On-Demand Composition of Smart Service Systems in Decentralized Environments

The increasing number of smart systems inevitably leads to a huge number of systems that potentially provide independently designed, autonomously operating services. In near-future smart computing systems, such as smart cities, smart grids or smart mobility, independently developed and heterogeneous services need to be dynamically interconnected in order to develop their full potential in a rather complex collaboration with others. Since the services are developed independently, it is challenging to integrate them on-the-fly at run time. Due to the increasing degree of distribution, such systems operate in a decentralized and volatile environment, where central management is infeasible. Conversely, the increasing computational power of such systems also supersedes the need for central management. The four identified key problems of adaptable, collaborative Smart Service Systems are on-demand composition of complex service structures in decentralized environments, the absence of a comprehensive, serendipity-aware specification, a discontinuity from design-time specification to run-time execution, and the lack of a development methodology that separates the development of a service from that of its role essential to a collaboration. This approach utilizes role-based models, which have a collaborative nature, for automated, on-demand service composition. A rigorous two-phase development methodology is proposed in order to demarcate the development of the services from that of their role essential to a collaboration. Therein, a collaboration designer specifies the collaboration including its abstract functionality using the proposed role-based collaboration specification for Smart Service Systems. Thereof, a partial implementation is derived, which is complemented by services developed in the second phase. The proposed middleware architecture provides run-time support and bridges the gap between design and run time. It implements a protocol for coordinated, role-based composition and adaptation of Smart Service Systems. The approach is quantitatively and qualitatively evaluated by means of a case study and a performance evaluation in order to identify limitations of complex service structures and the trade-off of employing the concept of roles for composition and adaptation of Smart Service Systems.:1 Introduction 1.1 Motivation 1.2 Terminology 1.3 Problem Statement 1.4 Requirements Analysis 1.5 Research Questions and Hypothesis 1.6 Focus and Limitations 1.7 Outline 2 The Role Concept in Computer Science 2.1 What is a Role in Computer Science? 2.2 Roles in RoleDiSCo 3 State of the Art & Related Work 3.1 Role-based Modeling Abstractions for Software Systems 3.1.1 Classification 3.1.2 Approaches 3.1.3 Summary 3.2 Role-based Run-Time Systems 3.2.1 Classification 3.2.2 Approaches 3.2.3 Summary 3.3 Spontaneously Collaborating Run-Time Systems 3.3.1 Classification 3.3.2 Approaches 3.3.3 Summary 3.4 Summary 4 On-Demand Composition and Adaptation of Smart Service Systems 4.1 RoleDiSCo Development Methodology 4.1.1 Role-based Collaboration Specification for Smart Service Systems 4.1.2 Derived Partial Implementation 4.1.3 Player & Context Provision 4.2 RoleDiSCo Middleware Architecture for Smart Service Systems 4.2.1 Infrastructure Abstraction Layer 4.2.2 Context Management 4.2.3 Local Repositories & Knowledge 4.2.4 Discovery 4.2.5 Dispatcher 4.3 Coordinated Composition and Subsequent Adaptation 4.3.1 Initialization and Planning 4.3.2 Composition: Coordinating Subsystem 4.3.3 Composition: Non-Coordinating Subsystem 4.3.4 Competing Collaborations & Negotiation 4.3.5 Subsequent Adaptation 4.3.6 Terminating a Pervasive Collaboration 4.4 Summary 5 Implementing RoleDiSCo 5.1 RoleDiSCo Development Support 5.2 RoleDiSCo Middleware 5.2.1 Infrastructure Abstraction Layer 5.2.2 Knowledge Repositories and Local Class Discovery 5.2.3 Planner 6 Evaluation 6.1 Case Study: Distributed Slideshow 6.1.1 Scenario 6.1.2 Phase 1: Collaboration Design 6.1.3 Phase 2: Player Complementation 6.1.4 Coordinated Composition and Adaptation at Run Time 6.2 Runtime Evaluation 6.2.1 General Testbed Setup and Scenarios 6.2.2 Discovery Time 6.2.3 Composition Time 6.2.4 Discussion 6.3 The ›Role‹ of Roles 6.4 Summary 7 Conclusion 7.1 Summary 7.2 Research Results 7.3 Future Wor

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

An Architecture for Integrated Intelligence in Urban Management using Cloud Computing

With the emergence of new methodologies and technologies it has now become possible to manage large amounts of environmental sensing data and apply new integrated computing models to acquire information intelligence. This paper advocates the application of cloud capacity to support the information, communication and decision making needs of a wide variety of stakeholders in the complex business of the management of urban and regional development. The complexity lies in the interactions and impacts embodied in the concept of the urban-ecosystem at various governance levels. This highlights the need for more effective integrated environmental management systems. This paper offers a user-orientated approach based on requirements for an effective management of the urban-ecosystem and the potential contributions that can be supported by the cloud computing community. Furthermore, the commonality of the influence of the drivers of change at the urban level offers the opportunity for the cloud computing community to develop generic solutions that can serve the needs of hundreds of cities from Europe and indeed globally.Comment: 6 pages, 3 figure

Rentable Internet of Things Infrastructure for Sensing as a Service (S2aaS)

Sensing as a Service (S2aaS) model [1] [2] is inspired by the traditional Everything as a service (XaaS) approaches [3]. It aims to better utilize the existing Internet of Things (IoT) infrastructure. S2aaS vision aims to create 'rentable infrastructure' where interested parties can gather IoT data by paying a fee for the infrastructure owners