Efficient content delivery through fountain coding in opportunistic information-centric networks

Opportunistic networks can increase network capacity, support collaborative downloading of content and offload traffic from a cellular to a cellular-assisted, device-to-device network. They can also support communication and content exchange when the cellular infrastructure is under severe stress and when the network is down or inaccessible. Fountain coding has been considered as espe- cially suitable for lossy networks, providing reliable multicast transport without requiring feedback from receivers. It is also ideal for multi-path and multi- source communication that fits exceptionally well with opportunistic networks. In this paper, we propose a content-centric approach for disseminating con- tent in opportunistic networks efficiently and reliably. Our approach is based on Information-Centric Networking (ICN) and employs fountain coding. When tied together, ICN and fountain coding provide a comprehensive solution that overcomes significant limitations of existing approaches. Extensive network simulations indicate that our approach is viable. Cache hit ratio can be increased by up to five times, while the overall network traffic load is reduced by up to four times compared to content dissemination on top of the standard Named Data Networking architecture

Smart Garage Implementation and Design Using Whatsapp Communication Media

WhatsApp is a social networking app that serves as a communication medium. According to the Online Audience Measurement Standards named comScore, in 2017, WhatsApp application users in Indonesia amounted to 35.8 million people. As the most popular mobile application with the most users in the country, in this research the authors chose Whatsapp as a communication medium that will be integrated into one application of Internet of Things (IoT), that is Smart Garage. Smart Garage is a combination of information technology and computing technology that is applied to a house by relying on efficiency and device automation. The results of this research shows that it is better to use mobile data networks than using the wireless networks. The maximum delay when using mobile data is 7.5 s and 7.7 s when using wireless networks. The research using WhatsApp application still rare especially in the field of IoT

Online Learning and Experimentation via Interactive Learning Resources

Recent trends in online learning like Massive Open Online Courses (MOOCs) and Open Educational Resources (OERs) are changing the landscape in the education sector by allowing learners to self-regulate their learning and providing them with an abundant amount of free learning materials. This paper presents FORGE, a new European initiative for online learning and experimentation via interactive learning resources. FORGE provides learners and educators with access to world- class facilities and high quality learning materials, thus enabling them to carry out experiments on e.g. new Internet protocols. In turn, this supports constructivist and self-regulated learning approaches, through the use of interactive learning resources, such as eBooks

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

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