Harun Baraki

A resource oriented integration architecture for the Internet of Things

The vision of the Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. These services when integrated with the traditional enterprise level services form the creation of ambient intelligence for a wide range of applications. To facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT resources, service oriented computing and resource oriented approaches have been widely used as promising technologies. However, a reference architecture integrating IoT services into either of these two technologies is still an open research challenge. In this article, we adopt the resource oriented approach to provide an end-to-end integration architecture of front-end IoT devices with the back-end business process applications. The proposed architecture promises a programmer friendly access to IoT services, an event management mechanism to propagate context information of IoT devices, a service replacement facility upon service failure, and a decentralized execution of the IoT aware business processes.

Heuristic Approaches for QoS-Based Service Selection

In a Service Oriented Architecture (SOA) business processes are commonly implemented as orchestrations of web services, using the Web Services Business Process Execution Language (WS-BPEL). Business processes not only have to provide the required functionality, they also need to comply with certain Quality-of-Service (QoS) constraints which are part of a service-level agreement between the service provider and the client. Different service providers may offer services with the same functionality but different QoS properties, and clients can select from a large number of service offerings. However, choosing an optimal collection of services for the composition is known to be an NP-hard problem.

FRASAD: A framework for model-driven IoT Application Development

This paper presents FRASAD, an effective model-driven software development framework to manage the complexity of Internet of Things (IoT) applications. We propose a node-centric software architecture and a rule-based programming model that allow designers to describe their application using only sensor node domain concepts. The final application code is successively generated from the initial models through an automatic model transformation process. The evaluation results show that our framework enables a fast way to develop IoT applications by reducing the cost of dealing with the heterogeneity and complexity exhibited by sensor nodes and their operating systems.

Context-Aware Prediction of QoS and QoE Properties for Web Services

Web Services are commonly used for integrating applications between partners over the Internet. Since services with the same functionality are advertised with different Quality of Service (QoS) levels and are assessed with different Quality of Experience (QoE), choosing the right service may be quite challenging. It is essential for a user to predict QoS and QoE values as accurately as possible in order to find a suitable service. Usually collaborative filtering is applied using similar users and services for predictive purposes. We hypothesize a correlation between context data and QoS and QoE dimensions which can be additionally incorporated to improve predictive accuracy and scalability. In this paper we present the two algorithms PredReg and PredNet in order to predict QoS and QoE values for Web Services. The PredReg algorithm is based on multiple linear regression. The PredNet algorithm uses additionally a neural network for prediction. Both algorithms include context data of users and services generating personalized predictions for the requesting user. In addition, PredNet is able to process categorical variables so that user profiles can also be considered for predictions. We evaluated PredReg and PredNet and compared them with the state-of-the-art approach WSRec [1] which is a memory-based collaborative filtering approach. Our experiments demonstrated that PredReg and PredNet provide a higher predictive accuracy and a significantly improved scalability. Therefore, we recommend the application of PredReg and PredNet for future personalized predictions.

From requirement to design patterns for ubiquitous computing applications

Ubiquitous Computing describes a concept where computing appears around us at any time and any location. Respective systems rely on context-sensitivity and adaptability. This means that they constantly collect data of the user and his context to adapt its functionalities to certain situations. Hence, the development of Ubiquitous Computing systems is not only a technical issue and must be considered from a privacy, legal and usability perspective, too. This indicates a need for several experts from different disciplines to participate in the development process, mentioning requirements and evaluating design alternatives. In order to capture the knowledge of these interdisciplinary teams to make it reusable for similar problems, a pattern logic can be applied. In the early phase of a development project, requirement patterns are used to describe recurring requirements for similar problems, whereas in a more advanced development phase, design patterns are deployed to find a suitable design for recurring requirements. However, existing literature does not give sufficient insights on how both concepts are related and how the process of deriving design patterns from requirements (patterns) appears in practice. In our work, we give insights on how trust-related requirements for Ubiquitous Computing applications evolve to interdisciplinary design patterns. We elaborate on a six-step process using an example requirement pattern. With this contribution, we shed light on the relation of interdisciplinary requirement and design patterns and provide experienced practitioners and scholars regarding UC application development a way for systematic and effective pattern utilization.

