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- Resource allocation model for sensor clouds under the sensing as a service paradigmPublication . Guerreiro, Joel; Rodrigues, Luis; Correia, NoéliaThe Sensing as a Service is emerging as a new Internet of Things (IoT) business model for sensors and data sharing in the cloud. Under this paradigm, a resource allocation model for the assignment of both sensors and cloud resources to clients/applications is proposed. This model, contrarily to previous approaches, is adequate for emerging IoT Sensing as a Service business models supporting multi-sensing applications and mashups of Things in the cloud. A heuristic algorithm is also proposed having this model as a basis. Results show that the approach is able to incorporate strategies that lead to the allocation of fewer devices, while selecting the most adequate ones for application needs.
- Virtual sensor networks: collaboration and resource sharingPublication . Guerreiro, Joel David Valente; Correia, N.This thesis contributes to the advancement of the Sensing as a Service (SeaaS), based on cloud infrastructures, through the development of models and algorithms that make an efficient use of both sensor and cloud resources while reducing the delay associated with the data flow between cloud and client sides, which results into a better quality of experience for users. The first models and algorithms developed are suitable for the case of mashups being managed at the client side, and then models and algorithms considering mashups managed at the cloud were developed. This requires solving multiple problems: i) clustering of compatible mashup elements; ii) allocation of devices to clusters, meaning that a device will serve multiple applications/mashups; iii) reduction of the amount of data flow between workplaces, and associated delay, which depends on clustering, device allocation and placement of workplaces. The developed strategies can be adopted by cloud service providers wishing to improve the performance of their clouds. Several steps towards an efficient Se-aaS business model were performed. A mathematical model was development to assess the impact (of resource allocations) on scalability, QoE and elasticity. Regarding the clustering of mashup elements, a first mathematical model was developed for the selection of the best pre-calculated clusters of mashup elements (virtual Things), and then a second model is proposed for the best virtual Things to be built (non pre-calculated clusters). Its evaluation is done through heuristic algorithms having such model as a basis. Such models and algorithms were first developed for the case of mashups managed at the client side, and after they were extended for the case of mashups being managed at the cloud. For the improvement of these last results, a mathematical programming optimization model was developed that allows optimal clustering and resource allocation solutions to be obtained. Although this is a computationally difficult approach, the added value of this process is that the problem is rigorously outlined, and such knowledge is used as a guide in the development of better a heuristic algorithm.
- Resource design in federated sensor networks using RELOAD/CoAP overlay architecturesPublication . Rodrigues, Luis; Guerreiro, Joel; Correia, NoéliaSensor networks can be federated for wide-area geographical coverage using RELOAD/CoAP architectures. In this case, proxy nodes of constrained environments form a P2P overlay to announce device resources or sensor data. Although this is a standard-based solution, consistency problems may arise because P2P resources (data objects stored at the overlay network) may end up including similar device resource entries. This is so because device resource entries, or sensor data, can be announced under different P2P resource umbrellas, meaning that any update to them will require changing multiple P2P resources. Here in this article, a multi-layer approach is proposed to solve this issue, allowing for a more efficient storage/retrieval of IoT data. Information at the overlay network is kept consistent, although additional P2P anonymous resources must be created. A mathematical model is proposed for the planning of such multi-layer organization of P2P resources, together with a heuristic algorithm. A required overlay service is also discussed.
- RELOAD/CoAP architecture for the federation of wireless sensor networksPublication . Pisco, Luís; Guerreiro, Joel; Correia, NoéliaSensing devices are expected to interconnect over large geographical areas and federations of wireless sensor networks are expected in a near future. In such environments a critical issue is how to discover the resources available at devices in a scalable manner. For this purpose, a Constrained Application Protocol (CoAP) Usage for REsource LOcation And Discovery (RELOAD), a generic self-organizing Peer-to-Peer (P2P) overlay network service, has been defined to be used as a lookup service, to store available device resources and as a cache for sensor data. Each P2P resource, at the RELOAD/CoAP overlay, includes references to device resources, hosted at one or more constrained nodes, but no provision is made for the insertion of bindings/references to P2P resources already available at the P2P overlay network. Such feature would increase the efficiency and consistency of storage, avoiding duplicate references, a very relevant issue for future IoT applications relying on the federation of wireless sensor networks. In this article an extension to the service provided by CoAP Usage is proposed so that resource bindings can be managed. Two binding models, and a heuristic algorithm for their implementation, are proposed. Results show that such models lead to a better resource organization, reducing the number of sensor resource entries and/or fetches to the P2P overlay.
- Allocation of resources in SAaaS Clouds managing thing mashupsPublication . Guerreiro, Joel; Rodrigues, Luis; Correia, NoéliaThe sensing and actuation as-a-service is an emerging business model to make sensors, actuators and data from the Internet of Things more attainable to everyday consumer. With the increase in the number of accessible Things, mashups can be created to combine services/data from one or multiple Things with services/data from virtual Web resources. These may involve complex tasks, with high computation requirements, and for this reason cloud infrastructures are envisaged as the most appropriate solution for storage and processing. This means that cloud-based services should be prepared to manage Thing mashups. Mashup management within the cloud allows not only the optimization of resources but also the reduction of the delay associated with data travel between client applications and the cloud. In this article, an optimization model is developed for the optimal allocation of resources in clouds under the sensing and actuation as-a-service paradigm. A heuristic algorithm is also proposed to quickly solve the problem.
- RELOAD/CoAP architecture with resource aggregation/disaggregation servicePublication . Pisco, Luís; Guerreiro, Joel; Correia, NoéliaM2M communication is expected to occur at a global level and for this reason federations of device networks are also expected. In such large scale environments, a critical issue is how to discover the available resources in a scalable manner. For this purpose CoAP Usage for RELOAD, a generic self-organizing P2P overlay network service, has been proposed to be used as a lookup service, to store available resources and as a cache for sensor data. However, such approach alone does not allow building an aggregate resource hierarchy, a very relevant issue for an efficient organization of data in future IoT applications. Here we address this issue and propose an architecture incorporating a resource aggregation/disaggregation service.