The wide availability of services and devices within contemporary smart home environments make their management a challenging and rewarding task. Maintaining complex smart home systems throughout their lifecycle entails considerable resources and effort. These challenges have stimulated the need for dynamic auto-configurable services amongst such distributed systems. Although significant research has been directed towards achieving auto-configuration, none of the existing solutions is sufficient to achieve auto-configuration within smart home environments. This research aims to devise methods to automate the activities associated with customised service delivery for dynamic home environments by exploiting recent advances in the field of ubiquitous robotics and Semantic Web technologies. It introduces a novel approach called the Knowledge-based Auto-configuration Ubiquitous Robotics for Smart Home Environments, which utilises the Sobot to achieve auto-configuration of the system. A proof-of-concept testbed has been designed, implemented and validated via several case studies.

Ubiquitous robotics for knowledge-based auto-configuration system within smart home environment

In this paper we present an approach to add self-adaptive features to software systems not initially designed to be self-adaptive. Rapid changes in users needs, available resources, and types of system faults are everyday concerns in operating complex systems. The ability to face these issues in a (semi-)automatic fashion is a welcome feature. MAPE-K (Monitor, Analyze, Plan, Execute — Knowledge), or one of its variations, is the basic architectural pattern around which most adaptation engines are built. The knowledge (K) element in that pattern is usually a collection of dynamic and static models representing relevant aspects of the system and its environment. Knowledge-based features can be encoded using various techniques and serve a number of disparate roles: providing dynamic views of the system (Reflection Models), representing reconfiguration policies (Evaluation Models), mapping reconfigurations into system-level adaptations (Execution Models), and so forth. In our approach all these models are unified by using ontologies and Semantic Web technologies; the resulting knowledge base is then used to drive adaptation activities. We discuss how the various MAPE-K components can be designed in order to take advantage of this knowledge base by applying our approach to a real-word case study: a deployed system that was not designed to perform automatic adaptation. We then discuss merits and limits of our proposal both in the context of this specific case study and in a broader scope.

An application of semantic technologies to self adaptations

Today's software systems increasingly work in changing environments, where rapid modifications in user needs, resource variabilities and system faults require remarkable administrative efforts. In order to mitigate the costs for governing these activities, software systems are expected to dynamically self-adapt. The problem of supporting auto-adaptation, which is complex activity in itself, is further exacerbated when applied to legacy systems which have not been developed for this purpose. In this paper we introduce a novel approach to self-adaptation based on the MAPE-K paradigm, where semantic models are used to provide an unified view of the heterogeneous elements composing these systems, and reasoning mechanisms are leveraged to drive adaptation strategies. We present the implementation of an adaptation engine based these concepts that uses ontologies and Semantic Web technologies, and discuss its application in a real world case study. From this experience, we offer recommendations for future research in this area.

Semantic Run-Time Models for Self-Adaptive Systems: A Case Study

There is a large variety of Internet of things (IoT) devices and their peripherals available in consumer markets, and IoT deployers should work on customizing device drivers that are compatible with their peripherals. Implementing compatible device drivers, however, often requires a burden of work. This paper proposes a generic platform that enables plug-and-play (PnP) integration for sensors and actuators to allow the addition and removal of IoT device peripherals without re-customizing all the device drivers. To this end, we employ IoT ontologies and semantics to represent IoT device characteristics and to infer IoT device behaviors. IoT device behavior is then passed to the generic device driver to cover device-specific operation. Since the generic device driver selectively operates most of the available function calls required in IoT devices, most of the programming work that is normally required for device customization is removed, and management overhead for software installation and maintenance can be minimized. To this end, we employ IoT ontologies and semantics as well as generic programming techniques in the generic platform in order to configure and control IoT devices. In the proposed platform, IoT device characteristics, including I/O functions and configuration rules, are defined using custom-built IoT ontologies, and operational behaviors are inferred through SPARQL queries. The generic platform then passes function-call name and configuration rules corresponding to the newly added peripheral device’s specification. The experimental results show that our generic platform covers most of the popular sensors available in the market. Our solution therefore enables a true PnP experience of sensors and actuator peripherals in IoT devices.

