Hypriot Cluster Lab: An ARM-Powered Cloud Solution Utilizing Docker

Following the establishment of virtualization approaches, cloud services within data center environments have become easily manageable. Modern infrastructures use virtual machines as a platform for service delivery, requiring powerful servers. Conjointly, the uprising of the Internet of Things implies new challenges to provide applications that can successfully manage data and communicate with a large number of connected devices. The standards of entry have resulted in extreme difficulties for small enterprises and educational institutions trying to provide their own virtualized services. The Hypriot Cluster Lab (HCL) - made publicly available on Github1 - offers cloud functionality while running on ARM processors, thereby minimizing costs. Due to the fact that such processors offer less computational power, services are packaged into lightweight containers built using the Docker framework, which avoid the overhead associated with virtual machine

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

iGateLink: A Gateway Library for Linking IoT, Edge, Fog and Cloud Computing Environments

In recent years, the Internet of Things (IoT) has been growing in popularity, along with the increasingly important role played by IoT gateways, mediating the interactions among a plethora of heterogeneous IoT devices and cloud services. In this paper, we present iGateLink, an open-source Android library easing the development of Android applications acting as a gateway between IoT devices and Edge/Fog/Cloud Computing environments. Thanks to its pluggable design, modules providing connectivity with a number of devices acting as data sources or Fog/Cloud frameworks can be easily reused for different applications. Using iGateLink in two case-studies replicating previous works in the healthcare and image processing domains, the library proved to be effective in adapting to different scenarios and speeding up the development of gateway applications, as compared to the use of conventional methods

A smart campus design: data-driven and evidence-based decision support solution design

The growth and the availability of the smart devices is becoming ubiquitous today and inter-networking of these devices make up what is commonly called the Internet of Things (IoT). IoT is being used to update, enhance, simplify and automate individual lives and communities. Most of the cities in general and universities in special are adopting IoT technologies in order to create a smart sustainable living and working environments. Based on the existing literature of smart campus domain, it can be observed that there is only a small number of models as such. This study attempts to bridge the following knowledge gaps of smart campus domain. This project falls into the concept of Smart Campus and aims to design a Smart Campus solution for Staffordshire University. The primarily goal is to design a solution architecture able to collect data from remote sensor networks and analyse them with the support of data analytics and machine learning techniques for sound business decision making. The project has two stages. The first stage is the business side of the project where a business requirement study has been done to extract the exact business requirements and once this complete the second stage was the technical implementation of one or many requirements and evaluation of the solution. The scope of this paper limits to the first stage of the project. A quantitative approach was chosen by considering the nature of this study. A self-administered online questionnaire was developed around several key challenges and directed especially to the staff members, in order to identify what are the expectations of university staff in relation to thematic topics. Subsequently, business requirements under each key challenge were ranked based on MoSCoW prioritisation method. Energy management, space utilisation and occupancy, cleanliness recognition, smarter car parking, internet enabled café, network and physical security and environment (temperature) control are the key business challenges identified. Moreover, intended system qualities and specific project benefits were also identified to scope the project well