Sanjin Sehic

Provisioning Software-Defined IoT Cloud Systems

Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units - a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.

Context-driven personalized service discovery in pervasive environments

Pervasive environments are characterized by a large number of embedded devices offering their services to the user. Which of the available services are of most interest to the user considerably depends on the user’s current context. User context is often rich and very dynamic; making an explicit, user-driven discovery of services impractical. Users in such environments would instead like to be continuously informed about services relevant to them. Implicit discovery requests triggered by changes in the context are therefore prevalent. This paper proposes a proactive service discovery approach for pervasive environments addressing these implicit requests. Services and user preferences are described by a formal context model called Hyperspace Analogue to Context, which effectively captures the dynamics of context and the relationship between services and context. Based on the model, we propose a set of algorithms that can continuously present the most relevant services to the user in response to changes of context, services or user preferences. Numeric coding methods are applied to improve the algorithms’ performance. The algorithms are grounded in a context-driven service discovery system that automatically reacts to changes in the environment. New context sources and services can be dynamically integrated into the system. A client for smart phones continuously informs users about the discovery results. Experiments show, that the system can efficiently provide the user with continuous, up-to-date information about the most useful services in real time.

PatRICIA -- A Novel Programming Model for IoT Applications on Cloud Platforms

Cloud computing technologies have recently been intensively exploited for the development and management of large-scale IoT systems, due to their capability to integrate diverse types of IoT devices and to support big IoT data analytics in an elastic manner. However, due to the diversity, complexity and scale of IoT systems, the need to handle large volumes of IoT data in a nontrivial manner, and the plethora of domain-dependent IoT controls, programming IoT applications on cloud platforms still remains a great challenge. To date, existing work neglects high-level programming models and focuses on low-level IoT data and device integration. In this paper, we outline PatRICIA, which aims at providing an end-to-end solution for high-level programming and provisioning of IoT applications on cloud platforms. We present a novel programming model, based on the concept of intent and intent scope. Further, we introduce its runtime for dealing with the complexity, diversity and scale of IoT systems in the cloud. Our programming model defines abstractions to enable easier, efficient and more intuitive development of cloud-scale IoT applications. To illustrate our programming model, we present a case study with real-world applications for controlling and managing electric vehicles.

COPAL: An adaptive approach to context provisioning

Context-aware services need to acquire context information from heterogeneous context sources. The diversity of service requirements posts challenges on context provisioning systems as well as their programming models. This paper proposes COPAL (COntext Provisioning for ALl) — an adaptive approach to context provisioning. COPAL is at first a runtime middleware, which provides loose-coupling between context and its processing. The component architecture of COPAL ensures that new context processing functions can be added dynamically. A set of context processing patterns are proposed to customize context attributes and compose context provisioning schemes. The COPAL components and models are reflected in a Domain Specific Language (DSL), which can further reduce the development efforts of context provisioning using automatic code generation. A motivating scenario is used throughout the paper to illustrate COPAL approach.

MADCAT: A Methodology for Architecture and Deployment of Cloud Application Topologies

The cloud computing paradigm introduces new possibilities and challenges for application design and deployment. On-demand resource provisioning, as well as resource and cost elasticity, need to be considered when realizing large-scale distributed applications for cloud environments. Current approaches do not sufficiently address the challenges of efficiently architecting and deploying cloud applications in a holistic manner and do not deal with the specific challenges encountered in cloud infrastructures. In this paper we introduce a methodology tackling the practical problems encountered when designing and deploying cloud applications. It enables the structured creation of cloud-native applications, addressing the complete application development lifecycle, from architectural design to concrete deployment topologies provisioned and executed on cloud infrastructure. By using iterative refinement and seamless provenance documentation of decisions made in the process, the methodology eases communication with relevant stakeholders and enables efficient design and deployment of distributed cloud applications.

LEONORE -- Large-Scale Provisioning of Resource-Constrained IoT Deployments

Internet of Things (IoT) devices are usually considered as external dependencies that only provide data, or process and execute simple instructions. Recently, IoT devices with embedded execution environments emerged that allow practitioners to deploy and execute custom application logic on the device. This approach fundamentally changes the overall process of designing, developing, deploying and managing IoT systems. However, these devices exhibit significant differences in available execution environments, processing, and storage capabilities. To accommodate this diversity, a structured approach is needed to uniformly and transparently deploy application components onto a large number of heterogeneous devices. This is especially important in the context of current large-scale IoT systems, such as in the smart city domain. In this paper, we present LEONORE, a service oriented infrastructure that provides elastic provisioning of application components on resource-constrained and heterogeneous edge devices in large-scale IoT deployments. LEONORE supports push-based as well as pull-based deployments and we show that our solution is able to elastically provision large numbers of devices using a testbed based on a real-world industry scenario.

Web-Scale Service Delivery for Smart Cities

Smart cities encompass services in diverse business and technological domains. Presently, most of these services are delivered through domain-specific, tightly coupled systems, which entail limited scalability and extensibility. The authors propose Web-scale service delivery that addresses these limitations and encourage the creation of novel services based on a domain-independent, cloud-based service-delivery platform.

COLT Collaborative Delivery of Lightweight IoT Applications

Todays IoT solutions are typically delivered by domain-specific solution providers. In these solutions, components are highly customized for specific project requirements, limiting their users to the offerings of their providers. To overcome these limitations, we propose a novel mechanism that opens the market for OEMs and third-party developers. This paper introduces the IoT Application Market, where a range of stakeholders can distribute, sell, share and contribute features for lightweight device-specific IoT applications. Based on the offerings in the IoT Application Market, users can buy applications and deploy them in their environment through self-service.

COPAL-ML: a macro language for rapid development of context-aware applications in wireless sensor networks

Application development on wireless sensor networks is becoming more and more challenging due to increasing complexity of applications and lack of dedicated programming models. Developers should concentrate on the application logic, while network designers should ensure the network and sensor performance. However, in reality, these two roles often overlap because the architectural and programming abstraction between the network and application is missing. Research on middleware and language that bridges these two abstraction levels is still in a preliminary stage. This paper proposes a macro language based on our previous work COPAL (COntext Provisioning for ALl). COPAL is a runtime context provisioning middleware that, via a loosely-coupled and composable architecture, ensures context information from wireless sensor networks and other sources can be processed for the needs of context-aware applications. COPAL-ML is a macro language that extends Java programming language and is tailored for the application development using COPAL. Its main task is to reduce development efforts, hide the inherent complexity of COPAL API, and separate concerns of the context-aware application from underlining wireless sensor network.

Green software services: from requirements to business models

In recent years, green software research is gaining momentum because of the acute need for sustainable development. Most past research has been focused on the definitions, metrics and technical solutions for green software, but few has addressed green software from the business perspective. In this paper, we present the analysis on three key elements of Green Software Services (GSS)-stakeholders, their requirements, and business models. The stakeholders of GSS are detailed with the services each stakeholder can provide and consume, thus clarifying their interests to GSS. Based on this analysis, we present the domain-independent, high-level requirements to GSS that cover diverse needs of different stakeholders. Six business models are then proposed to promote collaborations of stakeholders on the delivery of GSS. In the end, the relationship between GSS and cloud is discussed and a GSS marketplace is envisioned.