/pdf/a-generic-internet-of-things-iot-platform-supporting-plug-fz90bplhw1.pdf

A generic Internet of things (IoT) platform supporting plug-and-play device management based on the semantic web

Abstract This research is focused on creating a web-based mobile application to address the Landslide issue in Sri Lanka. Sri Lanka is a tropical country which suffers from Landslides continuously due to primarily the monsoon rain season and geographical terrain factors. Rain-induced Landslides affect the lives of the people, their public and private properties, the infrastructure of the country as well as the national economy. The south-western and central provinces are the most highly affected areas in the country. Due to the dynamic weather changes and unpredictable causal factors of the Landslides, it is difficult to predict this hazard. Sri Lanka is a third world country and the government struggles with limited resources to help the victims who suffer from this catastrophe. The Web and mobile technology are two technological mechanisms which can be used for convenient data gathering and interactive information sharing. This research has developed a web-based mobile solution which can represent information which is related to Landslides in Sri Lanka in a convenient and interactive manner. Due to the fact that the outcome of this research is a technical prototype, Design Science Research (DSR) methodology is the desired methodology for this study. This study will structure its research process to reflect on the vital phases of DSR. A literature review process, a development process and an evaluation process will be carried out to determine the desired outcome. The data are fetched through web sources and the processed data will be visualized on the geospatial map interface of the application which uses the Google Map Application Platform Interface (API). The heavy rainfall is the primitive factor for the rain-induced Landslides in Sri Lanka. The weather history and weather forecasts are fetched through AccuWeather Web API. The locations of the previous Landslide events are fetched from the Global Landslide Index API by NASA. The National Building Research Organization is the governmental body which sketches the hazard prone locations which could trigger

Addressing Landslide Issue in Sri Lanka Using a Web-based Mobile Application

The large diversity and heterogeneity of collaborating objects and services in modern networking environments such as personal, vehicular or home networks makes difficult the efficient configuration, deployment and management of composite dynamic systems without a major participation of human actors. This paper proposes an autonomic architecture based on decision models built on ontologies and aimed at self-configuring and self-adapting information networks. Our work follows a top-down approach based on well-known and accepted standards. A detailed structure composite diagram is illustrated to describe AODA modules and show how they interact together. Our proposition had been implemented as an ecosystem-wide ontology aimed at characterising the properties of services and events related to consumers and producers entities participating in the information network. We propose a smart metering use case to compute the overload generated by our solution in handling a large number of objects.

AODA: an Autonomic and Ontology-Driven Architecture for service-oriented and event-driven systems

The large diversity and heterogeneity of collaborating objects and services in modern networked environments such as personal, vehicular or home networks makes difficult the efficient configuration, deployment and management of composite dynamic systems without a major participation of human actors. This paper proposes an autonomic architecture based on decision models built on ontologies and aimed at self-configuring and self-adapting service-oriented and event-driven distributed systems. Our solution follows a top-down approach based on well-known and accepted standards and implemented as an ecosystem-wide ontology aimed at characterizing the properties of services and events related to consumers and producers entities participating in the autonomic collaborative environment.

AODA: An Autonomic and Ontology-Driven Architecture for Service-Oriented and Event-Driven Systems

IoT paradigm applies to systems composed by numerous devices sharing information and making cooperative decisions with little or no human intervention. The IoT standard defined by oneM2M intiative furnishes a standardised framework for interoperable services that satisfies most of IoT modelling requirements. Prior standards and also oneM2M, while focusing on achieving interoperability at the communication level, do not achieve interoperability at the semantic level. In addition, the oneM2M standard does not address the issue of system adaptations to address system increasing complexity and evolving execution context. In this paper, the OM2M platform is presented as a solution for the interoperability challenge of IoT. An expressive ontology for IoT called IoT-O, making best use of already defined ontologies in specific domains such as sensor, observation, service, quantity kind, units, or time, is described to support semantic data. Finally, FRAMESELF framework is introduced to retrofit self-management capabilities into an IoT system.

Semantic-Aware IoT Platforms

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AODA: an Autonomic and Ontology-Driven Architecture for service-oriented and event-driven systems

AODA: An Autonomic and Ontology-Driven Architecture for Service-Oriented and Event-Driven Systems

Semantic-Aware IoT Platforms