) facilitate easy integration of data application, (iii) deduce and extract new knowledge to build applications providing smart solutions, and (iv) facilitate inter-operability among various data processes including representation, management and storage of data. In this tutorial, our focus will be on the combination of Web technologies, Semantic Web, and IoT technologies and we will present to our audience that how a merger of these technologies is leading towards an evolution from IoT to Web of Things (WoT) to Semantic Web of Things. This tutorial will introduce the basics of Internet of Things, Web of Things and Semantic Web and will demonstrate tools and techniques designed to enable the rapid development of semantics-based Web of Things applications. One key aspect of this tutorial is to familiarize its audience with the open source tools designed by different semantic Web, IoT and WoT based projects and provide the audience a rich hands-on experience to use these tools and build smart applications with minimal efforts. Thus, reducing the learning curve to its maximum. We will showcase real-world use case scenarios which are designed using semantically-enabled WoT frameworks (e.g. CityPulse, FIESTA-IoT and M3).