Optimization of non-functional properties in Internet of Things applications

A major challenge in designing Internet of Things (IoT) systems is to meet various non-functional requirements such as lifetime, reliability, throughput, delay, and so forth. Furthermore, IoT systems tend to have competing requirements, which exacerbate these design challenges. We analyze this problem in detail and propose a model-driven approach to optimize an IoT application regarding to its non-functional requirements. Our approach defines optimizing as finding the best set of adjustable application parameters, which satisfies a given objective function. The relevant parameters are extracted during a simulation process. We apply a source code transformation that updates the source code with the generated adjustable parameter values and executes the compiler to create a new binary image of the application. Our experiment results demonstrate that non-functional requirements such as power consumption and reliability can be improved substantially during the optimization process.

An Approach Towards a Service Co-evolution in the Internet of Things

In the envisioned Internet of Things (IoT), we expect to see the emergence of complex service-based applications that integrate cloud services, connected objects and a wide variety of mobile devices. These applications will be smarter, easier to communicate with and more valuable for enriching our environment. They interact via interfaces and services. However, the interfaces and services can be modified due to updates and amendments. Such modifications require adaptations in all participating parties. Therefore, the aim of this research is to present a vision of service co-evolution in IoT. Moreover, we propose a novel agent architecture which supports the evolution by controlling service versions, updating local service instances and enabling the collaboration of agents. In this way, the service co-evolution can make systems more adaptive, efficient and reduce costs to manage maintenance.

Service Co-evolution in the Internet of Things

The envisioned Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. This service-based integration of IoT will be smarter, easier to communicate with and more valuable for enriching our environment. However, the interfaces and services can be modified due to updates and amendments. Such modifications require adaptations in all participating parties. Therefore, the aim of this research is to present a vision of service co-evolution in IoT. Moreover, we propose a novel agent architecture which supports the evolution by controlling service versions, updating local service instances and enabling the collaboration of agents. In this way, the service co-evolution can make systems more adaptive, efficient and reduce costs to manage maintenance.

MOCCAA: A Delta-synchronized and Adaptable Mobile Cloud Computing Framework.

Mobile Cloud Computing (MCC) requires an infrastructure that is merging the capabilities of resourceconstrained but mobile and context-aware devices with that of immovable but powerful resources in the cloud. Application execution shall be boosted and battery consumption reduced. However, a solution’s practicability is only ensured, if the provided tools, environment and framework themselves are performant too and if developers are able to adopt, extend and apply it easily. In this light, we introduce our comprehensive and extendable framework MOCCAA (MObile Cloud Computing AdaptAble) and demonstrate its effectiveness. Its performance gain is mainly achieved through minimized monitoring efforts for resource consumption prediction, scalable and location-aware resource discovery and management, and, in particular, through our graph-based delta synchronization of local and remote object states. This allows us to reduce synchronization costs significantly and improve quality dimensions such as latency and bandwidth consumption.

Lifting Low-Level Workflow Changes Through User-Defined Graph-Rule-Based Patterns

In dynamic service-oriented architectures, services and service compositions underlie constant evolution that may not only affect the own workflow but dependent services too. Subsequently, required adaptations necessitate an effective detection of the changes and their effects. Merely capturing a sequence of low-level changes and analyzing each of them demands much coordination and may lead to an incomplete picture. An abstraction that summarizes a combination of low-level changes will facilitate the detection and reduce the number of changes that shall be considered for adaptation. In this paper, we propose an abstraction that is formulated through graph-based patterns, since service compositions are workflows that can be mapped to directed labeled graphs. The characteristics and granularity of a graph pattern can be adjusted by domain experts to the respective workflow language and application case. In particular, graph-based patterns are crucial when workflows are represented in two different formats. This could be the case if there exists one representation for the execution and one for the verification. We present implementation details and a detailed example that shows the feasibility and simplicity of our solution.