This paper presents PlanIoT, a middleware approach for enabling adaptive data flow management in IoT-enhanced spaces (e.g., buildings) using automated planning methodologies. Today’s sensorized spaces deploy applications falling to diverse categories such as analytics, real-time, transactional, video streaming and emergency response. Depending on the category, applications have different QoS requirements related to timely delivery, networking resources, accuracy, etc. Typically, state-of-the-art data exchange systems introduce policies for bandwidth allocation or prioritization for specific data types and applications (e.g., camera data). PlanIoT introduces a generic QoS model to evaluate the performance of data flowing in Edge infrastructures and generates their performance metrics dataset. Such a dataset is used as input to automated planning representations to intelligently satisfy QoS requirements of deployed applications. The experimental results show that PlanIoT improves the end-to-end response time of time-sensitive flows by more than 50%, especially with an overloaded Edge infrastructure. We also show the adaptivity of our approach by considering emergency cases that require Edge infrastructure reconfiguration.

This paper presents the implementation and guideline of PlanIoT, an adaptive flow management framework for IoT-enhanced spaces. Such spaces are composed of applications deployed at the Edge with varying QoS requirements in terms of response time, timely delivery, throughput, etc. Configuring the Edge infrastructure requires tuning multiple parameters for optimal QoS satisfaction of applications. This is a complex task especially when the system has to be re-adapted (e.g., emergency situations). The PlanIoT framework manages application data flows in an adaptive manner. This is achieved via the following core software components: (i) a queueing network composer; (ii) an automated planning modeler; and (iii) an AI planner. This artifact presents implementation details of these components as well as guidelines for using the PlanIoT framework.

Sensor-cloud was introduced to create a cloud environment where a geographically distributed pool of sensors (physical or logical) and cloud resources can be shared between numerous applications. In addition, sensor-cloud hides the complexity and heterogeneity of underlying physical/logical sensors from applications. Based on the applications' requirements, the sensor-cloud creates virtual sensors that provide an abstract layer for applications to interact with real sensors through a model. Each virtual sensor abstracts one given real sensor. In this work, we focus mainly on the performance aspect of sensor-cloud infrastructure. In fact, since same data can be requested by applications, if the sensor-cloud allocates several real sensors and creates multiple virtual sensors to retrieve it, many resources will be wasted. To overcome this problem, we propose to share real and virtual sensors between different applications based on their requirements to minimize the network and system resources utilization. The proposed mechanism permits indeed to minimize the number of instantiated virtual sensors in the sensor-cloud, as well the traffic to/from the physical/logical sensors while achieving the QoS objectives of applications. The performed simulations, show that by adopting this strategy, a further optimization of resources within the sensor-cloud infrastructure is possible without compromising the requested QoS.

The Cloud offers virtually unlimited resources and the ability to scale up or down applications as needed on the fly. Hence, the Cloud emerged as a suitable solution for large-scale IoT applications to cope with the rapidly increasing devices and data volume. Furthermore, IoT broadened the scope of the Cloud to the real world and enabled new service models such as the Sensing as a Service model. The convergence of both technologies stimulated innovations in both fields, we refer to this convergence as the Cloud of Things. The Cloud of Things enables users to request a complex IoT service (IoT application composed of several interconnected micro services) and deploy it seamlessly. However, deploying a complex IoT service in the Cloud of Things infrastructure is a difficult process due to the different types of physical nodes (Cloud data centers, IoT devices, gateways, etc.) and multiple architectures to collect and process data. Furthermore, network usage largely depends on the placement of different services across the network. In this paper, we present an efficient provisioning model of IoT services formulated as a Mixed Integer Problem. The objective is to minimize the cost of the deployment of IoT services in Cloud of Things infrastructure, through optimizing resources usage across physical nodes and bandwidth consumption over the network.

In this paper we present a novel approach to add collaborative autonomic management facilities to component-based applications. We introduce an autonomic container having the needed generic functionalities for autonomic management in dynamic environments. The purpose of this container is to discharge the application developer from the burden of the non-functional facilities of management in order to focus merely on the business of the application. We give an architectural description of an application's components, and encapsulate each component in its own autonomic container. Here in, we described the different relationships that could occur between the different containers and how these relationships allow the collaboration of the containers in order to full-fill a global objective.

