Data-Centric Resource Management in Edge-Cloud Systems for the IoT

A major challenge in emergent scenarios such as the Cloud-assisted Internet of Things is efficiently managing the resources involved in the system while meeting requirements of applications. From the acquisition of physical data to its transformation into valuable services or information, several steps must be performed, involving the various players in such a complex ecosystem. Support for decentralized data processing on IoT devices and other devices near the edge of the network, in combination with the benefits of cloud technologies has been identified as a promising approach to reduce communication overhead, thus reducing delay for time sensitive IoT applications. The interplay of IoT, edge and cloud to achieve the final goal of producing useful information and value-added services to end user gives rise to a management problem that needs to be wisely tackled. The goal of this work is to propose a novel resource management framework for edge-cloud systems that supports heterogeneity of both devices and application requirements. The framework aims to promote the efficient usage of the system resources while leveraging the Edge Computing features, to meet the low latency requirements of emergent IoT applications. The proposed framework encompasses (i) a lightweight and data-centric virtualization model for edge devices, (ii) a set of components responsible for the resource management and the provisioning of services from the virtualized edge-cloud resources

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture

IoT DEVICE MANAGEMENT AND CONFIGURATION

As the number of IoT devices grows, the management and configuration of IoT devices becomes crucial in resource constraint networks. It is hard to manage and configure a large amount of heterogeneous resource constraint IoT devices because people need to know how they connect to each other, what internet-enabled services are available to provide, and how people interact with things through the internet. The thing-centric approach focuses on user experience when engaging things, but the cloud- centric approach switch the focus to IoT services that can process data streams collected from things and applications that help get people joined in the IoT world. To manage IoT populations effectively in a centralized manner, not only does it mean that moving computational power closer to the edge is a way to reduce bandwidth and latency, but it also implies that it is necessary to build an architecture which can scale and manage tons of connected devices by a uniform interface. In particular, RESTful Web services can provide a uniform interface that operates resources by HTTP methods. For example, users can read and write data by a uniform interface, and a flowerpot can write data and be triggered to water plants by a uniform interface. Thus, in the scope of IoT, embedded middleware can implement uniform interface by REST model. Virtualizing physical things has emerged as a design pattern to build IoT systems. Resource less constraint devices are capable of being virtualized with enough CPU power, memory, networking, but they are more expensive and power consuming. However, resource highly constraint devices take advantage of low energy consumption and cheaper price, but they cannot be virtualized because they do not have ability to even run a single multi-threaded program. Therefore, it is very important to select the right platforms for the right roles. In our case, we use Raspberry Pi 3 as a middleware and Nordic nRF52832 as a BLE endpoint. In this thesis, a REST-based IoT management system based on Service-Oriented Architecture is built, and the performance of the system has been tested, including the response time of HTTP GET and POST requests of the centralized server in a Fog domain and a script engine onto a BLE-enabled endpoint