Cloud computing is a recent paradigm in information technology enabling an economic model for virtual resources provisioning. For this paradigm, service oriented Architecture (SOA) is a pillar block to build applications. To efficiently manage service-based applications in the cloud, monitoring and reconfiguration remain important tasks but it is still a challenge to find a solution that reconciles the scalability, the memory consumption, and the efficiency. In this paper, we propose a framework that adds monitoring and reconfiguration facilities to services of a service-based application and deploys them in the cloud using a scalable micro-container. Unlike the existing initiatives, our framework adds dynamically these facilities to services that were not designed to be monitored or reconfigured. This makes the developers' task easier, letting them focusing on the business of their services instead of the non functional property of monitoring and reconfiguration. Moreover, our framework uses a scalable micro-container for services' deployment in the cloud to be inline with the scalability of this environment. The evaluation that we performed proves the efficiency and the flexibility of our approach for service-based applications in the cloud.

Monitoring and reconfiguration are critical issues in Cloud environments. Monitoring allows to detect violations and specific events, while reconfiguration allows to activate corrective mechanisms or runtime modifications. In this paper we propose an extension for Open Cloud Computing Interface (OCCI) to enable monitoring and reconfiguration. The extension describes the needed elements to manage (i.e., to monitor and reconfigure) cloud resources on demand. The definition entails the introduction of new OCCI Resources, Links and Mixins. We define on the one hand new types needed to monitor metrics based on a previously established SLA. On the other hand we define the needed types to reconfigure our managed resources when needed. The newly added elements are OCCI entities defined as generic Kinds, that are specialized using OCCI Mixins. Using these elements, the user is provided with a monitoring and reconfiguration infrastructure on demand. We propose herein, a real use case based on HTTP rendering showing how to establish and link the described elements of the infrastructure.

An important feature of the intelligent environments is that they monitor user activities and help them in making decisions based on their progress in activities. Many such environments have been designed previously for healthcare management. However, more than often users are reluctant to consider the feedback from the environment alone. So we consider expert's recommendations and social network of the user as additional entities that form part of the intelligent environment. However, unlike traditional approaches, they influence the user indirectly through various persuasion techniques. The main idea is to change the behavior of the user for improving healthcare management. In this article we identify salient features of our framework for healthcare self-management in intelligent environments that combines ubiquitous and social computing as persuasion media. The framework enables social interactions between the patients, doctors, and other users in their online social community through a web portal as well as through their smart phones. Both user's behavior and preferences are taken into account to help them in adopting healthy behavior. This is done by using different persuasion strategies created on the basis of the user's behavior model. As a case study we consider diabetes self-management in this article.

As the number of devices in a pervasive environment is increased, the number of components available on the network also grows rapidly. In such cases, it is possible to compose various applications through a combination of different sets of components. Considering the multifaceted problem of having varying device capabilities supporting a different set of protocols, and each device hosting a number of components providing the same functionality, it becomes very difficult to choose a particular device hosting a required component which can be the best-fit for the user. This becomes practically impossible when the required components are distributed across various devices in the networked environment. We propose a solution for dynamic user task composition considering user preferences, device capabilities, and heterogeneity of communication protocols. With our proposed approach, a user task can be instantiated in different environments using a different set of devices and components, depending upon their capabilities and user preferences. We propose mechanisms for modeling device capabilities and user preferences and for modeling the user task as a graph. We then propose algorithms for selection of devices based on user preferences and task requirements. Since the underlying network is also modeled as a graph, we describe an algorithm for mapping of services in the user task on to the components distributed across devices in the pervasive environment. We also give an overview of our initial implementation and some results of our evaluations.

This paper is an overview of how capacity-driven Web services are defined and put into action. Because of the specificities of these Web services compared to regular(i.e., mono-capacity) Web services, the way they are looked into is different and occurs through four steps known as description, discovery, composition, and enactment. A Web service that is empowered with several capacities, that are in fact operations to execute, has to know which capacity out of several it has to choose and then, trigger at run-time.For this purpose, this Web service takes into account the requirements like data and network that are put on each capacity it was empowered with. A feasibility discussion on the implementation of capacity-driven Web services is presented in this paper, as well.

The lack of semantics in Web Services Description Language (WSDL) prevents automatic discovery and hence automatic invocation and composition. In our work, we are interested in extending existing approaches for the description of Semantic Web Services. Previously, we have extended the W3C recommendation on Semantics for Web Services (SAWSDL) and have proposed the use of two types of ontologies: a Technical Ontology type containing concepts defining semantics of services, their QoS,etc. and a Domain Ontology type containing the concepts defining the semantics of the business domain. The aim of this paper is to present Yet Another Semantic Annotation for WSDL (YASA4WSDL) and define a technical service ontology for YASA4WSDL. This ontology integrates useful concepts of WSDL meta-model, and OWL-S and WSMO ontologies. The integration of these ontologies is based on different matching techniques according to an ontology mapping process. The resulting ontology covers specific Web services semantic concepts.